CN111488889A - Intelligent image processor for image edge extraction - Google Patents
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Abstract
The invention discloses an intelligent image processor for image edge extraction, and particularly relates to the field of image processing. The invention sets artificial intelligence to classify the images in advance, classifies the images to be extracted in large batch, and digitalizes the image information, does not need an image edge extraction algorithm to carry out image identification and image optimization, and uses an FPGA chip as a processing circuit, thereby improving the positioning precision of the images, reducing the data volume in the subsequent processing of the images and facilitating the effective and rapid operation of image edge extraction.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to an intelligent image processor for image edge extraction.
Background
The human cognition is in an objective world and the human-to-human interaction, the main media is images, the images are main sources of information acquired by people, scientific data shows that the information amount acquired by people through visual images is about 4 times of the information amount acquired by other ways, the image edge is the most basic characteristic of the whole image, contains a large amount of important information of the image, is the characteristic of the low level of the image, and is defined as follows: in the place where the image gray level change rate is the largest (where the image gray level changes the most severely), the edge caused by the discontinuity of the image gray level in the surface normal change is generally considered as the edge extraction to reserve the area where the image gray level changes the most severely, and mathematically, the most intuitive method is differentiation (difference for digital images), and from the viewpoint of signal processing, it can also be said that a high-pass filter is used, that is, a high-frequency signal is reserved.
The edge information is the most important information in the image, theoretically, all information in the image can be recovered through the edge information, and therefore edge detection is the important content of image analysis; is key to dealing with many problems; the traditional edge detection mainly uses difference operators in the horizontal direction and the vertical direction to detect edges in the horizontal direction and the vertical direction respectively, then certain gradient is synthesized for edge detection, only the difference between the two directions needs to be solved when a computer is implemented, and then the two directions are synthesized.
However, the edge detection algorithm based on mathematical morphology, the edge detection algorithm based on graph theory, and the edge detection algorithm based on multi-color features, which are used at present, have certain limitations and low intelligence degree, when a large amount of image edge information is extracted, due to the non-intelligence of the algorithms, the image edge extraction algorithm must be optimized when being executed, the required specification of the image is relatively specific, and incompatibility is easy to cause, and the architecture is simple, and the image can not be presorted, so when different types of images appear, the algorithm identification is required in advance and then the edge extraction is performed, which causes the defects of increased operation amount, slow processing effect and the like, and efficient large amount of image processing can not be realized, and further optimization is required.
It is therefore desirable to provide an intelligent image processor for image edge extraction.
Disclosure of Invention
In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide an intelligent image processor for image edge extraction, which sets artificial intelligence to classify images in advance, performs mass classification on the images to be extracted, and digitizes the image information without performing image recognition and image optimization by using an image edge extraction algorithm, and uses an FPGA chip as a processing circuit, thereby improving the positioning accuracy of the images, reducing the data amount in the subsequent processing of the images, facilitating the effective and rapid operation of the image edge extraction, performing the datamation conversion on the novel information of the images by using a preferential image classification system, and extracting the characteristic information content of a large number of images, and when performing the image edge extraction, directly using the processed image optimized data information to perform the image edge extraction, and simplifying the algorithm steps of the image edge extraction, the problem of incompatibility of an image edge extraction algorithm and image optimization is avoided, so that the problems in the background art are solved.
In order to achieve the purpose, the invention provides the following technical scheme: the utility model provides an intelligent image processor for image edge draws, includes that the image collection acquires module and FPGA chip, the output electric connection that the image collection acquireed the module has AD converting circuit, AD converting circuit's output electric connection has matrix information extractor, matrix information extractor's output electric connection has characteristic to draw module and original image data output, the inside that the module was drawed to the characteristic is equipped with image information analysis module, deep neural network, the output electric connection that the module was drawed to the characteristic has the module of comparing, the output electric connection that the module was compared has image classification output, the output of original image data output, image classification output and the input electric connection of FPGA chip, the inside of FPGA chip is provided with image edge and draws the module, the image collection acquires the module and includes camera subassembly, image classification and carries out image classification output, the input electric connection of FPGA chip, the output end of the camera assembly is electrically connected with the input ends of the storage and the image set transmission module.
