KR20150108567A - Object identification apparatus, method thereof and computer readable medium having computer program recorded therefor - Google Patents

Abstract

In the present invention, disclosed are an object identification apparatus, a method thereof, and a recording medium recorded with a computer program. Accordingly, the present invention, in learning features for object identification, applies a random pattern map on an image with respect to a pixel in a dead zone, and calculates the average brightness of the dead zone by designating a white pattern and a black pattern to have a feature index expressed, thereby rapidly calculating the brightness of a partial region by applying a descriptor of a high velocity average method.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an object identification apparatus, an object identification apparatus, a method thereof, and a recording medium on which a computer program is recorded.

The present invention relates to an object identification apparatus, a method thereof, and a recording medium on which a computer program is recorded. In particular, a random pattern map for pixels in a rectangular area is applied to an image, a white pattern and a black pattern are designated, An object identification device for calculating a brightness average of a rectangular area, a method thereof, and a recording medium on which a computer program is recorded.

Learning method Object identification is based on features (or feature points) obtained from an image (or image), and expresses each feature with various types of descriptors.

This learning method object identification uses a Harr-like feature method, and the corresponding halftone feature method increases computation time and requires a lot of training by applying a specific pattern to an image.

Korean Registered Patent No. 10-1064080 [Title: An Automatic Shape-Based Fill-Line Image Separation Device and Method Using Brightness Value Distribution and Slope Information in Magnetic Resonance Imaging]

It is an object of the present invention to provide an object identifying apparatus for calculating feature information on an image by applying a feature pattern divided into a plurality of regions to an image, a method thereof, and a recording medium on which a computer program is recorded.

Another object of the present invention is to provide a method and apparatus for calculating a brightness average for a partial area of an image through a feature map that is preset (or arranged) in a random pattern of white patterns and black patterns, An object identifying device for calculating information, a method thereof, and a recording medium on which a computer program is recorded.

The object identification apparatus according to an embodiment of the present invention applies a feature pattern of a quadrangle divided into a plurality of regions to an image and generates a feature map including at least one white Pattern regions and one or more black pattern regions, respectively, and information on one or more white pattern regions and one or more black pattern regions selected for each of the one or more unit items included in the feature map, A control unit for calculating information; And a storage unit for storing image feature information of an area where the calculated feature pattern is located.

As an example related to the present invention, each of the plurality of regions included in the characteristic pattern may be formed of one pixel or a plurality of pixels.

As an example related to the present invention, the white pattern area and the black pattern area may each include information on one or more of the plurality of areas included in the feature map.

As an example related to the present invention, the controller calculates the average value of the luminance values of one or more white pattern regions and the average value of the luminance values of one or more black pattern regions, which are respectively selected for one or more unit items, .

As an example related to the present invention, the controller may calculate an average value of the luminance values of one or more white pattern regions and an average value of the luminance values of one or more black pattern regions for each unit item, and calculate an average value of the luminance values of the calculated white pattern region, The difference between the average values of the luminance values of the black pattern regions can be calculated as the image characteristic information of the region where the characteristic pattern is located.

As an example related to the present invention, the control unit slides and applies a feature pattern of a quadrangle divided into a plurality of regions to all the remaining regions of the image, calculates a plurality of regions image feature information for a plurality of regions of the entire image, Learning can be performed on the basis of a plurality of region-by-region image feature information for the entire image.

The object identification apparatus according to an embodiment of the present invention may include a plurality of image patterns for a plurality of images set as an average value of luminance values of one or more white pattern regions and unit luminance values of one or more black pattern regions, A storage unit for storing image characteristic information for each region of the image; And a similarity between a plurality of image feature information for each region calculated by applying a feature pattern of a quadrangle divided into a plurality of regions to an image and a plurality of image feature information for each region for a plurality of images stored in the storage, And a controller for determining whether a target object exists in the image based on the determined similarity.

As an example related to the present invention, when the identified similarity degree is equal to or greater than a preset reference value, the controller can determine whether or not a target object exists in the image and identify the object in the image.

As an example related to the present invention, when the determined similarity degree is smaller than a preset reference value, it can be determined that there is no object in the image.

According to an embodiment of the present invention, there is provided a method for identifying an object, the method comprising: applying a feature pattern of a rectangle divided into a plurality of regions to an image through a control unit; Selecting at least one white pattern area and at least one black pattern area included in a unit item of the feature map based on a preset feature map corresponding to the feature pattern through the control unit; Calculating at least one white pattern area and at least one black pattern area selected for each of the one or more unit items included in the feature map through the controller as image feature information of an area where the feature pattern is located in the image; And storing image feature information of an area in which the feature pattern calculated in the storage unit is located.

