City Research Online

Abstract

A primary issue in 3D reconstruction is the realtime efficacy of different coding methods for the multiple decisions among competing 3D solutions. A common model framework making such coding decisions is the boundary limited drift-diffusion model, which has been developed in parallel in various branches of science from quantum physics to economics. A common property of all such models is the linear increase in variance of the diffusion processes over time, implying an inability to focus on the current information in the environment, and the inevitability of a forced random decision in the absence of any reliable evidence. We have developed an alternative, more plausible model framework for Bayesian information accumulation that solves both problems and provides an accurate account of many features of context effects in human 3D reconstruction performance. © 2011 IEEE.

Publication Type:	Conference or Workshop Item (Paper)
Additional Information:	© 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher Keywords:	3D reconstruction, Bayesian, decision-making, drift diffusion models, neural networks
Subjects:	R Medicine > RE Ophthalmology
Departments:	School of Health & Psychological Sciences > Optometry & Visual Sciences

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