Performance of optical flow techniques
International Journal of Computer Vision
Disambiguating Visual Motion Through Contextual Feedback Modulation
Neural Computation
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
A Fast Biologically Inspired Algorithm for Recurrent Motion Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Exploitation of the Lucas and Kanade Smoothness Constraint
ICMLA '08 Proceedings of the 2008 Seventh International Conference on Machine Learning and Applications
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Is the homunculus "aware" of sensory adaptation?
Neural Computation
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In the brain, both neural processing dynamics as well as the perceptual interpretation of a stimulus can depend on sensory history. The underlying principle is a sensory adaptation to the statistics of the input collected over some timespan, allowing the system to tune its detectors, e.g. by better sampling the input space and adjusting the response. Here, we show how a model for adaptation in visual motion processing can be set up from first principles using a generative formulation and casting the problem of adaptation in terms of optimal estimation over time. The model leads to an online adaptation of velocity tuning curves, inducing shifts in the velocity tuning and changes in the tuning curve widths that are compatible with observations from physiological experiments on macaque MT neurons. We also show how such an adaptation leads to a greater computational efficiency by a better sampling of the velocity space, requiring less motion detectors to achieve a desired level of estimation accuracy.