Performance of optical flow techniques
International Journal of Computer Vision
The computation of optical flow
ACM Computing Surveys (CSUR)
Matrix computations (3rd ed.)
International Journal of Computer Vision
Computing Optical Flow with Physical Models of Brightness Variation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Direct Estimation of Range Flow on Deformable Shape From a Video Rate Range Camera
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Role of Total Least Squares in Motion Analysis
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
A Total Least Squares Framework for Low-Level Analysis of Dynamic Scenes and Processes
Mustererkennung 1999, 21. DAGM-Symposium
Differential Range Flow Estimation
Mustererkennung 1999, 21. DAGM-Symposium
Reliable Estimates of the Sea Surface Heat Flux from Image Sequences
Proceedings of the 23rd DAGM-Symposium on Pattern Recognition
Complex motion in environmental physics and live sciences
IWCM'04 Proceedings of the 1st international conference on Complex motion
Fluid flow estimation through integration of physical flow configurations
Proceedings of the 29th DAGM conference on Pattern recognition
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We present a novel technique to eliminate strong biases in parameter estimation were part of the data matrix is not corrupted by errors. Problems of this type occur in the simultaneous estimation of optical flow and the parameter of linear brightness change as well as in range flow estimation. For attaining highly accurate optical flow estimations under real world situations as required by a number of scientific applications, the standard brightness change constraint equation is violated. Very often the brightness change has to be modelled by a linear source term. In this problem as well as in range flow estimation, part of the data term consists of an exactly known constant. Total least squares (TLS) assumes the error in the data terms to be identically distributed, thus leading to strong biases in the equations at hand. The approach presented in this paper is based on a mixture of ordinary least squares (OLS) and total least squares, thus resolving the bias encountered in TLS alone. Apart from a thorough performance analysis of the novel estimator, a number of applications are presented.