Classifications

• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T7/00—Image analysis
  • G06T7/10—Segmentation; Edge detection
  • G06T7/11—Region-based segmentation
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T7/00—Image analysis
  • G06T7/40—Analysis of texture
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T7/00—Image analysis
  • G06T7/10—Segmentation; Edge detection
  • G06T7/187—Segmentation; Edge detection involving region growing; involving region merging; involving connected component labelling
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
  • H04N9/00—Details of colour television systems
  • H04N9/64—Circuits for processing colour signals
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T2207/00—Indexing scheme for image analysis or image enhancement
  • G06T2207/10—Image acquisition modality
  • G06T2207/10024—Color image

Definitions

• the present invention relates to a color image segmentation method, and more particularly, to a color image segmentation method for segmenting a color image.
• 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.
• another conventional color image segmentation method for performing an automatic segmentation is not robust with respect to an input image containing noise
• 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.
• 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
• the step (c) segments the converted image based on a region growing method
• 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
• 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
• 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
• the predetermined scale is preferably a gray scale having values between 0 and 255
• a 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
• 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
• 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
• 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
• 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
• FIG 1 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)
• 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 Z, 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
• 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
• 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
• J k is the J-value obtained with respect to a k-region
• M k is the number of pixel points of a k-th region
• N is the total number of pixel points in the class-map
• 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
• 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
• 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
• ./ of an image before segmentation is 0 238, but ,7 of the image after segmentation is 0 105
• 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
• J of an image before segmentation is 0 494, but J of the image after segmentation is 0 093
• 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
• 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
• J of an image before segmentation is 0 438
• J of the image after segmentation is 0 061
• 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
• 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
• a color image can be automatically segmented without user's assistance and is robust with respect to an input image containing noise

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• Engineering & Computer Science (AREA)
• Computer Vision & Pattern Recognition (AREA)
• Physics & Mathematics (AREA)
• General Physics & Mathematics (AREA)
• Theoretical Computer Science (AREA)
• Multimedia (AREA)
• Signal Processing (AREA)
• Image Analysis (AREA)
• Facsimile Image Signal Circuits (AREA)
• Color Image Communication Systems (AREA)
• Image Processing (AREA)

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Publications (1)

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Citations (1)

• 2000
  • 2000-03-22 KR KR10-2001-7011526A patent/KR100436499B1/ko not_active IP Right Cessation
  • 2000-03-22 JP JP2000614663A patent/JP3853156B2/ja not_active Expired - Fee Related
  • 2000-03-22 CN CNB008036039A patent/CN1292593C/zh not_active Expired - Fee Related
  • 2000-03-22 WO PCT/KR2000/000248 patent/WO2000065839A1/en active IP Right Grant
  • 2000-03-22 AU AU36807/00A patent/AU3680700A/en not_active Abandoned
  • 2000-03-22 EP EP00915565A patent/EP1192809A4/en not_active Withdrawn
  • 2000-04-21 MY MYPI20001737A patent/MY130272A/en unknown
  • 2000-07-12 TW TW089106869A patent/TW469412B/zh not_active IP Right Cessation

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