In a preferred embodiment, the image set acquisition module comprises: when the scanning strategy acquires the information of the first image in real time, the resolution strategy extracts the definition value of the first image and compares the definition value with the image definition threshold, and when the definition value of the first image is lower than the image definition threshold, the image processing unit outputs re-shooting information; otherwise, numbering the first image and sending second image extraction information to the camera assembly; when the scanning strategy acquires the information of a second image in real time, the resolution strategy extracts the definition value of the second image, compares the definition value with the image definition threshold, and outputs re-shooting information when the definition value of the second image is lower than the image definition threshold; otherwise, numbering the second image, comparing the numbered first image with the second image in definition, and outputting the current image information with the highest definition.
In a preferred embodiment, the storage includes a random access memory and a Flash storage, the image set transmission module is a wired or wireless signal transmission device, and an input end of the image set transmission module is electrically connected with an output end of the random access memory.
In a preferred embodiment, the image information analysis module includes a viewpoint change process for identifying whether an object is photographed or acquired as an original or multi-dimensionally rotated image, a zoom change for identifying whether the object is an image with different sizes of the same object, an internal change for identifying all correct categories of all the same photographed objects for category discrimination, and a joint process for applying one or more of the viewpoint change process, the zoom change, and the internal change to a collaborative recognition analysis.
In a preferred embodiment, the deep neural network is internally provided with machine learning, the machine learning comprises a traditional logic research, a cognitive model and a theoretical analysis, the traditional logic research is composed of an auxiliary machine learning model, the cognitive model is composed of a target machine learning model, the traditional logic research and the cognitive model are from different machine learning model categories, the cognitive model is a machine learning model with limited capacity, and the theoretical analysis is composed of a comparison component.
In a preferred embodiment, the traditional logic study is configured to assign a first score to an unlabeled observation, the cognitive model is configured to assign a second score to the unlabeled observation, the theoretical analysis is configured to compare the first score and the second score to determine a probability that the cognitive model has returned a false positive or a false negative result, the comparing component of the first score and the second score is further configured to perform the comparison comprising: determining a magnitude of a difference between the first fraction and the second fraction; determining that the target machine learning model has returned a false positive when the magnitude is negative; and determining that the target machine learning model has returned a false negative when the magnitude is positive.
In a preferred embodiment, the image edge extraction module includes a restored image circuit, an image filter circuit, an edge enhancement circuit, an edge detection circuit and an edge location circuit, an output terminal of the restored image circuit is electrically connected to an output terminal of the image filter circuit, an output terminal of the image filter circuit is electrically connected to an input terminal of the edge enhancement circuit, and an input terminal and an output terminal of the edge detection circuit are electrically connected to an output terminal of the edge enhancement circuit and an input terminal of the edge location circuit, respectively.
In a preferred embodiment, an input end of the FPGA chip is electrically connected to an image information sorting module, the image information sorting module is configured to correspond image classification information output by original image data to original image data information output by image classification, and an input end of the image restoration circuit is electrically connected to an output end of the matrix information extractor.
The invention has the technical effects and advantages that:
1. according to the invention, by setting artificial intelligence to carry out image classification in advance, the images to be extracted are classified in a large batch, the image information is digitalized, image identification and image optimization are not required to be carried out by an image edge extraction algorithm, and an FPGA chip is used as a processing circuit, so that the positioning precision of the images can be improved, the data volume in the subsequent processing of the images is reduced, and the effective and rapid operation of image edge extraction is facilitated;
2. according to the invention, through an artificial intelligence mode, the algorithm initially constructs a traditional logic research and cognition model through the existing artificial analysis and established logic, the difference between prediction and correct output is recorded through a machine learning mode, the input weight is tuned to improve the prediction accuracy, and the image classification system is continuously optimized as the use duration is increased, so that the image optimization and classification intelligent accuracy is gradually improved.
3. The invention carries out data conversion on the novel image information through the prior image classification system, extracts the characteristic information content of a large number of images, can directly utilize the processed image optimized data information to carry out image edge extraction when carrying out image edge extraction, simplifies the algorithm steps of the image edge extraction, and avoids the incompatibility problem of the image edge extraction algorithm and the image optimization.
Drawings
Fig. 1 is a schematic view of the overall structure of the present invention.
FIG. 2 is a schematic diagram of an image set acquisition module according to the present invention.
FIG. 3 is a schematic structural diagram of an image edge extraction module according to the present invention.
