10 Neuronal interactions and their role in solving the stereo correspondence problem Jason

Binocular vision provides important information about depth to help us navigate in a three-dimensional environment and allows us to identify and manipulate 3D objects. The relative depth of any feature with respect to fixation can be determined by triangulating the horizontal shift or disparity between the images of this feature projected onto the left and the right eyes. The computation is difficult because in any given visual scene, there are many similar features, which create ambiguity for matching 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 a RDS (Figure 10.1a), 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 bottomup 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). While these models are quite distinct in details, they share the common theme that organized neuronal interactions among disparity tuned neurons play