As an example related to the present invention, the step of calculating image feature information of a region in which a feature pattern is located may include calculating an average value of brightness values of at least one white pattern region and brightness values of at least one black pattern region, It can be calculated as the image feature information of the region where the feature pattern is located.

As an example related to the present invention, the step of calculating image feature information of an area in which a feature pattern is located may include calculating an average value of luminance values of one or more white pattern regions and an average value of luminance values of one or more black pattern regions, process; And calculating a difference between an average value of the luminance values of the calculated white pattern region and an average value of the luminance values of the calculated black pattern region as the image characteristic information of the region where the characteristic pattern is located.

Selecting one or more white pattern regions and one or more black pattern regions for each unit item for each of the plurality of unit items included in the feature map through the control unit as an example related to the present invention; Calculating a plurality of image feature information for each of a plurality of regions of the entire image by sliding a feature pattern of a quadrangle divided into a plurality of regions into all the remaining regions of the image through a control unit; And performing a learning based on a plurality of image feature information for each region of the entire image calculated through the control unit.

The object identification method according to an embodiment of the present invention applies a feature pattern of a quadrangle divided into a plurality of regions to an image through a control unit to generate at least one white pattern area for each unit item constituting a feature map previously set corresponding to the feature pattern Calculating a plurality of image feature information for each region represented by an average value of the brightness values of the black pattern regions and an average value of the brightness values of the at least one black pattern region; Checking the similarity between a plurality of image feature information for each region calculated through the control unit and a plurality of image feature information for each region for a plurality of images stored in the storage unit; And determining whether a target object exists in the image based on the similarity determined through the control unit.

According to an embodiment of the present invention, the step of determining whether an object exists may include determining whether the degree of similarity determined through the control unit is equal to or greater than a preset reference value; Determining whether a target object exists in the image through the control unit when the determined similarity is equal to or greater than a preset reference value, and identifying the object in the image; And determining that the target object does not exist in the image through the control unit when the determined similarity is smaller than a preset reference value.

A computer program for carrying out the method according to the above-described embodiment may be stored in the recording medium on which the computer program according to the embodiment of the present invention is recorded.

According to the present invention, by applying feature patterns divided into a plurality of regions to an image, feature information on the image is calculated, so that a region where features can be extracted at one time is wide, .

In addition, the present invention calculates the brightness average for a partial area of an image through a feature map that is preset (or arranged) in a white pattern and a black pattern at random for a feature pattern divided into a plurality of regions, The brightness of a certain area can be calculated quickly by applying a descriptor of a fast average method.

1 is a block diagram showing the configuration of an object identifying apparatus 10 according to an embodiment of the present invention.
2 is a cross- FIG. 8 is a diagram illustrating an example of a feature pattern according to an embodiment of the present invention.
3 is a diagram illustrating an example of a feature map according to an embodiment of the present invention.
Figure 4 1 is a flowchart illustrating an object identification method according to a first embodiment of the present invention.
5 illustrates an image according to an embodiment of the present invention.
6, 5 is a flowchart illustrating an object identification method according to a second embodiment of the present invention.

It is noted that the technical terms used in the present invention are used only to describe specific embodiments and are not intended to limit the present invention. In addition, the technical terms used in the present invention should be construed in a sense generally understood by a person having ordinary skill in the art to which the present invention belongs, unless otherwise defined in the present invention, Should not be construed to mean, or be interpreted in an excessively reduced sense. In addition, when a technical term used in the present invention is an erroneous technical term that does not accurately express the concept of the present invention, it should be understood that technical terms that can be understood by a person skilled in the art can be properly understood. In addition, the general terms used in the present invention should be interpreted according to a predefined or prior context, and should not be construed as being excessively reduced.

Furthermore, the singular expressions used in the present invention include plural expressions unless the context clearly dictates otherwise. The term "comprising" or "comprising" or the like in the present invention should not be construed as necessarily including the various elements or steps described in the invention, Or may further include additional components or steps.

Furthermore, terms including ordinals such as first, second, etc. used in the present invention can be used to describe elements, but the elements should not be limited by terms. Terms are used only for the purpose of distinguishing one component from another. For example, without departing from the scope of the present invention, the first component may be referred to as a second component, and similarly, the second component may also be referred to as a first component.

