Object perception as Bayesian inference

Annual Review of Psychology

Volumn 55, Issue , 2004, Pages 271-304

  Kersten, Daniel ^a   Mamassian, Pascal ^b   Yuille, Alan ^c  

^a UNIVERSITY OF MINNESOTA   (United States)

^b UNIVERSITY OF GLASGOW   (United Kingdom)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Computer vision; Depth; fMRI; Material; Neural; Psychophysics; Shape; Vision

Indexed keywords

BAYES THEOREM; DECISION MAKING; HUMAN; LEARNING; PHYSIOLOGY; PSYCHOLOGICAL THEORY; PSYCHOPHYSICS; REVIEW; VISION;

BAYES THEOREM; DECISION MAKING; HUMANS; LEARNING; PSYCHOLOGICAL THEORY; PSYCHOPHYSICS; VISUAL PERCEPTION;

EID: 1842433590     PISSN: 00664308     EISSN: None     Source Type: Book Series    
DOI: 10.1146/annurev.psych.55.090902.142005     Document Type: Article

References (149)

1. Albert MK. 2000. The generic viewpoint assumption and Bayesian inference. Perception 29:601-8
2. Albright TD, Stoner GR. 2002. Contextual influences on visual processing. Annu. Rev. Neurosci. 25:339-79
3. Atick JJ, Griffin PA, Redlich AN. 1996. Statistical approach to shape from shading: reconstruction of three-dimensional face surfaces from single two-dimensional images. Neural Comput. 8:1321-40
4. Barlow HB. 1962. A method of determining the overall quantum efficiency of visual discriminations. J. Physiol. 160:155-68
5. Berger J. 1985. Statistical Decision Theory and Bayesian Analysis. New York: Springer-Verlag
6. Bertamini M. 2001. The importance of being convex: an advantage for convexity when judging position. Perception 30:1295-310
7. Biederman I. 2000. Recognizing depth-rotated objects: a review of recent research and theory. Spat. Vis. 13:241-53
8. Blake A, Bülthoff HH. 1990. Does the brain know the physics of specular reflection? Nature 343:165-69
9. Bloj MG, Kersten D, Hurlbert AC. 1999. Perception of three-dimensional shape influences colour perception through mutual illumination. Nature 402:877-79
10. Brady MJ, Kersten D. 2003. Bootstrapped learning of novel objects. J. Vis. 3:413-22
11. Brainard DH, Freeman WT. 1997. Bayesian color constancy. J. Opt. Soc. Am. A 14:1393-411
12. Buckley D, Frisby JP, Freeman J. 1994. Lightness perception can be affected by surface curvature from stereopsis. Perception 23:869-81
13. Bullier J. 2001. Integrated model of visual processing. Brain Res. Brain Res. Rev. 36:96-107
14. Bülthoff HH, Mallot HA. 1988. Integration of depth modules: stereo and shading. J. Opt. Soc. Am. A 5:1749-58
15. Bülthoff HH, Yuille A. 1991. Bayesian models for seeing surfaces and depth. Comments Theor. Biol. 2:283-314
16. Burgi PY, Yuille AL, Grzywacz NM. 2000. Probabilistic motion estimation based on temporal coherence. Neural Comput. 12:1839-67
17. Clark JJ, Yuille AL. 1990. Data Fusion for Sensory Information Processing. Boston: Kluwer Acad.
18. Dana KJ, van Ginneken B, Nayar SK, Koenderink JJ. 1999. Reflectance and texture of real world surfaces. ACM Trans. Graph. 18:1-34
19. Dayan P, Hinton GE, Neal RM, Zemel RS. 1995. The Helmholtz machine. Neural Comput. 7:889-904
20. Debevec PE. 1998. Rendering synthetic objects into real scenes: bridging traditional and image-based graphics with global illumina tion and high dynamic range photography. Presented at SIGGRAPH
21. Dror RO, Leung TK, Adelson EH, Willsky AS. 2001. Statistics of real-world illumination. Presented at Proc. CVPR, Hawaii
