IMAGE SAMPLING IN STOCHASTIC MODEL-BASED COMPUTER VISION

• US 20100202659A1
• Filed: 06/13/2008
• Published: 08/12/2010
• Est. Priority Date: 06/15/2007
• Status: Abandoned Application

• Current Assignee: Virtual Air Guitar Company Oy
• Original Assignee: Virtual Air Guitar Company Oy
• Inventors: Hämäläinen, Perttu
• Application Number:
  US12/664,847
• Publication Number:
  US 20100202659A1
• Time in Patent Office: Days
• US Class Current: 382/103
• CPC Class Codes: G06T 2207/10016 Video; Image sequence
• CPC Class Codes: G06T 2207/30201 Face
• CPC Class Codes: G06T 7/20 Analysis of motion motion estimation for coding, decoding, compressing or decompressing digital video signals H04N19/43, H04N19/51

Abstract

A method for tracking a target in computer vision is disclosed. The method generates an integral image (22) based on the input image. Then the image is split into portions (24). For each new portion a definite integral corresponding to the portion is computed using an integral image (25). Based on the definite integrals a new portion is chosen for splitting (26). The new portion is processed correspondingly and the processing is repeated until a termination condition is reached (27).

Claims

1-28. -28. (canceled)

29. A method for tracking a target in computer vision, the method comprising:

acquiring an input image;

generating an integral image based on the input image;

selecting an initial portion;

characterized in that the method further comprises;

splitting the selected portion into new portions;

for each new portion, using the integral image to determine the definite integral corresponding to the portion;

selecting a portion from said split portions;

repeating the sequence of said splitting, determining and selecting until a termination condition has been fulfilled;

30. The method according to claim 29, characterized in that the termination condition is the number of passes or a minimum size of a portion.

31. The method according to claim 29, characterized in that the selection probability of a portion is proportional to the determined definite integral corresponding to the portion.

32. The method according to claim 29, characterized in that the portions are rectangles.

33. The method according to claim 32, characterized in that the definite integral corresponding to a rectangle is determined as i_i(x₂,y₂)−

i_i(x₁,y₂)−

i_i(x₂,y₁)+i_i(x₁,y₁), where x₁,y₁ and x₂,y₂ are the coordinates of the corners of the rectangle, and i_i(x,y) is the intensity of the integral image at coordinates x,y.

34. The method according to claim 29, characterized in that choosing the selected portion among the new portions.

35. The method according to claim 29, characterized in that generating at least one integral image by using at least one of the following methods:

processing the input image with an edge detection filter;

comparing the input image to a model of the background;

or

subtracting consecutive input images to obtain a temporal difference image.

36. The method according to claim 29, characterized in that the method further comprises determining at least one parameter of a model of the tracked target based on the last selected portion.

37. The method according to claim 36, characterized in that determining at least one parameter of a model of the tracked target using at least one of the following methods:

setting a parameter proportional to the horizontal or vertical location of the last selected portion;

or

setting a parameter proportional to the horizontal or vertical location of a point randomly selected within the last selected portion.

38. A computer program for tracking a target in computer vision embodied in a computer readable medium, wherein the computer program is embodied on a computer-readable medium comprising program code means adapted to perform the following steps when the program is executed in a computing device:

acquiring an input image;

generating an integral image based on the input image;

selecting an initial portion;

characterized in that the method further comprises;

splitting the selected portion into new portions;

for each new portion, using the integral image to determine the definite integral corresponding to the portion;

selecting a portion from said split portions;

repeating the sequence of said splitting, determining and selecting until a termination condition has been fulfilled.

39. The computer program according to claim 38, characterized in that the termination condition is the number of passes or a minimum size of a portion.

40. The computer program according to claim 38, characterized in that the selection probability of a portion is proportional to the determined definite integral corresponding to the portion.

41. The computer program according to claim 38, characterized in that the portions are rectangles.

42. The computer program according to claim 41, characterized in that the definite integral corresponding to a rectangle is determined as i_i(x₂,y₂)−

i_i(x₁,y₂)−

i_i(x₂,y₁)+i_i(x₁,y₁), where x₁,y₁ and x₂,y₂ are the coordinates of the corners of the rectangle, and i_i(x,y) is the intensity of the integral image at coordinates x,y.

43. The computer program according to claim 38, characterized in that the selected portion is chosen among the new portions.

44. The computer program according to claim 38, characterized in that generating at least one integral image by using at least one of the following methods:

processing the input image with an edge detection filter;

comparing the input image to a model of the background;

or

subtracting consecutive input images to obtain a temporal difference image.

45. The computer program according to claim 38, characterized in that the program further comprises determining at least one parameter of a model of the tracked target based on the last selected portion.

46. The computer program according to claim 45, characterized in that determining at least one parameter of a model of the tracked target using at least one of the following methods:

setting a parameter proportional to the horizontal or vertical location of the last selected portion;

or

setting a parameter proportional to the horizontal or vertical location of a point randomly selected within the last selected portion.

47. A system for tracking a target in computer vision, wherein the system comprises means for receiving and processing data, which system is configured to:

acquire an input image;

generate an integral image based on the input image;

select an initial portion;

characterized in that the system is further configured to;

split the selected portion into new portions;

for each new portion, use the integral image to determine the definite integral corresponding to the portion;

select a portion from said split portions;

repeat the sequence of said splitting, determining and selecting until a termination condition has been fulfilled.

48. The system according to claim 47, characterized in that the termination condition is the number of passes or a minimum size of a portion.

49. The system according to claim 47, characterized in that the selection probability of a portion is proportional to the determined definite integral corresponding to the portion.

50. The system according to claim 47, characterized in that the portions are rectangles.

51. The system according to claim 50, characterized in that the definite integral corresponding to a rectangle is determined as i_i(x₂,y₂)−

i_i(x₁,y₂)−

i_i(x₂,y₁)+i_i(x₁,y₁), where x₁,y₁ and x₂,y₂ are the coordinates of the corners of the rectangle, and i_i(x,y) is the intensity of the integral image at coordinates x,y.

52. The system according to claim 47, characterized in that the selected portion is chosen among the new portions.

53. The system according to claim 47, characterized in that system is configured to generate at least one integral image by using at least one of the following methods:

processing the input image with an edge detection filter;

comparing the input image to a model of the background;

or

subtracting consecutive input images to obtain a temporal difference image.

54. The system according to claim 47, characterized in that the system is further configured to determine at least one parameter of a model of the tracked target based on the last selected portion.

55. The system according to claim 54, characterized in that the system is configured to determine at least one parameter of a model of the tracked target using at least one of the following methods:

setting a parameter proportional to the horizontal or vertical location of the last selected portion;

or

setting a parameter proportional to the horizontal or vertical location of a point randomly selected within the last selected portion.

56. The system according to claim 47, wherein the system is a computing device.