Neural Mechanisms of Motion Detection, Integration, and Segregation: From Biology to Artificial Image Processing Systems
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Research Article Neural Mechanisms of Motion Detection, Integration, and Segregation: From Biology to Artificial Image Processing Systems Jan D. Bouecke,1 Emilien Tlapale,2 Pierre Kornprobst,2 and Heiko Neumann1 1 Faculty
of Engineering and Computer Sciences, Institute for Neural Information Processing, Ulm University, James-Franck-Ring, 89069 Ulm, Germany 2 Equipe Projet NeuroMathComp, Institut National de Recherche en Informatique et en Automatique (INRIA), Unit´e de recherche INRIA Sophia Antipolis, Sophia Antipolis Cedex, 06902, France Correspondence should be addressed to Heiko Neumann, [email protected] Received 15 June 2010; Accepted 2 November 2010 Academic Editor: Elias Aboutanios Copyright © 2011 Jan D. Bouecke et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Object motion can be measured locally by neurons at different stages of the visual hierarchy. Depending on the size of their receptive field apertures they measure either localized or more global configurationally spatiotemporal information. In the visual cortex information processing is based on the mutual interaction of neuronal activities at different levels of representation and scales. Here, we utilize such principles and propose a framework for modelling neural computational mechanisms of motion in primates using biologically inspired principles. In particular, we investigate motion detection and integration in cortical areas V1 and MT utilizing feedforward and modulating feedback processing and the automatic gain control through center-surround interaction and activity normalization. We demonstrate that the model framework is capable of reproducing challenging data from experimental investigations in psychophysics and physiology. Furthermore, the model is also demonstrated to successfully deal with realistic image sequences from benchmark databases and technical applications.
1. Introduction and Motivation A key visual competency of many species, including humans, is the ability to rapidly and accurately ascertain the sizes, locations, trajectories, and identities of objects in the environment. For example, noticing a deer moving behind a thicket, or steering around obstacles through a crowded environment, indicates that many of the tasks of vision serve as a basis to guide behaviour based on the spatiotemporally changing visual input. The analysis and interpretation of moving objects based on motion estimations is thus a major task in everyday vision. However, motion can locally be measured only orthogonal to an extended contrast (aperture problem), while this ambiguity can be resolved at localized image features, such as corners or junctions from nonoccluding geometrical configurations. Several models have been suggested that focus on the problem of how to integrate localized and mostly ambiguous local motion estimates. For example, the vector sum approach averages
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