22. Eckstein MP, Thomas JP, Palmer J, Shimozaki SS. 2000. A signal detection model predicts the effects of set size on visual search accuracy for feature, conjunction, triple conjunction, and disjunction displays. Percept. Psychophys. 62:425-51
23. Elder JH, Goldberg RM. 2002. Ecological statistics of gestalt laws for the perceptual organization of contours. J. Vis. 2:324-53
24. Ernst MO, Banks MS. 2002. Humans integrate visual and haptic information in a statistically optimal fashion. Nature 415:429-33
25. Evgeniou T, Pontil M, Poggio T. 2000. Regularization networks and support vector machines. Adv. Comput. Math. 13:1-50
26. Feldman J. 2001. Bayesian contour integration. Percept. Psychophys. 63:1171-82
27. Field DJ. 1987. Relations between the statistics of natural images and the response properties of cortical cells. J. Opt. Soc. Am. A 4:2379-94
28. Fine I, MacLeod DIA, Boynton GM. 2003. Visual segmentation based on the luminance and chromaticity statistics of natural scenes. Special issue on Bayesian and statistical approaches to vision. J. Opt. Soc. Am. A 20:1283-91
29. Fleming RW, Dror RO, Adelson EH. 2003. Real-world illumination and the perception of surface reflectance properties. J. Vis. 3:347-68
30. Freeman WT. 1994. The generic viewpoint assumption in a framework for visual perception. Nature 368:542-45
31. Freeman WT, Pasztor EC, Carmichael OT. 2000. Learning low-level vision. Int. J. Comput. Vis. 40:25-17
32. Freund Y, Schapire R. 1999. A short introduction to boosting. J. Jpn. Soc. Artif. Intell. 14:771-80
33. Geisler WS, Albrecht DG. 1995. Bayesian analysis of identification performance in monkey visual cortex: nonlinear mechanisms and stimulus certainty. Vis. Res. 35:2723-30
34. Geisler WS, Kersten D. 2002. Illusions, perception and Bayes. Nat. Neurosci. 5:508-10
35. Geisler WS, Perry JS, Super BJ, Gallogly DP. 2001. Edge co-occurrence in natural images predicts contour grouping performance. Vis. Res. 41:711-24
36. Gepshtein S, Banks MS. 2003. Viewing geometry determines how vision and haptics combine in size perception. Curr.Biol. 13:483-88
37. Gold JI, Shadlen MN. 2001. Neural computations that underlie decisions about sensory stimuli. Trends Cogn. Sci. 5:10-16
38. Green DM, Swets JA. 1974. Signal Detection Theory and Psychophysics. Huntington, NY: Krieger
39. Grenander U. 1996. Elements of Pattern Theory. Baltimore, MD: Johns Hopkins Univ. Press
40. Grill-Spector K. 2003. The neural basis of object perception. Curr. Opin. Neurobiol. 13:1-8
41. Grill-Spector K, Kourtzi Z, Kanwisher N. 2001. The lateral occipital complex and its role in object recognition. Vis. Res. 41:1409-22
42. Hastie T, Tibshirani R, Friedman J. 2001. The Elements of Statistical Learning. New York: Springer
43. Helmholtz H. 1867. Handbuch der Physiologischen Optik. Leipzig: Voss. (English tranl. 1924 JPC Southall as Treatise on Physiological Optics)
44. Helmholtz H. 1867. Handbuch der Physiologischen Optik. Leipzig: Voss. (English tranl. 1924 JPC Southall as Treatise on Physiological Optics)
45. Hill H, Bruce V. 1993. Independent effects of lighting, orientation, and stereopis on the hollow-face illusion. Perception 22:887-97
46. Hillis JM, Ernst MO, Banks MS, Landy MS. 2002. Combining sensory information: mandatory fusion within, but not between, senses. Science 298:1627-30
