WO2014175481A1 - Méthode de production de descripteur et appareil matériel la mettant en œuvre - Google Patents

Méthode de production de descripteur et appareil matériel la mettant en œuvre Download PDF

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Publication number
WO2014175481A1
WO2014175481A1 PCT/KR2013/003521 KR2013003521W WO2014175481A1 WO 2014175481 A1 WO2014175481 A1 WO 2014175481A1 KR 2013003521 W KR2013003521 W KR 2013003521W WO 2014175481 A1 WO2014175481 A1 WO 2014175481A1
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WO
WIPO (PCT)
Prior art keywords
descriptor
points
feature point
region
interest
Prior art date
Application number
PCT/KR2013/003521
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English (en)
Korean (ko)
Inventor
최병호
이상설
황영배
장성준
김정호
Original Assignee
전자부품연구원
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Publication date
Application filed by 전자부품연구원 filed Critical 전자부품연구원
Priority to PCT/KR2013/003521 priority Critical patent/WO2014175481A1/fr
Publication of WO2014175481A1 publication Critical patent/WO2014175481A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/50—Extraction of image or video features by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/46—Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features
    • G06V10/462—Salient features, e.g. scale invariant feature transforms [SIFT]

Definitions

  • the present invention relates to a method for generating a descriptor and a hardware device for implementing the same.
  • a hardware structure design technique for generating a descriptor of Speeded Up Robust Features (SURF) which is an algorithm for object extraction using an input image. It is about.
  • the Speeded Up Robust Feature (SURF) algorithm extracts feature points and descriptors of an object.
  • Fast-Hessian detectors are used to extract feature points that are resistant to size changes, and the extracted feature points are given descriptors that are resistant to rotational changes.
  • the conventional SURF algorithm generates a descriptor by dividing the peripheral region of the feature point into 16 regions based on the main direction in order to give a strong descriptor to the rotational change.
  • the SURF algorithm determines the size of the surrounding area to be sampled using the scale information extracted from the feature points and the size of the principal component.
  • the coordinates for rotating the rectangle to generate the descriptor are calculated using the main direction of the feature point.
  • the pixel that is the center of the calculation does not have a constant pattern, but the calculation is performed at non-uniform intervals according to the rotation.
  • FIG. 1 illustrates an integrated image point necessary for generating a descriptor in the related art
  • FIG. 2 illustrates an integrated image point for a conventional haar-wavelet response operation
  • FIG. 3 illustrates a conventional memory. An illustration of patching.
  • the horizontal axis represents a pixel address in the x-axis direction of the integrated image
  • the vertical axis represents a pixel address in the y-axis direction of the integrated image
  • the integrated image point P1 is obtained by approximating the integral image point required by the calculation based on the origin of the feature point. Therefore, the integrated image point P1 is a value that changes depending on the origin of the feature point and the amount of rotation of the main axis. Therefore, as shown in FIG. 1, the integrated image point P1 is not uniform.
  • FIG. 2 shows the integral image point required for one ha-wavelet calculation.
  • peripheral point P3 when the peripheral point P3 performs memory patching three times in the x-axis direction, all of the peripheral points P3 arranged in the y-axis direction may be acquired.
  • the descriptor generation unit reads the integrated image point in the y-axis direction by applying an address value in the x-axis direction in which the integrated image point is desired to be obtained from the memory in which the integrated image is stored. That is, the operation of storing the necessary data by reading a large amount of integrated image points in the y-axis direction according to one address value in the x-axis direction is memory patching.
  • the memory patch can be performed only after the calculation of each accumulated image point. Therefore, the accumulated image points are unpredictable and cannot be memory patched in advance. This delays the processing speed of the memory.
  • an aspect of the present invention is to provide a method and an apparatus capable of reducing the number of times of memory patching for acquiring data of an integrated image required for a descriptor operation.
  • a descriptor generation method is a method in which a hardware device generates a descriptor based on a feature point of an image, setting a region of interest in which integral image points are evenly disposed around the feature point, and memory patching. Calculating all of the integrated image points disposed in the ROI, selecting integral image points included in a descriptor generation region among the integrated image points, and generating a descriptor based on the selected integrated image points It includes a step.
  • the ROI in the form of a circle may be set based on the origin of the feature point.
  • the operation of calculating a point of interest based on integrated image points acquired through memory patching in one basic unit may be repeated to calculate all integrated image points disposed in the ROI.
  • the descriptor generation region includes a plurality of regions consisting of l and k determined through the following equation,
  • Sample X and sample Y are coordinate values indicated by the candidate points
  • x and y represent the coordinates of the origin of the feature point
  • co si are cosine values (cos ( ⁇ )) corresponding to the inclination ( ⁇ ) of the main axis
  • each represents a sine value (sin ( ⁇ ))
  • Scal represents scale information of a feature point
  • l determines a horizontal region of the descriptor generating region
  • k determines a vertical region of the descriptor generating region.
  • a hardware device is a hardware device for calculating a feature point and a descriptor of an image, an integrated image memory for storing an integrated image for the image, a feature point extracting unit for extracting the feature point based on the integrated image, And setting a region of interest in which integral image points are evenly disposed around the feature point, and performing memory patching to calculate all of the integral image points disposed in the region of interest from the integral image memory, and among the integrated image points.
