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Optic Flow Integration at Multiple Spatial Frequencies – Neural Mechanism and Algorithm

  • Cornelia Beck
  • Pierre Bayerl
  • Heiko Neumann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4291)

Abstract

In this work we present an iterative multi-scale algorithm for motion estimation that follows mechanisms of motion processing in the human brain. Keeping the properties of a previously presented neural model of cortical motion integration we created a computationally fast algorithmic implementation of the model. The novel contribution is the extension of the algorithm to operate on multiple scales without the disadvantages of typical coarse-to-fine approaches. Compared to the implementation with one scale our multi-scale approach generates faster dense flow fields and reduces wrong motion estimations. In contrast to other approaches, motion estimation on the fine scale is biased by the coarser scales without being corrupted if erroneous motion cues are generated on coarser scales, e.g., when small objects are overlooked. This multi-scale approach is also consistent with biological observations: The function of fast feedforward projections to higher cortical areas with large receptive fields and feedback connections to earlier areas as suggested by our approach might contribute to human motion estimation.

Keywords

Input Image Motion Estimation Neural Model Coarse Scale Feedback Connection 
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

  • Cornelia Beck
    • 1
  • Pierre Bayerl
    • 1
  • Heiko Neumann
    • 1
  1. 1.Dept. of Neural Information ProcessingUniversity of UlmGermany

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