Template matching method and target image area extraction apparatus

• US 20050152604A1
• Filed: 10/21/2004
• Published: 07/14/2005
• Est. Priority Date: 01/09/2004
• Status: Abandoned Application

• Current Assignee: MediaTek Singapore Pte. Ltd. (MediaTek, Inc.)
• Original Assignee: NuCORE Technology, Inc. (MediaTek, Inc.)
• Inventors: Sato, Kazuhiro, Watanabe, Seiichiro, Kuroda, Tadahiro, Kitagawa, Shuji
• Application Number:
  US10/970,804
• Publication Number:
  US 20050152604A1
• Time in Patent Office: Days
• US Class Current: 382/209
• CPC Class Codes: G06V 10/752 Contour matching
• CPC Class Codes: G06V 40/162 using pixel segmentation or colour matching

Abstract

According to a template matching method, a matching value representing matching between a process area image and a target image is calculated by using the process area image in an arbitrary process area acquired from the edge image of an input image and template data representing the shape feature of the target image. The area of the target image in the input image is specified on the basis of the matching value. The template data is formed from positive points representing positions at which the edge of the target image exists in the process area and negative points representing positions at which no edge exists in the process area. The matching value is calculated in accordance with the positional relationship between the positive points, the negative points, and each edge present within the process area image. A target image area extraction apparatus is also disclosed.

Claims

1. A template matching method of calculating a matching value representing matching between a process area image and a target image by using the process area image in an arbitrary process area acquired from an edge image of an input image and template data representing a shape feature of the target image, and specifying an area of the target image in the input image on the basis of the matching value, the template data being formed from positive points representing positions at which an edge of the target image exists in the process area and negative points representing positions at which no edge exists in the process area, comprising:

the first step of calculating the matching value in accordance with a positional relationship between the positive points, the negative points, and each edge present within the process area image.

2. A method according to claim 1, wherein

the edge image is formed from a plurality of pixels in which edge images contained in the input image are represented by gray values, and

the first step comprises the second step of calculating, for each positive point and each negative point, evaluation values on the basis of gray values of the pixels of the process area image that correspond to positions of the positive point and the negative point, and the third step of calculating the matching value from the evaluation values of the positive point and the negative point.

3. A method according to claim 2, wherein

the second step comprises the step of calculating, as the evaluation value for each positive point, the gray value of the pixel of the process area image that corresponds to the position of the positive point, and the step of calculating, as the evaluation value for each negative point, a value by subtracting, from the number of gray levels of the edge image, the gray value of the pixel of the process area image that corresponds to the position of the negative point, and

the third step comprises the step of calculating the matching value from a sum of the evaluation values calculated in the second step.

4. A method according to claim 1, wherein the first step comprises the fourth step of generating, as matching candidate information, a plurality of types of parameters used for coordinate transformation of the positive points and the negative points of the template data, and the fifth step of transforming coordinates of the positive points and the negative points of the template data on the basis of the matching candidate information and generating new template data used to calculate the matching value.

5. A method according to claim 4, wherein the fourth step comprises the step of holding a plurality of individuals having chromosome information containing the plurality of types of parameters used for coordinate transformation of the positive points and the negative points of the template data, the step of designating as parent individuals a plurality of individuals among the individuals, the step of generating a child individual from the parent individuals on the basis of a genetic algorithm, and the step of outputting as the matching candidate information a parameter contained in chromosome information of the child individual.

6. A method according to claim 5, wherein the fourth step comprises the step of selecting, as a replacement candidate from the held individuals, an individual having the smallest matching value obtained by using new template data generated on the basis of matching candidate information of the individual, and the step of, when the matching value obtained by using new template data generated on the basis of matching candidate information of the child individual is larger than the matching value of the replacement candidate, holding the child individual as a new individual instead of the replacement candidate.

7. A method according to claim 1, wherein the negative points are so arranged as not to overlap another edge of the target image that exists around the edge of the target image.

8. A method according to claim 7, wherein the negative points are arranged within an area formed by the edge of the target image.

9. A method according to claim 7, wherein the negative points are arranged outside an area formed by the edge of the target image.

10. A method according to claim 1, wherein the negative points are arranged along and around a shape formed by an arrangement of the positive points.

11. A method according to claim 1, wherein the positive points are arranged at a density corresponding to a ratio at which the edge of the target image appears in an area where the positive points are arranged.

12. A method according to claim 1, wherein the negative points are arranged at a density corresponding to a ratio at which an edge other than the edge of the target image appears in an area where the negative points are arranged.

13. A method according to claim 1, wherein the target image is formed from a person'"'"'s face image, and the edge of the target image is formed from an edge representing a lower contour of the person'"'"'s face image.

14. A method according to claim 1, wherein the template data is formed from face template data having positive points representing positions at which an edge representing a lower contour of a person'"'"'s face image exists and negative points representing positions at which the edge representing the lower contour of the person'"'"'s face image does not exist.

15. A method according to claim 14, wherein the edge representing the lower contour of the person'"'"'s face image uses a semielliptic shape.

