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Decoding MT Motion Response for Optical Flow Estimation: An Experimental Evaluation

Abstract : Motion processing in primates is an intensely studied problem in visual neurosciences and after more than two decades of research, representation of motion in terms of motion energies computed by V1-MT feedforward interactions remains a strong hypothesis. Thus, decoding the motion energies is of natural interest for developing biologically inspired computer vision algorithms for dense optical flow estimation. Here, we address this problem by evaluating four strategies for motion decoding: intersection of constraints, maximum likelihood, linear regression on MT responses and neural network based regression using multi scale-features. We characterize the performances and the current limitations of the different strategies, in terms of recovering dense flow estimation using Middlebury benchmark dataset widely used in computer vision, and we highlight key aspects for future developments.
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Contributor : N V Kartheek Medathati Connect in order to contact the contributor
Submitted on : Thursday, March 12, 2015 - 10:19:12 PM
Last modification on : Friday, November 25, 2022 - 6:50:05 PM
Long-term archiving on: : Monday, April 17, 2017 - 11:14:43 AM


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  • HAL Id : hal-01131100, version 1


N V Kartheek Medathati, Manuela Chessa, Guillaume S. Masson, Pierre Kornprobst, Fabio Solari. Decoding MT Motion Response for Optical Flow Estimation: An Experimental Evaluation. [Research Report] RR-8696, INRIA Sophia-Antipolis, France; University of Genoa, Genoa, Italy; INT la Timone, Marseille, France; INRIA. 2015. ⟨hal-01131100⟩



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