Parsing images into region and curve processes

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2352, Issue , 2002, Pages 393-407

  Tu, Zhuowen ^a   Zhu, Song Chun ^a  

^a OHIO STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; D REGION; GLOBAL OPTIMIZATION; HIDDEN MARKOV MODELS; MONTE CARLO METHODS; ONE DIMENSIONAL; ROPE; STOCHASTIC SYSTEMS; TEXTURES;

DIFFERENCE OF GAUSSIANS; GEOMETRIC SMOOTHNESS; LAYERED REPRESENTATION; MARKOV CHAIN MONTE CARLO METHOD; NATURAL SCENES; POSTERIOR PROBABILITY; REVERSIBLE JUMP; STOCHASTIC ALGORITHMS;

IMAGE SEGMENTATION;

EID: 84949936079     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-47977-5_26     Document Type: Conference Paper

References (13)

1. J. August and S.W. Zucker. A generative model of curve images with a completelycharacterized non-gaussian joint distribution. In Proc. of Workshop on Stat. and Comp. Theories of Vis., July, 2001.
2. M. Isard and A. Blake. Contour tracking by stochastic propagation of conditional density. Proc. ECCV, 1996.
3. T.F. Cootes, D. Cooper, C.J. Taylor and J. Graham. Active shape models - their training and application. Computer Vision and Image Understanding, vol.61, no. 1, Jan. pages 38-59,1995.
4. U. Grenander and M. I. Miller. Representation of knowledge in complex systems. J. R. Stat. Soc., B, vol 56, 549-603, 1994
5. P. J. Green. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, vol. 82, 711-732, 1995.
6. J. Malik, S. Belongie, T. Leung, and J. Shi. Contour and texture analysis for image segmentation. IJCV, vo. 43, no.1, June 2001.
7. S.G. Mallat and Z. Zhang. Matching pursuits with time-frequency dictionaries. IEEE Tranc. on Signal Processing, vol. 41, no. 12, Dec. 1993.
8. D. Mumford. Algebraic geometry and its applications, chaper elastic and computer vision, pp. 491-506. Springer-Verlag, 1994.
9. B. A. Olshausen and D. J. Field. Sparse coding with an overcomplete basis set: a strategy employed by v1?. Vision Res., vol. 37, no. 23 3311-25 1997.
10. M. Kass, A. Witkin and D. Terzopoulos. Snakes: active contour models. IJCV, 1, 1988.
11. Z. Tu, S.C. Zhu., and H.Y. Shum. Image segmentation by data driven markov chain monte carlo. Proc. ICCV, 2001.
12. S. C. Zhu and A. L. Yuille. Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation. IEEE Trans. PAMI vol.18, No.9, 1996.
13. S.C. Zhu. Embedding gestalt laws in markov random fields- a theory for shape modeling and perceptual organization. PAMI, vol. 21, no.11, Nov. 1999.