  • a descriptor generator configured to select integral image points included in the descriptor generation area and generate a descriptor based on the selected integrated image points.
  • a region of interest in a circular shape is set based on the origin of the feature point, and the feature point obtained from the feature point extractor is located within the region of interest based on scale information from which the feature point is extracted, the origin of the feature point, and the principal axis direction of the feature point Descriptor generation area can be determined.
  • the descriptor generation region is determined based on coordinate values of all candidate points disposed in the ROI acquired through the memory patching, origin coordinates of the feature point, slope of a main axis, and scale information of the feature point, and the candidate point. By determining whether each of the coordinate values is included in the descriptor generation region, points of interest necessary for generating the descriptor may be selected from the candidate points.
  • the processing speed of the descriptor operation can be improved.
  • the pixel of interest that changes in accordance with the main axis direction is conventionally used, according to the embodiment of the present invention, since the fixed pixel structure is used, it is suitable for the parallel hardware structure.
  • the hardware structure of the same structure can be applied irrespective of the position of various scales and object feature points, it is possible to design a flexible hardware block.
  • the shape of the region of interest is considered as a prototype, the point of interest for the descriptor operation is reduced, and thus, the computational amount of the descriptor is reduced, thereby improving the computational processing speed of the descriptor.
  • FIG. 2 is an exemplary diagram of computing an integrated image point for a conventional haar-wavelet response operation.
  • FIG. 3 is an exemplary diagram of conventional memory patching.
  • FIG. 4 is a schematic configuration diagram of a speeded up robust feature (SURF) hardware device according to an embodiment of the present invention.
  • SURF speeded up robust feature
  • FIG. 5 illustrates components of a descriptor to which an embodiment of the present invention is applied.
  • 6 and 7 illustrate indexing of sixteen regions to which embodiments of the present invention are applied.
  • FIG. 8 is a flowchart illustrating a descriptor generation method according to an embodiment of the present invention.
  • FIG. 9 is an exemplary diagram of integral image points required when patching memory for one haar-wavelet response operation according to an embodiment of the present invention.
  • FIG. 10 illustrates a memory patching process according to an embodiment of the present invention.
  • FIG. 11 illustrates a descriptor generation region according to an embodiment of the present invention.
  • ... unit means a unit for processing at least one function or operation, which may be implemented in hardware or software or a combination of hardware and software.
  • FIG. 4 is a schematic configuration diagram of a speeded up robust feature (SURF) hardware device according to an embodiment of the present invention.
  • SURF speeded up robust feature
  • the SURF hardware device 100 includes an image storage unit 101, an integrated image generator 103, an integrated image memory 105, a feature point extractor 107, and a descriptor.
  • the generation unit 109 is included.
  • the image storage unit 101 stores the input black and white image.
  • the integrated image generator 103 generates an integrated image of the black and white image stored in the image storage unit 101 and stores the integral image in the integrated image memory 105.
  • the feature point extractor 107 generates image pyramids and extracts feature points from the generated image pyramid to represent a scale space for extracting feature points from the input black and white image.
  • the integral image used is the core of the SURF algorithm.
  • the descriptor generator 109 When the main direction of the feature point is determined, the descriptor generator 109 generates a descriptor by dividing the area around the feature point into 16 areas based on the main direction.
  • the descriptor generator 109 includes an integrated image obtained from the integrated image memory 105, an image size including height and width, origin (x, y) coordinates, and scale of feature points. Generates a descriptor by receiving SURF information.
  • the image size is obtained when the initial SURF hardware device 100 is implemented, and the SURF information is obtained from the feature point extractor 107.
  • SURF information is the origin (x, y) coordinates and scale information of a feature point determined to have a large amount of change or maintain a constant pattern when compared with the surrounding area on the integrated image.
  • the feature point depends on the threshold of the feature point extractor 107.
  • the descriptor generator 109 calculates an integrated image point necessary for generating a descriptor from feature information, that is, x, y coordinates, scale information, and main direction information, to which an integral image point of a pixel required for descriptor generation is input.
  • FIG. 5 illustrates components of a descriptor to which an embodiment of the present invention is applied.
  • the descriptor generator 109 may sample using the scale information from which the feature point P7 is extracted, the origin point (x, y) of the feature point P7 of the main axis, and the magnitude of the component of the main axis direction P9. The size of the peripheral area P11 is determined.
  • the descriptor generator 109 rotates the integrated image based on the main axis direction P9 and divides the image into 16 areas P11. Then, ⁇ dx, ⁇
  • extracted from each region P11. Create a total of 64 descriptors ( 4 ⁇ 4 ⁇ 4) using the components.
  • 6 and 7 illustrate indexing of sixteen regions to which embodiments of the present invention are applied.
  • the descriptor output order of the 16 regions is sequentially from Region 0_1 to Region_3_4, that is, Region 0_1-> Region 0_2-> Region 0_3->. -> Region 3_3-> Region 3_4
  • FIG. 8 is a flowchart illustrating a descriptor generation method according to an embodiment of the present invention
