Image Flow Segmentation and Estimation by Constraint Line Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Experiments in the machine interpretation of visual motion
Experiments in the machine interpretation of visual motion
Computing optical flow across multiple scales: an adaptive coarse-to-fine strategy
International Journal of Computer Vision
Performance of optical flow techniques
International Journal of Computer Vision
Optical flow estimation: advances and comparisons
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
A robust algorithm for optical flow estimation
Computer Vision and Image Understanding
The computation of optical flow
ACM Computing Surveys (CSUR)
Determining Optical Flow
A low-cost 3D human interface device using GPU-based optical flow algorithms
Integrated Computer-Aided Engineering
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In this paper, a robust estimation of the optical flow field that preserves the boundaries of the movement is shown. Arising from the techniques based on the Optical Flow Constraint (OFC), an estimation that takes several measures around a given pixel, discarding the erroneous ones, has been developed. This is done through performing a bidimensional clustering of the velocities obtained from the intersection of pairs of OFCs. In this way, the clustering is conducted in the velocity space and not in the (slope, intercept) parameter space of the OFCs. Finally, a hierarchical implementation that has a lesser error when large displacements are present in the image is shown.