EP1192809A1 - Color image segmentation method - Google Patents
Color image segmentation methodInfo
- Publication number
- EP1192809A1 EP1192809A1 EP00915565A EP00915565A EP1192809A1 EP 1192809 A1 EP1192809 A1 EP 1192809A1 EP 00915565 A EP00915565 A EP 00915565A EP 00915565 A EP00915565 A EP 00915565A EP 1192809 A1 EP1192809 A1 EP 1192809A1
- Authority
- EP
- European Patent Office
- Prior art keywords
- color image
- segmentation method
- image segmentation
- pixel
- values
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
- 238000000034 method Methods 0.000 title claims abstract description 94
- 238000003709 image segmentation Methods 0.000 title claims abstract description 80
- 230000011218 segmentation Effects 0.000 claims abstract description 29
- 230000002093 peripheral effect Effects 0.000 claims abstract description 9
- 238000003672 processing method Methods 0.000 claims description 6
- 238000004590 computer program Methods 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000013139 quantization Methods 0.000 description 1
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
- G06T7/10—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
- 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) s f y j
- 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
- 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
- the J-value at the center pixel is 0, and in the ciass-map of FIG 2C, the J-value at the center pixel is obtained as 0 855
- the J-value is 1 720, a relative large value
- the J-value is 0
- 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
- the pixels represented as + 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
- the pixels represented as + 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
- 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
Landscapes
- 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)
Abstract
Description
Claims
Applications Claiming Priority (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US13064399P | 1999-04-23 | 1999-04-23 | |
US130643P | 1999-04-23 | ||
PCT/KR2000/000248 WO2000065839A1 (en) | 1999-04-23 | 2000-03-22 | Color image segmentation method |
Publications (2)
Publication Number | Publication Date |
---|---|
EP1192809A1 true EP1192809A1 (en) | 2002-04-03 |
EP1192809A4 EP1192809A4 (en) | 2007-08-22 |
Family
ID=22445651
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
EP00915565A Withdrawn EP1192809A4 (en) | 1999-04-23 | 2000-03-22 | Color image segmentation method |
Country Status (8)
Country | Link |
---|---|
EP (1) | EP1192809A4 (en) |
JP (1) | JP3853156B2 (en) |
KR (1) | KR100436499B1 (en) |
CN (1) | CN1292593C (en) |
AU (1) | AU3680700A (en) |
MY (1) | MY130272A (en) |
TW (1) | TW469412B (en) |
WO (1) | WO2000065839A1 (en) |
Families Citing this family (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2000046748A1 (en) * | 1999-02-05 | 2000-08-10 | Samsung Electronics Co., Ltd. | Color image processing method and apparatus thereof |
US6778697B1 (en) * | 1999-02-05 | 2004-08-17 | Samsung Electronics Co., Ltd. | Color image processing method and apparatus thereof |
KR100788643B1 (en) | 2001-01-09 | 2007-12-26 | 삼성전자주식회사 | Searching method of image based on combination of color and texture |
KR100562937B1 (en) * | 2004-08-11 | 2006-03-22 | 엘지전자 주식회사 | Method and Apparatus for Processing Image of Display Device |
CN102087741B (en) * | 2009-12-03 | 2013-01-02 | 财团法人工业技术研究院 | Method and system for processing image by using regional architecture |
KR101223046B1 (en) | 2011-02-08 | 2013-01-17 | 경북대학교 산학협력단 | Image segmentation device and method based on sequential frame imagery of a static scene |
CN102629386A (en) * | 2012-03-28 | 2012-08-08 | 浙江大学 | Region segmentation method for colorful textile texture images |
CN103065317A (en) * | 2012-12-28 | 2013-04-24 | 中山大学 | Partial color transferring method and transferring device based on color classification |
KR101631953B1 (en) * | 2014-12-09 | 2016-06-20 | 삼성전자주식회사 | Method for processing image for blur region detection and image processing apparatus thereof |
Family Cites Families (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
KR100488422B1 (en) * | 1996-09-24 | 2005-09-02 | 주식회사 팬택앤큐리텔 | Grayscale-shaped information encoding / decoding device and method |
-
2000
- 2000-03-22 JP JP2000614663A patent/JP3853156B2/en not_active Expired - Fee Related
- 2000-03-22 AU AU36807/00A patent/AU3680700A/en not_active Abandoned
- 2000-03-22 CN CNB008036039A patent/CN1292593C/en not_active Expired - Fee Related
- 2000-03-22 EP EP00915565A patent/EP1192809A4/en not_active Withdrawn
- 2000-03-22 KR KR10-2001-7011526A patent/KR100436499B1/en not_active IP Right Cessation
- 2000-03-22 WO PCT/KR2000/000248 patent/WO2000065839A1/en active IP Right Grant
- 2000-04-21 MY MYPI20001737A patent/MY130272A/en unknown
- 2000-07-12 TW TW089106869A patent/TW469412B/en not_active IP Right Cessation
Non-Patent Citations (3)
Title |
---|
LEILA SHAFARENKO ET AL: "Automatic Watershed Segmentation of Randomly Textured Color Images" IEEE TRANSACTIONS ON IMAGE PROCESSING, IEEE SERVICE CENTER, PISCATAWAY, NJ, US, vol. 6, no. 11, November 1997 (1997-11), pages 1530-1544, XP011026240 ISSN: 1057-7149 * |
See also references of WO0065839A1 * |
YINING DENG ET AL: "Color Image Segmentation" CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, 1999. IEEE COMPUTER SOCIETY, vol. 2, 23 June 1999 (1999-06-23), pages 446-451, XP010347530 * |
Also Published As
Publication number | Publication date |
---|---|
EP1192809A4 (en) | 2007-08-22 |
MY130272A (en) | 2007-06-29 |
KR20010105382A (en) | 2001-11-28 |
KR100436499B1 (en) | 2004-06-22 |
AU3680700A (en) | 2000-11-10 |
TW469412B (en) | 2001-12-21 |
CN1340273A (en) | 2002-03-13 |
JP2002543691A (en) | 2002-12-17 |
JP3853156B2 (en) | 2006-12-06 |
WO2000065839A1 (en) | 2000-11-02 |
CN1292593C (en) | 2006-12-27 |
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RIN1 | Information on inventor provided before grant (corrected) |
Inventor name: MANJUNATH, B. S. DEP. OF ELEC. & COMP. ENGINEERIN Inventor name: SHIN, HYUN DOO Inventor name: CHOI, YANG LIM Inventor name: DENG, YINING DEP. OF ELEC. & COMP. ENGINEERING |
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