47. Hinton GE, Ghahramani Z. 1997. Generative models for discovering sparse distributed representations. Philos. Trans. R. Soc. London Ser. B 352:(1358):1177-90
48. Howe CQ, Purves D. 2002. Range image statistics can explain the anomalous perception of length. Proc. Natl. Acad. Sci. USA 99:13184-88
49. Humphrey GK, Goodale MA, Bowen CV, Gati JS, Vilis T, et al. 1997. Differences in perceived shape from shading correlate with activity in early visual areas. Curr. Biol. 1:144-47
50. Isard M, Blake A. 1998. Condensation - conditional density propagation for visual tracking. Int. J. Comput. Vis. 29:5-28
51. Jacobs RA. 2002. What determines visual cue reliability? Trends Cogn. Sci. 6:345-50
52. Jensen HW, Marschner SR, Levoy M, Hanrahan P. 2001. A practical model for subsurface light transport. Presented at Computer Graphics (SIGGRAPH)
53. Kanizsa G, Gerbino W. 1976. Convexity and symmetry in figure-ground organisation. In Vision and Artifact, ed. M Henle. New York: Springer
54. Kersten D. 1999. High-level vision as statistical inference. In The New Cognitive Neurosciences, ed. MS Gazzaniga, pp. 353-63. Cambridge, MA: MIT Press. 2nd ed.
55. Kersten D, Mamassian P, Knill DC. 1997. Moving cast shadows induce apparent motion in depth. Perception 26:171-92
56. Kersten D, Schrater PW. 2002. Pattern inference theory: a probabilistic approach to vision. In Perception and the Physical World, ed. R Mausfeld, D Heyer. Chichester: Wiley
57. Kersten D, Yuille A. 2003. Bayesian models of object perception. Curr. Opin. Neurobiol. 13:1-9
58. Knill DC. 1998. Discrimination of planar surface slant from texture: human and ideal observers compared. Vis. Res. 38:1683-711
59. Knill DC, Field D, Kersten D. 1990. Human discrimination of fractal images. J. Opt. Soc. Am. A 7:1113-23
60. Knill DC, Kersten D. 1991. Apparent surface curvature affects lightness perception. Nature 351:228-30
61. Knill DC, Kersten D, Yuille A. 1996. Introduction: a Bayesian formulation of visual perception. See Knill & Richards 1996, pp. 1-21
62. Knill DC, Richards W. 1996. Perception as Bayesian Inference. Cambridge, UK: Cambridge Univ. Press
63. Koechlin E, Anton JL, Burnod Y. 1999. Bayesian inference in populations of cortical neurons: a model of motion integration and segmentation in area MT. Biol. Cybern. 80:25-44
64. Koenderink JJ, van Doorn AJ, Kappers AM, Todd JT, 2001. Ambiguity and the 'mental eye' in pictorial relief. Perception 30:431-48
65. Konishi SM, Yuille AL, Coughlan JM, Zhu SC. 2003. Statistical edge detection: learning and evaluating edge cues. Pattern Anal. Mach. Intell. 1:37-48
66. Lamme VA, Roelfsema PR. 2000. The distinct modes of vision offered by feedforward and recurrent processing. Trends Neurosci. 23:571-79
67. Land EH, McCann JJ. 1971. Lightness and the retinex theory. J. Opt. Soc. Am. 61:1-11
68. Landy MS, Kojima H. 2001. Ideal cue combination for localizing texture-defined edges. J. Opt. Soc. Am. A Opt. Image Sci. Vis. 18:2307-20
69. Landy MS, Maloney LT, Johnston EB, Young M. 1995. Measurement and modeling of depth cue combination: in defense of weak fusion. Vis. Res. 35:389-412
70. Langer MS, Bülthoff HH. 2001. A prior for global convexity in local shape-from-shading. Perception 30:403-10
