KR101995294B1 - Image analysis apparatus and method - Google Patents

Abstract

The present invention provides an apparatus and a method which determine a classification standard of an object for classifying a detection target object. According to an embodiment of the present invention, the present invention relates to an image analysis method comprising the following steps: receiving an analysis target image including at least one object; using a model based on pre-learned deep learning to detect the object in the analysis target image; analyzing the detected object based on an actual measurement size of the detected object; and outputting an analysis result.

Description

[0001] IMAGE ANALYSIS APPARATUS AND METHOD [0002]

The present disclosure relates to an image analysis apparatus and method. More particularly, the present disclosure relates to an apparatus and method for considering an actual size of an object when an object is detected in an input image using a pre-learned deep learning based model.

In security facilities such as ports, airports or research facilities, there is a need to search for belongings of passersby to enhance security and prevent leakage of technology. At this time, an article search system using X-ray or the like is used as a technique for searching for belongings of a plurality of passers more quickly and efficiently.

Such an article retrieval system is widely used especially in customs electronic clearance system or security inspection system. For example, the Customs Electronic Customs clearance system is a computerized customs clearance service for import and export cargo, which can enhance efficiency of customs administration work in multilateral.

In addition, the security inspection system is a computerized security inspection task for judging whether there is any item that may cause a safety or security problem in a passenger's belongings, thereby enhancing the security of the security zone.

Deep learning, on the other hand, learns a very large amount of data, and when new data is input, it selects the highest probability with probability based on the learning result, and it can adaptively operate according to the image In the artificial intelligence field, there is an increasing tendency to utilize it in the field of artificial intelligence because it automatically finds the characteristic factor in the learning process of the model based on the data.

The conventional product retrieval system lacks research on more efficient and accurate data analysis using such techniques as deep running. Accordingly, research on an article search system for detecting an object from an input image in consideration of an actual size of an object to be detected is required.

The technical object of the present invention is to provide an article search system to which a deep running technique is applied.

It is another object of the present invention to provide an apparatus and method for analyzing an image obtained in an article search system using a pre-learned deep learning based model.

Another object of the present invention is to provide an apparatus and a method for considering an actual size of an object to be detected when an object is detected using a model of a deep learning based on a pre-learned model.

Another object of the present invention is to provide an apparatus and method for determining a classification criterion of an object for classifying an object to be detected.

The technical objects to be achieved by the present disclosure are not limited to the above-mentioned technical subjects, and other technical subjects which are not mentioned are to be clearly understood from the following description to those skilled in the art It will be possible.

According to an aspect of the present disclosure, there is provided a method of analyzing an object, the method comprising: receiving an analysis object image including at least one object; detecting an object in the analysis object image using a previously learned deep learning based model; Analyzing the detected object based on the measured size, and outputting the analysis result.

The image analysis method may further include receiving image information of the analysis target image, wherein the image information includes at least one of resolution information on the analysis target image and information on an actual size of the analysis target image .

The image analysis method may further include calculating a unit length per pixel of the analysis object image using the resolution information.

Analyzing the detected object may include calculating an actual size of the detected object using a unit length per pixel of the analysis target image and determining the type of the object based on the actual size of the detected object Step < / RTI >

Wherein the step of determining the type of the object determines the type of the object as the first type when the actual size of the object is larger than a preset value and if the actual size of the object is smaller than the predetermined value, The kind of object can be determined as the second kind.

Wherein the image analysis method further comprises the step of determining a classification criterion for the object, the determining criterion comprising: receiving a readout image comprising a readout object; receiving information about the readout object Updating the database using the read image and information about the read object, and determining a classification criterion for the object using the database.

The step of determining the classification criterion may determine the classification criterion for the object by adding different importance levels to the respective components of the reading object.

The step of determining the classification criterion may determine the classification criterion of the object based on the actual size information of the reading object.

Updating the database may include aligning the read image according to a predetermined angle and updating the database using information about the aligned read image and the aligned read image have.

Wherein the information on the reading object includes at least one of the type, width, height, aspect ratio of the reading object, degree of rotation of the reading object in the reading image, photographing date of the reading image, photographing time, And may include at least one.

According to another aspect of the present disclosure, there is provided an image processing apparatus including an analysis object image receiving unit that receives an analysis object image including at least one object, an object detection unit that detects an object in the analysis object image using a previously learned deep- And an object analyzing unit for analyzing the detected object based on the actual size of the detected object.

According to still another aspect of the present disclosure, there is provided a method of analyzing an object, the method comprising: receiving an analysis object image including at least one object; detecting an object in the analysis object image using a pre- Analyzing the detected object on the basis of the actual size of the object, and outputting the analysis result. The computer readable recording medium records the program.

The features briefly summarized above for this disclosure are only exemplary aspects of the detailed description of the disclosure which follow, and are not intended to limit the scope of the disclosure

According to the present disclosure, an article search system to which a deep running technique is applied can be provided.

Furthermore, according to the present disclosure, an apparatus and method for analyzing an image acquired in an article search system using a pre-learned deep learning based model can be provided.

Furthermore, according to the present disclosure, an apparatus and method for considering an actual size of an object to be detected when an object is detected using a model of a depth learning-based learning that has been learned in advance can be provided.

Furthermore, according to the present disclosure, an apparatus and method for determining a classification criterion of an object for classifying an object to be detected can be provided.

The effects obtainable from the present disclosure are not limited to the effects mentioned above, and other effects not mentioned can be clearly understood by those skilled in the art from the description below will be.

1 is a diagram for explaining an article search system according to an embodiment of the present disclosure.
2 is a block diagram showing a configuration of an image analysis apparatus 200 according to an embodiment of the present disclosure.
3 is a diagram for explaining a process of reading an image.
4 is a diagram for explaining an application range of artificial intelligence in an image reading process according to an embodiment of the present disclosure.
FIG. 5 is a view showing an embodiment of an image enhancement apparatus for performing image enhancement according to the present disclosure.
FIG. 6 is a diagram for explaining a process of separating an object and a background from an image including a single object and generating position information of the object according to an embodiment of the present disclosure;
7 is a view showing an image in which a hue is expressed based on the physical properties of an object according to an embodiment of the present disclosure;
8 is a diagram for explaining a process of generating an output image based on color distribution information of an image according to an embodiment of the present disclosure.
FIG. 9 is a diagram for explaining a process of acquiring a final output image obtained by combining an image obtained using color distribution information and an image obtained by applying edge-based filtering or smoothing filtering according to an embodiment of the present disclosure.
10 is a diagram for explaining a process of obtaining a final output image using a graphical model according to an embodiment of the present disclosure.
11 is a view for explaining an image enhancement method according to an embodiment of the present disclosure.
12 is a diagram for explaining context analysis according to an embodiment of the present disclosure.
13 is a diagram illustrating a process of generating and analyzing context information of an image according to an embodiment of the present disclosure.
14 is a diagram for explaining a process of analyzing an image and identifying an object according to an image analysis apparatus according to an embodiment of the present disclosure.
15 is a diagram for explaining the operation of the image analysis apparatus according to an embodiment of the present disclosure.
16 is a view for explaining an embodiment of a composite neural network for generating a multi-channel feature map.
17 is a view for explaining an embodiment of the pulling technique.
18 is a block diagram showing a configuration of an image synthesizing apparatus according to an embodiment of the present disclosure.
19 is a view illustrating a process of generating a multi-object image using two images including a single object according to an embodiment of the present disclosure.
20 is a diagram illustrating a process of learning a composite-object neural network using a multi-object image according to an embodiment of the present disclosure.
21 is a diagram for explaining a process of analyzing an actual image using the image synthesizing apparatus according to an embodiment of the present disclosure.
FIG. 22 is a view for explaining an image synthesizing method according to an embodiment of the present disclosure; FIG.
23 is a flowchart illustrating an image analysis method according to an embodiment of the present disclosure.
24 is a flowchart for explaining an image analysis method according to another embodiment of the present disclosure.
25 is a flowchart for explaining a method of determining a classification criterion of an object according to an embodiment of the present disclosure.
FIG. 26 is a diagram for explaining a method of determining a classification criterion of an object according to an embodiment of the present disclosure; FIG.
27 is a diagram showing an example of classification criteria of an object.
28 is a diagram of a database of objects in accordance with one embodiment of the present disclosure;
29 is a diagram for explaining image analysis results according to some embodiments of the present disclosure.
30 is another block diagram showing a configuration of an image analysis apparatus according to an embodiment of the present disclosure.

Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings, which will be easily understood by those skilled in the art. However, the present disclosure may be embodied in many different forms and is not limited to the embodiments described herein.

In the following description of the embodiments of the present invention, a detailed description of known functions and configurations incorporated herein will be omitted when it may make the subject matter of the present disclosure rather unclear. Parts not related to the description of the present disclosure in the drawings are omitted, and like parts are denoted by similar reference numerals.

In the present disclosure, when an element is referred to as being "connected", "coupled", or "connected" to another element, it is understood that not only a direct connection relationship but also an indirect connection relationship May also be included. Also, when an element is referred to as " comprising "or" having "another element, it is meant to include not only excluding another element but also another element .

In the present disclosure, the terms first, second, etc. are used only for the purpose of distinguishing one element from another, and do not limit the order or importance of elements, etc. unless specifically stated otherwise. Thus, within the scope of this disclosure, a first component in one embodiment may be referred to as a second component in another embodiment, and similarly a second component in one embodiment may be referred to as a first component .

In the present disclosure, the components that are distinguished from each other are intended to clearly illustrate each feature and do not necessarily mean that components are separate. That is, a plurality of components may be integrated into one hardware or software unit, or a single component may be distributed into a plurality of hardware or software units. Thus, unless otherwise noted, such integrated or distributed embodiments are also included within the scope of this disclosure.

In the present disclosure, the components described in the various embodiments are not necessarily essential components, and some may be optional components. Thus, embodiments consisting of a subset of the components described in one embodiment are also included within the scope of the present disclosure. Also, embodiments that include other elements in addition to the elements described in the various embodiments are also included in the scope of the present disclosure.

Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings.

1 is a diagram for explaining an article search system according to an embodiment of the present disclosure.

The article retrieval system 100 may include a reading unit 110 and / or a learning unit 120. [ The reader 110 may include an image analyzer 112 and / or an output device 114. The learning unit 120 may include a database 122, a deep learning learning unit 124, an algorithm verification unit 126, and / or a learned model storage unit 128. The reading unit 110 may function as a reading interface, and the learning unit 120 may function as a centrally managed intelligent data center.

Hereinafter, the case where the article search system according to the present disclosure is applied to an electronic clearance system or a security search system will be described as an example. However, the article retrieval system according to the present disclosure is not limited to this use. In addition, the article retrieval system according to the present disclosure can be utilized in a system that performs a role of identifying a specific article according to various purposes.

The input 130 of the article retrieval system 100 may include image, article information, and / or control information.

The image may be an image relating to an article comprising at least one object. For example, it may be an X-ray image of an article taken by an X-ray reading device. The image may be a raw image taken by an X-ray imaging device or an image in any form (format) for storing or transmitting the image. The image may be obtained by capturing image data captured by an X-ray reading device and transmitting the image data to an output device such as a monitor and then data. The image may be enhanced before being output to the output device 114 or before being input to the image analysis device 112. A method of enhancing an image will be described later. The output device 114 may output an image or an enhanced image. The image analysis apparatus 112 may receive an image or an enhanced image and may perform an operation of the image analysis apparatus 112 described later.

The image information may be transmitted through an X-ray reading device, or may be information about an attribute of the image itself input by the reading source 114 or the user. For example, the image information may include resolution information, resolution information, luminance range information, color difference range information, pixel information, information on the actual size of the image, angle information of the image, Information on the date of image capture, date of image capture, and image capture time. In addition, various information for indicating the attribute of the transmitted image itself may be included in the image information.

The article information may be information on the article included in the corresponding image. For example, if the goods entered into the image analysis device is a cargo in the e-commerce system, the goods information may include information on import declaration and / or customs listing information. As another example, when the article input to the image analysis apparatus is an article in the security check system, the article information may include identification information of the passer, the security level of the passer, and / or the item information of the passer.

The article information may be subjected to a predetermined preprocessing process before being input to the image analysis apparatus 112. For example, refinement of the name of the article may be performed on the list of articles, the information of imports, etc. contained in the article information. Refining work of a product name may mean an operation of unifying the names of various articles inputted for the same or similar articles.

The input of article information may be optional. For example, the article search system 100 of the present disclosure can operate only by inputting images without inputting article information. The article may include any kind of article as an article to be inspected or read. For example, when the image analysis apparatus according to the present disclosure is used in an electronic clearance system, the article may be at least one of a express cargo, a postal cargo, a container cargo, a traveler cargo and a traveler himself. As another example, when the image analysis apparatus according to the present disclosure is used in a security search system, the article may be at least one of a passenger belonging and a passenger himself.

