2 results
0 - Introduction
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- By D.C. Knill, University of Minnesota, D. Kersten, University of Minnesota, A. Yuille, Harvard University
- Edited by David C. Knill, University of Pennsylvania, Whitman Richards, Massachusetts Institute of Technology
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- Book:
- Perception as Bayesian Inference
- Published online:
- 05 March 2012
- Print publication:
- 13 September 1996, pp 1-22
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- Chapter
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Summary
Overview
Bayesian approaches have enjoyed a great deal of recent success in their application to problems in computer vision (Grenander, 1976-1981; Bolle & Cooper, 1984; Geman & Geman, 1984; Marroquin et al., 1985; Szeliski, 1989; Clark & Yuille, 1990; Yuille & Clark, 1993; Madarasmi et al., 1993). This success has led to an emerging interest in applying Bayesian methods to modeling human visual perception (Bennett et al., 1989; Kersten, 1990; Knill & Kersten, 1991; Richards et al., 1993). The chapters in this book represent to a large extent the fruits of this interest: a number of new theoretical frameworks for studying perception and some interesting new models of specific perceptual phenomena, all founded, to varying degrees, on Bayesian ideas. As an introduction to the book, we present an overview of the philosophy and fundamental concepts which form the foundation of Bayesian theory as it applies to human visual perception. The goal of the chapter is two-fold: first, it serves as a tutorial to the basics of the Bayesian approach to readers who are unfamiliar with it, and second, to characterize the type of theory of perception the approach is meant to provide. The latter topic, by its meta-theoretic nature, is necessarily subjective. This introduction represents the views of the authors in this regard, not necessarily those held by other contributors to the book.
First, we introduce the Bayesian framework as a general formalism for specifying the information in images which allows an observer to perceive the world.
6 - Implications of a Bayesian formulation of visual information for processing for psychophysics
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- By D.C. Knill, University of Pennsylvania, D. Kersten, University of Minnesota, P. Mamassian, University of Minnesota
- Edited by David C. Knill, University of Pennsylvania, Whitman Richards, Massachusetts Institute of Technology
-
- Book:
- Perception as Bayesian Inference
- Published online:
- 05 March 2012
- Print publication:
- 13 September 1996, pp 239-286
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- Chapter
- Export citation
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Summary
Introduction
The previous chapters have demonstrated the many ways one can use a Bayesian formulation for computationally modeling perceptual problems. In this chapter, we look at the implications of a Bayesian view of visual information processing for investigating human visual perception. We will attempt to outline the elements of a general program of empirical research which results from taking the Bayesian formulation seriously as a framework for characterizing human perceptual inference. A major advantage of following such a program is that it supports a strong integration of psychophysics and computational theory, since its structure is the same as that of the Bayesian framework for computational modeling. In particular, it provides the foundation for a psychophysics of constraints, used to test hypotheses about the 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. Thus, it not only provides a framework for modeling solutions to specific perceptual problems; it also guides the definition of the problems.
The chapter is organized into four major sections. In the next section, we develop a framework for characterizing human perception in Bayesian terms and analyze its implications for studying human perceptual performance. The third and fourth sections of the chapter apply the framework to two specific problems: the perception of 3-D shape from surface contours and the perception of 3-D object motion from cast shadow motion.