Disambiguating Visual Motion Through Contextual Feedback Modulation

DOI10.1162/0899766041732404zbMath1084.68906DBLPjournals/neco/BayerlN04OpenAlexW2082223698WikidataQ45033858 ScholiaQ45033858MaRDI QIDQ3160475

Pierre Bayerl, Heiko Neumann

Publication date: 9 February 2005

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1162/0899766041732404

Mathematics Subject Classification ID

Machine vision and scene understanding (68T45)

Related Items (9)

Biologically plausible learning in neural networks with modulatory feedback ⋮ A dynamical model of visual motion processing for arbitrary stimuli including type II plaids ⋮ A Bio-Inspired, Computational Model Suggests Velocity Gradients of Optic Flow Locally Encode Ordinal Depth at Surface Borders and Globally They Encode Self-Motion ⋮ Computing with a Canonical Neural Circuits Model with Pool Normalization and Modulating Feedback ⋮ Globally consistent depth sorting of overlapping 2D surfaces in a model using local recurrent interactions ⋮ Bifurcation analysis applied to a model of motion integration with a multistable stimulus ⋮ Motion detection, noise reduction, texture suppression, and contour enhancement by spatiotemporal Gabor filters with surround inhibition ⋮ A neurally plausible model of the dynamics of motion integration in smooth eye pursuit based on recursive Bayesian estimation ⋮ Interaction of feedforward and feedback streams in visual cortex in a firing-rate model of columnar computations

Cites Work

• Computations underlying the measurement of visual motion
• Bayesian inference in populations of cortical neurons: A model of motion integration and segmentation in area MT
• Neural Mechanisms for the Robust Representation of Junctions

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