BIOGRAPHY
Our ability to move through an environment and to recognize and grasp objects depends on the brain's capacity to organize the visual stimulation in a perceived three-dimensional layout. When humans move in a 3D environment, continuous geometrical distortions are projected onto their retinas. The brain can make perfect sense of these unstable images, giving rise to a conscious experience of a stable world. This puzzling phenomenon is the main focus of my research. The problem in which I am interested is how the visual system interprets moving features. In particular, what is the specific 3D shape that the brain recovers from moving images? And what is the specific 3D motion? The basic approach to solve this problem is to mathematically describe the set of retinal velocities produced by the moving images, called optic flow, and than address the properties of the optic flow that may be relevant to the visual system for deriving 3D properties from 2D projections.
My approach to this problem can be characterized by two main hypotheses: 1) the visual system relies on properties of the optic flow that are not necessarily sufficient for deriving the projected object and its 3D motion; 2) the derivation of the 3D structure and motion is based primarily on a heuristic analysis of the optic flow. I have provided evidence for these hypotheses by studying human performance in a variety of perceptual tasks that involved judgments of 3D structure and 3D motion. These empirical outcomes can be predicted by a model that derives local orientation of 3D surfaces and their 3D motion from a property of the optic flow called deformation. Since this property is inherently ambiguous, the model does not, in general, derive the correct solution. Nevertheless, it can still predict the results presented so far in the literature, challenging the traditional hypothesis that 3D structure and motion are recovered by means of a vertical mathematical analysis of the optic flow.
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