  • FIG. 9 is an integral image point required when patching a memory for one haar-wavelet response operation according to an embodiment of the present invention.
  • 10 is an exemplary diagram, and FIG. 10 illustrates a memory patching process according to an embodiment of the present invention, and FIG. 11 illustrates a descriptor generation area according to an embodiment of the present invention.
  • the descriptor generator 109 sets a region of interest (ROI) having a radius of a predetermined size around a feature point (S101), which is the ROI.
  • ROI region of interest
  • S101 feature point
  • the radius of the circle may be a value determined by the operator.
  • the integrated image points P15 are evenly distributed in the region of interest ROI P13. All of the integrated image points P15 distributed in the ROI P13 are candidate points for descriptor generation. Hereinafter, all the integrated image points P15 distributed in the region of interest ROI P13 are described as candidate points P15.
  • the descriptor generator 109 acquires candidate points P15 from the integrated image memory 105 through memory patching S103, and the method of obtaining the descriptor points P15 is the same as FIG. 10.
  • FIG. 10 illustrates a portion of candidate points P15.
  • the peripheral point in order to calculate the first point of interest 1, the second point of interest 2, and the third point of interest 3, the first point of interest 1 and the second point of interest 2, as described with reference to FIG. 2.
  • a total of eight peripheral points arranged in the left, right, and up and down directions with respect to the third point of interest 3 are required. Therefore, the peripheral point must be obtained through the basic three memory patching in the x-axis direction.
  • the candidate points P15 acquired through the first first (1) memory patching are stored in the first buffer.
  • the candidate points P15 acquired through the next second (2) memory patching are stored in the second buffer.
  • candidate points P15 obtained through the next third memory patching are stored in the third buffer.
  • the three first points of interest 1 may be calculated based on the candidate points P15 obtained through the three times of memory patching.
  • the second point of interest 2 becomes a peripheral point through the fourth (4) memory patching.
  • the candidate points P15 may be obtained. Therefore, the descriptor generator 109 may empty the first buffer and store peripheral candidate points P15 obtained through the fourth memory patching in the first buffer. In this manner, nine first points of interest 1, second points of interest 2, and third points of interest 3 may be calculated through a total of five times of memory patching.
  • the descriptor generation unit 109 determines 16 descriptor generation regions based on the feature point P7 through the values of l and k. And l, k is calculated through the following equation (1).
  • Sample X and sample Y mean coordinate values indicated by integrated image points (or candidate points) disposed in the ROI.
  • x and y represent the origin coordinates of the feature point P7.
  • co and si represent cosine values (cos ( ⁇ )) and sine values (sin ( ⁇ )) corresponding to the slope ⁇ of the main axis (P9 of FIGS. 5, 6 and 7), respectively.
  • Scal represents scale information of a feature point.
  • the region in the horizontal direction among the 16 regions is determined through the k value based on the feature point P7, and the region in the vertical direction is determined by the l value.
  • the sixteen areas P11 are 12 from the feature point 7 to the left and 12 to the right and 12 up and down, respectively, that is, l: -12 to 12, based on the feature point P7.
  • k It is classified into the range from -12 to 12.
  • the center coordinates (xs, ys) P17 of each region P11 are calculated to perform a haar-wavelet operation for each of the sixteen regions P11 to generate a descriptor.
  • the descriptor generator 109 may include candidates included in the descriptor generation area determined in step S105, that is, the 16 areas P11. The points P15 are selected (S107).
  • the selection process applies the coordinate values (Sample X, sample Y) of the candidate points (P15) to the equation (1) to determine whether the candidate points (P15) are included in the 16 areas (P11) consisting of l, k You can judge.
  • the descriptor generation unit 109 performs a descriptor generation operation based on the candidate points P15 selected in step S107 (S109).
  • step S109 a descriptor generation operation is performed using the above-described equations ( ⁇ dx, ⁇
  • a dx buffer that stores the amount of the direction component on the x-axis of the region in which the candidate points are included among the 16 areas
  • a dy buffer that stores the direction component on the y-axis
  • which stores the absolute value of the direction component of the y-axis.
  • the point of interest necessary for generating the descriptor may be obtained by distinguishing the candidate point in the ROI corresponding to the actual descriptor generating region. Therefore, since candidate points P15 are unevenly distributed in the related art, there is no problem that an operation must be performed for each candidate point P15. This allows the descriptor generator 109 to be suitable for hardware and to operate at high speed.
  • the integrated image points existing in the circular ROI required for the descriptor generation are illustrated in the pixel P19.
  • the integrated image point is represented by numbers (1, 2, 3, 4, 5, 6, 7).
  • the descriptor generator 109 performs a memory patch for each horizontal line to patch the first five lines, seven descriptor points may be calculated at a time. That is, the wavelet calculation is performed through the box P21 having the peripheral point set to 1, and the point of interest 3 located in the center is calculated. In this way, box P21 sets the peripheral points 2, 3, 4,... If set to, 7, the points of interest 1, 2, 3, 4, 5, 6, and 7 located in the center of the box P21 may be calculated. The next one line of memory patching computes nine points of interest, and each subsequent line of patch can compute 11 points of interest.
  • the embodiments of the present invention described above are not only implemented through the apparatus and the method, but may be implemented through a program for realizing a function corresponding to the configuration of the embodiments of the present invention or a recording medium on which the program is recorded.

