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Integration of Multiple Temporal and Spatial Scales for Robust Optic Flow Estimation in a Biologically Inspired Algorithm

  • Cornelia Beck
  • Thomas Gottbehuet
  • Heiko Neumann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4673)

Abstract

We present a biologically inspired iterative algorithm for motion estimation that combines the integration of multiple temporal and spatial scales. This work extends a previously developed algorithm that is based on mechanisms of motion processing in the human brain [1]. The temporal integration approach realizes motion detection using one reference frame and multiple past and/or future frames leading to correct motion estimates at positions that are temporarily occluded. In addition, this mechanism enables the detection of subpixel movements and therefore achieves smoother and more precise flow fields. We combine the temporal integration with a recently proposed spatial multi scale approach [2]. The combination further improves the optic flow estimates when the image contains regions of different spatial frequencies and represents a very robust and efficient algorithm for optic flow estimation, both on artificial and real-world sequences.

Keywords

Motion Estimate Motion Detection Coarse Scale Temporal Integration Multiple Spatial Scale 
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 2007

Authors and Affiliations

  • Cornelia Beck
    • 1
  • Thomas Gottbehuet
    • 1
  • Heiko Neumann
    • 1
  1. 1.Inst. for Neural Information Processing, University of UlmGermany

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