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Motion-Driven Segmentation by Competitive Neural Processing

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Abstract

Bio-inspired energy models compute motion along the lines suggested by the neurophysiological studies of V1 and MT areas in both monkeys and humans: neural populations extract the structure of motion from local competition among MT-like cells. We describe here a neural structure that works as a dynamic filter above this MT layer for image segmentation and takes advantage of neural population coding in the cortical processing areas. We apply the model to the real-life case of an automatic watch-out system for car-overtaking situations seen from the rear-view mirror. The ego-motion of the host car induces a global motion pattern whereas an overtaking vehicle produces a pattern that contrasts highly with this global ego-motion field. We describe how a simple, competitive, neural processing scheme can take full advantage of this motion structure for segmenting overtaking-cars

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Authors and Affiliations

  1. Departamento de Arquitectura y Tecnología de Computadores, E.T.S.I. Informática, Universidad de Granada, Periodista Daniel Saucedo Aranda s/n, 18071, Granada, Spain

    Sonia Mota, Eduardo Ros, Javier Díaz, Eva M. Ortigosa & Alberto Prieto

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  1. Sonia Mota
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  2. Eduardo Ros
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  3. Javier Díaz
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  4. Eva M. Ortigosa
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  5. Alberto Prieto
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Correspondence to Sonia Mota.

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Mota, S., Ros, E., Díaz, J. et al. Motion-Driven Segmentation by Competitive Neural Processing. Neural Process Lett 22, 125–147 (2005). https://doi.org/10.1007/s11063-005-2158-1

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