Edge-based binocular stereopsis algorithm - A matching mechanism with probabilistic feedback

Neural Networks

Volumn 9, Issue 3, 1996, Pages 379-395

  Hongo, Sadayuki ^a   Sonehara, Noboru ^b   Yoroizawa, Isamu ^b  

^a NTT CORPORATION   (Japan)

^b NONE

Author keywords

Binocular stereopsis; Computational theory; Edge based stereo matching; Markov random field; Neural network model; Vision

Indexed keywords

BINOCULAR VISION; COMPUTATION THEORY; COMPUTER SIMULATION; MARKOV PROCESSES; NEURAL NETWORKS; PROBABILITY; STEREO VISION;

EDGE BASED BINOCULAR STEREOPSIS ALGORITHM; MARKOV RANDOM FIELD; NEURAL NETWORK MODEL;

ALGORITHMS;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; BINOCULAR VISION; PRIORITY JOURNAL; REVIEW;

EID: 0030131034     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/0893-6080(95)00127-1     Document Type: Article

References (25)

1. Albus, K. (1975). A quantitative study of the projection area of the central and paracentral visual field in area 17 of the cat. I. The perception of the topography. Experimental Brain Research, 24, 159-179.
2. Bellman, R., & Dreyfus, S. (1962). Applied dynamic programming. Princeton, NJ: Princeton University Press.
3. Blake, A., & Zisserman, A. (1987). Visual reconstruction. Cambridge, MA: MIT Press.
4. Bülthoff, H. H. (1991). Shape from X: Psychophysics and computation. In M. S. Landy & J. A. Movshon (Eds.), Computational models of visual processing. (Chapter 20). London: A Bradford Book.
5. Geman, S., & Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transaction on Pattern Analysis and Machine Intelligence, PAMI-6, 721-741.
6. Gilbert, C. D., & Wiesel, T. N. (1983). Clustered intrinsic connections in cat visual cortex. Journal of Neuroscience, 3, 1116-1133.
7. Grimson, W. E. L. (1982). A computational theory of visual surface interpolation. Philosophical Transaction of Royal Society of London B, 298, 395-427.
8. Grimson, W. E. L. (1983). An implementation of a computational theory of visual surface interpolation. Computer Vision, Graphics, and Image Processing, 22, 39-69.
9. Grimson, W. E. L., & Marr, D. (1979). A computer implementation of a theory of human stereo vision. In Proceedings of Image Understanding Workshop (pp. 41-47).
10. Hongo, S., & Yoroizawa, I. (1994). Stochastic relaxation for continuous values - standard regularization based on Gaussian MRF. Transactions of the Institute of Electronics, Information and Communication Engineers, E77-D, 425-432.
11. Hongo, S., Kawato, M., Inui, T., & Miyake, S. (1989). Contour extraction of images on parallel computer - local, parallel and stochastic algorithm which learns energy parameters. In Proceedings of International Joint Conference on Neural Networks (Vol. 1, pp. 161-168).
12. Hubel, D. H., & Wiesel, T. N. (1970). Cells sensitive to binocular depth in area 18 of the macaque monkey cortex. Nature, 225, 41-42.
13. Hubel, D. H., & Wiesel, T. N. (1977). Functional architecture of macaque monkey visual cortex. In Proceedings of Royal Society of London B, 198, 1-59.
14. Kawato, M., & Inui, T. (1990). Computational theory of the visual cortical areas. Transactions of the Institute of Electronics, Information and Communication Engineers, J73-D-II, 1111-1121 (in Japanese).
15. Kawato, M., Inui, T., Hongo, S., & Hayakawa, H. (1991). Computational theory and neural network models of interaction between visual cortical areas. (Technical Report TR-A-0105). Advanced Telecommunication Research Laboratories Technical Report, Kyoto, Japan.
16. Livingstone, M. S., & Hubel, D. H. (1988). Segregation of form, color, movement, and depth: Anatomy, physiology, and perception. Science, 240, 740-749.
17. Marr, D. (1982). Vision. New York: W. H. Freeman.
18. Marr, D., & Poggio, T. (1979). A computational theory of human stereo vision. In Proceedings of Royal Society of London B, 204, 301-328.
19. Marroquin, J. L. (1984). Surface reconstruction preserving discontinuities. (Technical Report 792). MIT AI Laboratory Technical Report, Cambridge.
20. Marroquin, J. L. (1985). Probabilistic solution of inverse problems. (Technical report). MIT AI Laboratory Technical Report, Cambridge.
21. Marroquin, J. L. (1987). Deterministic Bayesian estimation of Markov random fields with applications to computational vision. In Proceedings of IEEE First International Conference on Computer Vision (pp. 597-601).
22. Poggio, T., Torre, V., & Koch, C. (1985). Computational vision and regularization theory. Nature, 317, 314-319.
23. Ts'o, D. Y., Gilbert, C. D., & Wiesel, T. N. (1986). Relationship between horizontal interactions and functional architecture in cat striate cortexes revealed by cross-correlation analysis. Journal of Neuroscience, 6, 1160-1170.
24. Witkin, A. P. (1984). Scale space filtering: a new approach to multiscale description. In S. Ullman & W. Richards (Eds.), Image understanding 1984 (Chapter 3). New Jersey: Ablex Publishing Corporation.
25. Zeki, S., & Shipp, S. (1988). The functional logic of cortical connections. Nature, 335, 311-317.