IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance of optical flow techniques
International Journal of Computer Vision
Robust computation of optical flow in a multi-scale differential framework
International Journal of Computer Vision
Computational Framework for Segmentation and Grouping
Computational Framework for Segmentation and Grouping
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Neural mechanisms for the robust representation of junctions
Neural Computation
Integration of form and motion within a generative model of visual cortex
Neural Networks - 2004 Special issue Vision and brain
Disambiguating Visual Motion Through Contextual Feedback Modulation
Neural Computation
Motion segmentation with accurate boundaries: a tensor voting approach
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Editorial: ECOVISION: Challenges in Early-Cognitive Vision
International Journal of Computer Vision
Action Recognition Using a Bio-Inspired Feedforward Spiking Network
International Journal of Computer Vision
Continuous dimensionality characterization of image structures
Image and Vision Computing
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
Action recognition via bio-inspired features: The richness of center-surround interaction
Computer Vision and Image Understanding
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
Bifurcation analysis applied to a model of motion integration with a multistable stimulus
Journal of Computational Neuroscience
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The neural mechanisms underlying motion segregation and integration still remain unclear to a large extent. Local motion estimates often are ambiguous in the lack of form features, such as corners or junctions. Furthermore, even in the presence of such features, local motion estimates may be wrong if they were generated near occlusions or from transparent objects. Here, a neural model of visual motion processing is presented that involves early stages of the cortical dorsal and ventral pathways. We investigate the computational mechanisms of V1-MT feedforward and feedback processing in the perception of coherent shape motion. In particular, we demonstrate how modulatory MT-V1 feedback helps to stabilize localized feature signals at, e.g. corners, and to disambiguate initial flow estimates that signal ambiguous movement due to the aperture problem for single shapes. In cluttered environments with multiple moving objects partial occlusions may occur which, in turn, generate erroneous motion signals at points of overlapping form. Intrinsic-extrinsic region boundaries are indicated by local T-junctions of possibly any orientation and spatial configuration. Such junctions generate strong localized feature tracking signals that inject erroneous motion directions into the integration process. We describe a simple local mechanism of excitatory form-motion interaction that modifies spurious motion cues at T-junctions. In concert with local competitive-cooperative mechanisms of the motion pathway the motion signals are subsequently segregated into coherent representations of moving shapes. Computer simulations demonstrate the competency of the proposed neural model.