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings, wherein like reference numerals refer to like or similar elements throughout the several views, and redundant description thereof will be omitted.

In the following description, well-known functions or constructions are not described in detail since they would obscure the invention in unnecessary detail. It is to be noted that the accompanying drawings are only for the purpose of facilitating understanding of the present invention, and should not be construed as limiting the scope of the present invention with reference to the accompanying drawings.

1 is a block diagram showing the configuration of an object identifying apparatus 10 according to an embodiment of the present invention.

As shown in FIG. 1, the object identification apparatus 10 includes a storage unit 100 and a control unit 200. Not all of the components of the object identification apparatus 10 shown in Fig. 1 are essential components, and the object identification apparatus 10 may be implemented by more components than the components shown in Fig. 1, The object identifying device 10 may also be implemented by a component.

The control unit 200 applies a feature pattern of a quadrangle divided into a plurality of regions to an image (or image). Thereafter, the control unit 200 selects one or more white pattern regions and one or more black pattern regions included in the unit items constituting the feature map, which correspond to the feature patterns. Thereafter, the control unit 200 compares the information (or the average value of the luminance values) of the at least one white pattern area and one or more black pattern areas selected for each of the plurality of unit items with the feature information Feature information). In addition, the controller 200 slides and applies a feature pattern of a quadrangle divided into a plurality of regions to all of the remaining regions of the image so as to apply image characteristic information (or a plurality of regions of image feature information ). Thereafter, the control unit 200 learns based on the plurality of image feature information for each region of the original image. In addition, the control unit 200 performs an object identification function for a plurality of image feature information for each region calculated from other images based on the plurality of image characteristic information for each region of the plurality of learned images.

The storage unit 100 stores a user interface (UI), a graphical user interface (GUI), and the like.

In addition, the storage unit 100 stores data and programs necessary for the object identification apparatus 10 to operate.

The storage unit 100 stores an image (or an image) captured through an input unit (not shown) (or a camera (not shown)) included in the object identification apparatus 10, (Not shown), and the like.

In addition, the storage unit 100 may store a feature pattern of a quadrangle divided into a plurality of regions, a preset feature map corresponding to the feature pattern, a plurality of (or more) unit items included in the feature map, One or more white pattern regions and one or more black pattern regions or the like. Here, the white pattern area or the black pattern area is formed by one pixel or a plurality of pixels. At this time, if the white pattern area or the black pattern area is formed of one pixel, the corresponding white pattern area or the black pattern area stores (or manages) the luminance value of the corresponding pixel. Further, when the white pattern region or the black pattern region is formed of a plurality of pixels, the white pattern region or the black pattern region stores an average of the brightness values of the plurality of pixels.

Also, the storage unit 100 stores a plurality of image feature information, a learning result, an identified (or recognized) object information for an image generated by the control unit 200, and the like.

The control unit 200 executes the overall control function of the object identification device 10. [

In addition, the controller 200 applies a rectangular feature pattern divided into a plurality of regions to an image (or image) stored in the storage unit 100 or an image received through the communication unit or the input unit. Here, the feature pattern has a predetermined size.

That is, as shown in FIG. 2, the controller 200 applies a rectangular feature pattern 210 divided into a plurality of regions to an image.

In the embodiment of the present invention, the 5 × 5 matrix type feature pattern is described, but the present invention is not limited thereto, and the design pattern may be variously changed according to the designer's design. In addition, the number (or zone number) in the feature pattern may be reset to different positions according to the designer's design.

Also, the control unit 200 sets a feature map corresponding to the feature pattern. Here, the feature map may be mutually shared among different object identification devices in order to extract feature information on the image. In addition, the feature map used in the object identifying apparatus 10 may be a feature map provided (shared) from another object identifying apparatus.

Also, as shown in FIG. 3, the feature map 300 includes a plurality of (or one or more) unit items (including 310, 320, 330, etc.). Herein, each unit item 310, 320, and 330 includes a white pattern area 302 and a black pattern area 303 including information on at least one of a plurality of areas included in the feature map 300 . At this time, the number of unit items included in the feature map (or the number of the feature indexes 301) and the number of regions included in the white pattern area and the black pattern area included in the unit item are variously set according to the designer's design .