71. Lee TS, Mumford D. 2003. Hierarchical Bayesian inference in the visual cortex. J. Opt. Soc. Am. A 20:1434-48
72. Lee TS, Yang CF, Romero RD, Mumford D. 2002. Neural activity in early visual cortex reflects behavioral experience and higher-order perceptual saliency. Nat. Neurosci. 5:589-97
73. Legge GE, Hooven TA, Klitz TS, Mansfield JS, Tjan BS. 2002. Mr. Chips 2002: new insights from an ideal-observer model of reading. Vis. Res. 42:2219-34
74. Lennie P. 2003. The cost of cortical computation. Curr. Biol. 13:493-97
75. Leopold DA, O'Toole AJ, Vetter T, Blanz V. 2001. Prototype-referenced shape encoding revealed by high-level aftereffects. Nat. Neurosci. 4:89-94
76. Lerner Y, Hendler T, Ben-Bashat D, Harel M, Malach R. 2001. A hierarchical axis of object processing stages in the human visual cortex. Cereb. Cortex 11:287-97
77. Liu Z, Kersten D. 1998. 2D observers for human 3D object recognition? Vis. Res. 38:2507-19
78. Liu Z, Kersten D. 2003. 3D symmetric shapes are discriminated more efficiently than asymmetric ones. J. Opt. Soc. Am. A 20:1331-40
79. Liu Z, Kersten D, Knill DC. 1999. Stimulus information or internal representation? - A case study in human object recognition. Vis. Res. 39:603-12
80. Liu Z, Knill DC, Kersten D. 1995. Object classification for human and ideal observers. Vis. Res. 35:549-68
81. Lorenceau J, Shiffrar M. 1992. The influence of terminators on motion integration across space. Vis. Res. 32:263-73
82. MacKay DM. 1956. The epistemological problem for automata. In Automata Studies, ed. CE Shannon, J McCarthy, pp. 235-50. Princeton: Princeton Univ. Press
83. Maloney LT. 2002. Statistical decision theory and biological vision. In Perception and the Physical World, ed. D Heyer, R Mausfeld, pp. 145-89. Chichester, UK: Wiley
84. Mamassian P, Goutcher R. 2001. Prior knowledge on the illumination position. Cognition 81:B1-9
85. Mamassian P, Knill DC, Kersten D. 1998. The perception of cast shadows. Trends Cogn. Sci. 2:288-95
86. Mamassian P, Landy MS. 1998. Observer biases in the 3D interpretation of line drawings. Vis. Res. 38:2817-32
87. Mamassian P, Landy MS. 2001. Interaction of visual prior constraints. Vis. Res. 41:2653-68
88. Mamassian P, Landy MS, Maloney LT. 2002. Bayesian modelling of visual perception. See Rao et al. 2002, pp. 13-36
89. Marschner SR, Westin SH, Lafortune EPF, Torrance KE. 2000. Image-based measurement of the bidirectional reflectance distribution function. Appl. Opt. 39:2592-600
90. McDermott J, Weiss Y, Adelson EH. 2001. Beyond junctions: nonlocal form constraints on motion interpretation. Perception 30:905-23
91. Mumford D. 1992. On the computational architecture of the neocortex. II. The role of cortico-cortical loops. Biol. Cybern. 66:241-51
92. Murray SO, Kersten D, Olshausen BA, Schrater P, Woods DL. 2002. Shape perception reduces activity in human primary visual cortex. Proc. Natl. Acad. Sci. USA 99:15164-69
93. Nakayama K, Shimojo S. 1992. Experiencing and perceiving visual surfaces. Science 257:1357-63
94. Oliva A, Schyns PG. 2000. Diagnostic colors mediate scene recognition. Cogn. Psychol. 41:176-210
95. Oliva A, Torralba A. 2001. Modeling the shape of the scene: a holistic representation of the spatial envelope. Int. J. Comput. Vis. 42:145-75
96. Olshausen BA, Field DJ. 2000. Vision and the coding of natural images. Am. Sci. 88:238-45
