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A Highly Robust Estimator Through Partially Likelihood Function Modeling and Its Application in Computer Vision
January 1992 (vol. 14 no. 1)
pp. 19-35

The authors present a highly robust estimator, known as the model fitting (MF) estimator for general regression. They explain that high robustness becomes possible through partially but completely modeling the unknown log likelihood function. The partial modeling takes place by taking the Bayesian statistical decision rule and a number of important heuristics into consideration while maximizing the log likelihood function. Applications include the automatic selection of multiple thresholds, single rigid motion estimation or multiple rigid motion segmentation, and estimation from two perspective views. It is believed that the proposed MF estimator will aid in solving many robust estimation problems that demand an estimator that is either highly robust or capable of handling contaminated Gaussian mixture models.

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Index Terms:
model fitting estimator; pattern recognition; picture processing; partially likelihood function modeling; computer vision; unknown log likelihood function; Bayesian statistical decision rule; single rigid motion estimation; multiple rigid motion segmentation; contaminated Gaussian mixture models; Bayes methods; computer vision; decision theory; estimation theory; pattern recognition; picture processing
Citation:
X. Zhuang, T. Wang, P. Zhang, "A Highly Robust Estimator Through Partially Likelihood Function Modeling and Its Application in Computer Vision," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 1, pp. 19-35, Jan. 1992, doi:10.1109/34.107011
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