Color image segmentation based on adaptive local thresholds

Image and Vision Computing

Volumn 23, Issue 1, 2005, Pages 69-85

  Navon, Ety ^a   Miller, Ofer ^a   Averbuch, Amir ^a  

^a TEL AVIV UNIVERSITY   (Israel)

Author keywords

Homogeneity; Image segmentation; Local thresholds; Merging; Splitting

Indexed keywords

ALGORITHMS; AUTOMATION; COLOR; GRAPH THEORY; ITERATIVE METHODS; MERGING; STATISTICAL METHODS; THREE DIMENSIONAL COMPUTER GRAPHICS; WATERSHEDS;

HISTOGRAM; HOMOGENEITY; LOCAL THRESHOLD; SPLITTING;

IMAGE SEGMENTATION;

EID: 9544229696     PISSN: 02628856     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.imavis.2004.05.011     Document Type: Article

References (44)

1. D. Wang, Unsupervised video segmentation based on watersheds and temporal tracking, IEEE Transactions on Circuits and Systems for Video Technology 8 (5) (1998).
2. H. Gao, W.-C. Siu, C.-H. Hou, Improved techniques for automatic image segmentation, IEEE Transactions on Circuits and Systems for Video Technology 11 (12) (2001).
3. F. Dufaux, F. Moscheni, A. Lippman, Spatio-temporal segmentation based on motion and static segmentation, IEEE Proceedings of International Conference on Image Processing 95, Washington, DC, October 1995.
4. P. Suetens, P. Fua, A.J. Hanson, Computational strategies for object recognition, ACM Computing Surveys 24 (1992) 5-61.
5. P. Besl, R. Jain, Three-dimensional object recognition, ACM Computing Surveys 17 (1985) 75-145.
6. M. Kunt, M. Benard, R. Leonardi, Recent results in high compression image coding, IEEE Transactions on Circuits and Systems 34 (1987) 1306-1336.
7. K. Belloulata, J. Konrad, Fractal Image compression with region-based functionality, IEEE Transactions on Image Processing 11 (4) (2002) 351.
8. H. Radha, M. Vetterli, R. Leonardi, Image compression using binary space partitioning trees, IEEE Transactions on Image Processing 5 (12) (1996).
9. E. Ozyildiz, N. Krahnst-over, R. Sharm, Adaptive texture and color segmentation for tracking moving objects, Pattern Recognition 35 (2002) 2013-2029.
10. |10] D.S. Yang, H.I. Choi, Moving object tracking by optimizing models, Proceedings of the International Conference of Pattern Recognition, Brisbane, Australia, 1998, pp. 738-740.
11. R. Murrieta-Cid, M. Briot, N. Vandapel, Landmark identification and tracking in natural environment, IEEE International Conference on Intelligent Robots and Systems, Victoria, BC, Canada, 1998, pp. 738-740
12. N. Pal, S. Pal, A review on image segmentation techniques, Pattern Recognition 26 (1993) 1277-1294.
13. R.M. Haralick, L.G. Shapiro, Survey: image segmentation techniques, Computer Vision, Graphics and Image Processing 29 (1985) 100-132.
14. B. Schacter, L. Davis, A. Rosenfeld, Scene Segmentation by Cluster Detection in Color Space, Department of Computer Science, University of Maryland, College Park, MD, 1975.
15. A. Sarabi, J.K. Aggarwal, Segmentation of chromatic images, Pattern Recognition 13 (6) (1981) 417-427.
16. S.A. Underwood, J.K. Aggarwal, Interactive computer analysis of aerial color infrared photographs, Computer Graphics and Image Processing 6 (1977) 1-24.
17. J.M. Tenenbaum, T.D. Garvey, S. Weyl, H.C. Wolf, An interactive facility for scene analysis research. Artificial Intelligent Center, Stanford Research Institute, Menlo Park, CA, Technical Report 87, 1974.
18. H.-D. Cheng, Y. Sun, A hierarchical approach to color image segmentation using homogeneity, IEEE Transactions on Image Processing 9 (12) (2000).
19. A. Jain, Fundamentals of Digital Image Processing, Prentice-Hall, Englewood Cliffs, NJ, 1989.
20. J. Canny, A computational approach to edge detection, IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-8 (1986) 679-698.
21. R. Deriche, Using Canny's criteria to derive a recursively implemented optimal edge detector, International Journal of Computer Vision 1987; 167-187.
