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Proceedings Paper

Regularization model of human binocular vision
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Paper Abstract

Binocular reconstruction of a 3D shape is an ill-conditioned inverse problem: in the presence of visual and oculomotor noise the reconstructions based solely on visual data are very unstable. A question, therefore, arises about the nature of a priori constraints that would lead to accurate and stable solutions. Our previous work showed that planarity of contours, symmetry of an object and minimum variance of angles are useful priors in binocular reconstruction of polyhedra. Specifically, our algorithm begins with producing a 3D reconstruction from one retinal image by applying priors. The second image (binocular disparity) is then used to correct the monocular reconstruction. In our current study, we performed psychophysical experiments to test the importance of these priors. The subjects were asked to recognize shapes of 3D polyhedra from unfamiliar views. Hidden edges of the polyhedra were removed. The recognition performance, measured by detectability measure d¢, was high when shapes satisfied regularity constraints, and was low otherwise. Furthermore, the binocular recognition performance was highly correlated with the monocular one. The main aspects of our model will be illustrated by a demo, in which binocular disparity and monocular priors are put in conflict.

Paper Details

Date Published: 11 March 2005
PDF: 12 pages
Proc. SPIE 5674, Computational Imaging III, (11 March 2005); doi: 10.1117/12.598015
Show Author Affiliations
Zygmunt Pizlo, Purdue Univ. (United States)
Yunfeng Li, Purdue Univ. (United States)
Moses W Chan, Purdue Univ. (United States)

Published in SPIE Proceedings Vol. 5674:
Computational Imaging III
Charles A. Bouman; Eric L. Miller, Editor(s)

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