2 results
7 - Neuronal interactions and their role in solving the stereo correspondence problem
- from Part I - Depth processing and stereopsis
-
- By Jason M. Samonds, Carnegie Mellon University, Tai Sing Lee, Carnegie Mellon University
- Edited by Laurence R. Harris, York University, Toronto, Michael R. M. Jenkin, York University, Toronto
-
- Book:
- Vision in 3D Environments
- Published online:
- 05 August 2011
- Print publication:
- 07 July 2011, pp 137-160
-
- Chapter
- Export citation
-
Summary
Introduction
Binocular vision provides important information about depth to help us navigate in a three-dimensional environment and allow us to identify and manipulate 3D objects. The relative depth of any feature with respect to the fixation point can be determined by triangulating the horizontal shift, or disparity, between the images of that feature projected onto the left and right eyes. The computation is difficult because, in any given visual scene, there are many similar features, which create ambiguity in the matching of corresponding features registered by the two eyes. This is called the stereo correspondence problem. An extreme example of such ambiguity is demonstrated by Julesz's (1964) random-dot stereogram (RDS). In an RDS (Figure 7.1a), there are no distinct monocular patterns. Each dot in the left-eye image can be matched to several dots in the right-eye image. Yet when the images are fused between the two eyes, we readily perceive the hidden 3D structure.
In this chapter, we will review neurophysiological data that suggest how the brain might solve this stereo correspondence problem. Early studies took a mostly bottom-up approach. An extensive amount of detailed neurophysiological work has resulted in the disparity energy model (Ohzawa et al., 1990; Prince et al., 2002). Since the disparity energy model is insufficient for solving the stereo correspondence problem on its own, recent neurophysiological studies have taken a more top-down approach by testing hypotheses generated by computational models that can improve on the disparity energy model (Menz and Freeman, 2003; Samonds et al., 2009a; Tanabe and Cumming, 2009).
24 - Neural Encoding of Scene Statistics for Surface and Object Inference
- Edited by Sven J. Dickinson, University of Toronto, Aleš Leonardis, University of Ljubljana, Bernt Schiele, Technische Universität, Darmstadt, Germany, Michael J. Tarr, Carnegie Mellon University, Pennsylvania
-
- Book:
- Object Categorization
- Published online:
- 20 May 2010
- Print publication:
- 07 September 2009, pp 451-474
-
- Chapter
- Export citation
-
Summary
Introduction
Visual scenes are often complex and ambiguous to interpret because of the myriad causes that generate them. To understand visual scenes, our visual systems have to rely on our prior experience and assumptions about the world. These priors are rooted in the statistical correlation structures of visual events in our experience. They can be learned and exploited for probabilistic inference in a Bayesian framework using graphical models. Thus, we believe that understanding the statistics of natural scenes and developing graphical models with these priors for inference are crucial for gaining theoretical and computational insights to guide neurophysiological experiments. In this chapter, we will provide our perspective based on our work on scene statistics, graphical models, and neurophysiological experiments.
An important source of statistical priors for inference is the statistical correlation of visual events in our natural experience. In fact, it has long been suggested in the psychology community that learning due to coherent covariation of visual events is crucial for the development of Gestalt rules (Koffka 1935) as well as models of objects and object categories in the brain (Gibson 1979; Roger and McClelland 2004). Nevertheless, there has been relatively little research on how correlation structures in natural scenes are encoded by neurons. Here, we will first describe experimental results obtained from multielectrode neuronal recording in the primary visual cortex of awake-behaving monkeys. Each study was conducted on at least two animals. These results reveal mechanisms at the neuronal level for the encoding and influence of scene priors in visual processing.