Fig. 4 is a schematic structural diagram of an image information analysis module according to the present invention.
FIG. 5 is a schematic diagram of a deep neural network structure according to the present invention.
The reference signs are: 1. an image set acquisition module; 11. a camera assembly; 12. a reservoir; 13. an image set transmission module; 2. an AD conversion circuit; 3. a matrix information extractor; 4. a feature extraction module; 41. an image information analysis module; 411. viewpoint change processing; 412. a zoom change; 413. an internal variation; 414. performing combined treatment; 42. a deep neural network; 421. machine learning; 5. outputting original image data; 6. a comparison module; 7. outputting the images in a classified manner; 8. an FPGA chip; 9. an image edge extraction module; 91. a restoration image circuit; 92. an image filtering circuit; 93. an edge enhancement circuit; 94. an edge detection circuit; 95. an edge positioning circuit.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1-5 show an intelligent image processor for image edge extraction, which includes an image collection acquisition module 1 and an FPGA chip 8, an output end of the image collection acquisition module 1 is electrically connected to an AD conversion circuit 2, an output end of the AD conversion circuit 2 is electrically connected to a matrix information extractor 3, an output end of the matrix information extractor 3 is electrically connected to a feature extraction module 4 and an original image data output 5, an image information analysis module 41 and a deep neural network 42 are disposed inside the feature extraction module 4, an output end of the feature extraction module 4 is electrically connected to a comparison module 6, an output end of the comparison module 6 is electrically connected to an image classification output 7, output ends of the original image data output 5 and the image classification output 7 are electrically connected to an input end of the FPGA chip 8, an image edge extraction module 9 is disposed inside the FPGA chip 8, and the image collection acquisition module 1 includes a camera component 11, a camera component, A storage 12 and an image set transmission module 13, wherein the output end of the camera assembly 11 is electrically connected with the input end of the storage 12 and the image set transmission module 13.
The implementation mode is specifically as follows: the image classification in advance is set by artificial intelligence, the images to be extracted are classified in a large batch, the image information is digitalized, image identification and image optimization are not required to be carried out by an image edge extraction algorithm, an FPGA chip is used as a processing circuit, the positioning precision of the images can be improved, the data volume in the subsequent processing of the images is reduced, the effective and rapid operation of image edge extraction is facilitated, the novel information of the images is subjected to digitalized conversion by a preferential image classification system, the characteristic information content of a large number of images is extracted, when the image edge extraction is carried out, the processed image-optimized data information can be directly used for image edge extraction, the algorithm steps of the image edge extraction are simplified, and the problem that the image edge extraction algorithm is incompatible with the image optimization is solved.
The image set obtaining module 1 includes: when the scanning strategy acquires the information of the first image in real time, the resolution strategy extracts the definition value of the first image and compares the definition value with the image definition threshold, and when the definition value of the first image is lower than the image definition threshold, the image processing unit outputs re-shooting information; otherwise, numbering the first image and sending second image extraction information to the camera assembly 11; when the scanning strategy acquires the information of the second image in real time, the resolution strategy extracts the definition value of the second image, compares the definition value with the image definition threshold value, and outputs re-shooting information when the definition value of the second image is lower than the image definition threshold value; otherwise, the second image is numbered, the numbered first image and the second image are compared in definition value, the current image information with the highest definition value is output, and the high-definition image is convenient for subsequent image processing.
The memory 12 includes a random memory and a Flash memory, the image set transmission module 13 is a wired or wireless signal transmission device, and an input end of the image set transmission module 13 is electrically connected to an output end of the random memory, and is configured to sort and transmit the image information of the image set acquisition module 1.
The image information analysis module 41 includes a viewpoint change process 411, a zoom change 412, an internal change 413, and a joint process 414, where the viewpoint change process 411 is used to identify whether an original or multi-dimensional rotated image is caused by the photographed or acquired object, the zoom change 412 is used to identify whether the image is an image of the same object with different sizes, the internal change 413 is used to identify all correct types of all the same photographed objects for type discrimination, and the joint process 414 is used to apply one or more of the viewpoint change process 411, the zoom change 412, and the internal change 413 to a collaborative identification analysis, and classify and identify the image by using multiple types of change analysis.