3, the control unit 200 determines whether or not the first unit item (or the unit item having the first unit item / feature index of 0) 510 (E.g., 0, 6, 7, 8, 9 zones) and one or more black pattern zones (e.g., 15, 16, 17 zones) included in the second unit item (E.g., 6, 19, 20, and 24 zones) and one black pattern zone (e.g., 0 zone) included in 520 the second unit item / ) And one white pattern area (e.g., area 19) included in the third unit item (or a unit item of the third unit item / feature index 2) 530 and one or more black pattern areas For example, 5, 6, 7, 8, and 24 zones, respectively.

Also, the control unit 200 may convert information (or an average value of luminance values of the corresponding area) of one or more white pattern areas and one or more black pattern areas of each unit item selected from among a plurality of (or one or more) (Or sets) the feature information of the region in which the image is located.

That is, the control unit 200 calculates an average value of luminance values of one or more white pattern regions for each unit item, and calculates an average value of luminance values of one or more black pattern regions for each unit item. Then, the controller 200 calculates (or sets) the average value of the luminance values of the calculated white pattern area and the calculated black pattern area as the feature information of the area in which the feature pattern is located.

For example, the control unit 200 calculates the average of the luminance values of the 0, 6, 7, 8, and 9 regions included in the white pattern area constituting the first unit item 510 shown in FIG. 3, The average value of the luminance values of the 15th, 16th, and 17th regions included in the black pattern region constituting the 1 unit item 510 can be calculated and the average value of the calculated white pattern region and the black pattern region can be calculated as the characteristic information.

Also, the controller 200 slides and applies a feature pattern of a quadrangle divided into the plurality of regions to all the remaining regions of the image, so that the image feature information (or a plurality of region-specific images (Or extracting / setting) the feature information.

In addition, the control unit 200 stores a plurality of image feature information for each region of the original image in the storage unit 100.

In addition, the controller 200 learns based on a plurality of image characteristic information for each area of the calculated image. At this time, in the case of the corresponding image, the control unit 200 may normalize the image to a plurality of predetermined sizes, and then calculate a plurality of image-specific image-specific information for each of the plurality of normalized images.

In addition, the controller 200 may perform learning based on a plurality of image feature information for each of the plurality of images.

In addition, the control unit 200 stores the learning result in the storage unit 100. [

The control unit 200 may also store a feature pattern of a rectangle divided into a plurality of regions with respect to other images (or images) stored in the storage unit 100 or other images received through a communication unit (not shown) or an input unit To thereby calculate a plurality of image feature information for each area. Here, the image characteristic information may include an average value of luminance values of one or more white pattern regions and a mean value of luminance values of one or more black pattern regions per unit item constituting a preset characteristic map corresponding to a characteristic pattern of a square divided into a plurality of regions . The image feature information may also be a difference between the average value of the luminance values of one or more white pattern regions and the average value of the luminance values of one or more black pattern regions per unit item.

In addition, the controller 200 stores a plurality of image feature information for each region in the storage unit 100.

In addition, the control unit 200 may store each of a plurality of image feature information for each region for a plurality of images stored (or learned / registered) in advance in the storage unit 100 and a plurality of images for each region (Or compares) the degree of similarity between feature information.

In addition, the control unit 200 determines whether the similarity between the plurality of image feature information of each region calculated and the plurality of images stored in the storage unit 100 is equal to or greater than a preset reference value (Or confirms).

That is, the control unit 200 determines whether each of the plurality of similarity degrees detected is equal to or greater than a preset reference value.

As a result of the determination, if the determined similarity is equal to or greater than a preset reference value, the control unit 200 classifies (or judges / confirms) the object as a target object in the image, identifies (or recognizes) And stores the identified object in the storage unit 100.

If the determined similarity is smaller than a preset reference value, the control unit 200 classifies the object as having no object in the image.

In this manner, feature information about an image can be calculated by applying a feature pattern divided into a plurality of regions to the image.

In this manner, the brightness average for a partial area of the image is calculated through feature maps that are preset (or arranged) in a random pattern of white patterns and black patterns with respect to feature patterns divided into a plurality of regions, Can be calculated.

Hereinafter, an object identification method according to the present invention will be described in detail with reference to FIGS. 1 to 6. FIG.

Figure 4 1 is a flowchart illustrating an object identification method according to a first embodiment of the present invention.

First, the control unit 200 applies a feature pattern of a quadrangle divided into a plurality of regions to a partial area of the original image. Here, the feature pattern has a predetermined size. Further, the region is formed by one pixel or a plurality of pixels.