97. Oram MW, Foldiak P, Perrett DI, Sengpiel F. 1998. The 'ideal Homunculus': decoding neural population signals. Trends Neurosci. 21:259-65
98. Parish DH, Sperling G. 1991. Object spatial frequencies, retinal spatial frequencies, noise, and the efficiency of letter discrimination. Vis. Res. 31:1399-115
99. Parraga CA, Troscianko T, Tolhurst DJ. 2000. The human visual system is optimised for processing the spatial information in natural visual images. Curr. Biol. 10:35-38
100. Pearl J. 1988. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Mateo, CA: Morgan Kaufmann
101. Pelli DG, Farell B, Moore DC. 2003. The remarkable inefficiency of word recognition. Nature 243:752-56
102. Pizlo Z. 1994. A theory of shape constancy based on perspective invariants. Vis. Res. 34:1637-58
103. Pizlo Z. 2001. Perception viewed as an inverse problem. Vis. Res. 41:3145-61
104. Platt ML, Glimcher PW. 1999. Neural corre lates of decision variables in parietal cortex. Nature 400:233-38
105. Poggio T, Edelman S. 1990. A network that learns to recognize three-dimensional objects. Nature 343:263-66
106. Poggio T, Girosi F. 1990. Regularization algorithms for learning that are equivalent to multilayer networks. Science 247:978-82
107. Portilla J, Simoncelli EP. 2000. A parametric texture model based enjoint statistics of complex wavelet coefficients. Int. J. Comput. Vis. 40:9-71
108. Pouget A, Dayan P, Zemel R. 2000. Information processing with population codes. Nat. Rev. Neurosci. 1:125-32
109. Ramachandran VS. 1985. The neurobiology of perception. Perception 14:97-103
110. Rao RP, Ballard DH. 1999. Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nat. Neurosci. 2:79-87
111. Rao RPN, Olshausen BA, Lewicki MS, eds. 2002. Probabilistic Models of the Brain: Perception and Neural Function. Cambridge, MA: MIT Press
112. Read JCA. 2002. A Bayesian model of stereopsis depth and motion direction discrimination. Biol. Cybern. 86:117-36
113. Riesenhuber M, Poggio T. 2002. Neural mechanisms of object recognition. Curr. Opin. Neurobiol. 12:162-68
114. Rock I. 1983. The Logic of Perception. Cambridge, MA: MIT Press
115. Sanger TD. 1996. Probability density estimation for the interpretation of neural population codes. J. Neurophysiol. 76:2790-93
116. Saunders JA, Knill DC. 2001. Perception of 3D surface orientation from skew symmetry. Vis. Res. 41:3163-83
117. Schölkopf B, Smola AJ. 2002. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press
118. Schrater PR, Kersten D. 2000. How optimal depth cue integration depends on the task. Int. J. Comput. Vis. 40:73-91
119. Schrater PR, Kersten D. 2002. Vision, psychophysics, and Bayes. See Rao et al. 2002b, pp. 39-64
120. Schrater PR, Knill DC, Simoncelli EP. 2000. Mechanisms of visual motion detection. Nat. Neurosci. 1:64-68
121. Simoncelli EP, Olshausen BA. 2001. Natural image statistics and neural representation. Annu. Rev. Neurosci. 24:1193-216
122. Sinha P, Adelson E. 1993. Recovering reflectance and illumination in a world of painted polyhedra. Presented at Proc. Int. Conf. Comput. Vis., 4th, Berlin
123. Sun J, Perona P. 1998. Where is the sun? Nat. Neurosci. 1:183-84
124. Tarr MJ, Bülthoff HH. 1995. Is human object recognition better described by geon structural descriptions or by multiple views? Comment on Biederman and Gerhardstein (1993). J. Exp. Psychol. Hum. Percept. Perform. 21:1494-505
