Title of Invention

"COLOUR IMAGE SEGMENTATION SYSTEM"

Abstract

A color image segmentation system for segmenting a color image into a plurality of regions, said system comprising: An input means (102) for inputting the color image; A first means (104,106) for calculating a predetermined value representing the degree of difference from the color of peripheral pixels based on pixel values of the input color image; A second means (108,110) for converting the calculated value into a value of a predetermined scale; and a segmenting means (112) for segmenting the converted image.

Full Text

COLOR IMAGE SEGMENTATION METHOD Technical Field The present invention relates to a color image segmentation method, and more particularly, to a color image segmentation method for segmenting a color image. Background Art The segmentation of a color image is a very important part of digital image processing and its applications. Conventional color image segmentation methods have a problem in that it is not easy to segment a color image containing texture. Also, another conventional color image segmentation method for performing an automatic segmentation is not robust with respect to an input image containing noise, and still another conventional color image segmentation method for again segmenting the image which a user segments preparatorily is robust with respect to an input image containing noise, but an automatic segmentation is not performed, therefore, it takes much time. Disclosure of the Invention To solve the above problems, it is an object of the present invention to provide a color image segmentation method capable of automatically segmenting a color image containing texture and being robust with respect to an input image containing noise. It is another object of the present invention is to provide a color image processing method containing the color image segmentation method. It is still another object of the present invention is to provide a medium in which a computer program performing the color image segmentation method is stored. Accordingly, to achieve the above object, according to one aspect of the present invention, there is provided a color image segmentation method. The color image segmentation method comprises the steps of: (a) calculating a predetermined value representing the degree of difference from the color of peripheral pixels by using pixel values of an input image; (b) obtaining a converted image by converting a calculated value into a value of a predetermined scale; and (c) segmenting the converted image. Preferably, the step (c) segments the converted image based on a region growing method. It is preferable that the color image segmentation method, prior to the step (a), further comprises the step of (p-a) quantizing pixel values of an image into a predetermined number of representative pixel values; wherein the pixel values are quantized pixel values. The representative pixel values preferably consist of 10-20 values. It is preferable that the color image segmentation method, prior to the step (a), further comprises the steps of: (p-a-1) defining a predetermined window containing a center pixel; and (p-a-2) calculating a predetermined value representing the degree of difference from the color of peripheral pixels with respect to pixels in a defined window. It is also preferable that the step (a) comprises the steps of: (a-1) defining a window B which is centered at a pixel p and has a size of d x d when d is a positive integer; and (a-2) classifying a pixel position Z, into a C number of classes when i is a number between 1 and C, and Z, is a set of all pixels in the window B; and (a-3) obtaining a J-value with respect to each pixel in a class-map as: (Equation Removed) where m, is the average of positions of N, data points in class Zt, (Equation Removed) d is preferably an integer inclusive of and between 3 and 10. The predetermined scale is preferably a gray scale having values between 0 and 255. In order to achieve the above object, according to another aspect of the present invention, there is provided a color image segmentation method. The color image segmentation method comprises the steps of: (a) quantizing pixel values of an image into a predetermined number of representative pixel values; (b) calculating a predetermined value representing the degree of difference from the color of pixels in a predetermined size window using quantized representative pixel values; (c) obtaining a converted image by converting a calculated value into a value of a predetermined scale; and (d) segmenting the converted image using a segmentation method based on a region growing method. In order to achieve another object, there is provided an object-based color image processing method for processing a color image according to a color image segmentation method. The color image segmentation method comprises the steps of: (a) calculating a predetermined value representing the degree of difference from the color of peripheral pixels using pixel values of an input image; (b) obtaining a converted image by converting a calculated value into a value of a predetermined scale; and (c) segmenting the converted image. In order to achieve still another object, there is provided a medium for storing program codes performing a color image segmentation method for segmenting a color image into a plurality of regions. The color image segmentation method comprises the steps of: (a) quantizing pixel values of an image into a predetermined number of representative pixel values; (b) calculating a predetermined value representing the degree of difference from the color of pixels in a predetermined size window using quantized representative pixel values; (c) obtaining a converted image by converting a calculated value into a value of a predetermined scale; and (d) segmenting the converted image using a segmentation method based on a region growing method. Brief Description of the Drawings The above objects and advantages of the present invention will become more apparent by describing in detail a preferred embodiment thereof with reference to the attached drawings in which: FIG. 1 is a flowchart illustrating a color image segmentation method according to a preferred embodiment of the present invention; FIGS. 2A through 2C illustrate class-maps and J-values formed according to a color image segmentation method of FIG. 1; FIGS. 3A and 3B illustrate segmented class-maps; FIG. 4A illustrates one image frame of a "container" as a test image and a test image segmented by the color image segmentation method according to the present invention; FIG. 4B illustrates one image frame of a "foreman" as a test image and a test image segmented by the color image segmentation method according to the present invention; FIG. 4C illustrates one image frame of a "coast" as a test image and a test image segmented by the color image segmentation method according to the present invention; FIG. 4D illustrates one image frame of a "flower garden" as a test image and a test image segmented by the color image segmentation method according to the present invention; and FIG. 4E illustrates one image frame of a "mother and daughter" as a test image and a test image segmented by the color image segmentation method according to the present invention. Best mode for carrying out the Invention Referring to FIG. 1, which illustrates a flowchart illustrating a color image segmentation method according to a preferred embodiment of the present invention, a color image is input (step 102), and pixel values of an input image are quantized into several representative pixel values (step 104) In order to classify an image in natural scenes, the representative pixel values consist of 10-20 values. In this embodiment, quantization is performed using three representative pixel values for convenience of explanation. Next, a class-map is formed by assigning labels corresponding to a quantized representative pixel values (step 106). More preferably, a window centered at a pixel to be processed in an entire image is defined. That is, when d is a positive integer, preferably between 3 and 10, a window B which is centered at a pixel p and has a size of d x d, is defined. Also, an assumption is made that i is the number between 1 and C, and Z, is a set of all the pixels in the window B. In other words, an assumption is made that Zi is classified into a C number of classes. Preferably, the d determining the size of the window is an integer inclusive of and between 3 and 10. Also, an assumption is made that mi is the average of positions of Ni data points in class Zi as: (equation 1) (Equation Removed) Also, Sr and Sw are defined by: (equation 2) (Equation Removed) (equation 3) (Equation Removed) respectively. Next, a J-value with respect to each pixel in a class-map is obtained (step 108). The J-value with respect to each pixel in the class-map is defined as follows, (equation 4) (Equation Removed) The J-values obtained by equation 3 are converted into a gray scale value between 0 and 255, so that a gray scale image having values and capable of being referred to as a J-image is obtained (step 110). The J-image has the same form as a three-dimensional topographic map containing valleys and mountains that actually represent region centers and region boundaries, respectively. Lastly, the J-image is segmented based on a region growing method (step 112). The region growing method is known to one of ordinary skill in the art as a method used for the segmentation of a digital image, therefore, an explanation thereof is not given. FIGS. 2A through 2C illustrate class-maps and J-values formed according to a color image segmentation method of FIG. 1. The J-value at the center pixel is 1.720 in the class-map of FIG. 2A, and in the class-map