For example, if the e-clearance system reads a traveler and the read traveler is an anxious traveler with an anomaly or a history of carrying a dangerous object in the past, the traveler's cargo may be of a higher level than other traveler's cargo Analysis and / or reading. For example, information that a specific article is a cargo of a critical traveler can be provided to the readers.

As another example, if the passer is a passenger with a high security level as a result of reading the passer, the passenger can perform analysis and / or reading at a higher level than the other passenger for the passenger's belongings. For example, information that a specific article is a belonging of a passenger with a high security level can be provided to a read-out person.

The control information may be information for controlling image reading or controlling the read image. For example, the control information may be input by the readout source 140. [ For example, the control information may include read source information, manager information, operation mode information, read sensitivity information, and / or user interface information. Specific use of the control information will be described later.

The article searching system 100 may receive the image, the article information and / or the control information 130 and transmit the same to the output apparatus 114 or the image analyzing apparatus 112. The image analyzing apparatus 112 can analyze the input image using the deep learning based model that has been learned in advance. The image analysis device 112 may transmit the analyzed result to the output device 114. [ The output device 114 outputs the input image, the article information and / or the control information 130, the image analysis result and / or the user interface transmitted from the image analysis device 112, It is possible to read out the output result of the memory 114. As described above, the refinement operation can be performed on the article information 130, and the image to be analyzed can be imaged before being input to the image analysis apparatus 112 and / Strengthening can be performed.

The output device 114 outputs all kinds of human-sensible signals such as a device for outputting visual information such as a monitor and a warning light, a device for outputting acoustic information such as a speaker, and a device for outputting tactile information such as a vibrator And the like. A user interface may be provided via the output device 114 and the reader may use the user interface to control the operation of the article retrieval system 100. For example, the reading source 140 can control the operation of the image analysis apparatus by inputting control information using the output user interface.

When the image of the image analyzing apparatus 112 includes an object to be detected, an object having an abnormality, or an object having a risk level equal to or higher than a threshold value, the information related thereto is output as an image analysis result through the output device 114 And the reading source 140 can confirm this. The image analyzing apparatus 112 may perform various processes for analyzing an analysis target image. For example, the image analyzer 112 may perform context analysis to more accurately analyze the image to be analyzed. Various processes and context analysis performed by the image analysis apparatus 112 will be described later.

The reading source 140 may determine whether to perform additional examination based on the image analysis result output through the output device 114. [ The additional inspection may include opening inspection directly opening an article related to the image and confirming an object included in the article. In this specification, the object to be searched may refer to an object having an abnormality or an object having a risk greater than a threshold value as described above. However, it is not so limited and may include various objects that are to be detected or searched by the system of the present disclosure.

As a result of the image analysis of the image analysis apparatus, the remediation test result inputted after the read source performs the direct remediation test and / or the matching result information in which the image analysis apparatus matches the image and the article information is transmitted to the learning unit 120 . The learning unit 120 stores the newly received information in the database 122 and the deep learning learning unit 124 can perform the deep learning learning using the information stored in the database 122. [ Or the deep learning learning unit 124 may directly receive all or a part of the learning data without being stored in the database 122. [ The learning result of the deep learning learning unit 124 may be verified by the algorithm verification unit 126 and the verified model may be stored as an updated model in the learned model storage unit 128. The model stored in the learned model storage unit 128 is transmitted again to the image analysis apparatus 112. The image analysis apparatus 112 can update the received model as a previously learned deep learning based model . The learning unit 120 may generate a composite image by receiving and combining a plurality of images. Also, the virtual image analysis result, the refinement inspection result and / or the matching result information corresponding to the composite image are generated using the image analysis result, the refinement inspection result and / or the matching result information for each of the plurality of images . The learning unit 120 may use the composite image and the generated virtual information as learning data. According to this, even if the number of learning data is absolutely small, a sufficient amount of learning data necessary for learning of the artificial intelligence model can be generated by combining or merging these learning data. Synthesis of images and generation of virtual information on the synthesized image will be described later.

The reading unit 110 and the learning unit 120 may be implemented as separate devices or may be implemented in the same device. Also, some or all of the configurations included in the reading unit 110 and the learning unit 120 may be configured by hardware or software.

Artificial intelligence technology allows computers to learn data and make decisions like a person. Artificial neural network is a mathematical model that is inspired by neural networks of biology. Neurons can mean the entire model with problem solving ability by changing the synaptic bond strength through learning. Artificial neural networks are generally composed of an input layer, a hidden layer and an output layer. The neurons contained in each layer are connected via weights. The linear combination of weights and neuron values, Through an activation function, an artificial neural network can have a form that can approximate a complex function. The objective of artificial neural network learning is to find a weight that minimizes the difference between the output of the output layer and the actual output.

The deep neural network is an artificial neural network composed of several hidden layers between the input layer and the output layer. It can model complex nonlinear relations through many hidden layers. By increasing the number of layers in this way, The structure is called deep learning. Deep learning learns a very large amount of data, and when new data is input, it chooses the highest possible answer based on the learning result, so it can operate adaptively according to the image, In the course of learning, you can automatically find the characteristic parameter.

According to one embodiment of the present disclosure, the deep learning-based model may be a fully convolutional neural network, a convolutional neural network, But is not limited to, at least one of a neural network, a recurrent neural network, a restricted Boltzmann machine (RBM), and a deep belief neural network (DBN). Alternatively, a machine running method other than deep running may be included. Or a hybrid model combining deep running and machine running. For example, a feature of an image may be extracted by applying a deep learning-based model, and a model based on a machine learning may be applied when an image is classified or recognized based on the extracted feature. The machine learning based model may include, but is not limited to, a support vector machine (SVM), an AdaBoost, and the like.

Also, according to one embodiment of the present disclosure, a method of learning a deep learning based model may include at least one of supervised learning, unsupervised learning, or reinforcement learning , But is not limited thereto. Map learning is performed by using a series of learning data and a corresponding label (label, target output value), and a neural network model based on map learning is a model model in which a function is inferred from training data . Map learning receives a series of learning data and a corresponding target output value, finds an error through learning to compare the actual output value with the target output value for the input data, and modifies the model based on the result do. Map learning can be divided into regression, classification, detection, and semantic segmentation depending on the type of the result. The function derived from the map learning can be used again to predict new results. As such, the neural network model based on the map learning can optimize the parameters of the neural network model through learning of a large number of learning data.

According to one embodiment of the present disclosure, a deep learning-based model can use information about an input image and an article for learning, and even after generating a learned model, information about images and articles obtained in the apparatus of this disclosure Can be used to update the neural network model. Further, a deep learning based model according to an embodiment of the present disclosure may be applied to an analysis result output by the method of the present disclosure, for example, anomaly or risk for an identified object, information about an object, The neural network model can be updated by using prediction results such as whether the target object is a target object, comparison information on the prediction result and the final open inspection result, evaluation degree or reliability information on the prediction result, and the like.

2 is a block diagram showing a configuration of an image analysis apparatus 200 according to an embodiment of the present disclosure. The image analysis apparatus 200 of FIG. 2 is an embodiment of the image analysis apparatus 112 of FIG.

The image analysis apparatus 200 may include an image receiving unit 210, an article information matching unit 220, and / or an image analysis unit 230. As described above, since the input of the article information is optional, the image analyzing apparatus 200 may not include the article information matching unit 220. The description of the input of the article information is as described with reference to Fig.

The image receiving unit 210 may receive an image related to an article including one or more objects. The description of the image received by the image receiving unit 210 is as described with reference to FIG.

The article information matching unit 220 receives the article information and the image received by the image receiving unit 210 as input, and performs matching of the article information and the image. The description of the article information is as described with reference to Fig. The matched image and article information may be output to the read source to assist in reading the read source. Alternatively, the matched image and article information may be transmitted to the learning unit 120 of FIG. 1 and used for learning of the deep learning model. The matched image and article information are stored in the database 122 of the learning unit 120 of FIG. 1 and then refined by the reading object and / or the reading task, Alternatively, the learning can be performed using the refined data for each reading task to be applied. The object to be read may include the express cargo, the postal cargo, the container cargo, the traveler cargo and the traveler. Further, the object to be read may include a traveler's belongings and a traveler. The reading task may include determining whether an object included in the object is abnormal or dangerous, determining whether the identified object is an object to be searched, determining whether the object is matched with the object, And may include a determination as to whether or not it has not been reported. As described above, the model learned by the learning unit 124 may be input to the image analysis unit 230 to update the existing model. At this time, an appropriate artificial intelligence may be updated depending on the object to be read. Also, as described above, the learning unit 124 may generate new learning data using existing learning data and may use it for learning. The new learning data can be generated by combining existing data and merging data as described above.

The image analyzing unit 230 receives the image (image to be analyzed) or image and article information, analyzes the image using a deep learning-based model that has been previously learned, and outputs the analyzed result to an output device have.

When only a video is received, the video analyzer 230 may identify the object included in the video and determine the presence or absence of an anomaly or the risk of the identified object. The image analyzing unit 230 may improve the accuracy of object identification by performing the context analysis process described below.

For example, if it is determined that the identified object is prohibited or not suitable, the image analysis unit 230 may determine that the object is abnormal or dangerous. The risk can be represented by a numerical value, and it can be determined whether or not it is a dangerous object by comparing with a predetermined threshold value. The numerical value of the risk and / or the predetermined threshold value may be adaptively determined according to the read object and / or the read task.

When both the image and the article information are received, the image analyzing unit 230 can more accurately analyze the object included in the image using the image and the article information. For example, the type, quantity and / or size information of the articles listed in the article list, the security level of the passer, and / or the authorized item information of the passer may be additionally used to identify the object from the image. If there is a discrepancy between the identified object and the item information by analyzing the image, it can be outputted as the image analysis result.

The image analysis result output by the image analysis unit 230 may include at least one of the risk level, type, quantity, number, size, and position of the object. If the image analysis result is the position of the object, the position of the object can be displayed on the image to be analyzed and output to the output device. The position of the object may be displayed in coordinates, but the object may be highlighted and displayed at the corresponding position in the output image so that the read source can easily read it. For example, an object may be highlighted by highlighting the edges of the object or by displaying a square box surrounding the object. In addition, a predetermined object area can be strengthened so that the readout source can more easily identify the object through the image enhancement process to be described later. For example, the region corresponding to a predetermined color can be enhanced to convert the image so that the region can be more clearly identified.

Alternatively, the image analysis unit 230 may determine whether or not a search target object (for example, an object for which customs clearance is prohibited or not suitable) is included in the analysis target image. For this, the image analysis unit 230 may receive or store information on the object to be searched. In addition, the image analysis unit 230 may identify an object included in the image and determine whether the identified object is an object to be searched.

3 is a diagram for explaining a process of reading an image.

FIG. 3 (a) is a flowchart of a conventional reading process, and FIG. 3 (b) is a flowchart of a reading process according to an embodiment of the present disclosure.

As shown in FIG. 3A, according to an existing reading process, when image and / or article information is input 311, it is provided as information 312 to a reader. The reader selects (313) an article that requires a remedial inspection based on the image and / or article information. The result of performing the opening inspection is input 314 as a result of inspection.

As shown in FIG. 3 (b), according to the reading process according to an embodiment of the present disclosure, when the image and / or article information is input 321, the image analysis device 322 performs pre- Based model, and provides the analyzed result to the reader as information (324). The image analysis apparatus 322 may transmit the learning data to the artificial intelligence data center 323, and the artificial intelligence data center 323 may learn the learning data. The artificial intelligence data center 323 may transmit the learned model to the image analysis apparatus 322 as a reading task assistant artificial intelligence to be read later.

The reader can select (325) an article requiring the inspection of the opening, based on the analysis result of the image analysis device 322, the image and / or the article information. The result of performing the remade inspection can be input 326 as a result of the inspection. The result of the inspection may be transmitted to the artificial intelligence data center 323 and used as learning data.

4 is a diagram for explaining an application range of artificial intelligence in an image reading process according to an embodiment of the present disclosure.

As shown in FIG. 4, randomly sampled samples 420 among all the products 410 can be selected (450) as a management object. Or perform a risk analysis 440 on all the articles 410 using the screening assistant artificial intelligence 430 to select the objects to be managed, thereby selecting the objects to be managed 450.

The use of artificial intelligence is not limited to the risk analysis 440 of the article described above. For example, when the management object is selected (450), it can be utilized as an assistant artificial intelligence 460 for assisting the inspection thereafter. For example, by applying the test assistant artificial intelligence 460, it is possible to assist in the examination of the reading source by identifying the object, determining whether there is an abnormality or risk of the identified object, and / or providing the reading source with information about the object to be searched . The reader can perform the close inspection 470 using the information provided by the test assistant AI.

