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研究生:甘敏成
研究生(外文):Ming-Cheng Kan
論文名稱:適合彩色影像切割之較佳色彩座標系
論文名稱(外文):Better Color Coordinates for Consistent Color Image Segmentation
指導教授:郭鐘榮
指導教授(外文):Chung J. Kuo
學位類別:碩士
校院名稱:國立中正大學
系所名稱:電機工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2001
畢業學年度:89
語文別:英文
論文頁數:59
中文關鍵詞:色彩座標系彩色影像切割
外文關鍵詞:Color CoordinatesColor Image Segmentation
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彩色影像切割通常最直接是利用組成個別 (R,G,B) 色彩影像而成。之前有很多色彩座標被提出使用,所以必然存在一組最適合影像切割的座標,本論文提出agreement、orthogonality和spectral similarity三種測量方式當作決定哪一個是最佳的切割標準,根據所提出的測試方法,對於影像切割而言,我們模擬結果 一直是最佳座標。
關於本論文架構敘述如下,第二章回顧對於影像切割使用分水嶺運算和有關色彩座標的相關背景。對於決定哪一個座標是最佳的測試標準和步驟則陳述在第三章,同時第四章為實驗結果。而最後在第五章是結論與討論。

Color image segmentation is achieved through direct combination of the segmented image from color component images. Since many color coordinates are available, there must be one that is most suitable for image segmentation. This thesis proposed agreement, orthogonality and spectral similarity measure as criteria to decide which coordinate is the best. According to our simulation results, is always the best coordinate for color image segmentation no matter which measure is employed.
The organization of this thesis is as follows. Chapter 2 reviews the watershed algorithm used for image segmentation and some backgrounds about color coordinates. The criteria and procedures to decide which color coordinate is the best is shown in Chapter 3 and Chapter 4 is experimental results. Finally the conclusion and discussions are given in Chapter 5.

ABSTRACTII
CONTENTSIII
LIST OF ILLUSTRATIONSIV
LIST OF TABLESVIII
CHAPTER 1 INTRODUCTION1
CHAPTER2 LITERATURE REVIEW3
2.1 WATERSHED ALGORITHM3
2.2 EXISTING COLOR COORDINATES4
CHAPTER 3 BETTER COORDINATE BASED ON EXISTING COLOR COORDINATES6
3.1 AGREEMENT MEASURE13
3.2 ORTHOGONALITY MEASURE16
3.3 SPECTRAL SIMILARITY MEASURE18
CHAPTER 4 EXPERIMENTAL RESULTS21
CHAPTER 5 CONCLUSIONS AND DISCUSSIONS33
APPENDIX A RELATIONSHIP BETWEEN EXISTING COLOR COORDINATES35
REFERENCES47

[1]“Managing Color With ColorSync,” Apple Computer Technical Publications, http://developer.apple.com/techpubs/macos8/MultimediaGraphics/ColorSyncManager/ManagingColorWithColorSync/frameset.html
[2]A. Moga , B. Cramariuc, & M. Gabbouj, “An efficient watershed segmentation algorithm suitable for parallel implementation," IEEE International Conference on Image Processing, vol. 2, pp. 101-104 , 1995
[3]A.K. Jain, Fundamentals of Digital Image Processing, Prentice-Hall, Englewood Cli_s, NJ, USA, 1992
[4]Adrian Ford, Alan Roberts, “Color Space Conversions,” http://wwwzenger.informatik.tu-muenchen.de/lehre/vorlesungen/graphik/info/csc/COL_.htm
[5]Chuang Gu; Ming-Chieh Lee, “Semantic video object tracking using region-based classification,” 1998 International Conference on Image Processing, pp. 643 —647, Vol. 1998
[6]Gatica-Perez, D.; Ming-Ting Sun; Chuang Gu, ”Semantic video object extraction based on backward tracking of multivalued watershed,” 1999 International Conference on Image Processing, Vol. 2 , pp.145 -149, 1999
[7]Gauch, J.M., “Image segmentation and analysis via multiscale gradient watershed hierarchies,” IEEE Transactions on Image Processing, vol. 8, pp. 69-79, Jan. 1999
[8]Saarinen, K., “Color image segmentation by a watershed algorithm and region adjacency graph processing,” IEEE Transactions on Image Processing, vol. 3, pp.1021-1025, 1994
[9]Shafarenko, L.; Petrou, M.; Kittler, J., “Automatic watershed segmentation of randomly textured color images,” IEEE Transactions on Image Processing, vol. 6,pp. 1530-1544, Nov. 1997
[10]Vincent, L.; Soille, P., “Watersheds in digital spaces:An efficient algorithm based on immersion simulations,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, pp. 583-598, June 1991
[11]Munchurl Kim; Jae Gark Choi; Daehee Kim; Hyung Lee; Myoung Ho Lee; Chieteuk Ahn; Yo-Sung Ho,”A VOP generation tool: automatic segmentation of moving objects in image sequences based on spatio-temporal information,” IEEE Transactions on Circuits and Systems for Video Technology, Vol. 9, No 8,pp. 1216 -1226, Dec. 1999
[12]Vandenbroucke, N.; Macaire, L.; Postaire, J.-G., “Color pixels classification in an hybrid color space,” IEEE Transactions on Image Processing, vol. 1, pp. 176-180,1998
[13]Seaborn, M., “On the use of color in content based image retrieval,” http://www.brunel.ac.uk/~eepgmas/report/report.html
[14]Sean Dunn, “Digital Color,” http://davis.wpi.edu/~matt/courses/color/
[15]Shiji, A.; Hamada, N., “Color image segmentation method using watershed algorithm and contour information,” 1999 International Conference on Image Processing, Vol. 4, pp. 305 —309, 1999
[16]Toklu, C.; Murat Tekalp, A.; Tanju Erdem, A. “Semi-automatic video object segmentation in the presence of occlusion,” IEEE Transactions on Circuits and Systems for Video Technology, Vol. 10, pp. 624 —629, June 2000.
[17]Vandenbroucke, N.; Macaire, L.; Postaire, J.-G., “Color pixels classification in an hybrid color space,” 1998 International Conference on Image Processing, Vol.1, pp. 176-180, 1998
[18]Y.I. Ohta, T. Kanade, & T. Sakai, “Color information for region segmentation,” Computer Graphics & Image Processing, vol. 13, pp. 222-241,1980

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