of FIG. 2B, the J-value at the center pixel is 0, and in the class-map of FIG. 2C, the J-value at the center pixel is obtained as 0.855. Here, like in the class-map of FIG. 2A, in the case where pixels represented as + located at the left of the center pixel, pixels represented as 0 located at the right of the center pixel, and pixels represented as * located at the right of the center pixel form regions most clearly, the J-value is 1.720, a relative large value. Also, like in the class-map of FIG. 2B, in the case where the pixels represented as +, the pixels represented as 0, and the pixels represented as * are uniformly distributed and hardly form regions, the J-value is 0. Furthermore, like in the class-map of FIG. 2C, in the case where the pixels represented as * located at the right of the center pixel form regions, but the pixels represented as 0 and * hardly form regions, the J-value is 0.855. That is, the larger the J-value is, the more likely that the pixel is near to a region boundary, therefore, a segmentation based on the region growing method by using this point can be performed. FIGS. 3A and 3B illustrate segmented class-maps. It is necessary to check whether a segmentation is performed well with respect to each region in the segmented class-maps and to represent the same as quantized values. For this purpose, when Jk is the J-value obtained with respect to a k-region, and Mk is the number of pixel points of a k-th region, and N is the total number of pixel points in the class-map, the averaged J-value is calculated as: (equation 5) (Equation Removed) The calculated values are represented as quantized values whether a segmentation is performed well with respect to each region in the segmented class-maps or not. In the case of the segmented class-map shown in FIG. 3A, J is 0, on the other hand, in the case of the segmented class-map shown in FIG. 3B, J is 0.05. That is, in the case of regions of a fixed number, especially in the case of better segmentation, the averaged J-value is small. This occurs because the region contains a few uniformly distributed color classes in the case where a region is well segmented. Accordingly, the averaged J-value is small. FIG. 4A illustrates one image frame of a "container" as a test image and a test image segmented by the color image segmentation method according to the present invention. Referring to FIG. 4A, J of an image before segmentation is 0.232, but, J of the image after segmentation is 0.071. Also, it is evident that regions in the test image are well segmented. FIG. 4B illustrates one image frame of a "foreman" as a test image and a test image segmented by the color image segmentation method according to the present invention. Referring to FIG. 4B, .7 of an image before segmentation is 0.238, but ./ of the image after segmentation is 0.1 05. Also, it is evident that regions in the test image are well segmented. FIG. 4C illustrates one image frame of a "coast" as a test image and a test image segmented by the color image segmentation method according to the present invention. Referring to FIG. 4C, 7 of an image before segmentation is 0.494, but J of the image after segmentation is 0.093. Also, it is evident that regions in the test image are well segmented. FIG. 4D illustrates one image frame of a "flower garden" as a test image and a test image segmented by the color image segmentation method according to the present invention. Referring to FIG. 4D, J of an image before segmentation is 0.435, but J of the image after segmentation is 0.088. Also, it is evident that regions in the test image are well segmented. FIG. 4E illustrates one image frame of a "mother and daughter" as a test image and a test image segmented by the color image segmentation method according to the present invention. Referring to FIG. 4E, J of an image before segmentation is 0.438, but J of the image after segmentation is 0.061. Also, it is evident that regions in the test image are well segmented. That is, as described referring to FIG. 4A through 4E, J of the image segmented by the color image segmentation method according to the present invention is smaller than J of the image before segmentation. In the above embodiment, the calculation of a specific function is explained as an example, however, this is only for explanation. The scope of the present invention defined in the appended claims is not limited to the embodiment, and it is obvious that one of ordinary skill in the art can use another modified function representing the degree of difference from the color of peripheral pixels. Furthermore, the above color image segmentation method can be embodied in a computer program. Codes and code segments composing the program can be easily inferred to by a skilled computer programmer in the art. Also, the program can be stored in computer readable media, read and executed by a computer, and it can