Skip to Main content Skip to Navigation
New interface
Conference papers

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, linear decoding through learned weights on MT responses, maximum likelihood and regression with neural network 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 .
Complete list of metadata

Cited literature [14 references]  Display  Hide  Download
Contributor : Pierre Kornprobst Connect in order to contact the contributor
Submitted on : Monday, October 19, 2015 - 6:07:00 PM
Last modification on : Friday, November 25, 2022 - 6:50:05 PM
Long-term archiving on: : Thursday, April 27, 2017 - 4:25:14 AM


Files produced by the author(s)


  • HAL Id : hal-01215526, version 1



Manuela Chessa, N. V. Kartheek Medathati, Guillaume S. Masson, Fabio Solari, Pierre Kornprobst. Decoding MT Motion Response For Optical Flow Estimation : An Experimental Evaluation. 23rd European Signal Processing Conference (EUSIPCO), Aug 2015, Nice, France. ⟨hal-01215526⟩



Record views


Files downloads