Titlebook: Data Segmentation and Model Selection for Computer Vision; A Statistical Approa Alireza Bab-Hadiashar,David Suter Book 2000 Springer Scienc

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978-1-4684-9508-9Springer Science+Business Media New York 2000

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Gian Paolo Cimellaro Ph.D., P.E.,Marta Piquéll deviations from assumed models. This chapter provides an overview of basic concepts and tools of robust statistics. In the first part we focus on regression models and discuss the most important classes of robust procedures for estimation and inference, which have been developed in the past two d

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Advances in Numerical Mathematicshas a parametric expression that combines a deterministic input-output relationship and random fluctuations. However, most of the problems encountered in computer vision do not fit in this framework. In this chapter, we illustrate this difference by taking line fitting as a typical example. First, w

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Computational Modeling and Explanationast squares) model-fitting. The essential idea is that if the value of K is smaller than a certain value (determined by the fraction of the total data that belongs to the given segment), the K-th order statistic is totally insensitive to outliers. Generally, these approaches try to optimize the valu

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2D and 3D Scene Segmentation for Robotic Vision topic of intense and ongoing research in image processing, computer vision, and intelligent robotics..The extent to which domain specific knowledge can be effectively applied versus the quest for generality has provided a lasting tension and an interesting challenge which parallels the reaction ver

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Robust Measures of Evidence for Variable Selectionthe Wald test procedure of Sommer and Huggins [204]. In the case of linear regression, we relate them to Mallows’ . and the robust version . of Ronchetti and Staudte. Then we propose a new method for robustly finding acceptable submodels using weights of evidence for hypotheses regarding the noncent