Hereinafter, referring to Figs. 5 to 11, an embodiment of a method for enhancing an image before being input to the image analysis apparatus 112 of Fig. 1 and before being output to the output apparatus 114 will be described.

FIG. 5 is a view showing an embodiment of an image enhancement apparatus for performing image enhancement according to the present disclosure.

The image enhancement apparatus of FIG. 5 may be configured separately or as a part of the image analysis apparatus 112 of FIG.

The image enhancement apparatus 500 may include an image receiving unit 510, an object image extracting unit 520, a color distribution analyzing unit 530, and / or an image enhancing unit 540. It should be noted, however, that this shows only some components necessary for explaining the present embodiment, and the components included in the image enhancement apparatus 500 are not limited to the above-described examples. For example, two or more constituent units may be implemented in one constituent unit, and an operation performed in one constituent unit may be divided and executed in two or more constituent units. Also, some of the constituent parts may be omitted or additional constituent parts may be added.

The image enhancement apparatus 500 according to an exemplary embodiment of the present invention receives an input image 550, extracts an object included in the input image 550, and converts the object image including the object into one or more regions Determining one or more weights for at least some of the one or more areas based on the color distribution information, and determining one or more of the weights determined among the one or more areas The first output image 560 for the object image can be generated by applying the first output image 560 to at least a part of the object image.

Each pixel constituting the image may have a predetermined brightness and hue by a combination of a luminance value representing luminance (brightness) and a hue value representing hue. At this time, the hue value may be represented by a combination of values of three or more hue elements, depending on various ways of expressing the hue. For example, the color value may be represented by an RGB value which is a combination of three color elements (Red (R), Green (G), and Blue (B)). For example, each of R, G, and B has a value from 0 to 255, so that the intensity of each color element can be expressed. The range of values that each of R, G, and B can have is determined based on the number of bits representing each of R, G, and B. [ For example, when represented by 8 bits, each of R, G, and B may have a value of one of 0 to 255. [

Acquiring the color distribution information may mean acquiring various statistical values that can be obtained therefrom by analyzing the color components of the color values of the pixels included in the region. For example, the statistical value may be information on which color element has the largest average value among the color elements of the color values of the pixels included in the corresponding region. For example, based on the values of R, G, and B of all the pixels included in the area, it is possible to determine which color element has the greatest total or average among R, G, and B. Alternatively, for each pixel, a color element having the largest value among R, G, and B is determined as the dominant color of the corresponding pixel, and the color of which the dominant color is determined most for all the pixels included in the corresponding region It can be judged. In this way, what is the dominant color of a given area can be determined. For example, if R of the three color components (R, G, B) has the largest value for the color values of a majority of pixels included in a predetermined region, the dominant color of the predetermined region is red It can be judged. In the above description, the color distribution information or dominant color is analyzed based on each of R, G, and B. However, the present invention is not limited to this, and may be analyzed based on various colors represented by a combination of two or more of R, G, For example, if the color to be identified is orange, it can be determined whether the dominant color of the pixel in the corresponding region is orange based on a part or all of R, G, and B representing orange.

Hereinafter, an embodiment of a process of enhancing an image by applying a weight to a region where a dominant color is red is assumed as an object of image enhancement will be described in detail. If it is determined that the dominant color of a predetermined region is red based on the color distribution information, one or more weights may be determined for the region. The weights can be determined for all or part of R, G, B and luminance. For example, when enhancing red, the weight for R may be a value greater than one. Applying the weight may mean that the color element value of the pixel in the corresponding area is multiplied by the corresponding weight. In this case, the weight for G and / or B may be a value less than one. By doing so, the red dominant region can be enhanced to a more red region.

In the above description, the enhancement of a specific color of an image has been described. However, the enhancement of the image of the present disclosure is not limited to this, and may include both a change in color value or a change in brightness value. Therefore, if necessary, the brightness value may be weighted to enhance the image.

Hereinafter, each component of the image enhancement apparatus 500 will be described.

The image receiving unit 510 may receive an input image 550 including one or more objects. The input image 550 may be an image before being input to the image analysis apparatus 112 and / or an image before being output to the output apparatus 114.

The object image extracting unit 520 may extract an object included in the input image received by the image receiving unit 510 and divide the object image including the object into one or more regions. For example, the object image extracting unit 520 may extract an object included in the input image by comparing the pixel value of the image to be analyzed with a predetermined threshold value to binarize the pixel values and grouping the binarized pixel values. Here, extracting an object may mean separating the object from the background, the object means a specific object in the image, and the background may be a part excluding the object from the image. The background of the image may be expressed in a predetermined color depending on the image capturing method or the image capturing apparatus. For example, the predetermined color may be white. If a color representing the background of the image is specified, the background and the object may be separated based on the specified background color. For example, an object may be identified by deleting a specified background color area from the input image 550.

For example, the object image may be obtained by specifying a bounding box surrounding the object region, and the object image extracting unit 520 may generate position information of the separated object based on the specified rectangular box have. That is, the square box may refer to an object recognition box.

According to one embodiment of the present disclosure, when an input image is an X-ray image of an item photographed by an X-ray reading device, since a background portion is not necessary, the corresponding background portion is cut off, It can be analyzed only by region. In particular, it can be said that it is important to acquire an area for the article in a real environment where the articles continue to pass through the X-Ray reading device through the conveyor belt. A detailed process of dividing the object and the background and generating the location information of the object will be described in detail with reference to FIG.

FIG. 6 is a diagram for explaining a process of separating an object and a background from an image including a single object and generating position information of the object according to an embodiment of the present disclosure; The object image extracting unit 600 of FIG. 6 may be an embodiment of the object image extracting unit 520 of FIG. The input image 610 may be the input image 550 described with reference to FIG. 5, and may be, for example, an image relating to the article including the bag 612 as a single object.

The object image extracting unit 600 roughly cuts the surrounding area based on the bag 612 by performing a cropping operation on the input image 610 including one bag 612 A cropped image 620 can be acquired. Then, the object image extracting unit 600 may obtain the binarized image 630 by binarizing the pixel value by thresholding the pixel value of the cropped image 620 with a predetermined threshold value. Then, the object image extracting unit 600 can obtain the grouped image 640 by grouping adjacent pixels (clustering, morphology, closing) to select a portion of the object from the binarized image 630. [ Then, the object image extracting unit 600 performs labeling and hole filling operations on the grouped image 640 to form a pixel group formed in the largest shape as an area 652 for the object And determine the remainder as the area 654 for the background, thereby obtaining the object 650 extracted image. In addition, the object image extracting unit 600 can determine the position of the object in the input image 610 using the information about the extracted object image. For example, the object image extracting unit 600 may specify a rectangular box surrounding the object area, and may generate position information of the object based on the specified rectangular box. 6, the object image extracting unit 600 can specify a rectangular box 662 surrounding the bag 612 and acquire the position information of the bag 612 based on the specified rectangular box . For example, the positional information of the bag 612 may be position information of four vertices forming the rectangular box 662, but is not limited thereto. For example, the position information may be represented by the coordinates (x, y) of one vertex of the rectangular box 662 and the width and height of the rectangular box. The coordinates (x, y) of the one vertex may be the coordinates of the upper left vertex of the square box 662. The coordinates (x, y) of the vertex can be specified based on the coordinates (0, 0) of the upper left vertex of the input image 610.

Referring again to FIG. 5, the object image extracting unit 520 may divide the object image into one or more regions based on the size of the object image. Each of the one or more regions may be square. For example, the object image extracting unit 520 may determine the number and size of regions for dividing the object image based on the size of the object image. For example, if the object image is relatively large or has a size larger than a predetermined threshold value, it can be divided to have more divided areas. Also, the size of each of the regions dividing the object image may not be equal to each other.

If the object image is not a square, the object image extracting unit 520 may convert the object image into a square by up-sampling or down-sampling the object image, Regions. ≪ / RTI > For example, since the object image is acquired based on a rectangular box surrounding the object extracted by the object image extracting unit 520, the object image may not be a square. In this case, the object image extracting unit 520 may divide the object image into one or more regions, but it may acquire a square object image by up-sampling or down-sampling the object image in the horizontal or vertical direction, An object image of a square may be divided into one or more regions.

For example, referring to FIG. 8, an object image 800 may be composed of 9 pixels in width and 12 pixels in height and may not be square. In this case, according to the present disclosure, the object image 800 may be divided into square regions of 3x3 size (in this case, the horizontal size of the object image / the horizontal size of the divided region) x (Vertical size of the region) = (9/3) x (12/3) = 12, having a total of 12 areas), up-sampling the width of the object image 800, And divides it into a 3x3 size area and divides it into a total of 16 areas. The shape of one or more regions dividing the object image is not limited to a square. For example, the region may have the form n x m, where n and m are positive integers that are different. In this case, the above-described upsampling or downsampling may not be performed.

Referring again to FIG. 5, the color distribution analyzing unit 530 obtains the color distribution information for each of the divided regions in the object image extracting unit 520, and generates color distribution information for at least a part of the regions One or more weights can be determined.

The color distribution information may include information on each of n (n is an integer greater than 1) color expression ranges. The color representation range may vary depending on the type and performance of the image acquisition device. Further, for example, "color" for R (red) may mean only the color of a pixel whose pixel value has (R, G, B) = (255,0,0) in an 8- Refers to not only when the pixel value is (R, G, B) = (255, 0, 0) but also includes similar color within a predetermined range based on the pixel value. For example, the "color expression range" for R may range from (R, G, B) = (150 to 255, 0 to 100, 0 to 100). That is, a pixel having (R, G, B) = (150, 100, 100) can also be defined as being included in the color expression range of red (R). The "color expression range" may be defined for the color to be identified. In the above example, the range of the color gamut of red is described, but the range of the color gamut of green (G) or blue (B) may be defined. Or a color expression range for any color (yellow, orange, sky blue, etc.) represented by a combination of some or all of R, G, and B may be defined. For example, when an area of an object represented by orange color is to be enhanced among the objects included in the image, as a result of analysis of the color distribution information, a weight is applied to a region where a number of pixels included in an orange color expression range is dominant, The image enhancement according to the present disclosure can be performed. The method of applying the weight is as described above.

If the image acquisition device expresses a color by a combination of three color elements of R, G (green), and B (blue), the color distribution information may include information on a part or all of the three color elements. If the color elements are five colors R, G, B, Y (yellow), and P (purple), the color distribution information may include information on some or all of the five color elements.

An X-ray image of an article taken by an X-ray reading device is used to determine a range of color expressions according to the physical properties of the objects included in the image (for example, whether the object is organic, inorganic, The applied X-ray image is being used. By reading the X-ray image to which the color is added, it is possible to identify not only the shape of the object included in the image but also the physical properties of the object to some extent. In the image enhancement of the present disclosure, an X-ray image added with a color according to the physical properties of an object is used as an input image, and the color distribution information is analyzed. Based on the analyzed color distribution information, The accuracy and the readability of the readout source for reading the image can be improved.

7 is a view showing an image in which a hue is expressed based on the physical properties of an object according to an embodiment of the present disclosure;

Referring to FIG. 7, a bag image 700, a medicine container image 710, and a traveler's baggage carrier image 720 taken by an X-ray reading device are shown. In the case of the bag ring 702, the bag zipper 704, the medicine bottle 712 and the bottle 722, it can be seen that the color expression range (applied color) differs according to the physical properties of the object. On the other hand, while the bag ring 702, the bag zipper 704, the medicine 712, and the bottle 722 are relatively clearly colored so that they can be distinguished from other objects, ), It is difficult to identify what the arbitrary content 724 is in the traveler's baggage image 720 and it is not easy to distinguish it from other objects. This is due to the properties of the object. For example, metals or minerals are expressed in a relatively clear and distinct color so that they can be clearly distinguished from the background, while organic matter is expressed in a light color, so that the distinction with the background becomes unclear. For areas of color that represent organic matter, you can enhance them with a clear, distinct color that can be clearly distinguished from the background by enhancing that color.

In other words, it is necessary to vary the degree of enhancement of the image according to the color expression range, using the feature that the range of color expression is different according to the physical properties of the object. For this purpose, the color distribution for each of the divided regions may be analyzed to apply a weight to at least some of the regions.

The one or more weights may include weights for at least some of n color elements representing n color representation ranges or colors. For example, if one area has n color expression ranges or color elements, the number of weights in the corresponding area may be 1 to n.

