Robust regression and outlier detection
Robust regression and outlier detection
On a Constraint Equation for the Estimation of Displacement Rates in Image Sequences
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
A Total Least Squares Framework for Low-Level Analysis of Dynamic Scenes and Processes
Mustererkennung 1999, 21. DAGM-Symposium
Dense Parameter Fields from Total Least Squares
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Statistical Image Sequence Processing for Temporal Change Detection
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Mixed OLS-TLS for the Estimation of Dynamic Processes with a Linear Source Term
Proceedings of the 24th DAGM Symposium on Pattern Recognition
On performance analysis of optical flow algorithms
Proceedings of the 15th international conference on Theoretical Foundations of Computer Vision: outdoor and large-scale real-world scene analysis
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We present a new technique for estimating the sea surface heat flux from infrared image sequences. Based on solving an extension to the standard brightness change constraint equation in a total least squares (TLS) sense, the total derivative of the sea surface temperature with respect to time is obtained. Due to inevitable reflexes in field data the TLS framework was further extended to a robust estimation based on a Least Median of Squares Orthogonal Distances (LMSOD) scheme. From this it is possible for the first time to compute accurate heat flux densities to a high temporal and spatial resolution. Results obtained at the Heidelberg Aeolotron showed excellent agreement to ground truth and field data was obtained on the GasExII experiment.