Neural Mechanisms of Motion Detection, Integration, and Segregation: From Biology to Artificial Image Processing Systems

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.

• Publication year

  2011

• Venue

  EURASIP Journal on Advances in Signal Processing

• Publication date

  Unknown publication date

• Fields of study

  Biology, Computer Science

• Identifiers
  DOI 10.1155/2011/781561
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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