thereby realize the color image processing method. The media can include magnetic media, optical media, and carrier waves. As described above, according to the present invention, a color image can be automatically segmented without user's assistance and is robust with respect to an input image containing noise. Industrial Applicability In the above color image segmentation method according to the present invention, a robust segmentation is possible even when segmenting an image containing much noise or texture. Furthermore, an automatic segmentation is possible without user's assistance such as segmentation performed manually by a user, therefore, the segmentation speed is high. The color image segmentation method can be applied to object-based image processing such as that used in MPEG-7. What is claimed is: 1. A color image segmentation method for segmenting a color mage into a plurality of regions, comprising the steps of: (a) calculating a predetermined value representing the degree of difference from the color of peripheral pixels by using pixel values of an input image; (b) obtaining a converted image by converting a calculated value into a value of a predetermined scale; and (c) segmenting the converted image. 2. The color image segmentation method according to claim 1, wherein the step (c) segments the converted image based on a region growing method. 3. The color image segmentation method according to at least one of claim 1 or claim 2, prior to.the step (a), further comprising the step of (p-a) quantizing pixel values of'an image into a predetermined number of representative pixel values; wherein the pixel values are quantized pixel values. 4. The color image segmentation method according to claim 3, wherein the representative pixel values consist of 10-20 values. 5. The color image segmentation method according to at least one of claim 1 or claim 2 or claim 4, prior to the step (a), further comprising the steps of: (p-a-1) defining a predetermined window containing a center pixel; and (p-a-2) calculating a predetermined value representing the degree of difference from the color of peripheral pixels with respect to pixels in a defined window. 6. The color image segmentation method according to claim 3, prior to the step (a), further comprising the steps of: (p-a-1) defining a predetermined window containing a center pixel; and (p-a-2) calculating a predetermined value representing the degree of difference from the color of peripheral pixels with respect to pixels in a defined window. 7. The color image segmentation method according to at least one of claim 1 or claim 2, wherein the step (a) comprises the steps of: (a-1) defining a window B which is centered at a pixel p and has a size of d x d when d is a positive integer; and (a-2) classifying a pixel position Z, into a C number of classes when i is a number between 1 and C, and Z, is a set of all pixels in the window B; and (a-3) obtaining a J-value with respect to each pixel in a class-map as: (Equation Removed) where m, is the average of positions of A/, data points in class Z„ (Equation Removed) 8. The color image segmentation method according to claim 3, wherein the step (a) comprises the steps of: (a-1) defining a window B which is centered at a pixel p and has a size of d x d when d is a positive integer; and (a-2) classifying a pixel position Z, into a C number of classes when i is a number between 1 and C, and Z, is a set of all pixels in the window B; and (a-3) obtaining a J-value with respect to each pixel in a class-map as: (Equation Removed) where m, is the average of positions of N, data points in ciass Z„ (Equation Removed) 9. The color image segmentation method according to claim 4, wherein the step (a) comprises the steps of: (a-1) defining a window B which is centered at a pixel p and has a size of d x d when d is a positive integer; and (a-2) classifying a pixel position Z, into a C number of classes when i is a number between 1 and C, and Z, is a set of all the pixels in the window B; and (a-3) obtaining a J-value with respect to each pixel in a class-map as: (Equation Removed) where m, is the average of positions of N, data points in class Z„ (Equation Removed) 10. The color image segmentation method according to claim 5, wherein the step (a) comprises the steps of. (a-1) defining a window B which is centered at a pixel p and has a size of d x d when d is a positive integer; and (a-2) classifying a pixel position Z. into a C number of classes when i is a number between 1 and C, and Z, is a set of all pixels in the window B; and (a-3) obtaining a J-value with respect to each pixel in a class-map as: where m, is the average of positions of w, data points in ciass Z„ (Equation Removed) 11. The color image segmentation method according to claim 6, wherein the step (a) comprises the steps of: (a-1) defining a window B which is centered at a pixel p and has a size of d x d when d is a positive integer, and (a-2) classifying a pixel position Z, into a C number of classes when i is a number between 1 and C, and Z, is a set of all pixels in the window B; and (a-3) obtaining a J-vaiue with respect to each pixel in a class-map as: (Equation Removed) S where m, is the average of positions of N} number of data points of ciass Z„ (Equation Removed) 12. The color image segmentation method according to claim 7, wherein the d is an integer inclusive of and between 3 and 10. 