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Bio-inspired Motion-Based Object Segmentation

  • Sonia Mota
  • Eduardo Ros
  • Javier Díaz
  • Rodrigo Agis
  • Francisco de Toro
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4141)

Abstract

Although motion extraction requires high computational resources and normally produces very noisy patterns in real sequences, it provides useful cues to achieve an efficient segmentation of independent moving objects. Our goal is to employ basic knowledge about biological vision systems to address this problem. We use the Reichardt motion detectors as first extraction primitive to characterize the motion in scene. The saliency map is noisy, therefore we use a neural structure that takes full advantage of the neural population coding, and extracts the structure of motion by means of local competition. This scheme is used to efficiently segment independent moving objects. In order to evaluate the model, we apply it to a real-life case of an automatic watch-up system for car-overtaking situations seen from the rear-view mirror. We describe how a simple, competitive, neural processing scheme can take full advantage of this motion structure for segmenting overtaking-cars.

Keywords

Receptive Field Motion Detection Rigid Body Motion Intelligent Vehicle Velocity Channel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sonia Mota
    • 1
  • Eduardo Ros
    • 2
  • Javier Díaz
    • 2
  • Rodrigo Agis
    • 2
  • Francisco de Toro
    • 3
  1. 1.Departamento de Informática y Análisis NuméricoUniversidad de CórdobaCórdobaSpain
  2. 2.Departamento de Arquitectura y Tecnología de ComputadoresUniversidad de GranadaGranadaSpain
  3. 3.Departamento de Teoría de la Señal, Telemática y ComunicacionesUniversidad de GranadaGranadaSpain

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