Comutations underlying the measuremnt of visual motion.
Artificial Intelligence
On the estimation of optical flow: relations between different approaches and some new results
Artificial Intelligence
Performance of optical flow techniques
International Journal of Computer Vision
Layered 4D Representation and Voting for Grouping from Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural models of motion integration and segmentation
Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
Neural mechanisms for the robust representation of junctions
Neural Computation
Disambiguating Visual Motion by Form-Motion Interaction--a Computational Model
International Journal of Computer Vision
A Fast Biologically Inspired Algorithm for Recurrent Motion Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mumford-Shah regularizer with contextual feedback
Journal of Mathematical Imaging and Vision
Switching Hidden Markov Models for Learning of Motion Patterns in Videos
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Neural mechanisms for mid-level optical flow pattern detection
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
EURASIP Journal on Advances in Signal Processing - Special issue on biologically inspired signal processing: analyses, algorithms and applications
Optic flow integration at multiple spatial frequencies – neural mechanism and algorithm
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
A hidden markov model based approach for facial expression recognition in image sequences
ANNPR'10 Proceedings of the 4th IAPR TC3 conference on Artificial Neural Networks in Pattern Recognition
Motion-based prediction is sufficient to solve the aperture problem
Neural Computation
Neural mechanisms for form and motion detection and integration: biology meets machine vision
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
Learning representations for animated motion sequence and implied motion recognition
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
Bifurcation analysis applied to a model of motion integration with a multistable stimulus
Journal of Computational Neuroscience
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Motion of an extended boundary can be measured locally by neurons only orthogonal to its orientation (aperture problem) while this ambiguity is resolved for localized image features, such as corners or nonocclusion junctions. The integration of local motion signals sampled along the outline of a moving form reveals the object velocity. We propose a new model of V1-MT feedforward and feedback processing in which localized V1 motion signals are integrated along the feedforward path by model MT cells. Top-down feedback from MT cells in turn emphasizes model V1 motion activities of matching velocity by excitatory modulation and thus realizes an attentional gating mechanism. The model dynamics implement a guided filling-in process to disambiguate motion signals through biased on-center, off-surround competition. Our model makes predictions concerning the time course of cells in area MT and V1 and the disambiguation process of activity patterns in these areas and serves as a means to link physiological mechanisms with perceptual behavior. We further demonstrate that our model also successfully processes natural image sequences.