13. The color image segmentation method according to claim 8, wherein the d is an integer inclusive of and between 3 and 10. 14. The color image segmentation method according to claim 9, wherein the d is an integer inclusive of and between 3 and 10. 15. The color image segmentation method according to claim 10, wherein the d is an integer inclusive of and between 3 and 10. 16. The color image segmentation method according to claim 11, wherein the d is an integer inclusive of and between 3 and 10. 17. The color image segmentation method according to at least one of claim 1 or claim 2, wherein the predetermined scale is a gray scale having values between 0 and 255 18. The color image segmentation method according to claim 3. wherein the predetermined scale is a gray scale having values between 0 and 255. 19. The color image segmentation method according to claim 4, wherein the predetermined scale is a gray scale having values between 0 and 255. 20. The color image segmentation method according to claim 5, wherein the predetermined scale is a gray scale having values between 0 and 255. 21. The color image segmentation method according to claim 6, wherein the predetermined scale is a gray scaie having values between 0 and 255. 22 The color image segmentation method according to claim 7, wherein the predetermined scaie is a gray scaie having values between 0 and 255. 23 The color image segmentation method according to claim 8, wherein the predetermined scale is a gray scale having values between 0 and 255. 24. The color image segmentation method according to claim 9, wherein the predetermined scale is a gray scale having values between 0 and 255. 25. The color image segmentation method according to claim 10, wherein the predetermined scale is a gray scale having values between 0 and 255. 26. The color image segmentation method according to claim 11, wherein the predetermined scale is a gray scale having values between 0 and 255 27. The color image segmentation method according to claim 12, wherein the predetermined scale is a gray scale having values between 0 and 255. 28. The color image segmentation method according to claim 13, wherein the predetermined scale is a gray scale having values between 0 and 255. 29. The color image segmentation method according to claim 14, wherein the predetermined scale is a gray scale having values between 0 and 255. 30. The color image segmentation method according to claim 15, wherein the predetermined scale is a gray scale having values between 0 and 255. 31. The color image segmentation method according to claim 16, wherein the predetermined scale is a gray scale having values between 0 and 255. 32. An object-based color image processing method for processing a color image according to a color image segmentation method, wherein the color image segmentation method comprises the steps of: (a) calculating a predetermined value representing the degree of difference from the color of peripheral pixels using pixel values of an input image; (b) obtaining a converted image by converting a calculated value into a value of a predetermined scale; and (c) segmenting the converted image 33 The color image processing method according to claim 32, wherein the color image processing method complies with the MPEG-7 standard. 34. A color image segmentation method for segmenting a color image into a plurality of regions, comprising the steps of: (a) quantizing pixel values of an image into a predetermined number of representative pixel values; (b) calculating a predetermined value representing the degree of difference from the color of pixels in a predetermined size window using quantized representative pixel values; (c) obtaining a converted image by converting a calculated value into a value of a predetermined scale; and (d) segmenting the converted image using a segmentation method based on a region growing method. 35 The color image segmentation method according to claim 34, wherein the step (a) comprises the steps of: (a-1) defining a window B which is centered at a pixel p and has a size of d x d when d is a positive integer; and (a-2) classifying a pixel position Z, into a C number of classes when i is a number between 1 and C, and Z, is a set of all pixels in the window B; and (a-3) obtaining a J-value with respect to each pixel in a class-map as: (Equation Removed) where m, is the average of positions of N, data points in class Z„ (Equation Removed) 36. The color image segmentation method according to claim 35, wherein d is an integer inclusive of between 3 and 10. 