The deep neural network 42 is built by utilizing the theoretical analysis and the comparison component, the deep neural network 42 is realized, the intellectualization of image classification is realized by utilizing the deep neural network 42, and the accuracy of the deep neural network is improved.
Wherein the traditional logic study is configured to assign a first score to the unlabeled observation, the cognitive model is configured to assign a second score to the unlabeled observation, the theoretical analysis is configured to compare the first score and the second score to determine a probability that the cognitive model has returned a false positive or a false negative result, the comparing component of the first score and the second score is further configured to perform a comparison comprising: determining a magnitude of a difference between the first score and the second score; when the magnitude is negative, determining that the target machine learning model has returned a false positive; and when the amplitude is positive, determining that the target machine learning model returns false negative, and realizing automatic optimization and upgrade of the image intelligent recognition classification system so as to realize intellectualization.
The image edge extraction module 9 includes a restored image circuit 91, an image filter circuit 92, an edge enhancement circuit 93, an edge detection circuit 94 and an edge positioning circuit 95, an output end of the restored image circuit 91 is electrically connected to an output end of the image filter circuit 92, an output end of the image filter circuit 92 is electrically connected to an input end of the edge enhancement circuit 93, an input end and an output end of the edge detection circuit 94 are electrically connected to an output end of the edge enhancement circuit 93 and an input end of the edge positioning circuit 95, and an extraction program is extracted due to the whole image edge detection.
The input end of the FPGA chip 8 is electrically connected to an image information sorting module, the image information sorting module is used for corresponding image classification information of the original image data output 5 to original image data information of the image classification output 7, the input end of the restoration image circuit 91 is electrically connected to the output end of the matrix information extractor 3, processed image optimized data information is directly used for image edge extraction, and the algorithm steps of image edge extraction are simplified.
The model of the camera assembly 11 is a SONY IMX362 model.
The working principle of the invention is as follows:
the first step is as follows: after the camera assembly 11 is used for obtaining images, the image set transmission module 13 is used for transmitting a large number of images stored in the storage 12 to the AD conversion circuit 2 in a data transmission mode, the AD conversion circuit 2 is used for converting the images into gray-scale images of 28 × 28 so as to form a pixel matrix, image information is converted into a data signal form to facilitate algorithm identification and analysis, and then information of the pixel matrix is extracted through the matrix information extractor 3;
the second step is that: the matrix information extractor 3 respectively transmits the extracted image matrix information to the feature extraction module 4 and the FPGA chip 8, the image matrix information is processed at the feature extraction module 4 by using the image information analysis module 41 to perform viewpoint change processing 411, zoom change 412, internal change 413 and joint processing 414 under the intervention of the variables of the deep neural network 42, the content of the image is quantized, then each quantized image is compared with quantized data under the action of the comparison module 6, similarity analysis is performed, and the classified output value is output to the FPGA chip 8;
the third step: then, under the action of the FPGA chip 8, the image edge information is extracted through a series of circuits including a restored image circuit 91, an image filtering circuit 92, an edge enhancement circuit 93, an edge detection circuit 94 and an edge positioning circuit 95, and the FPGA chip is used as a processing circuit, so that the image positioning precision can be improved, the data volume in the subsequent processing of the image is reduced, and the effective and rapid operation of image edge extraction is facilitated.
The points to be finally explained are: first, in the description of the present application, it should be noted that, unless otherwise specified and limited, the terms "mounted," "connected," and "connected" should be understood broadly, and may be a mechanical connection or an electrical connection, or a communication between two elements, and may be a direct connection, and "upper," "lower," "left," and "right" are only used to indicate a relative positional relationship, and when the absolute position of the object to be described is changed, the relative positional relationship may be changed;
secondly, the method comprises the following steps: in the drawings of the disclosed embodiments of the invention, only the structures related to the disclosed embodiments are referred to, other structures can refer to common designs, and the same embodiment and different embodiments of the invention can be combined with each other without conflict;
and finally: the above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that are within the spirit and principle of the present invention are intended to be included in the scope of the present invention.