For example, as shown in FIG. 5, the controller 200 applies a rectangular feature pattern 520 divided into a plurality of regions to a partial area 510 of the original image (S410).

Thereafter, the control unit 200 selects (or confirms) one or more white pattern regions and one or more black pattern regions included in the unit items constituting the feature map, based on a preset feature map corresponding to the feature pattern . At this time, the controller 200 repeats the process of selecting at least one white pattern area and at least one black pattern area included in each of the plurality of (or one or more) unit items included in the feature map.

For example, as shown in FIG. 3, the controller 200 determines whether the feature map 300 included in the feature map 300 has been generated based on the preset feature map 300 corresponding to the feature pattern 210 of the rectangle shown in FIG. One or more white pattern areas 302 and one or more black pattern areas 303 corresponding to the first unit item 310, the second unit item 320 and the third unit item 330, respectively.

That is, as shown in FIG. 3, the control unit 200 displays one or more white patterns included in the first unit item (or the unit item having the first unit item / feature index 0) 310 in the feature map 300 (E.g., zones 0, 6, 7, 8, 9) and one or more black pattern zones (e.g., zones 15, 16, 17) (E.g., 6, 19, 20, and 24 zones) and one black pattern zone (e.g., 0 zone) included in the third unit (E.g., 19 zones) and one or more black pattern zones (e.g., 5, 6, 7, and 8) included in the item (or unit item with the third unit item / 8, and 24) are selected (S420).

Thereafter, the control unit 200 converts information (or an average value of the luminance values of the corresponding area) of one or more white pattern areas and one or more black pattern areas for each selected unit item among the plurality of (or one or more) Is calculated as the feature information of the area in which the "

That is, the control unit 200 calculates an average value of luminance values of one or more white pattern regions for each unit item, and calculates an average value of luminance values of one or more black pattern regions for each unit item. Then, the controller 200 calculates (or sets) the average value of the luminance values of the calculated white pattern area and the calculated black pattern area as the feature information of the area in which the feature pattern is located. At this time, the controller 200 may calculate the difference between the average value of the luminance values of the calculated white pattern region and the average value of the luminance values of the calculated black pattern region as the feature information of the region where the feature pattern is located.

For example, the control unit 200 may include one or more white pattern areas (e.g., 0, 6, 7, 8, 9 areas) and one or more black pattern areas For example, 15, 16, and 17 zones) and the first black pattern zone average value. In addition, the control unit 200 may include one or more white pattern regions (e.g., 6, 19, 20, and 24 zones) and one black pattern zone (e.g., 0 zone) and the second black pattern zone average value. In addition, the control unit 200 may include one white pattern area (for example, area 19) and one or more black pattern areas (for example, 5, 6, 7, 8, and 24) and the third black pattern zone average value. Thereafter, the control unit 200 calculates a first white pattern zone average value and a first black pattern zone average value calculated for the first unit item 310, a second white pattern zone average value calculated for the second unit item 320, The second black pattern zone average value, the third black pattern zone average value, the third white pattern zone average value, and the third black pattern zone average value calculated for the third unit item 330 are calculated as the image feature information of the partial region in which the quadrangular feature pattern is located .

Further, the control unit 200 calculates the difference between the calculated first white pattern zone average value and the first black pattern zone average value, the calculated difference between the calculated second white pattern zone average value and the second black pattern zone average value, The difference between the average value and the average value of the third black pattern zone may be calculated as image feature information of a partial region where the feature pattern of the quadrangle is located in the corresponding image (S430).

Then, the control unit 200 slides and applies a feature pattern of a quadrangle divided into the plurality of regions to all the remaining regions of the original image, (Or extracting / setting) the image feature information.

In addition, the controller 200 stores a plurality of image feature information for each of the regions of the original image in the storage unit 100 (S440).

Thereafter, the control unit 200 learns based on the plurality of image feature information for each region of the original image.

In addition, the control unit 200 stores the learning result in the storage unit 100 (S450).

6, 5 is a flowchart illustrating an object identification method according to a second embodiment of the present invention.

First, the control unit 200 applies a feature pattern of a quadrangle divided into a plurality of regions to an image (or image) stored in the storage unit 100 or an image received through a communication unit (not shown) or an input unit To thereby calculate a plurality of image feature information for each area. Here, the image characteristic information may include an average value of luminance values of one or more white pattern regions and a mean value of luminance values of one or more black pattern regions per unit item constituting a preset characteristic map corresponding to a characteristic pattern of a square divided into a plurality of regions . The image feature information may also be a difference between the average value of the luminance values of one or more white pattern regions and the average value of the luminance values of one or more black pattern regions per unit item.

