Object perception: Generative image models and bayesian inference

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

Volumn 2525, Issue , 2002, Pages 207-218

  Kersten, Daniel ^a  

^a UNIVERSITY OF MINNESOTA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; COMPUTER VISION; INFERENCE ENGINES;

BAYESIAN DECISION THEORY; BAYESIAN INFERENCE; GENERATIVE MODEL; NON TRIVIAL PROBLEMS; OBJECT PERCEPTION; OPTIMAL THEORIES; OPTIMAL UTILIZATION; VISUAL INFERENCES;

DECISION THEORY;

EID: 84931305624     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-36181-2_21     Document Type: Conference Paper

References (35)

1. Bloj, M. G., Kersten, D., & Hurlbert, A. C. (1999). Perception of three-dimensional shape influences colour perception via mutual illumination. Nature, 402, 877-879.
2. Clark, J. J., & Yuille, A. L. (1990). Data Fusion for Sensory Information Processing. Boston: Kluwer Academic Publishers.
3. Brainard, D. H., & Freeman, W. T. (1997). Bayesian color constancy. J Opt Soc Am A, 14, (7), 1393-411.
4. Biilthoff, H. H., & Mallot, H. A. (1988). Integration of depth modules: stereo and shading. Journal of the Optical Society of America, A, 5, (10), 1749-1758.
5. Drew, M., & Funt, B. (1990). Calculating surface reflectance using a single-bounce model of mutual reflection. Proceedings of the 3rd International Conference on Computer Vision Osaka: 393-399.
6. Foley, J., van Dam, A., Feiner, S., & Hughes, J. (1990). Computer Graphics Principles and Practice, (2nd ed.). Reading, Massachusetts: Addison-Wesley Publishing Company.
7. Gegenfurtner, K. R. (1999). Reflections on colour constancy. Nature, 402, 855-856.
8. Geisler, W. S, & Kersten, D. (2002). Illusions, perception and Bayes. Nat Neurosci, 5, (6), 508-10.
9. Green, D. M., & Swets, J. A. (1974). Signal Detection Theory and Psychophysics. Huntington, New York: Robert E. Krieger Publishing Company. 1974.
10. Grenander, U. (1996). Elements of Pattern theory. Baltimore: Johns Hopkins University Press.
11. Grill-Spector, K., Kourtzi, Z., & Kanwisher, N. (2001). The lateral occipital complex and its role in object recognition. Vision Res, 41, (10-11), 1409-22.
12. Jacobs, R. A. (2002). “What determines visual cue reliability?” Trends Cogn Sci 6(8): 345-350.
13. Kersten, D. (1997). Inverse 3D Graphics: A Metaphor for Visual Perception. Behavior Research Methods, Instruments, & Computers, 29, (1), 37-46.
14. Kersten, D. (1999). High-level vision as statistical inference. In Gazzaniga, M. S. (Ed.), The New Cognitive Neurosciences-2nd Edition(pp. 353-363). Cambridge, MA: MIT Press.
15. Kersten, D., & Schrater, P. R. (2002). Pattern Inference Theory: A Probabilistic Approach to Vision. In Mausfeld, R.,& Heyer, D. (Ed.), Perception and the Physical World pp. Chichester: John Wiley & Sons, Ltd.
16. Knill, D. C, & Richards, W. (1996). Perception as Bayesian Inference. Cambridge: Cambridge University Press.
17. Landy, M. S., Maloney, L. T., Johnston, E. B., & Young, M. J. (1995). Measurement and modeling of depth cue combination: In defense of weak fusion. Vision Research, 35, 389-412.
18. Lerner, Y., Hendler, T., & Malach, R. (2002). Object-completion Effects in the Human Lateral Occipital Complex. Cereb Cortex, 12, (2), 163-77.
19. Lorenceau, J., & Shiffrar, M. (1992). The influence of terminators on motion integration across space. Vision Res, 32, (2), 263-73.
20. Lorenceau, J., & Alais, D. (2001). Form constraints in motion binding. Nat Neurosci, 4, (7), 745-51.
21. Madison, C, Thompson, W., Kersten, D., Shirley, P., & Smits, B. (2001). Use of interreflection and shadow for surface contact. Perception and Psychophysics, 63, (2), 187-194.
22. Mamassian, P., Knill, D. C, & Kersten, D. (1998). The Perception of Cast Shadows. Trends in Cognitive Sciences, 2, (8), 288-295.
23. McDermott, J., Weiss, Y., & Adelson, E. H. (2001). Beyond junctions: nonlocal form constraints on motion interpretation. Perception, 30, (8), 905-23.
24. Mumford, D. (1992). On the computational architecture of the neocortex. II. The role of cortico-cortical loops. Biol Cybern, 66, (3), 241-51.
25. Murray, S. O, Kersten, D, Olshausen, B. A, Schrater P, & Woods, D.L. (Under review) Shape perception reduces activity in human primary visual cortex. Submitted to the Proceedings of the National Academy of Sciences.
26. Pearl, J. (1988). Probabilistic reasoning in intelligent systems: networks of plausible inference, (Rev. 2nd printing, éd.). San Mateo, Calif.: Morgan Kaufmann Publishers.
27. Poggio, T., Torre, V., & Koch, C. (1985). Computational vision and regularization theory. Nature, 317, 314-319.
28. Rao, R. P., & Ballard, D. H. (1999). Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects [see comments]. Nat Neurosci, 2, (1), 79-87.
29. Ripley, B.. Pattern Recognition and Neural Networks. Cambridge University Press. 1996.
30. Schrater, P. R., &¿ Kersten, D. (2000). How optimal depth cue integration depends on the task. International Journal of Computer Vision, 40, (1), 73-91.
31. Schrater, P., & Kersten, D. (2001). Vision, Psychophysics, and Bayes. In Rao, R. P. N., Olshausen, B. A., & Lewicki, M. S. (Ed.), Probabilistic Models of the Brain: Perception and Neural Function{pp. Cambridge, Massachusetts: MIT Press.
32. Simoncelli, E. P. (1997). Statistical Models for Images: Compression, Restoration and Synthesis. Pacific Grove, CA.: IEEE Signal Processing Society.
33. Weiss, Y., Simoncelli, E. P., & Adelson, E. H. (2002). Motion illusions as optimal percepts. Nat Neurosci, 5, (6), 598-604.
34. Yuille, A. L., & Biilthoff, H. H. (1996). Bayesian decision theory and psychophysics. In D.C., K., & W., R. (Ed.), Perception as Bayesian Inference pp. Cambridge, U.K.: Cambridge University Press.
35. Zhu, S.C., Wu, Y., and Mumford, D. (1997). “Minimax Entropy Principle and Its Application to Texture Modeling”. Neural Computation. 9(8).