For example, when one weight is determined for one area, the determined weight can be applied to all color elements included in the one area or all the color expression ranges. Alternatively, the determined weight may be applied to at least a part of all color elements included in the one area or all the color expression ranges. For example, in order to enhance the image, the determined weight may be applied only to a predetermined color expression range of a predetermined color element or n color expression ranges among n color elements.

Alternatively, for example, weights may be determined for each of n color elements or n color representation ranges. That is, the number of weights for one area may be n. In this case, a weight corresponding to each color element or color expression range included in the area may be applied to the corresponding color element or color expression range. The weight may be given a relatively high weighting value for a predetermined color element or a color expression range to be subjected to image enhancement. For example, a weight value greater than 1 may be given to multiply the value of the color element or the pixel value belonging to the color representation range.

Or, for example, a weight can be determined for each of m color elements or color representation ranges greater than 1 and less than n. That is, the number of weights for one area may be m. In this case, the weighted value may be applied only to a color element or a color expression range to which a weight is assigned, among the color elements included in the area or the color expression range. As described above, a relatively high weight is given to a predetermined color element or color expression range to be subjected to image enhancement.

As described above, the weights can be determined to be relatively high for a predetermined color element or color expression range among n color elements or color expression ranges. For example, when an object included in an X-ray image is an organic material, the boundaries are often expressed less clearly in an image as compared with objects having different physical properties (metal, inorganic materials, etc.). This is because the color of the object, which is organic, is not clearly represented so that it can be distinguished from other objects or backgrounds around. For example, by being represented by a light orange color, it may not be distinguished from the background of white color. Therefore, by imparting a relatively high weight to the portion corresponding to the color expression range representing the organic matter in the divided regions, the corresponding color can be enhanced, for example, the light orange color can be changed to the deep orange color. By strengthening the image in this way, it is possible to further clarify the distinction between the surrounding object or the background and the object to be strengthened.

The predetermined color element or color representation range to which a relatively high weight is given may be one or more. For example, when the total color element or the color expression range is n, the predetermined color element or color expression range to which a relatively high weight value is given may be 1 to n. When there are a plurality of predetermined color elements or color expression ranges, the degree of image enhancement required for each may be different, and thus different weights may be given to each. For example, when an image is clearly expressed in the order of metal-> inorganic-> organic matter, a relatively high weight can be given only to a color element or color expression range for an organic material. However, A high weight may be given. At this time, a relatively high weight can be given to the organic matter rather than the inorganic matter.

A specific process of acquiring color distribution information and determining a weight value for each of the divided regions will be described in detail with reference to FIG.

8 is a diagram for explaining a process of generating an output image based on color distribution information of an image according to an embodiment of the present disclosure.

Referring to FIG. 8, an object image 800 may be divided into one or more regions such as a first region 810, a second region 820, and the like. The process of dividing regions in the object image 800 is as described for the object image extraction unit 520 in FIG. Hereinafter, the process of obtaining the color distribution information and determining the weight in the first region 810 will be described in detail. It is assumed that the first area 810 has a total of 9 pixels as a 3x3 area and has five color expression ranges (n = 5) (hereinafter, the color expression range can be replaced with color elements) . The image enhancement apparatus according to an exemplary embodiment may acquire color distribution information including information on five color representation ranges for the first area 810 and may generate color distribution information including at least a part of a 3x3 size area One or more weights may be determined for the < / RTI >

Alternatively, only information on a predetermined color expression range that is an object of image enhancement may be acquired and used as color distribution information. For example, when distribution information on a predetermined color expression range is equal to or greater than a predetermined threshold value, the region is determined as an enhancement target, and a relatively high weight can be given to the region.

More specifically, in one embodiment for determining the weight based on the color distribution information, n color channel images reflecting information on each of n color rendering ranges may be obtained for each of the divided areas. For example, since the first area 810 has information on five (n = 5) color expression ranges, five color channel images can be obtained, and the first color channel image 830, A color channel image 840, a third color channel image 850, a fourth color channel image 860, and a fifth color channel image 870. For example, if the X-ray reading device supports five color components R, G, B, Y and P, the first color channel image 830, the second color channel image 840, The image 840, the fourth color channel image 860, and the fifth color channel image 870 may correspond to R, G, B, Y, and P color elements, respectively.

Each of the first to fifth color channel images 830 to 870 is generated by mapping each pixel to a color channel image corresponding to the corresponding color information based on the color information of each of the constituent pixels of the first area 810 . For example, the first pixel 812 is mapped to a pixel 852 at the corresponding position of the third color channel image 850, and the second pixel 814 is mapped to the pixel 852 at the corresponding position of the first color channel image 830 And the third pixel 816 is mapped to the pixel 872 at the corresponding position of the fifth color channel image 870 and the fourth pixel 818 is mapped to the second color channel image 840 And the fifth pixel 820 is mapped to the pixel 874 at the corresponding position of the fifth color channel image 870 and the sixth pixel 822 is mapped to the pixel 842 at the corresponding position of the fifth color channel image 870, The seventh pixel 824 is mapped to the pixel 844 at the corresponding position of the second color channel image 840 and the seventh pixel 824 is mapped to the pixel 844 at the corresponding position of the second color channel image 840, The pixel 826 is mapped to the pixel 878 at the corresponding position of the fifth color channel image 870 and the ninth pixel 828 is mapped to the pixel 880 at the corresponding position of the fifth color channel image 870 So that the first to fifth colors Phase channel images 830 to 870 can be generated.

On the other hand, if the color expression range is at most n, color channel images less than n can be obtained. For example, in the first region 810, pixels having a color corresponding to the fourth color channel image 860 A total of four color channel images excluding the fourth color channel image 860 can be obtained.

When the color channel images are acquired, the first color channel image 830, the second color channel image 840, the third color channel image 850, the fourth color channel image 860, and the fifth color channel image 870 , Weights a1, a2, a3, a4, and a5 can be applied to each of them.

The weights can be determined in consideration of the color distribution of the pixels constituting each region, for example, the weights can be determined to be proportional to the color distribution of the pixels. Alternatively, the weights can be determined to be relatively high for a predetermined color expression range and relatively low for a remaining color expression range.

Referring again to FIG. 5, the image enhancement unit 540 may apply one or more weights determined in the color distribution analysis unit 530 to at least a part of one or more regions to generate a first output image for the object image .

8, a first color channel image 830, a second color channel image 840, a third color channel image 850, a fourth color channel image 860, and a fifth color channel image 870, A1, a2, a3, a4, and a5 are applied to the first to fifth color channel images to which the weight is applied, and the first region 810-1 to which the weight is applied is obtained by combining the first to fifth color channel images. The first output image may be finally generated by repeating the process for the remaining regions of the object image 800 as well. The weights may be determined in consideration of the color distribution of the pixels constituting each region, a relatively high weight for a predetermined color expression range, and a relatively low weight for the remaining color expression range may be determined. For example, since the portion corresponding to the hue representing the organic matter in each of the divided regions is not clearly distinguished from the background, the boundary portion is relatively not expressed clearly in the image, so that the weight is determined to be relatively high, Since the part is relatively distinct from the background, the weight can be determined to be relatively low since the boundary part is expressed relatively clearly in the image. Applying the weighting may mean replacing pixels in the region to be enhanced with new pixel values multiplied by weights, as described above.

Or as described above, if a predetermined color representation range to be subjected to image enhancement is dominant or has a distribution of a predetermined threshold value or more as a result of color distribution analysis of the region 810 included in the object image 800, 810) can be set relatively high.

For example, when the color expression range to be enhanced is red (R) and the dominant color of a predetermined region is red, one or more weights may be applied to the region. For example, (R, G, B) = (240, 20, 20) can be enhanced by applying the weight 2 when (R, G, B) = (120,10,10) have. Alternatively, a weight 2 may be applied to only some of R, G, and B, e.g., red, so that (R, G, B) = (240, 10, 10). (R, G, B) = (240, 5, 5) is applied to a part of R, G and B, for example red, . The above example relates to the case of reinforcing the red color, but it is not limited thereto, and any color can be determined as the color to be strengthened.

The predetermined threshold value and / or the weight value may be arbitrarily determined or may be determined based on the accumulated image processing information. Alternatively, by performing learning on the threshold and / or weight through an artificial intelligence based learning model, optimal thresholds and / or weights can be continuously updated.

In addition, the image enhancement unit 540 may apply edge-based filtering or smoothing filtering to at least a portion of the one or more regions to generate a second output image for the object image. In addition, the image enhancement unit 540 may generate a third output image for the object image based on the generated first output image and the second output image.

Edge-based filtering or smoothing filtering is a technique for enhancing the contrast of an image including, but not limited to, Wiener filtering, unsharp mask filtering, histogram equalization, and linear contrast adjustment techniques, May include techniques for enhancing < / RTI >

FIG. 9 is a diagram for explaining a process of acquiring a final output image obtained by combining an image obtained using color distribution information and an image obtained by applying edge-based filtering or smoothing filtering according to an embodiment of the present disclosure. The object image 900, the first area 910, and the weighted first area 910-1 of FIG. 9 correspond to the object image 800, the first area 810, and the weighted first area 910-1, And 810-1, respectively. 9, the image enhancement unit 540 may generate a first region 910-2 to which the filtering is applied to the first region 910, and may include a first region 910-1 to which weighting is applied, And the first area 910-2 to which the filtering is applied, to generate the final first area 910-3. The image enhancement unit 540 may generate a second output image to which the above-described filtering techniques are applied to the remaining regions, and a third output image that combines the first output image and the second output image.

The process of generating the weighted region (e.g., 910-1), the filtered region (e.g., 910-2), and / or the final region 910-3 using the two may be performed on a domain basis. However, the present invention is not limited to this, and the process may be performed in units of object images. For example, it is possible to obtain a weighted object image (first output image) by applying a weight to each of the regions included in the object image. Also, it is possible to acquire an object image (second output image) by applying the filtering to each of the regions included in the object image. In addition, the final image (third output image) can be generated by combining the weighted object image and the enhanced edge object image.

On the other hand, for example, when the organic matter in the divided region is less than the other materials, the influence on the first output image may be relatively small by combining the second output image with the first output image. In this case, The weight for distribution information can be determined to be relatively higher. Also, for example, by combining the first output image and the second output image, it is possible to more accurately recognize an object even when a plurality of objects overlap each other.

10 is a diagram for explaining a process of obtaining a final output image using a graphical model according to an embodiment of the present disclosure.

The image enhancement apparatus according to an exemplary embodiment determines each of the color expression ranges included in the color distribution information as individual nodes, and determines a relative relationship between the determined individual nodes, a first output image, a second output image, , A graphical model of a hierarchical structure can be generated.

Referring to FIG. 10, if the color distribution information includes n color expression ranges in each of the divided regions, the lowest-order node is the first color distribution information 1010-1 to the nth color distribution information 1010-n ≪ / RTI > Then, the first output image 1020 can be obtained by applying a weight to each of the color representation ranges of the corresponding divided region or the corresponding divided region based on the respective color distribution information. The first output image 1020 may be determined as the final output image. A third output image 1040 is generated based on the first output image 1020 and the second output image 1030. The second output image 1030 is generated by applying a contrast enhancement technique You may.

11 is a view for explaining an image enhancement method according to an embodiment of the present disclosure. The image enhancement method of FIG. 11 is performed by the image enhancement apparatus of FIG. 5, and the description of the image enhancement apparatus of FIG. 5 may be applied to the image enhancement method of FIG.

In step S1100, an input image can be received.

The object included in the input image can be extracted in step S1110. For example, a pixel value of an input image may be compared with a predetermined threshold value to binarize the pixel value, and binarized pixel values may be grouped to extract an object included in the analysis target image.

In step S1120, the object image including the object may be divided into one or more regions. For example, the number and size of regions for dividing the object image can be determined based on the size of the object image. Also, the size of each of the regions dividing the object image may not be equal to each other. In addition, when the object image is not a square, the object image may be converted into a square by up-sampling or down-sampling the object image, and then the object image may be divided into one or more regions have.

In step S1130, the color distribution information may be obtained for each of the one or more areas. For example, the color distribution information may include information on each of n (n is an integer greater than 1) color expression ranges.

In step S1140, based on the color distribution information, one or more weights may be determined for at least some of the one or more areas. For example, the one or more weights may include weights for at least some of the n color representation ranges. For example, if one area has n color expression ranges, the number of weights in the corresponding area may be 1 to n.

In step S1150, the determined one or more weights may be applied to at least one of the one or more regions to generate a first output image for the object image.

