Likelihood Calculation for a Class of Multiscale Stochastic Models, with Application to Texture Discrimination

IEEE Transactions on Image Processing

Volumn 4, Issue 2, 1995, Pages 194-207

  Luettgen, Mark R ^a   Willsky, Alan S ^b  

^a Alphatech Inc ^*  (Canada)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CALCULATIONS; COMPUTATIONAL COMPLEXITY; ESTIMATION; FAST FOURIER TRANSFORMS; MARKOV PROCESSES; MATHEMATICAL MODELS; OPTIMIZATION; PARALLEL PROCESSING SYSTEMS; RANDOM PROCESSES; TEXTURES; TREES (MATHEMATICS);

LIKELIHOOD CALCULATION; MULTISCALE STOCHASTIC MODELS; SCALE RECURSIVE DYNAMICS; TEXTURE DISCRIMINATION;

IMAGE PROCESSING;

EID: 0029253301     PISSN: 10577149     EISSN: 19410042     Source Type: Journal    
DOI: 10.1109/83.342185     Document Type: Article

References (22)

1. C. Bouman and M. Shapiro, “Multispectral image segmentation using a multiscale model’’, in Proc. IEEE lnt. Conf. ASSP, 1992, pp. 565-568.
2. R. Chellappa and S. Chatterjee, “Classification of textures using Gaussian Markov random fields,” IEEE Trans. Acoust., Speech, Signal Processing, vol. 33, pp. 959-963, 1985.
3. R. Chellappa and R. Kashyap, “Digital image restoration using spatial interaction models,” IEEE Trans. Acoust., Speech, Signal Processing, vol. 30, pp. 461-472, 1982.
4. R. Chellappa, B. Manjunath, and T. Simchony, “Texture segmentation with neural networks,” in Neural Networks for Signal Processing, B. Kosko, Ed. Englewood Cliffs: Prentice-Hall, 1992, pp. 37-61.
5. K. Chou, A. Willsky, and A. Benveniste, “Multiscale recursive estimation, data fusion and regularization,” Massachusetts Inst. Technol., Lab. for Inform, and Decision Syst., LIDS-P-2085, 1991; also IEEE Trans. Automat. Contr., vol. 39, no. 3, pp. 463-478, Mar. 1994.
6. K. Chou et al., “Recursive and iterative estimation algorithms for multiresolution stochastic processes,” in Proc. IEEE Conf. Decision and Contr., Dec. 1989.
7. F. Cohen, Z. Fan, and M. Patel, “Classification of rotated and scaled textured images using Gaussian Markov random field models,” IEEE Trans. Pattern Anal. Machine IntelL., vol. 13, pp. 192-202, 1991.
8. I. Daubechies. “Orthonormal bases of compactly supported wavelets,” Commun. Pure and Applied Math., vol. 91, pp. 909-996, 1988.
9. H. Derin et al., “Modeling and segmentation of speckled images using complex data,” IEEE Trans. Geosci. Remote Sensing, vol. 28, pp. 76-87, 1990.
10. R. Kashyap, R. Chellappa, and A. Khotanzad, “Texture classification using features derived from random field models,” Pattern Recognit. Letts., vol. 1, pp. 43-50, 1982.
11. P. Kelly, H. Derin, and K. Hartt, “Adaptive segmentation of speckled images using a hierarchical random field model,” IEEE Trans. Acoust., Speech, Signal Processing, vol. 36, pp. 1628-1641, 1988.
12. M. Luettgen. “Image Ph.D. thesis, Massachusetts Inst. Technol., Cambridge, MA, Dept. Elec. Eng. and Comput. Sci., May 1993.
13. M. Luettgen et al., “Multiscale representations of Markov random fields,” IEEE Trans. Signal Processing, vol. 41, pp. 3377-3396, 1993.
14. M. Luettgen and A. Willsky. “Efficient multiscale regularization with applications to the computation of optical flow,” IEEE Trans. Image Processing, vol. 3, pp. 41-64, 1994.
15. M. Luettgen and A. Willsky. “Multiscale smoothing error models,” Massachusetts Inst. Technol., Lab. for Inform, and Decision Syst., LIDS-P-2234, 1994; to appear in IEEE Trans. Automat. Contr., Jan. 1995.
16. S. Mallat. “Multi-frequency channel decomposition of images and wavelet models,” IEEE Trans. Acoust., Speech, Signal Processing, vol. 37, pp. 2091-2110, 1989.
17. B. S. Manjunath and R. Chellappa, “Unsupervised texture segmentation using Markov random field models,” IEEE Trans. Pattern Anal. Machine Intell., vol. 13, pp. 478-182, 1991.
18. D. Munson and R. Visentin, “A signal processing view of strip-mapping synthetic aperture radar,” IEEE Trans. Acoust., Speech, Signal Processing, vol. 37, pp. 2131-2147, 1989.
19. L. Novak and M. Burl. “Optimal speckle reduction in polarimetric SAR imagery,” IEEE Trans. Aerosp. Electron. Syst., vol. 26, pp. 293-305, 1990.
20. L. Novak et al., “Optimal processing of polarimetric synthetic aperture radar imagery", Lincoln Lab. J., vol. 3, pp. 273-290, 1990.
21. J. Pearl. Probabilistic Reasoning in Intelligent Systems. San Mateo, CA: Morgan Kaufmann, 1988.
22. L. Rabiner and B. Juang, “An introduction to hidden Markov models,” IEEE ASSP Mag., 1986, pp. 4-16.