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  • General Physics & Mathematics (AREA)
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Abstract

L'invention concerne une méthode de production d'un descripteur et un appareil matériel la mettant en œuvre. En cela, une méthode de production d'un descripteur, qui est une méthode permettant à un appareil matériel de produire un descripteur en fonction d'une caractéristique d'une image, comprend les étapes suivantes : configurer une région d'intérêt dans laquelle des points d'image intégrale sont agencés uniformément avec la caractéristique au centre ; calculer tous les points d'image intégrale agencés dans la région d'intérêt grâce à la correction de mémoire ; sélectionner, parmi les points d'image intégrale, des points d'image intégrale compris dans une zone de production de descripteur ; et produire un descripteur en fonction des points d'image intégrale sélectionnés.
PCT/KR2013/003521 2013-04-24 2013-04-24 Méthode de production de descripteur et appareil matériel la mettant en œuvre WO2014175481A1 (fr)

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6111584A (en) * 1995-12-18 2000-08-29 3Dlabs Inc. Ltd. Rendering system with mini-patch retrieval from local texture storage
WO2010148539A1 (fr) * 2009-06-26 2010-12-29 Intel Corporation Techniques pour détecter des copies vidéo
KR20110002043A (ko) * 2008-04-23 2011-01-06 미쓰비시덴키 가부시키가이샤 이미지 식별을 위한 스케일 안정적 특징-기반 식별자
KR20110091763A (ko) * 2008-11-12 2011-08-12 노키아 코포레이션 특징 디스크립터를 표현하고 식별하는 방법, 장치 및 컴퓨터 판독가능 저장 매체
KR101166722B1 (ko) * 2011-03-17 2012-07-19 인하대학교 산학협력단 이미지 검색을 위한 국부적 색변화 기반 관심점 검출과 디스크립터 생성 방법 및 그 기록매체

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6111584A (en) * 1995-12-18 2000-08-29 3Dlabs Inc. Ltd. Rendering system with mini-patch retrieval from local texture storage
KR20110002043A (ko) * 2008-04-23 2011-01-06 미쓰비시덴키 가부시키가이샤 이미지 식별을 위한 스케일 안정적 특징-기반 식별자
KR20110091763A (ko) * 2008-11-12 2011-08-12 노키아 코포레이션 특징 디스크립터를 표현하고 식별하는 방법, 장치 및 컴퓨터 판독가능 저장 매체
WO2010148539A1 (fr) * 2009-06-26 2010-12-29 Intel Corporation Techniques pour détecter des copies vidéo
KR101166722B1 (ko) * 2011-03-17 2012-07-19 인하대학교 산학협력단 이미지 검색을 위한 국부적 색변화 기반 관심점 검출과 디스크립터 생성 방법 및 그 기록매체

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