Although not shown in FIG. 11, edge-based filtering or smoothing filtering may be applied to at least a portion of the one or more regions to generate a second output image for the object image. Also, for example, a third output image for the object image may be generated based on the generated first output image and the second output image

In the embodiments described with reference to FIGS. 5 to 11, an example of receiving an image including a single object and separating an object and a background has been described. However, the present invention is not limited thereto, and the input image may be an image including two or more objects. In this case, it is possible to distinguish two or more objects and backgrounds from the input image, and generate position information for each of the two or more objects. Also, in this case, in the description with reference to FIG. 6, when a plurality of pixel groups are formed, it can be determined that not only the pixel group formed in the largest shape but also the other pixel groups are regions for the object. The process of generating the position information of each determined object is the same as described for the image including one object.

At least some of the steps of the components of the image enhancement apparatus of the present disclosure and the image enhancement method may be performed using an artificial intelligence based or deep run based model. For example, a weight determined on the basis of the size, number and color distribution information of an area generated by dividing an object image, various thresholds mentioned in the present disclosure, whether or not a second output image is generated, Model can be learned, and information according to the learned model can be used.

Hereinafter, an embodiment of a context analysis method performed by the image analysis apparatus 112 will be described with reference to FIGS. 12 to 17. FIG.

The image analysis apparatus 1200 of FIG. 12 may be an embodiment of the image analysis apparatus 112 of FIG. Alternatively, the image analyzing apparatus 1200 of FIG. 12 may be included in the image analyzing apparatus 112 of FIG. 1, or may be a separate apparatus for performing context analysis.

Referring to FIG. 12, the image analysis apparatus 1200 may include a feature extraction unit 1210, a context generation unit 1220, and / or a feature and context analysis unit 1230. It should be noted, however, that this shows only some components necessary for explaining the present embodiment, and the components included in the image analysis apparatus 1200 are not limited to the above-described examples.

The image analysis apparatus 1200 extracts the characteristics of the input image (analysis target image), generates context information based on the extracted features, and analyzes the analysis target image based on the extracted features and the generated context information have. For example, the image analysis apparatus 1200 can classify an image or locate an object of interest using extracted features and generated context information.

The input image of the image analysis apparatus 1200 may be the same as the input image of the image analysis apparatus 112 of FIG.

The feature extracting unit 1210 can extract an image feature by analyzing the input image. For example, the feature may be a local feature for each region of the image. The feature extraction unit 1210 according to an exemplary embodiment may extract features of an input image using a general convolutional neural network (CNN) scheme or a pooling scheme. The pooling scheme may include at least one of a max pooling scheme and an average pooling scheme. However, the pulling technique referred to in the present disclosure is not limited to the max-pulling technique or the average-pulling technique, and includes any technique of obtaining a representative value of an image area of a predetermined size. For example, the representative value used in the pooling technique may be at least one of a variance value, a standard deviation value, a mean value, a most frequent value, a minimum value, a weighted average value, etc., in addition to the maximum value and the average value.

The composite neural network of the present disclosure may be used to extract "features" such as borders, line colors, etc. from input data (images) and may include multiple layers. Each layer can receive input data and process the input data of the layer to generate output data. The composite neural network can output the feature map generated by convoluting the input image or the input feature map with filter kernels as output data. The initial layers of the composite product neural network may be operated to extract low level features such as edges or gradients from the input. The next layers of the neural network can extract gradually more complex features such as eyes, nose, and so on. The concrete operation of the composite neural network will be described later with reference to FIG.

The composite neural network may also include a pooling layer in which a pooling operation is performed in addition to the convolutional layer in which the convolution operation is performed. The pooling technique is a technique used to reduce the spatial size of data in the pooling layer. Specifically, the pooling technique includes a max pooling technique for selecting a maximum value in a corresponding area and an average pooling technique for selecting an average value of the corresponding area. In the field of image recognition, a max pooling technique is generally used do. In the pooling technique, the window size and interval (stride) of the pooling are generally set to the same value. Here, the stride means adjusting the interval to move the filter when applying the filter to the input data, i.e., the interval at which the filter moves, and the stride can also be used to adjust the size of the output data. The specific operation of the pulling technique will be described later with reference to FIG.

The feature extraction unit 1210 according to an embodiment of the present disclosure is a pre-processing for extracting a feature of an analysis object image, and can apply filtering to the analysis object image. The filtering may be Fast Fourier Transform (FFT), histogram equalization, motion artifact rejection, or noise rejection. However, the filtering of the present disclosure is not limited to the methods listed above, and may include any type of filtering that can improve the quality of the image. Or enhancement of the image described with reference to Figs. 5 to 11 as pre-processing may be performed.

The context generation unit 1220 can generate the context information of the input image (analysis target image) using the characteristics of the input image extracted from the feature extraction unit 1210. [ For example, the context information may be a representative value indicating all or a part of the region to be analyzed. Also, the context information may be global context information of the input image. The context generation unit 1220 according to an embodiment may generate context information by applying the resultant synthesis neural network technique or the pulling technique to the features extracted from the feature extraction unit 1210. [ The pooling technique may be, for example, an average pooling technique.

The feature and context analyzing unit 1230 can analyze the image based on the features extracted from the feature extracting unit 1210 and the context information generated in the context generating unit 1220. The feature and context analyzing unit 1230 according to an embodiment concatenates the local features of each region of the image extracted by the feature extracting unit 1210 and the global context reconstructed from the context generating unit 1220, Or the like to classify an input image or use it to find a position of an object of interest included in an input image. Since the information at the specific two-dimensional position in the input image includes not only the local feature information but also the global context information, the feature and context analyzing unit 1230 uses the information to distinguish the local feature information Can more accurately recognize or classify similar input images.

As described above, the invention according to one embodiment of the present disclosure allows for more accurate and efficient learning and image analysis by using global context information as well as local features used by a general artificial neural network technique do. From this point of view, the neural network to which the invention according to the present disclosure is applied can be referred to as a 'depth neural network through context analysis'.

13 is a diagram illustrating a process of generating and analyzing context information of an image according to an embodiment of the present disclosure.

The feature extraction unit 1310, the context generation unit 1320, and the feature and context analysis unit 1330 of FIG. 13 respectively correspond to the feature extraction unit 1210, the context generation unit 1220, May be one embodiment of the < RTI ID = 0.0 >

13, the feature extraction unit 1310 may extract a feature from the input image 1312 using the input image 1312 and generate a feature image 1314 including the extracted feature information. The extracted feature may be a feature of the local region of the input image. The input image 1312 may include an input image of the image analysis apparatus or a feature map of each layer in the composite neural network model. Also, the feature image 1314 may include a feature map and / or feature vector obtained by applying a composite neural network technique and / or a pulling technique to the input image 1312.

The context generation unit 1320 may generate context information by applying a composite neural network technique and / or a pooling technique to the feature image 1314 extracted by the feature extraction unit 1310. [ For example, the context generation unit 1320 can generate context information of various scales such as an entire image, a quadrant region, and a 9th region by variously adjusting the pooling interval. Referring to FIG. 13, a full context information image 1322 including context information on an image of a full size of an image, a quadrature context information image 1322 including context information on a quadruple image having a size divided into four quadrants, 1324), and a 9-ary context information image 1326 including context information for a 9-ary image having a size divided into nine equal parts of the whole image.

The feature and context analyzer 1330 can more accurately analyze the specific region of the analysis target image using both the feature image 1314 and the context information images 1322, 1324, and 1326.

For example, when an image including a boat having a shape similar to a car is an input image, a characteristic image 1314 including a local feature extracted by the feature extraction unit 1310, Can not accurately determine whether it is a car or a boat. That is, although the feature extracting unit 1310 can recognize the shape of the object based on local features, there are cases where it is not possible to correctly identify and classify the object only by the shape of the object.

The context generation unit 1320 according to an embodiment of the present disclosure generates context information 1322, 1324, and 1326 based on the analysis object image or the feature image 1314 to more accurately identify and classify objects . For example, if the feature extracted for the whole image is recognized or classified as "natural landscape ", the feature extracted for the quadrant image is recognized or classified as" Quot; natural scenery ", "lake ", and" water "as the context information.

The feature and context analyzer 1330 according to an embodiment of the present disclosure can identify the object having the shape of the boat or car as a "boat" by utilizing the context information.

In the embodiment described with reference to FIG. 13, the context information for the entire image, the context information for the quadruple image, and the context information for the nine-divided image are generated and utilized. However, But is not limited thereto. For example, context information for an image having a size other than the image of the above-described size may be generated and utilized.

The resultant artificial neural network technique and pulling according to an embodiment of the present disclosure will be described later with reference to FIG. 16 and FIG.

14 is a diagram for explaining a process of analyzing an image and identifying an object according to an image analysis apparatus according to an embodiment of the present disclosure.

For example, the image analysis apparatus 1400 receives the image 1410 and generates information about image regions of various sizes, thereby accurately identifying and / or classifying the objects included in the image 1410. The input image 1410 may be, for example, an X-ray image including a bag. The image analysis apparatus 1400 analyzes the input image 1410 according to the above description, extracts features of the entire image, features of a partial region of the image, and accurately identifies the objects included in the image 1410 can do. The feature 1422 for the entire image may be, for example, a feature of the shape of the bag. The features for a portion of the image may include, for example, features 1424 for the handle, features 1426 for the zipper, features for the ring 1428, and the like.

The image analysis apparatus 1400 can accurately identify that the object included in the image 1410 is a "bag" by utilizing the generated features 1422, 1424, 1426, and 1428 as context information.

If some of the generated features are unrelated to the "bag ", the image analysis apparatus 1400 may determine that the object contained in the image 1410 can not be identified as a" bag " Can not be identified as "bag ". Alternatively, if some of the context information is not related to other context information, an abnormality of the object can be output. For example, when an unstructured space that is not related to a normal characteristic of the "bag ", a space of a certain thickness or more is detected, the" bag "

As described above, when context information that is not related to the normal context information is included, such fact can be output to the readout source, and the readout source can perform close inspection or refinement inspection Can be performed.

15 is a diagram for explaining the operation of the image analysis apparatus according to an embodiment of the present disclosure.

In step S1500, the image analysis apparatus can extract the characteristics of the analysis target image.

The image analysis apparatus according to an exemplary embodiment can extract characteristics of an input image by using a general artificial neural network technique or a pulling technique. The feature of the analysis object image may be a local feature for each region of the image, and the pooling technique may include at least one of a max pooling technique and an average pooling technique.

In step S1510, the image analysis apparatus can generate context information based on the features extracted in step S1500.

The image analysis apparatus according to an exemplary embodiment may generate context information by applying the resultant artificial neural network technique and / or the pulling technique to the feature extracted in step S1500. The context information may be a representative value indicating all or a part of the region of the image to be analyzed. Also, the context information may be global context information of the input image. Also, the pooling technique may be, for example, an average pooling technique.

In step S1520, the image analysis apparatus may analyze the analysis target image based on the feature extracted in step S1500 and the context information generated in step S1510.

For example, the image analysis apparatus may classify the input image by combining the local features of each region of the image extracted in operation S1500 and the global context reconstructed in operation S1510, or locate the object of interest included in the input image have. Therefore, since the information at a specific two-dimensional position in the input image is included from the local information to the global context, it is possible to more accurately recognize or classify input images which are different in actual contents but local information is similar to each other. Or context information that is not related to other context information.

16 is a view for explaining an embodiment of a composite neural network for generating a multi-channel feature map.

The image processing based on the composite neural network can be applied to various fields. For example, image processing apparatuses for image object recognition, image processing apparatuses for image reconstruction, image processing apparatuses for semantic segmentation, image processing for scene recognition, Device or the like.

The input image 1610 may be processed through the composite neural network 1600 to output the feature map image. The outputted feature map image can be utilized in various fields as described above.

The composite neural network 1600 may be processed through a plurality of layers 1620, 1630, and 1640, and each layer may output multi-channel feature map images 1625 and 1635. The plurality of layers 1620, 1630, and 1640 according to an exemplary embodiment may extract a feature of an image by applying a filter having a predetermined size from the upper left end to the lower right end of the input data. For example, the plurality of layers 1620, 1630, and 1640 multiply the weighted value by the weighted upper left NxM pixel of the input data and map it to a neuron at the upper left of the feature map. In this case, the weight to be multiplied will also be NxM. The NxM may be, for example, 3x3, but is not limited thereto. Thereafter, in the same process, the plurality of layers 1620, 1630, and 1640 scans input data from left to right and from top to bottom by k squares, and maps the weights to neurons in the feature map. The k-th column means a stride for moving the filter when performing the product multiplication, and can be set appropriately to adjust the size of the output data. For example, k may be one. The NxM weight is called a filter or filter kernel. That is, the process of applying the filter in the plurality of layers 1620, 1630, and 1640 is a process of performing a convolution operation with the filter kernel. As a result, the extracted result is referred to as a "feature map" Map image ". In addition, the layer on which the convolution operation is performed may be referred to as a convolution layer.

