The computation of optical flow
ACM Computing Surveys (CSUR)
Motion Segmentation Using Occlusions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Disambiguating Visual Motion Through Contextual Feedback Modulation
Neural Computation
A Roadmap to the Integration of Early Visual Modules
International Journal of Computer Vision
A Fast Biologically Inspired Algorithm for Recurrent Motion Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
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We present a biologically inspired iterative algorithm for motion estimation that combines the integration of multiple temporal and spatial scales. This work extends a previously developed algorithm that is based on mechanisms of motion processing in the human brain [1]. The temporal integration approach realizes motion detection using one reference frame and multiple past and/or future frames leading to correct motion estimates at positions that are temporarily occluded. In addition, this mechanism enables the detection of subpixel movements and therefore achieves smoother and more precise flow fields. We combine the temporal integration with a recently proposed spatial multi scale approach [2]. The combination further improves the optic flow estimates when the image contains regions of different spatial frequencies and represents a very robust and efficient algorithm for optic flow estimation, both on artificial and real-world sequences.