In addition, the controller 200 stores a plurality of image feature information for each region in the storage unit 100 (S610).

Thereafter, the controller 200 calculates the similarity between the calculated image feature information of each of the plurality of regions and the plurality of image characteristic information of each region of the plurality of images stored (or learned / registered) in the storage unit 100 Confirm (or compare) each.

For example, the controller 200 checks the similarity between the calculated image feature information of each of the plurality of regions and the image characteristic information of each of the plurality of regions with respect to the ◯◯ images previously learned in the storage unit 100 (S620 ).

Thereafter, the control unit 200 determines whether the similarity between the plurality of image feature information of each area calculated and the plurality of image characteristic information of each of the plurality of images stored in the storage unit 100 is equal to or greater than a preset reference value (Or confirms).

That is, the control unit 200 determines whether each of the plurality of similarity degrees detected is equal to or greater than a preset reference value.

For example, the control unit 200 determines whether the determined degree of similarity is equal to or greater than a preset reference value (S630).

As a result of the determination, if the determined similarity is equal to or greater than a preset reference value, the control unit 200 classifies (or judges / confirms) the object as a target object in the image, identifies (or recognizes) And stores the identified object in the storage unit 100.

For example, when there is a degree of similarity that is equal to or greater than a preset reference value out of the identified similarity degrees, the controller 200 determines a plurality of region-based image feature information And identifies the object in the input image, and stores the identified object in the storage unit 100 (S640).

If the determined similarity is smaller than a preset reference value, the control unit 200 classifies the object as having no object in the image.

For example, when all of the determined similarity degrees are less than a preset reference value, the control unit 200 classifies the object as not existing in the corresponding image (S650).

The object identification apparatus and method according to the embodiment of the present invention can be written in a computer program, and the codes and code segments constituting the computer program can be easily deduced by a computer programmer in the field. In addition, the computer program is stored in a computer readable medium readable by a computer and read and executed by a computer, an object identification device, a user device (or a terminal) according to an embodiment of the present invention, Device and method thereof.

The information storage medium includes a magnetic recording medium, an optical recording medium, and a carrier wave medium. The object identification apparatus according to an embodiment of the present invention and a computer program implementing the method can be stored and installed in an internal memory such as an object identification apparatus, a user apparatus, and the like. Alternatively, an external memory such as a smart card storing and installing an object identifying device according to an embodiment of the present invention and a computer program implementing the method may be mounted on an object identifying device, a user device, or the like through an interface.

As described above, in the embodiment of the present invention, characteristic information about an image is calculated by applying a feature pattern divided into a plurality of regions with respect to an image, and a region where features can be extracted at one time is wide, , And feature extraction performance can be improved.

In addition, as described above, in the embodiment of the present invention, the brightness average (brightness) of a partial area of an image through a feature map that is preset (or arranged) in a random pattern of white patterns and black patterns The feature information of the image is calculated, and the brightness of some areas can be calculated quickly by applying the descriptor of the fast average method.

It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit or essential characteristics thereof. Therefore, the embodiments disclosed in the present invention are intended to illustrate rather than limit the scope of the present invention, and the scope of the technical idea of the present invention is not limited by these embodiments. The scope of protection of the present invention should be construed according to the following claims, and all technical ideas within the scope of equivalents should be construed as falling within the scope of the present invention.

The present invention applies a random pattern map for pixels in a rectangular area to an image in learning of feature points for object identification, calculates a brightness index of a rectangular area by expressing a feature index by designating a white pattern and a black pattern, It can be used widely in image processing field, object identification / recognition field, etc., by calculating the brightness of some areas quickly by applying an average method descriptor.