The term " multiple-channel feature map "refers to a set of feature maps corresponding to a plurality of channels, and may be, for example, a plurality of image data. Channel feature maps 1625 and 1635 may be inputs at any hierarchy and may be output according to a feature map operation result, such as a convolution operation. According to one embodiment, 1630, 1640, also referred to as " layers "or" convolutional layers. &Quot; Each layer sequentially receives the multi-channel feature maps generated in the previous layer, Channel characteristic maps generated in the L-1th layer (not shown) in the L (L is an integer) th layer 1640. The multi-channel characteristic maps generated in the L-1th layer Maps can be generated.

16, the feature maps 1625 having the channel K1 are outputs according to the feature map operation 1620 in the layer 1 with respect to the input image 1610 and the feature maps 1630 ≪ / RTI > In addition, feature maps 1635 with channel K2 are outputs according to feature map operation 1630 at layer 2 for input feature maps 1625 and feature map operations (not shown) at layer 3, ≪ / RTI >

Referring to FIG. 16, the multi-channel feature maps 1625 generated in the first layer 1620 include feature maps corresponding to K1 (K1 is an integer) channels. In addition, the multi-channel feature maps 1635 generated in the second layer 1630 include feature maps corresponding to K2 (K2 is an integer) channels. Here, K1 and K2, which represent the number of channels, may correspond to the number of filter kernels used in the first layer 1620 and the second layer 1630, respectively. That is, the number of multi-channel feature maps generated in the Mth layer (M is an integer equal to or greater than 1 and equal to or smaller than L-1) may be equal to the number of filter kernels used in the Mth layer.

17 is a view for explaining an embodiment of the pulling technique.

As shown in FIG. 17, the window size of the pulling is 2x2, the stride is 2, and the output image 1790 can be generated by applying the maximum pulling to the input image 1710. [

17A, a 2x2 window 1710 is applied to the upper left of the input image 1710 and a representative value (here, maximum value 4) among the values in the window 1710 area is calculated to output the output image 1790 At a corresponding position 1720 of the display unit 1720.

Then, in FIG. 17B, the window is shifted by stride, that is, by 2, and the maximum value 3 of the values in the window 1730 region is input to the corresponding position 1740 of the output image 1790.

If the window can not be moved to the right side, the above process is repeated from the left side of the input image by a stride. That is, as shown in FIG. 17C, the maximum value 5 of the values in the window 1750 area is input to the corresponding position 1760 of the output image 1790.

Thereafter, as shown in Fig. 17D, the window is shifted by a stride, and the maximum value 2 of the values in the window 1770 region is input to the corresponding position 1780 of the output image 1790.

The above process is repeated until the window is located in the lower right region of the input image 1710, thereby generating an output image 1790 in which the pulling is applied to the input image 1710.

Hereinafter, an embodiment of a method of generating a new composite image and / or corresponding virtual article information by utilizing a plurality of image and / or article information will be described with reference to FIGS. 18 to 22. FIG.

18 is a block diagram showing a configuration of an image synthesizing apparatus according to an embodiment of the present disclosure.

18, the image synthesizing apparatus 1800 includes an object image extracting unit 1810, an object position information generating unit 1820, an image synthesizing unit 1830, and / or an object detecting deep learning model learning unit 1840 . It should be noted, however, that this shows only some of the components necessary for explaining the present embodiment, and the components included in the image synthesizer 1800 are not limited to the above-described examples. For example, two or more constituent units may be implemented in one constituent unit, and an operation performed in one constituent unit may be divided and executed in two or more constituent units. Also, some of the constituent parts may be omitted or additional constituent parts may be added. Or, among the components of the image analyzing apparatus 112 of FIG. 1, the image enhancing apparatus 500 of FIG. 5, the image analyzing apparatus 1200 of FIG. 12, and the image synthesizing apparatus 1800 of FIG. 18, The component performing the function may be implemented as one component.

The image synthesizing apparatus 1800 according to an embodiment of the present disclosure receives a first image including a first object and a second image including a second object, and for each of the first image and the second image, The first object and the second object are generated based on the position information of the first object and the position information of the second object, 3 image, and learn the object detection deep learning model using the position information of the first object, the position information of the second object, and the third image.

Referring to FIG. 18, the input image 1850 may include an image including a single object. The description of the input image 1850 is the same as the description of the input image described with reference to FIG.

The object image extracting unit 1810 may receive the image 1850 including a single object and may divide the received image into an object and a background. The description of the object image extracting unit 1810 is the same as the description of the object image extracting unit 520 described with reference to FIG. 5 and FIG.

The object location information generation unit 1820 can determine the location of the object extracted from the object image extraction unit 1810. For example, the object position information generation unit 1820 specifies a bounding box surrounding the object region, and generates position information of the object classified by the object image extraction unit 1810 based on the specified rectangular box can do. The description of the method of generating the location information of the object is the same as the description of the method referring to Fig.

According to an embodiment of the present disclosure, since position information of an object included in an image can be automatically generated, it is possible to avoid the hassle of a readout source for directly inputting the positional information of an object for each image for artificial intelligence learning .

Referring again to FIG. 18, the image synthesizing unit 1830 receives the position information of the object through the object image extracting unit 1810 and the object position information generating unit 1820, Can be generated. For example, the first image including the first object and the second image including the second object are respectively transmitted through the object image extracting unit 1810 and the object position information generating unit 1820, The position information of the second object is obtained and the image combining unit 1830 generates a third image including the first object and the second object based on the obtained position information of the first object and the position information of the second object can do. A detailed process of generating a multi-object image will be described in detail with reference to FIG.

19 is a view illustrating a process of generating a multi-object image using two images including a single object according to an embodiment of the present disclosure. The image combining unit 1900 of FIG. 19 is an embodiment of the image combining unit 1830 of FIG. 19, the image synthesizing unit 1900 includes a first single object image 1910, a second single object image 1920, and a first single object image 1920 obtained through an object image extracting unit and an object position information generating unit Object image 1940 and the multi-object image 1940 in which the first single object image 1910 and the second single object image are combined using the position information of the second single object image 1920 and the second single object image 1920, Location information 1950 for the objects that are being processed. The image combining unit 1900 may also use the image 1930 for the background separated from the object when the first single object image 1910 and the second single object image 1920 are combined. The position information of the first single object image 1910 and the position information of the second single object image 1920 may be arbitrarily modified. In addition, image synthesis may be performed based on the corrected position information. Thus, it is possible to generate a large amount of composite images and virtual position information.

As described above, it is possible to generate as many synthetic images and / or corresponding virtual position information as necessary for learning through synthesis. Therefore, even when the absolute number of images and / or position information required for learning is small, a sufficient number of learning data can be generated to sufficiently learn the artificial intelligence model.

Referring again to FIG. 18, the object detection deep learning model learning unit 1840 can learn the object detection deep learning model using the position information of the first object, the position information of the second object, and the third image. For example, the object detection deep learning model learning unit 1840 can learn a combined-effect neural network model. The position information of the first object, the position information of the second object, and the third image may be used for learning of the compound neural network model.

20 is a diagram illustrating a process of learning a composite-object neural network using a multi-object image according to an embodiment of the present disclosure. The object detection deep learning model learning unit 2000 of FIG. 20 is an embodiment of the object detection deep learning model learning unit 1840 of FIG. Referring to FIG. 20, it is possible to use a multi-object image 2010 synthesized using single object images and position information of objects as data necessary for learning. The object detection deep learning model learning unit 2000 can learn the composite neural network 2020 by projecting the position information of each of the single objects with respect to the multi object image 2010. [ According to one embodiment, when there are a plurality of objects in an object passing through an X-ray searcher in an electronic search system, an overlapping X-ray image of a plurality of objects can be obtained. According to this disclosure, Since the artificial neural network is learned by using the shape of each object together with the position information of the objects of the object, the more accurate detection result can be obtained even when the overlap between the objects occurs.

21 is a diagram for explaining a process of analyzing an actual image using the image synthesizing apparatus according to an embodiment of the present disclosure.

The image synthesizing apparatus 2100 of FIG. 21 is an embodiment of the image synthesizing apparatus 1800 of FIG. The operation of the object image extracting unit 2104, the object position information generating unit 2106, the image synthesizing unit 2108 and the object detecting deep learning model learning unit 2110 included in the image synthesizing apparatus 2100 of FIG. The object image extracting unit 1810, the object position information generating unit 1820, the image synthesizing unit 1830 and the object detecting deep learning model learning unit 1840 included in the image synthesizing apparatus 1800 . Accordingly, the image synthesizing apparatus 2100 includes an object image extracting unit 2104, an object position information generating unit 2106, an image synthesizing unit 2108, and an object detecting deep learning model learning unit 2104 for a plurality of single object images 2102, Lt; RTI ID = 0.0 > 2110 < / RTI > The object detecting apparatus 2120 can detect each object using the artificial neural network model learned in the image processing apparatus 2100 with respect to the image 2122 including multiple objects in the real environment.

According to one embodiment, when the present invention is applied to an article search system, the image synthesizing apparatus 2100 of the present disclosure generates a new multi-object embedded image based on a single object region extraction in an X-ray image . In addition, the object detection apparatus 2120 can find an area where there are multiple objects included in an article passing through the X-ray scanner. Accordingly, by automatically extracting the position of the object with respect to the X-ray image, it is possible to more easily perform the image inspection operation by the readout source, and further, And can be used for comparison of computerized information.

FIG. 22 is a view for explaining an image synthesizing method according to an embodiment of the present disclosure; FIG.

In step S2200, the first image including the first object and the second image including the second object may be input, and the object and the background may be distinguished for the first image and the second image, respectively. For example, a pixel value of an input image may be compared with a predetermined threshold value to binarize the pixel value, and binarized pixel values may be grouped to distinguish objects included in the input image.

In step S2210, the location information of the first object and the second object may be generated. For example, a rectangular box surrounding the object area may be specified, and position information of the object classified in step S2200 may be generated based on the specified rectangular box.

In step S2220, a third image including the first object and the second object may be generated based on the position information of the first object and the position information of the second object. For example, the third image including the first object and the second object may be generated based on the position information of the first object and the position information of the second object obtained in step S2210.

In step S2230, the object detection deep learning model may be learned using the position information of the first object, the position information of the second object, and the third image. For example, the location information of the first object generated in step S2210, the location information of the second object, and the third image generated in step S2220 may be used for learning the articulated neural network model. .

In the embodiment described with reference to FIGS. 18 to 22, an example in which an image including a single object is received and an object and a background are separated has been described. However, the present invention is not limited thereto, and the input image may be an image including two or more objects. In this case, it is possible to distinguish two or more objects and backgrounds from the input image, and generate position information for each of the two or more objects.

Also, in the above-described embodiment, it has been described that the third image is generated based on the two single object images and the position information of the respective objects. However, the present invention is not limited to this, and a third image may be generated using two or more single object images and position information of each object. That is, the image processing method and apparatus according to the present disclosure can generate a third image based on two or more images each including one or more objects and position information of each object.

Hereinafter, with reference to FIGS. 23 to 30, a method for analyzing an analysis object image in consideration of an actual size of an object in the article search system according to the present disclosure will be described in detail.

In the conventional article search system, when there is a specific object in the analysis target image, the image obtained by separately marking it is provided to the readout source, and the reader can not tell the specific object in the analysis target image. However, by using the above-described CNN or deep learning based image analysis technique, the image analysis apparatus can provide the user with information on what specific object in the analysis target image is.

Since the above-described deep learning-based image analysis method is performed using only the context information based on the features of the object and the extracted features in the analysis target image, the actual measurement size of the object Can not be considered.

For example, an object from which the same context information is extracted may be an object of a different kind depending on the actual size of the object. For example, smartphones and tablets are different types of objects, but because of their morphological similarity, it is difficult to distinguish between smartphones and tablets in the image to be analyzed.

That is, the above-described deep learning-based image analysis method does not analyze an image in consideration of an actual size of an object, and therefore, the same image analysis results are obtained for the same or similar objects regardless of the actual size.

Hereinafter, a method of improving the detection and analysis performance of the image analyzing apparatus using the actual size information of the object in the image to be analyzed is proposed.

The image analysis apparatus can receive information related to the attribute of the analysis object image or image information together with the analysis object image from the X-ray reading apparatus. The image analysis apparatus can calculate the actual size of the object to be analyzed and improve the image analysis performance based on the calculated actual size by using the received analysis object image and image information.

23 is a flowchart illustrating an image analysis method according to an embodiment of the present disclosure.

