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On the estimation of optical flow: relations between different approaches and some new results
Artificial Intelligence
Robust regression and outlier detection
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Image Flow Segmentation and Estimation by Constraint Line Clustering
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International Journal of Computer Vision
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ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Computational analysis of visual motion
Computational analysis of visual motion
A robust algorithm for optical flow estimation
Computer Vision and Image Understanding
Robust computation of optical flow in a multi-scale differential framework
International Journal of Computer Vision
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International Journal of Computer Vision
The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
Spatio-Temporal Image Processing: Theory and Scientific Applications
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Robot Vision
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Computer Vision and Image Understanding
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Pattern Recognition
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Image and Vision Computing
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Signal Processing
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International Journal of Computer Vision
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This paper formulates the optic flow problem as a set ofover-determined simultaneous linear equations. It then introduces andstudies two new robust optic flow methods. The first technique is based onusing the Least Median of Squares (LMedS) to detect the outliers. Then, theinlier group is solved using the least square technique. The second methodemploys a new robust statistical method named the Least Median of SquaresOrthogonal Distances (LMSOD) to identify the outliers and then uses totalleast squares to solve the optic flow problem. The performance of bothmethods are studied by experiments on synthetic and real image sequences.These methods outperform other published methods both in accuracy androbustness.