16. A method according to claim 14, wherein the positive points are arranged at a high density in a cheek contour area of the person'"'"'s face image and a low density in a chin contour area of the person'"'"'s face image.

17. A method according to claim 14, wherein the negative points are arranged at a high density in a cheek contour area of the person'"'"'s face image in an area outside an area formed by the edge representing the lower contour of the person'"'"'s face image, and a low density in a chin contour area of the person'"'"'s face image.

18. A target image area extraction apparatus which specifies an area of a person'"'"'s face image contained in an input image by using a template matching method defined in claim 1.

19. A target image area extraction apparatus comprising:

an image input unit which generates an edge image representing a contour of an object from an input image;

an image holding unit which holds and stores the edge image;

a template data holding unit which holds and stores template data representing a shape feature of a target image to be extracted;

a matching candidate output unit which generates, as matching candidate information, a plurality of types of parameters used for coordinate transformation of the template data;

a template coordinate calculation unit which performs coordinate transformation for the template data read out from said template data holding unit on the basis of the matching candidate information, and generates new template data;

a matching calculation unit which calculates matching values representing matching between process area images extracted from process areas sequentially set in the edge image of said image holding unit and the template data generated by said template coordinate calculation unit;

a target area specifying unit which, when a matching value obtained from said matching calculation unit exceeds a predetermined reference value, specifying a process area having the matching value as an area containing the target image; and

an area information extraction unit which outputs area information in a target image area on the basis of a coordinate position of the specified process area and the template data,

wherein the template data is formed from positive points representing positions at which an edge of the target image exists in the process area and negative points representing positions at which no edge exists in the process area, and

said template coordinate calculation unit calculates the matching value in accordance with a positional relationship between the positive points, the negative points, and each edge present within the process area image.

20. An apparatus according to claim 19, wherein the edge image is formed from a plurality of pixels in which edge images contained in the input image are represented by gray values, and

said template coordinate calculation unit calculates, for each positive point and each negative point, evaluation values on the basis of gray values of the pixels of the process area image that correspond to positions of the positive point and the negative point, and calculates the matching value from the evaluation values of the positive point and the negative point.

21. An apparatus according to claim 20, wherein said template coordinate calculation unit calculates, as the evaluation value for each positive point, the gray value of the pixel of the process area image that corresponds to the position of the positive point, calculates, as the evaluation value for each negative point, a value by subtracting from the number of gray levels of the edge image the gray value of the pixel of the process area image that corresponds to the position of the negative point, and calculates the matching value from a sum of the evaluation values.

22. An apparatus according to claim 19, wherein said matching candidate output unit comprises

a population holding unit which holds and stores a plurality of individuals having chromosome information containing the plurality of types of parameters used for coordinate transformation of the positive points and the negative points of the template data,

a parent individual designation unit which designates as parent individuals a plurality of individuals among the individuals held in said population holding unit, and

a new individual generation unit which generates a child individual from the parent individuals on the basis of a genetic algorithm, and outputs as the matching candidate information a parameter contained in chromosome information of the child individual.

23. An apparatus according to claim 22, wherein said matching candidate output unit further comprises

means for selecting, as a replacement candidate from the individuals held in said population holding unit, an individual having the smallest matching value obtained by using new template data generated on the basis of matching candidate information of the individual, and

means for, when the matching value obtained by using new template data generated on the basis of matching candidate information of the child individual is larger than the matching value of the replacement candidate, holding the child individual as a new individual instead of the replacement candidate.

24. A method comprising:

(a) calculating a matching value based on a positional relationship between positive points, negative points, and an edge of a target image, the target image to be extracted from a process area image, wherein the positive points represent positions at which the edge exists within a process area, and the negative points represent positions at which the edge does not exist within the process area, wherein template data are derived from the positive points and the negative points and represent a shape feature of the target image, wherein the matching value matches the process area image to the target image by using the process area image in an arbitrary process area, wherein the arbitrary process area is defined by the template data and by an edge image of an input image; and

(b) specifying an area of the target image in the input image on the basis of the matching value.

25. The method of claim 24, wherein the edge image is formed from a plurality of pixels in which edge images contained in the input image are represented by gray values, and wherein the calculating the matching value in (a) comprises:

(c) calculating an evaluation value, for each positive point and each negative point, on the basis of the gray values of the pixels of the process area image that correspond to positions of the positive point and the negative point; and

(d) calculating the matching value using the evaluation value of each positive point and each negative point.

26. The method of claim 25, wherein the calculating the evaluation value in (c) comprises a calculation, for each positive point, of the gray value of the pixel of the process area image that corresponds to the position of the positive point, and a calculation, for each negative point, of a value by subtracting, from the number of gray levels of the edge image, the gray value of the pixel of the process area image that corresponds to the position of the negative point, and wherein the calculating the matching value in (d) uses the evaluation value calculated in (c).