Referring to FIG. 23, an image analyzing method considering an actual size of an object according to an embodiment of the present disclosure may be performed by the article searching system 100 or the image analyzing apparatus 112. Hereinafter, it is assumed that the image analysis apparatus 112 performs the method for convenience. However, in a broad sense, the image analysis method considering the actual size of the object according to the present disclosure can also be interpreted as being performed by the article search system 100.

An image analyzing method considering an actual size of an object according to an embodiment of the present disclosure includes receiving an analyzing object image (S2300), detecting an object in the analyzing object image (S2310), measuring an actual size of the detected object (S2320) analyzing the detected object on the basis of the analyzed result (S2340) and / or outputting the analyzed result (S2340).

In step S2300, the image analysis apparatus can receive the analysis object image from the X-ray apparatus or the like. Here, the analysis target image may mean an image or a photograph including an object to be detected. The image analysis method according to the present disclosure may be a method of detecting an object included in an analysis object image, determining what kind of object is detected, and providing the object to a readout object.

In step S2310, the image analysis apparatus can detect an object in the analysis target image. The image analysis apparatus can detect an object in the analysis target image by using CNN or a pre-learned deep learning based model. The method of the image analyzing apparatus for detecting an object in the analysis object image is the same as that described with reference to FIG. 12 to FIG. 22, and a description thereof will be omitted.

In step S2320, the image analysis apparatus can perform an analysis on the detected object in step S2310 based on the actual size of the detected object. Here, the actual size may mean the actual size of the object to be detected. The actual size of the object to be detected can be calculated based on the resolution information of the analysis object image. For example, the image analyzing apparatus may calculate the length of the detection object in units of pixels, and then multiply the calculated pixel value by the length per unit pixel to obtain the actual size of the detection object. Here, the length of the object to be detected may include the length, length, and / or diagonal length of the object.

As another example, the image analysis apparatus may calculate the actual size of the object by calculating the area occupied by the object to be detected in the image to be analyzed. For example, the image analyzing apparatus can calculate the actual size of the detection target object by calculating the area of the object in pixels and multiplying the calculated pixel value by the width per pixel. For example, the number of pixels constituting an area of an object may be obtained, and the area occupied by one pixel may be multiplied by the number of pixels obtained to calculate the area of the object. At this time, the area occupied by one pixel can be obtained based on information (e.g., resolution, etc.) regarding the analysis target image. Alternatively, the area occupied by one pixel may be directly obtained by the product retrieval system 100 or the image analysis apparatus as information on the analysis target image.

The image analysis apparatus can determine the type of the object to be detected by comparing the actual size of the object to be detected with a value determined for each object type or a value defined in the database for each object type.

For example, the image analysis apparatus can compare an actual size of an object to be detected with a predetermined value. As a result of the comparison, when the actual size of the detection object is smaller than or equal to the predetermined value, the image analysis apparatus can determine the type of the detection object as the first type. As a result of the comparison, when the actual size of the detection object is larger than the predetermined value, the image analysis apparatus can determine the type of the detection object as the second type. As another example, when the actual size of the detection object is smaller than the preset value as a result of the comparison, the image analysis apparatus can determine the type of the detection object as the first type. When the actual size of the detection object is greater than or equal to the predetermined value as a result of the comparison, the image analysis apparatus can determine the type of the detection object as the second type.

It is an example of the present invention that the image analysis apparatus determines the type of the object to be detected as one of the first type and the second type and the scope of the present invention is not limited thereto. The image analysis apparatus can compare the size of the object to be detected with a plurality of preset values and determine the type of the object to be detected as one of a plurality of types. As another example, the image analysis apparatus can determine the type of the detection object as one of a plurality of types, depending on which of a plurality of predetermined ranges the size of the detection object is included in.

For example, if the image analysis apparatus determines that the object to be detected is primarily a smartphone, but the size of the object to be detected exceeds a preset value, the image analyzing apparatus may not display the object as a tablet, .

In another embodiment, the actual size of the detection object may be compared with a predetermined size range predetermined for each type of object. For example, when the actual size of the detection object belongs to a predetermined size range, the detection object may be determined as an object of a type related to the predetermined size range. For example, if the first size range for the smartphone and the second size range for the tablet are stored in advance, and the actual size of the detection object is included in the first size range, the detection object is determined to be a smartphone . Further, when the actual size of the detection object is included in the second size range, the detection object may be determined to be a tablet. If the actual size of the detection object is not included in any of the first size range and the second size range, the detection object is an object other than the smartphone and the tablet (e.g., a third liquid crystal device) Can be judged.

In step S2330, the image analysis apparatus may output the analysis result of step S2320. For example, the image analysis apparatus may provide the analysis result to the user or the readout source 140 via the output device 114. [

24 is a flow chart illustrating an image analysis method according to some embodiments of the present disclosure.

Referring to Fig. 24, a specific method by which the image analysis apparatus provides the detection result to the reading source will be described. An image analyzing method considering an actual size of an object according to an embodiment of the present disclosure includes receiving an analyzing object image (S2400), receiving image information (S2410), detecting an object in the analyzing object image S2420), calculating the length per pixel of the image to be analyzed (S2430), analyzing the detected object based on the per-pixel length (S2440), outputting the analyzed result (S2450) and / (S2460). ≪ / RTI >

Steps S2400, S2420, S2440, and S2450 may correspond to steps S2300, S2310, S2320, and S2330 of the image analysis apparatus described in FIG. 23, respectively.

Also, although not shown in FIG. 24, the image analysis method according to an embodiment of the present disclosure may include a step of performing a preprocessing on an analysis object image. The preprocessing of the image to be analyzed may mean a process of eliminating the interference to the entire image to be analyzed or a process of setting the color information of the X-ray input image as a preset in order to detect the image analyzing device. In addition, the image analysis apparatus can perform preprocessing on the analysis object image through the preprocessing method of the images described with reference to FIG. 5 to FIG.

As another example, an image preprocessing method according to an embodiment of the present disclosure may include a step of rotating an analysis object image to obtain an angle-corrected analysis object image. The angle correction of the image analysis device may be performed on a received analysis image or on a bounding box surrounding the detected object.

In step S2410, the image analysis apparatus can receive the image information. X-ray readers, etc., each has its own specification. Since specifications are disclosed, the image analysis apparatus can calculate the length per unit pixel of the analysis target image based on the specifications. The image analysis apparatus can construct a probability model considering the actual size of the object by adding or reflecting a probability model of the actual size of the object to the CNN or deep learning based probability model.

Here, the image information may be information transmitted through the X-ray reading device, information about the attribute of the analysis object image input by the reader or the user. Or the image information may be information previously input to the image analysis apparatus according to specifications of the disclosed X-ray reading apparatus. For example, the image information may include resolution information, resolution information, luminance range information, chrominance range information, pixel information, information on the actual size of the image, codec information, But is not limited to, at least one of X-ray reading device model information, image aspect ratio, imaging date and time, and image storage path. In addition, various information for indicating the attributes of the received analysis target image itself may be included in the image information. Hereinafter, for convenience, the case where the image information is resolution information of the analysis target image and / or information on the actual size of the image will be described as an example.

In step S2430, the image analysis apparatus can calculate the length per unit pixel of the analysis target image using the image information received in step S2410. For example, when the image information received by the image analyzing apparatus is resolution information on the image to be analyzed and information on the actual size of the image, the image analyzing apparatus measures the horizontal or vertical length of the image to be analyzed, And the resolution per unit pixel of the image to be analyzed.

For example, when the image to be analyzed is an image having X * Y resolution and the actual size of the image is Hcm * Wcm, the image analyzing apparatus calculates X / H (pixel / cm) or W / Y (pixel / cm).

The method of calculating the length per unit pixel of the image analyzing apparatus is one example, and the scope of the present invention is not limited to the above example. In addition, the image analysis apparatus may receive information on the length per unit pixel directly from the X-ray reading device or the reading source.

In step S2440, the image analysis apparatus can analyze the detected object in consideration of the length per unit pixel of the analysis object image calculated in step S2430. In order to analyze an object in consideration of the actual size of the object, the image analysis apparatus can classify the types of the detectable object in advance.

In step S2460, the image analysis apparatus can receive the verification result of the reading source for the analysis result. Here, the verification result may refer to the result of the opening inspection of the reading source described in Figs. 1 to 4. The results of the retrofit inspection can be input as inspection results. The result of the inspection may be transmitted to the artificial intelligence data center 323 and used as learning data. The method of using the result of the inspection of the image analysis apparatus as the verification result and the result of the inspection of the open / close operation as the learning data is the same as described above, and the description thereof is omitted.

25 is a flowchart for explaining a method of determining a classification criterion of an object according to an embodiment of the present disclosure.

The method for determining the classification criterion of an object according to an embodiment of the present disclosure can be performed by the article search system 100, the image analysis apparatus 112, or the learning unit 120. Hereinafter, it is assumed that the image analysis apparatus 112 determines the classification criterion of the object for convenience. However, in a broad sense, the method of determining classification criteria of an object according to the present disclosure can also be interpreted as being performed by the article search system 100 or the learning unit 120.

Referring to Fig. 25, a method in which an image analysis apparatus determines a classification criterion of an object will be described. A method for determining a classification criterion of an object according to an embodiment of the present disclosure includes receiving a read image including a read object (S2500), receiving information about the read object (S2510), analyzing the read image (S2520), and / or determining a classification criterion of the object (S2530).

In step S2500, the image analysis apparatus can receive the read image for determining the classification criterion of the object. For example, the read image may be input by a read source, a user, or an administrator who wants to determine the classification criteria of the object. Since the read image is an image input for database addition and determination of classification criteria, it may be an image including a single object differently from the analysis target image described above.

In step S2510, the image analysis apparatus can receive information on an object included in the read image. The information about the object included in the readout image can be input by a readout source, a user or an administrator to determine the classification standard of the object.

The image analysis apparatus can receive the information about the object included in the read image together with the read image and can convert the data into data. For example, the image analysis apparatus may be configured to determine the time and date at which the read image was received, the object image information, the image storage path, the degree or angle of rotation of the object contained in the read image, the width and height of the object, And the aspect ratio can be converted into data and constructed as a database.

In steps S2520 to S2530, the image analysis apparatus can analyze the object included in the read image based on the information about the object included in the read image and the read image, and determine the classification criterion for the object. Steps S2500 to S2520 for determining a classification criterion of an object may be repeatedly performed to generate a database for determining classification criteria of an object.

Even if the object is of the same type, the characteristics of the object shown through the X-ray image may vary depending on the use, material, and the like. In order to determine to what extent the type of object is to be determined, the image analysis apparatus can extract the feature vector from the read image.

The image analysis apparatus can extract a feature vector from a read image using at least one of a Scale Invariant Feature Transform (SIFT) or a Speeded Up Robust Feature (SURF) algorithm.

In another example, the image analysis device is provided with an output vector of the Convolutional Neural Network (CNN) using the context of the object, and the output vector is transformed to the optimal complexity by a vector dimension change algorithm such as Principal Component Analysis Can be converted into an output vector.

The algorithm described above is an example of an algorithm used to derive the feature vectors of the present disclosure and does not limit the scope of the present invention. The image analysis apparatus can extract or derive the feature vectors of the read image by using various algorithms for analyzing or extracting features of the image.

Here, SIFT may mean an algorithm for extracting feature points that are invariant to image size and rotation. In addition, SURF can refer to an algorithm that finds feature points that are invariant to environmental changes in consideration of environmental changes such as scale and illumination point from multiple images.

The image analysis apparatus can extract a feature vector using at least one of the algorithms described above. The image analysis device can project the feature vector extracted using PCA or Linear Discriminant Analysis (LDA) or the like to n dimensions and analyze the projected result. Here, PCA can mean an algorithm that transforms a high-dimensional feature vector into a low-dimensional feature vector.

Fig. 26 is a diagram for explaining a method of determining a classification criterion of an object according to an embodiment of the present disclosure, and Fig. 27 is a diagram showing an example of classification criterion of an object.

FIG. 26 shows an example of projecting feature vectors of various sagitious objects onto a two-dimensional plane. Specifically, FIG. 26 shows an example of clustering multiple feature vectors into a plurality of clusters 2600, 2610, and 2620. In the graph of FIG. 26, the x-axis and the y-axis may be one of various types of parameters for expressing the feature vector.

The feature vectors shown in FIG. 26 are all feature vectors for the swords, which can be largely classified into a first cluster 2600 on the lower left side and a second cluster 2610 and 2620 on the upper right side. The reason why the two clusters having different feature vectors are formed is that the feature vectors can be extracted differently depending on the use, material, and specific form of the object, even if the images are about the same object.

For example, the first cluster 2600 may be a clustered feature vector for a Swiss knife. The second cluster may be one in which feature vectors for the kitchen knife are clustered.

