Measurement error models
Performance of optical flow techniques
International Journal of Computer Vision
Optical flow estimation and the interaction between measurement errors at adjacent pixel positions
International Journal of Computer Vision
Computer Vision and Image Understanding
The statistics of optical flow
Computer Vision and Image Understanding
Statistical Optimization for Geometric Computation: Theory and Practice
Statistical Optimization for Geometric Computation: Theory and Practice
Errors-in-variables modeling in optical flow estimation
IEEE Transactions on Image Processing
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One of the difficulties in estimating optical flow is bias. Correcting the bias using the classical techniques is very difficult. The reason is that knowledge of the error statistics is required, which usually cannot be obtained because of lack of data. In this paper, we present an approach which utilizes color information. Color images do not provide more geometric information than monochromatic images to the estimation of optic flow. They do, however, contain additional statistical information. By utilizing the technique of instrumental variables, bias from multiple noise sources can be robustly corrected without computing the parameters of the noise distribution. Experiments on synthesized and real data demonstrate the efficiency of the algorithm.