Bayesian approaches have enjoyed a great deal of recent success in their application to problems in computer vision. This success has led to an emerging interest in applying Bayesian methods to modeling human visual perception.
We consider the implications of a Bayesian view of visual information processing for experimentally investigating human visual perception. We have outlined the elements of a general program of empirical research which results from taking the basic Bayesian formulation seriously not only as a means for objectively modeling image information through ideal observer analysis (e.g. see our work in object recognition), but also as a framework for characterizing human perceptual inference. A major advantage of following such a program is that, because its structure is the same as that of the Bayesian framework for computational modeling, it supports a strong integration of psychophysics and computational theory. In particular, it provides the foundation for a psychophysics of constraints in which one tests hypotheses regarding quantitative and qualitative constraints used in human perceptual inferences. The Bayesian approach also suggests new ways to conceptualize the general problem of perception and to decompose it into isolatable parts for psychophysical investigation; that is, it not only provides a framework for modeling solutions to specific perceptual problems; it also guides the definition of the problems.
Here are some papers from our publication list:
Kersten, D., Mamassian, P., & Yuille, A. (2004). Object perception as Bayesian Inference. Annual Review of Psychology, 55, 271-304.(pdf)
Kersten, D., & Yuille, A. (2003). Bayesian models of object perception. Current Opinion in Neurobiology, 13(2). (pdf)
Kersten, D. (2002) Object perception: Generative Image Models and Bayesian Inference. In Biologically Motivated Computer Vision. Second International Workshop, BMCV 2002, Tübingen, Germany, November, 2002. Proceedings. H.H. Bülthoff, S.-W. Lee, T.A. Poggio, C. Wallraven (Eds.). Lecture Notes in Computer Science 2525. Springer. (pdf)
Kersten, D. & Schrater, P. R., (2002). Pattern Inference Theory: A Probabilistic Approach to Vision. In R. Mausfeld, & D. Heyer (Ed.), Perception and the Physical World. Chichester: John Wiley & Sons, Ltd. (Draft pdf) (postscript)
e-mail: kersten@eye.psych.umn.edu