On the clustering basis, the second clusters all contain similar feature vectors, but the image analyzing apparatus can recognize that the second clusters can be divided into two different detailed clusters based on the information about the inputted products. 26, each of the two sub-clusters 2610 and 2620 includes a cluster 2610 of feature vectors of a knife made of wood and a cluster 2612 of feature vectors of a knife made of a plastic material (for example, 2620).

FIG. 27 shows an example in which the image analysis apparatus determines classification criteria for a sword object according to the cluster result in FIG. 26;

The image analyzing apparatus can determine to what extent the object type to be detected is to be subdivided in consideration of the similarity between the feature vectors. Fig. 27 shows an example in which the image analysis apparatus subdivides the swords into cutter knives, Swiss knives, and knives.

For example, the image analysis apparatus may add different importance levels to each of the components of the object, and use the importance to determine classification criteria of the object. For example, in the example of FIG. 26, the image analysis apparatus calculates the degree of importance of the handle portion to be low, so that the difference of the handle portion does not affect the object classification standard. The importance of each component can be pre-set according to the needs of the reader or administrator.

As another example, the image analysis apparatus can determine the classification criterion of the object based on the actual size information of the object. For example, since the transient, the Chinese, and the Japanese are all composed of the handle and the blade, the morphological similarity can be high. Since the objects with high morphological similarity are all likely to be clustered into a single cluster, the image analysis apparatus adds the actual size of the object to the object classification criterion, so that the transient, Can be included.

For example, since a smartphone and a tablet are both shown as objects including a liquid crystal of a rectangular shape, they can be clustered into one cluster according to their morphological similarity. At this time, the image analyzing device can consider the actual size of the object, so that the smartphone and the tablet are included in different clusters

28 is a diagram illustrating a database of objects according to an embodiment of the present disclosure;

28 shows an example of a database including information on objects included in a read image and a read image. For example, the database of objects may include at least one of the type of object, the date and time of imaging of the read image, the read image, the image storage path, the extent to which the object is rotated within the read image, the width, . Such information is only an example, and the scope of the present invention is not limited to the above example. The image analysis apparatus can add the read image to the database by using various information that can represent the actual size of the object.

In the conventional image analysis, the general size of the object is estimated by K-mean clustering technique or the like. However, this clustering is only effective when the shape of the object is close to the normal distribution, and thus may not be suitable for image analysis in which objects of various sizes are mixed. Particularly, when a plurality of objects in a non-destructive inspection are shown, it is difficult to construct a size-based probability model using a normal distribution.

To compare the entity size of an object under the same conditions, the image analysis apparatus may perform a preprocessing step of skew estimation of the object in the readout image or correcting the angle before adding the readout image to the database . For example, in the example of FIG. 28, the angles of the objects are -30, 60, and 30, respectively, but the image analysis apparatus rotates the read image so that the angles of the respective objects become the same, can do. At this time, the image analysis apparatus can database the width and height of the object based on the rotated read image.

29 is a diagram for explaining image analysis results according to some embodiments of the present disclosure.

A method of constructing a probability model for each type of object will be described with reference to FIG. As described above, even if the morphological characteristic is very similar, the type of the object may vary depending on the actual size. When the analysis of a single kind of object is performed, the distribution of the feature vectors follows the normal distribution, but when the objects that can be classified into different kinds are mixed, the distribution of the feature vectors does not follow the normal distribution.

For example, if the subject of the image analysis is a person, the distribution of the feature vectors to the human follows the normal distribution. However, if objects of various shapes, materials, and sizes exist even though the object types are the same as the swords, The distribution does not follow the normal distribution.

In consideration of this point, the image analysis apparatus does not construct the feature vectors belonging to the same category as one Gaussian probability model but uses a Gaussian Mixture Model (GMM) and / or a normalized histogram A mathematical probability model for the type of object can be constructed.

The graph of FIG. 29 may be a graph showing feature vectors for the case where objects of various types, materials, and sizes exist even though the types of objects are the same. In the graph of FIG. 29 (a), the horizontal axis may mean a parameter representing an actual size of an object, and the vertical axis may mean a distribution value of each sample. On the other hand, the horizontal axis of the graph of FIG. 29 (b) may mean a parameter representing an actual size of an object, and the vertical axis may mean a probability value of each interval. In the graph of FIG. 29 (a), the feature vectors can be largely divided into two clusters. In this case, each cluster is considered to follow a normal distribution, but no normal distribution is formed between the left cluster and the right cluster.

In this case, the image analysis apparatus can classify the type of the object by using the features expressing the size of the object. Each data value may be extracted through a linear transformation. For example, the left cluster of the graph of FIG. 29 may be an example of a normal distribution for a Swiss knife feature vector, and the right cluster of the graph may be an example of a normal distribution for a knife feature vector.

30 is another block diagram showing a configuration of an image analysis apparatus according to an embodiment of the present disclosure.

30, the image analyzing apparatus 3000 includes an analyzing object image receiving unit 3001, an image information receiving unit 3002, a classification criterion determining unit 3003, a database 3004, a measured distance calculating unit 3005, A pre-processing unit 3006, a probability model building unit 3007, and / or an image analysis unit 3008. [

It should be noted, however, that this shows only some components necessary for explaining the present embodiment, and the components included in the image analysis apparatus 3000 are not limited to the above-described examples. For example, two or more constituent units may be implemented in one constituent unit, and an operation performed in one constituent unit may be divided and executed in two or more constituent units. Also, some of the constituent parts may be omitted or additional constituent parts may be added. Among the components of the image analyzing apparatus 112 of FIG. 1, the image enhancing apparatus 500 of FIG. 5, the image analyzing apparatus 1200 of FIG. 12, and the image synthesizing apparatus 1800 of FIG. 18, The component performing the function may be implemented as one component.

The analysis object image receiving unit 3001 can receive the analysis object image provided from the X-ray reading device and the read image inputted for building the database. Here, the analysis object image receiving unit 3001 may be defined as an analysis object input unit.

The image information receiving unit 3002 can receive the image information of the analysis object image and the read image provided from the X-ray reading device. Here, the image information receiving unit 3002 may be defined as an image information input unit.

The classification criterion determining unit 3003 can determine the classification criterion of the detection object. The classification criterion determining section 3003 can determine the classification criterion of the detection object object in accordance with the method described in Fig.

The database 3004 may mean the learning unit 120 of FIG. 1 or the database 122 included in the learning unit. The database 3004 shown in FIG. 30 is shown as being included in the image analysis apparatus 3000, but the scope of the present disclosure is not limited thereto. Like the learning unit 120 shown in FIG. 1, a component having the same function as the database 3040 can be configured separately from the image analysis apparatus.

The actual distance calculation unit 3005 can calculate the length per unit pixel of the analysis target image using the information included in the image information. The preprocessing unit 3006 may perform preprocessing on the analysis object image or the read image. For example, the preprocessing unit 3060 may perform skew estimation or perform angle correction on an analysis object image or a read image.

The probability model construction unit 3007 constructs a probability model for analyzing the object of the analysis target image using the length per unit pixel provided from the actual distance calculation unit 3005 and the object classification reference included in the database 3004 Can be constructed.

The image analysis unit 3008 can analyze the analysis target image using the probability model generated by the probability model construction unit. Although not shown in FIG. 30, the image analysis unit 3008 may include an object detection unit and an object analysis unit. The object detection unit can detect an object from the analysis object image using a model based on CNN or a deep learning. The object analyzing unit can analyze the object based on the actual size of the detected object.

The specific operation of each component included in the image analysis apparatus 3000 is as described with reference to FIGS. 23 to 29.

The output device 3020 can display information processed in the image analysis apparatus 3000. [ For example, the output device 3030 can display execution screen information of an application program driven by the image analysis device 3000, or a user interface and GUI (Graphic User Interface) information according to the execution screen information.

The output device 3020 may be a liquid crystal display (LCD), a thin film transistor-liquid crystal display (TFT LCD), an organic light-emitting diode (OLED), a flexible display display, a 3D display, and an e-ink display.

Although the output device 3020 illustrated in FIG. 30 is shown as being configured as a separate apparatus from the image analyzing apparatus 3000, the scope of rights of the present disclosure is not limited thereto. An output unit having the same function as the output apparatus may be configured as a part of the image analyzing apparatus 3000.

Although the exemplary methods of this disclosure are represented by a series of acts for clarity of explanation, they are not intended to limit the order in which the steps are performed, and if necessary, each step may be performed simultaneously or in a different order. In order to implement the method according to the present disclosure, the illustrative steps may additionally include other steps, include the remaining steps except for some steps, or may include additional steps other than some steps.

The various embodiments of the disclosure are not intended to be all-inclusive and are intended to illustrate representative aspects of the disclosure, and the features described in the various embodiments may be applied independently or in a combination of two or more.

In addition, various embodiments of the present disclosure may be implemented by hardware, firmware, software, or a combination thereof. In the case of hardware implementation, one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays A general processor, a controller, a microcontroller, a microprocessor, and the like.

The scope of the present disclosure is to be accorded the broadest interpretation as understanding of the principles of the invention, as well as software or machine-executable instructions (e.g., operating system, applications, firmware, Instructions, and the like are stored and are non-transitory computer-readable medium executable on the device or computer.

Claims (18)

Comprising: receiving an analysis object image including at least one object;
Detecting an object in the analysis target image using a pre-learned deep learning based model;
Receiving image information including resolution information on the analysis target image;
Calculating a unit length per pixel of the analysis target image using the resolution information;
Analyzing the detected object based on an actual size of the detected object; And
And outputting the analysis result,
The step of analyzing the detected object
Calculating an actual size of the detected object using a unit length per pixel of the analysis target image; And
And determining the type of the object based on the actual size of the detected object. The method according to claim 1,
Wherein the image information includes information on an actual size of the analysis target image. delete delete The method according to claim 1,
Wherein the step of determining the type of the object comprises:
When the actual size of the object is larger than a preset value, the type of the object is determined as the first type,
And when the actual size of the object is smaller than the preset value, the type of the object is determined as the second type. The method according to claim 1,
Further comprising determining a classification criterion for the object,
Wherein the step of determining the classification criterion comprises:
The method comprising: receiving a read image including a read object;
Receiving information about the reading object;
Updating the database using the read image and the information about the read object; And
And determining a classification criterion for an object using the database. The method according to claim 6,
Wherein the step of determining the classification criterion comprises:
And assigning different importance levels to each of the components of the read object to determine a classification criterion for the object. The method according to claim 6,
Wherein the step of determining the classification criterion comprises:
And determining a classification criterion of the object based on the actual size information of the reading object. The method according to claim 6,
Wherein updating the database comprises:
Aligning the read image according to a predetermined angle; And
And updating the database using information about the aligned read image and the aligned read image. The method according to claim 6,
And updating the database using the information on the analysis target image, the analysis result, and the modification test result. The method according to claim 6,
Wherein the information on the reading object includes:
Wherein the image analysis includes at least one of a type, a width, a height, an aspect ratio of the read object, a degree to which the read object is rotated in the read image, a shooting date of the read image, Way. An analysis object image receiving unit for receiving an analysis object image including at least one object;
An object detecting unit that detects an object in the analysis target image using a pre-learned deep learning based model;
An image information receiver for receiving image information including resolution information of the analysis target image;
A measured distance calculating unit for calculating a unit length per pixel of the analysis target image using the resolution information; And
And an object analyzing unit for analyzing the detected object based on an actual size of the detected object,
The object analyzing unit,
Wherein the actual size of the detected object is calculated using the unit length per pixel of the analysis target image and the type of the object is determined based on the actual size of the detected object. 13. The method of claim 12,
Wherein the image information includes information on an actual size of the analysis target image. delete delete 13. The method of claim 12,
The object analyzing unit,
When the actual size of the object is larger than a preset value, the type of the object is determined as the first type,
And determines the type of the object as the second type when the actual size of the object is smaller than the predetermined value. 13. The method of claim 12,
Further comprising a classification criterion determining unit for determining a classification criterion for the object,
Wherein the classification-
A system for receiving a read image including a read object, receiving information about the read object, updating the database using the read image and information about the read object, and classifying the object using the database An image analyzing device that determines a reference. Comprising: receiving an analysis object image including at least one object;
Detecting an object in the analysis target image using a pre-learned deep learning based model;
Receiving image information including resolution information on the analysis target image;
Calculating a unit length per pixel of the analysis target image using the resolution information;
Analyzing the detected object based on an actual size of the detected object; And
And outputting the analysis result,
The step of analyzing the detected object
Calculating an actual size of the detected object using a unit length per pixel of the analysis target image; And
And determining the type of the object based on the actual size of the detected object. ≪ Desc / Clms Page number 19 >

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