Color Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 16, Issue 7, 1994, Pages 689-700

  Liu, Jianqing ^a   Yang, Yee Hong ^a  

^a UNIVERSITY OF SASKATCHEWAN   (Canada)

Author keywords

Color image segmentation; evaluation criteria; Markov random field (MRF); multiresolution scheme; scale space filter; simulated annealing

Indexed keywords

ALGORITHMS; COMPUTER VISION; CONVERGENCE OF NUMERICAL METHODS; ESTIMATION; IMAGE ANALYSIS; OPTIMIZATION; RANDOM PROCESSES; TREES (MATHEMATICS);

COLOR IMAGE SEGMENTATION; GLOBAL CLUSTERING INFORMATION; MARKOV RANDOM FIELD; MAXIMUM A POSTERIORI; MULTIRESOLUTION SCHEME; SCALE SPACE FILTER; SIMULATED ANNEALING;

COLOR IMAGE PROCESSING;

EID: 0028464041     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/34.297949     Document Type: Article

References (19)

1. J. Besag, “On the statistical analysis of dirty pictures,” J. Roy. Statist, Soc. B, vol. 48, pp. 259–302, 1986.
2. P. Burger and D. Gillies, Interactive Computer Graphics. New York: Addison-Wesley, 1989.
3. R. C. Dubes, A. K. Jain, S. G. Nadabar, and C. C. Chen, “MRF model-based based algorithms for image segmentation,” in Proc. 10th Int. Conf. Pattern Recognit., Atlantic City, NJ, 1990, pp. 808–814.
4. K. Fukunaga, Introduction to Statistical Pattern Recognition. San Diego, CA: Academic Press, 1982.
5. J. Gauch and C.W. Hsia, “A comparison of three color image segmentation algorithms in four color spaces,” in SPIE Visual Commun. Image Processing, vol. 1818 (preprint), 1992.
6. S. Geman and D. Geman, “Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 6, pp. 721–741, 1984.
7. D. Geman, S. Geman, C. Graffigne, and P. Dong, “Boundary detection by constrained optimization,” IEEE Trans. Pattern Anal. Machine Intell., vol. 12, pp. 609–628, Dec. 1990.
8. R. M. Haralick and L. G. Shapiro, “Survey—Image segmentation techniques,” Comput. Vision, Graphics, and Image Processing, vol. 29, pp. 100–132, 1985.
9. G. Healey, “Segmenting images using normalize color,” IEEE Trans. Syst., Man, Cybern., vol. 22, pp. 64–73, 1992.
10. C. L. Huang, T. Y. Cheng, and C. C. Chen, “Color images segmentation using scale space filter and Markov random field,” Pattern Recognit., vol. 25, pp. 1217–1229, 1992.
11. G. J. Klinker, S. A. Shafer, and T. Kanade, “A physical approach to color image understanding,” Int. J. Comput. Vision, vol. 4, pp. 7–38, 1990.
12. Y. W. Lim and S. U. Lee, “On the color image segmentation algorithm based on the thresholding and the fuzzy c-means techniques,” IEEE Trans. Pattern Anal. Machine Intell., vol. 12, pp. 609–628, 1990.
13. J. Liu, “Multiresolution Color Image Segmentation,” M.Sc. thesis, Comput. put. Vision Lab., Dept. of Computational Sci., Univ. of Saskatchewan, 1993.
14. R. Muzzolini, Y. H. Yang, and R. Pierson, “Multiresolution texture segmentation with application to diagnostic ultrasound images,” IEEE Trans. Med. Imaging, vol. 12, no. 1, pp. 108–123, 1993.
15. R. Ohlander, K. Price, and D. R. Reddy, “Picture segmentation using a recursive region splitting method,” Comput. Graphics and Image Processing, vol. 8, pp. 313–333, 1978.
16. Y. Ohta, T. Kanade, and T. Sakai, “Color information for region segmentation,” Comput. Graphics and Image Processing, vol. 13, pp. 222–241, 1980.
17. W. K. Pratt, Digital Image Processing. New York: John Wiley, 1978.
18. T. Taxt and E. Bolviken, “Relaxation using models from quantum mechanics,” Pattern Recognit., vol. 24, pp. 695–709, 1991.
19. A. P. Witkin, “Scale space filtering: A new approach to multiscale description,” Image Understanding, S. Ullman and W. Richards, Eds. NJ: Ablex Publishing, 1984, pp. 79–95.