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Adaptive Motion Pooling and Diffusion for Optical Flow Computation

Abstract : We propose to extend a state of the art bio-inspired model for optic flow computation through adaptive processing by focusing on the role of local context indicative of the local velocity estimates reliability. We set a network structure representative of cortical areas V1, V2 and MT, and incorporate three functional principles observed in primate visual system: contrast adaptation, adaptive afferent pooling and MT diffusion that are adaptive dependent upon the 2D image structure (Adaptive Motion Pooling and Diffusion, AMPD). We assess the AMPD performance on Middlebury optical flow estimation dataset, showing that the proposed AMPD model performs better than the baseline one and its overall performance is comparable with many computer vision methods.
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Contributor : Pierre Kornprobst Connect in order to contact the contributor
Submitted on : Tuesday, September 19, 2017 - 11:52:23 AM
Last modification on : Friday, November 25, 2022 - 6:50:05 PM


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  • HAL Id : hal-01589983, version 1


N S Kartheek Medathati, Manuela S Chessa, Guillaume S Masson, Pierre Kornprobst, Fabio S Solari. Adaptive Motion Pooling and Diffusion for Optical Flow Computation. WBICV 2017 : First International Workshop on Brain-Inspired Computer Vision, Sep 2017, Catania, Sicily, Italy. ⟨hal-01589983⟩



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