37. The color image segmentation method according to one of claim 34 to claim 36, wherein the predetermined scale is a gray scale having values between 0 and 255. 38. A medium for storing program codes performing a color image segmentation method for segmenting a color image into a plurality of regions, wherein the color image segmentation method comprises the steps of: (a) quantizing pixel values of an image into a predetermined number of representative pixel values; (b) calculating a predetermined value representing the degree of difference from the color of pixels in a predetermined size window using quantized representative pixel values; (c) obtaining a converted image by converting a calculated value into a value of a predetermined scale; and (d) segmenting the converted image using a segmentation method based on a region growing method. 39. The medium according to claim 38, wherein the step (a) comprises the steps of: (a-1) defining a window B which is centered at a pixel p and has a size of d x d when d is a positive integer; and (a-2) classifying a pixel position Z, into a C number of classes when i is a number between 1 and C, and Z, is a set of all pixels in the window B; and (a-3) obtaining a J-vaiue with respect to each pixel in a class-map as: (Equation Removed) where m, is the average of positions of N, data points in class Z„ (Equation Removed) 40. The medium according to claim 39, wherein d is set as an integer inclusive of and between 3 and 10. 41 The medium according to one of claim 38 to claim 40, wherein the predetermined scale is a gray scale having values between 0 and 255. 42. A colour image segmentation system for segmenting a colour image into a plurality of regions, said system comprising: a first means for inputting the colour image, a second means for quantizing pixel value of the input image to obtain representative pixel values; a third means for obtaining a class map by assigning labels corresponding to quantized representative pixel values; a fourth means for obtaining a J value with respect to each pixel in a class map; a fifth means for obtaining a J image by converting the J value into a gray scale value, and a sixth means for segmenting the J image based on region-growing method. 43. A system as claimed in claim 1, wherein the second means quantizes the pixel values using 3 representative pixel values. 44. A system as claimed in claim 1, wherein pixel values of the image input are quantized to obtain 10 to 20 representative pixel values. 45. A system as claimed in claim 1, wherein the third means defines a window 'B' centered at a pixel to be processed in an entire image. 46. A system as claimed in claim 4, wherein the window 'B' is centered at a pixel 'P' and has a size of d x d; wherein d is a positive integer between 3 and 10. 47 A system as claimed in claim 1, wherein the fourth means obtains the 'J' value defined by the formula: (Equation Removed) 48 A system as claimed in claim 6, wherein Stand Sw are defined by the formula: (Equation Removed) 49 A system as claimed in claim 7, wherein m, is the average of positions of N, data points in class Zt and is defined by the formula: 50 A system as claimed in claims 7 and 8, wherein Zx is a set of all pixel in the window 'B'. 51 A system as claimed in claim 9, wherein i lies between 1 and C. 52 A system as claimed in claim 1, wherein the fifth means converts the 'J' value into gray scale value between 0 and 225. 53 A color image segmentation method for segmenting a color image into a plurality of regions, substantially as herein described with reference to the accompanying drawings. 54 An object-based color image processing method for processing a color image according to a color image segmentation method, substantially as herein described with reference to the accompanying drawings. 55 A medium for storing program codes performing a color image segmentation method for segmenting a color image into a plurality of regions, substantially as herein described with reference to the accompanying drawings.

Documents:

in-pct-2001-682-abstract.pdf

in-pct-2001-682-correspondence-others.pdf

in-pct-2001-682-correspondence-po.pdf

IN-PCT-2001-682-DEL-Claims.pdf

in-pct-2001-682-del-drawings.pdf

in-pct-2001-682-del-form-1.pdf

in-pct-2001-682-del-form-13.pdf

in-pct-2001-682-del-form-19.pdf

in-pct-2001-682-del-form-2.pdf

in-pct-2001-682-del-form-26.pdf

in-pct-2001-682-del-form-3.pdf

in-pct-2001-682-del-form-4.pdf

in-pct-2001-682-del-form-5.pdf

in-pct-2001-682-del-pct-101.pdf

in-pct-2001-682-del-pct-210.pdf

in-pct-2001-682-del-pct-304.pdf

in-pct-2001-682-del-pct-308.pdf

in-pct-2001-682-del-pct-332.pdf

in-pct-2001-682-del-petition-137.pdf

in-pct-2001-682-del-petition-138.pdf