Conventional optical flow techniques provide a motion description that may be redundant for a human viewer. Computational effort may be wasted describing 'perceptually irrelevant motions'. This inefficient behavior may also give rise to false alarms and noisy flows. To solve this problem, hierarchical optical flow techniques have been proposed. They start from a low resolution motion estimate and new motion information is locally added only in certain regions. However, new motion information should be added only if it is 'perceptually relevant'. In this work we propose a definition of 'perceptually relevant motion information'. This definition is based on the entropy of the image representation in the human cortex (Watson JOSA 87, Daugman IEEE T.Biom.Eng. 89): an increment in motion information is perceptually relevant if it contributes to decrease the entropy of the cortex representation of the prediction error. Numerical experiments (optical flow computation and flow-based segmentation) show that applying this definition to a particular hierarchical motion estimation algorithm, more robust and meaningful flows are obtained.

Aknowledgements: 
This work has been partially funded by the projects CICYT TIC 1FD97-0279 and CICYT TIC 1FD97-1910, and the Fulbright-MEC PostDoc grant FU2000-29167406.
 

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