22. D. Marr, E. Hildreth, Theory of edge detection, in: Proceedings of Royal Society of London B, no. 207, 1980, pp. 187-217.
23. W.-Y. Ma, B.S. Manjunath, EdgeFlow: a technique for boundary detection and image segmentation, IEEE Transaction on Image Processing 9 (8) (2000).
24. S. Chen, W. Lin, C. Chen, Split-and-merge image segmentation based on localized feature analysis and statistical tests, CVGIP: Graph, Models Image Processing 53 (1991) 457-475.
25. Z. Wu, Homogeneity testing for unlabeled data: a performance evaluation, CVGIP: Graph, Models Image Processing 55 (1993) 370-380.
26. R. Beveridge, et al., Segmenting images using localized histograms and region merging, Computer Vision, Graphics and Image Processing 2 (1989) 311-347.
27. Y.-L. Chang, X. Li, Adaptive image region-growing, IEEE Transactions on Image Processing 3 (1994) 868-872.
28. O. Monga, An optimal region growing algorithm for image segmentation, Interantional Journal of Pattern Recognition and Artificial Intelligence 1 (4) (1987) 351-375.
29. S. Horowitz, T. Pavlidis, Picture segmentation by a directed split and- merge procedure, in: Proceedings of Second International Joint Conference on Pattern Recognition, 1974, pp. 424-433.
30. R. Adams, L. Bischof, Seeded region growing, IEEE Transactions on Pattern Analysis and Machine Intelligence 6 (1994).
31. S.A. Hojjatoleslami, J. Kittler, Region growing: a new approach, IEEE Transactions on Image Processing 7 (1998) 1079-1084.
32. O. Monga, An optimal region growing algorithm for image segmentation, International Journal of Pattern Recognition and Artificial Intelligence 1 (4) (1987) 351-375.
33. L. Vincent, P. Soille, Watershed in digital space: an efficient algorithm based on immersion simulation, IEEE Transactions on Pattern Analysis and Machine Intelligence 13 (1991) 583-598.
34. P. De Smet, D. De Vleeschauwer, Performance and scalability of a highly optimized rainfalling watershed algorithm, Proceedings of International Conference on Imaging Science, Systems and Technology, CISST 98, Las Vegas, NV, USA, July 1998, pp. 266-273.
35. T. Pavlidis, Y. Liow, Integrating region growing and edge detection, IEEE Transactions on Pattern Analysis and Machine Intelligence 12 (1990) 225-233.
36. K. Haris, S.N. Efstratiadis, N. Maglaveras, A.K. Katsaggelos, Hybrid image segmentation using watershed and fast region merging, IEEE Transactions on Image Processing 7 (1998) 1684-1699.
37. E. Sharon, A. Brandt, R. Basri, Fast multiscale image segmentation, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, I, South Carolina, 2000, pp. 70-77.
38. I.J. Cox, S.B. Rao, Y. Zhong, Ratio regions: a technique for image segmentation, Proceedings of International Conference on Pattern Recognition, B, August 1996, pp. 557-564.
39. P. Perona, W. Freeman, A factorization approach to grouping, Proceedings of European Conference on Computer Vision, Freiburg, Germany, 1998, pp. 655-670.
40. J. Shi, J. Malik, Normalized cuts and image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence 22 (8) (2000) 888-905 (also Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Puerto Rico, 1997, pp. 731-737).
41. J. Shi, J. Malik, Normalized cuts and image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence 22 (8) (2000) 888-905 (also Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Puerto Rico, 1997, pp. 731-737).
42. Z. Wu, R. Leahy, An optimal graph theoretic approach to data clustering: theory and its application to image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence 15 (1993) 1101-1113.
43. K. Jain Anil, Fundamental of Digital Image Processing, Prentice-Hall, Englewood Cliffs, NJ, 1989.
44. C. Thomas, L. Charles, R. Ronald, Introduction to Algorithms, MIT Press, Cambridge, MA, 1990.