On the estimation of optical flow: relations between different approaches and some new results
Artificial Intelligence
Computation of component image velocity from local phase information
International Journal of Computer Vision
Performance of optical flow techniques
International Journal of Computer Vision
The computation of optical flow
ACM Computing Surveys (CSUR)
Recovering 3-D motion parameters from optical flow field using randomized Hough transform
Pattern Recognition Letters
Motion analysis by random sampling and voting process
Computer Vision and Image Understanding
Robust Linear and Support Vector Regression
IEEE Transactions on Pattern Analysis and Machine Intelligence
The statistics of optical flow
Computer Vision and Image Understanding
Efficient randomized algorithms for robust estimation of circular arcs and aligned ellipses
Computational Geometry: Theory and Applications
The Linear l1 Estimator and the Huber M-Estimator
SIAM Journal on Optimization
Statistical Optimization and Geometric Visual Inference
AFPAC '97 Proceedings of the International Workshop on Algebraic Frames for the Perception-Action Cycle
Variational analysis of spherical images
CAIP'05 Proceedings of the 11th international conference on Computer Analysis of Images and Patterns
Optical flow computation for compound eyes: variational analysis of omni-directional views
BVAI'05 Proceedings of the First international conference on Brain, Vision, and Artificial Intelligence
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In this paper, we show that the randomized sampling and voting process detects optical flow. Using an appropriate number of images from a sequence of images, our method detects subpixel motion in this sequence. We use the accumulator space for the unification of these flow vectors which are computed from different time intervals. Numerical examples for the test image sequences show the performance of our method.