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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g this question. The amount of data involved is very large. The number of segments and types (models) are not known in advance (and can sometimes rapidly change over time). The sensors we have involve the introduction of noise. Usually, we require fast ("real-time" or near real-time) computation of solutions 978-1-4684-9508-9978-0-387-21528-0

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Advances in Numerical Mathematicsia such as cross-validation, jackknife, bootstrap, ., Bayesian Information Criterion (BIC), and Minimum Description Length (MDL). We conclude by discussing some of the fundamental issues that lie behind all these criteria.

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Data Segmentation and Model Selection for Computer VisionA Statistical Approa

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Model Selection Criteria for Geometric Inferenceia such as cross-validation, jackknife, bootstrap, ., Bayesian Information Criterion (BIC), and Minimum Description Length (MDL). We conclude by discussing some of the fundamental issues that lie behind all these criteria.

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Model Selection for Structure and Motion Recovery from Multiple Imageslculate exactly. Two new methods, GBIC and GRIC for approximating the posterior probability of each putative variety, are presented. This paper is intended as a pragmatic beginner’s guide to model selection, highlighting the pertinent problems and illustrating them using multiview geometry determina