Claims (8)
1. An intelligent image processor for image edge extraction comprises an image set acquisition module (1) and an FPGA chip (8), and is characterized in that: the output end of the image set acquisition module (1) is electrically connected with an AD conversion circuit (2), the output end of the AD conversion circuit (2) is electrically connected with a matrix information extractor (3), the output end of the matrix information extractor (3) is electrically connected with a feature extraction module (4) and an original image data output (5), an image information analysis module (41) and a deep neural network (42) are arranged inside the feature extraction module (4), the output end of the feature extraction module (4) is electrically connected with a comparison module (6), the output end of the comparison module (6) is electrically connected with an image classification output (7), the original image data output (5) and the output end of the image classification output (7) are electrically connected with the input end of an FPGA chip (8), and an image edge extraction module (9) is arranged inside the FPGA chip (8), the image set acquisition module (1) comprises a camera assembly (11), a storage (12) and an image set transmission module (13), wherein the output end of the camera assembly (11) is electrically connected with the input ends of the storage (12) and the image set transmission module (13).
2. The intelligent image processor for image edge extraction according to claim 1, wherein: the image set acquisition module (1) comprises: when the scanning strategy acquires the information of the first image in real time, the resolution strategy extracts the definition value of the first image and compares the definition value with the image definition threshold, and when the definition value of the first image is lower than the image definition threshold, the image processing unit outputs re-shooting information; otherwise, numbering the first image and sending second image extraction information to the camera assembly (11); when the scanning strategy acquires the information of a second image in real time, the resolution strategy extracts the definition value of the second image, compares the definition value with the image definition threshold, and outputs re-shooting information when the definition value of the second image is lower than the image definition threshold; otherwise, numbering the second image, comparing the numbered first image with the second image in definition, and outputting the current image information with the highest definition.
3. The intelligent image processor for image edge extraction according to claim 1, wherein: the storage (12) comprises a random storage and a Flash storage, the image set transmission module (13) is a wired or wireless signal transmission device, and the input end of the image set transmission module (13) is electrically connected with the output end of the random storage.
4. The intelligent image processor for image edge extraction according to claim 1, wherein: the image information analysis module (41) comprises viewpoint change processing (411), scaling change (412), internal change (413) and joint processing (414), wherein the viewpoint change processing (411) is used for identifying whether an object is photographed or acquired to cause an original or multi-dimensional rotated image, the scaling change (412) is used for identifying whether the object is an image with different sizes of the same object, the internal change (413) is used for identifying all correct types of all the same photographed objects for type distinguishing, and the joint processing (414) is used for applying one or more of the viewpoint change processing (411), the scaling change (412) and the internal change (413) to collaborative identification analysis.
5. The intelligent image processor for image edge extraction according to claim 1, wherein: the deep neural network (42) is provided with machine learning (421) in inside, machine learning (421) include traditional logic research, cognitive model and theoretical analysis, traditional logic research comprises supplementary machine learning model, cognitive model comprises target machine learning model, traditional logic research and cognitive model come from different machine learning model categories, cognitive model is the machine learning model of limited capacity, theoretical analysis comprises the comparison component.
6. The intelligent image processor for image edge extraction according to claim 5, wherein: the traditional logic study is configured to assign a first score to an unlabeled observation, the cognitive model is configured to assign a second score to the unlabeled observation, the theoretical analysis is configured to compare the first score and the second score to determine a probability that a cognitive model has returned a false positive or a false negative result, the comparing component of the first score and the second score is further configured to perform a comparison comprising: determining a magnitude of a difference between the first fraction and the second fraction; determining that the target machine learning model has returned a false positive when the magnitude is negative; and determining that the target machine learning model has returned a false negative when the magnitude is positive.
7. The intelligent image processor for image edge extraction according to claim 1, wherein: the image edge extraction module (9) comprises a restored image circuit (91), an image filtering circuit (92), an edge enhancement circuit (93), an edge detection circuit (94) and an edge positioning circuit (95), wherein the output end of the restored image circuit (91) is electrically connected with the output end of the image filtering circuit (92), the output end of the image filtering circuit (92) is electrically connected with the input end of the edge enhancement circuit (93), and the input end and the output end of the edge detection circuit (94) are respectively electrically connected with the output end of the edge enhancement circuit (93) and the input end of the edge positioning circuit (95).
8. The intelligent image processor for image edge extraction as claimed in claims 1 and 7, wherein: the input end of the FPGA chip (8) is electrically connected with an image information sorting module, the image information sorting module is used for corresponding image classification information of original image data output (5) and original image data information of image classification output (7), and the input end of the image restoration circuit (91) is electrically connected with the output end of the matrix information extractor (3).
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