10: Object identifying apparatus 100:
200:

Claims (16)

Applying a feature pattern of a quadrangle divided into a plurality of regions to an image and designating one or more white pattern regions and one or more black pattern regions included in a unit item of the feature map based on a predetermined feature map corresponding to the feature pattern A control unit for selecting at least one white pattern area and at least one black pattern area selected respectively for one or more unit items included in the feature map from the image to image feature information of an area where the feature pattern is located; And
And a storage unit for storing image feature information of an area where the calculated feature pattern is located. The method according to claim 1,
Wherein each of the plurality of regions included in the feature pattern is formed of one pixel or a plurality of pixels. The method according to claim 1,
Wherein the white pattern area and the black pattern area each include information on at least one of a plurality of areas included in the feature map. The method according to claim 1,
Wherein the controller calculates the average value of the luminance values of one or more white pattern regions and the average value of the luminance values of one or more black pattern regions each selected for the one or more unit items as image feature information of the region in which the feature pattern is located Object identification device. The method according to claim 1,
Wherein the control unit calculates the average value of the luminance values of one or more white pattern regions and the average value of the luminance values of the at least one black pattern region for each unit item and calculates an average value of the luminance values of the calculated white pattern region, And calculates the difference between the average values of the luminance values of the characteristic pattern as the image characteristic information of the region where the characteristic pattern is located. The method according to claim 1,
Wherein the control unit slides and applies a feature pattern of a quadrangle divided into the plurality of regions to all the remaining regions of the image to calculate a plurality of image feature information for each region for a plurality of regions of the entire image, And performs learning based on a plurality of region-by-region image feature information on the image. A storage unit for storing a plurality of image feature information for each of a plurality of images set to a mean value of luminance values of one or more white pattern regions and a mean value of luminance values of at least one black pattern region, ; And
A similarity between a plurality of image feature information of each region calculated by applying a feature pattern of a quadrangle divided into a plurality of regions to an image and a plurality of image feature information of each region of a plurality of images stored in the storage unit, And a controller for determining whether a target object exists in the image based on the determined similarity. 8. The method of claim 7,
Wherein the control unit determines whether a target object exists in the image when the determined similarity degree is equal to or greater than a preset reference value, and identifies an object in the image. 8. The method of claim 7,
Wherein the controller determines that a target object does not exist in the image when the determined similarity is smaller than a preset reference value. Applying a feature pattern of a quadrangle divided into a plurality of regions to an image through a control unit;
Selecting at least one white pattern area and at least one black pattern area included in a unit item of the feature map based on a preset feature map corresponding to the feature pattern through the controller;
Calculating information on at least one white pattern area and at least one black pattern area selected for each of the one or more unit items included in the feature map through the controller as image feature information of an area where the feature pattern is located in the image; And
And storing image feature information of an area where the calculated feature pattern is located in the storage unit. 11. The method of claim 10,
Wherein the calculating of the image feature information of the region in which the feature pattern is located includes calculating an average value of the brightness values of one or more white pattern regions and a mean value of brightness values of the at least one black pattern region, And calculating the image feature information of the region. 11. The method of claim 10,
Wherein the step of calculating, as image feature information of an area in which the feature pattern is located,
Calculating an average value of luminance values of one or more white pattern regions and an average value of luminance values of one or more black pattern regions for each unit item; And
And calculating a difference between an average value of the luminance values of the calculated white pattern region and an average value of the luminance values of the calculated black pattern region as image feature information of the region where the characteristic pattern is located. 11. The method of claim 10,
Repeating the process of selecting one or more white pattern areas and one or more black pattern areas for each of the plurality of unit items included in the feature map through the controller;
Calculating a plurality of image feature information for each of a plurality of regions of the entire image by sliding and applying a feature pattern of a quadrangle divided into the plurality of regions to all remaining regions of the image through the controller; And
And performing learning based on a plurality of image feature information for each region of the entire image through the control unit. A characteristic pattern of a quadrangle divided into a plurality of regions with respect to the image is applied to the image through the control unit so that the average value of the luminance values of one or more white pattern regions for each unit item constituting the characteristic map set in advance corresponding to the characteristic pattern, Calculating a plurality of image feature information for each region represented by an average value of luminance values of the region;
Confirming the similarity between the calculated image feature information for each region and the plurality of image feature information for each region for a plurality of images stored in the storage unit through the control unit; And
And determining whether a target object exists in the image based on the determined similarity through the control unit. 15. The method of claim 14,
Wherein the step of determining whether the object is present comprises:
Determining whether the determined similarity is equal to or greater than a preset reference value through the controller;
Determining whether a target object exists in the image through the control unit when the determined similarity is equal to or greater than the preset reference value and identifying an object in the image; And
And determining that a target object does not exist in the image through the control unit when the determined similarity is less than the preset reference value. 16. A recording medium on which a computer program for performing the method according to any one of claims 10 to 15 is recorded.

Images