27. The method of claim 24, wherein the calculating the matching value in (a) comprises:

(e) generating, as matching candidate information, a plurality of types of parameters used for coordinate transformation of the positive points and the negative points of the template data; and

(f) transforming coordinates of the positive points and the negative points of the template data on the basis of the matching candidate information, and generating new template data used to calculate the matching value.

28. The method of claim 27, wherein the generating the plurality of types of parameters in (e) comprises:

(g) choosing a plurality of individuals having chromosome information containing the plurality of types of parameters used for coordinate transformation of the positive points and the negative points of the template data;

(h) designating as parent individuals a plurality of individuals among the individuals;

(i) generating a child individual from the parent individuals on the basis of a genetic algorithm; and

(j) outputting, as the matching candidate, information of a parameter contained in chromosome information of the child individual.

29. The method of claim 28, wherein the generating the plurality of types of parameters in (e) comprises:

(k) selecting, as a replacement candidate from the chosen individuals, an individual having the smallest matching value obtained by using new template data generated on the basis of matching candidate information of the individual; and

(l) retaining the child individual as a new individual instead of the replacement candidate when the matching value obtained by using new template data generated on the basis of matching candidate information of the child individual is larger than the matching value of the replacement candidate.

30. The method of claim 24, wherein the negative points are so arranged as not to overlap another edge of the target image that exists around the edge of the target image.

31. The method of claim 30, wherein the negative points are arranged within an area formed by the edge of the target image.

32. The method of claim 30, wherein the negative points are arranged outside an area formed by the edge of the target image.

33. The method of claim 24, wherein the negative points are arranged along and around a shape formed by an arrangement of the positive points.

34. The method of claim 24, wherein the positive points are arranged at a density corresponding to a ratio at which the edge of the target image appears in an area where the positive points are arranged.

35. The method of claim 24, wherein the negative points are arranged at a density corresponding to a ratio at which an edge other than the edge of the target image appears in an area where the negative points are arranged.

36. The method of claim 24, wherein the target image is formed from a person'"'"'s face image, and the edge of the target image is formed from an edge representing a lower contour of the person'"'"'s face image.

37. The method of claim 24, wherein the template data are derived from face template data having positive points representing positions at which an edge representing a lower contour of a person'"'"'s face image exists and negative points representing positions at which the edge representing the lower contour of the person'"'"'s face image does not exist.

38. The method of claim 37, wherein the edge representing the lower contour of the person'"'"'s face image uses a semi-elliptical shape.

39. The method of claim 37, wherein the positive points are arranged at a high density in a cheek contour area of the person'"'"'s face image and at a low density in a chin contour area of the person'"'"'s face image.

40. The method of claim 37, wherein the negative points are arranged at a high density in a cheek contour area of the person'"'"'s face image in an area outside an area formed by the edge representing the lower contour of the person'"'"'s face image, and at a low density in a chin contour area of the person'"'"'s face image.

41. A target image area extraction apparatus that specifies an area of a person'"'"'s face image contained in an input image by using the method of claim 24.

42. A device comprising:

a template data holding unit that stores template data representing a shape feature of a target image to be extracted;

a template coordinate calculation unit that performs coordinate transformation on the template data based on matching candidate information and that generates new template data;

a matching calculation unit that calculates a matching value, wherein the matching value matches a process area to the new template data generated by the template coordinate calculation unit;

a target area specifying unit that specifies the process area associated with the matching value as an area containing the target image when the matching value exceeds a predetermined reference value; and

a target area information extraction unit that outputs area information for a target image area based on the new template data and a coordinate position of the specified process area, wherein the new template data are derived from positive points representing positions at which an edge of the target image exists within the process area and negative points representing positions at which no edge of the target image exists within the process area, and wherein the matching calculation unit calculates the matching value based on a positional relationship between the positive points, the negative points, and each edge present within the process area.

43. The device of claim 42, further comprising:

a matching candidate output unit that generates the matching candidate information, wherein the matching candidate information includes a plurality of types of parameters used for coordinate transformation of the template data.

44. The device of claim 42, further comprising:

an image holding unit that stores an edge image representing a contour of an object from an input image, wherein the device extracts process area images from process areas sequentially set in the edge image stores in the image holding unit.

45. The device of claim 44, wherein the edge image is formed from a plurality of pixels in which edge images contained in the input image are represented by gray values, and wherein the template coordinate calculation unit calculates, for each positive point and each negative point, an evaluation value based on a gray value of the pixel of a process area image that corresponds to each positive point and each negative point.

46. The device of claim 45, wherein the template coordinate calculation unit calculates the evaluation value for each negative point by subtracting the gray value of the pixel of the process area image that corresponds to the negative point from the number of gray levels of the edge image.

47. A device comprising:

an image holding unit that stores an edge image representing a contour of an object in an input image; and

means for specifying a process area in the input image as a specified process area that contains a target image and for extracting the target image from the input image.

48. The device of claim 47, wherein the means uses template data to represent the target image.

49. The device of claim 48, wherein the template data are derived from positive points representing positions at which an edge of the target image exists within the process area and negative points representing positions at which no edge of the target image exists within the process area.