125. Tenenbaum JB. 2000. Bayesian modeling of human concept learning. Advances in Neural Information Processing Systems, ed. Solla S, Leen T, Muller KR, 12:59-65. Cambridge, MA: MIT Press
126. Tenenbaum JB, Griffiths TL. 2001. Generalization, similarity, and Bayesian inference. Behav. Brain Sci. 24:629-40; discussion 652-791
127. Tenenbaum JB, Xu F. 2000. Word learning as Bayesian inference. Proc. Ann. Conf. Cogn. Sci. Soc., 22nd, eds. Gleitman LR, Joshi AK. Mahwah, NJ: Lawerence Erlbaum Assoc.
128. Tjan BS, Braje WL, Legge GE, Kersten D. 1995. Human efficiency for recognizing 3-D objects in luminance noise. Vis. Res. 35:3053-69
129. Troje NF, Kersten D. 1999. Viewpoint dependent recognition of familiar faces. Perception 28:483-87
130. Tu Z, Chen A, Yuille AL, Zhu SC. 2003. Image parsing. Proc. Int. Conf. Comput. Vis., Cannes, France
131. Tu Z, Zhu S-C. 2002. Image segmentation by data-driven Markov chain Monte Carlo. IEEE Trans. Pattern Anal. Mach. Intell. 24:657-73
132. Ullman S. 1996. High-Level Vision: Object Recognition and Visual Cognition. Cambridge, MA: MIT Press
133. Ullman S, Basri R. 1991. Recognition by linear combination of models. IEEE Trans. Pattern Anal. Mach. Intell. 13:992-1006
134. VanRullen R, Thorpe SJ. 2001. Is it a bird? Is it a plane? Ultra-rapid visual categorisation of natural and artifactual objects. Perception 30:655-68
135. Vapnik VN. 1998. Statistical Learning Theory. New York: Wiley
136. Vetter T, Troje NF. 1997. Separation of texture and shape in images of faces for image coding and synthesis. J. Opt. Soc. Am. A 14:2152-61
137. Viola P, Jones MJ. 2001. Robust real-time object detection. Proc. IEEE Workshop Stat. Comput. Theor. Vis., Vancouver, Can.
138. Weber M, Welling M, Perona P. 2000. Unsupervised Learning of Models for Recognition. Presented at Proc. Eur. Conf. Comp. Vis., 6th, Dublin, Ireland
139. Weiss Y, Simoncelli EP, Adelson EH. 2002. Motion illusions as optimal percepts. Nat. Neurosci. 5:598-604
140. Yonas A, ed. 2003. Development of space perception. In Encyclopedia of Cognitive Science, ed. R Anand, pp. 96-100. London, UK: Macmillan
141. Yu AJ, Dayan P. 2002. Acetylcholine in cortical inference. Neural Netw. 15:719-30
142. Yuille AL, Bülthoff HH. 1996. Bayesian decision theory and psychophysics. See Knill & Richards 1996, pp. 123-61
143. Yuille AL, Coughlan JM, Konishi S. 2001. The KGBR viewpoint-lighting ambiguity and its resolution by generic constraints. Presented at Proc. Int. Conf. Comput. Vis., Vancouver, Canada
144. Yuille A, Coughlan JM, Konishi S. 2003. The KGBR viewpoint-lighting ambiguity. J. Opt. Soc. Am. A Opt. Image Sci. Vis. 20:24-31
145. Yuille A, Grzywacz N. 1988. A computational theory for the perception of coherent visual motion. Nature 333:71-74
146. Zhu SC. 1999. Embedding gestalt laws in Markov random fields. IEEE Trans. Pattern Anal. Mach. Intell. 21:1170-87
147. Zhu SC, Mumford D. 1997. Prior learning and Gibbs reaction-diffusion. IEEE Trans. PAMI 19:1236-50
148. Zhu SC, Wu Y, Mumford D. 1997. Mini max entropy principle and its applications to texture modeling. Neural Comput. 9:1627-60
149. Zipser K, Lamme VA, Schiller PH. 1996. Contextual modulation in primary visual cortex. J. Neurosci. 16:7376-89