Dec 10

Neurons for Robots: A Tool Kit

Format: Paperback

Language: English

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Size: 14.19 MB

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Formal Written Examination: 1 x 1.5 hr(s) paper(s) to be taken in Winter 2016. Often a qualitative doctoral thesis such as an ethnographic study will have many points. Introduces contour and region-based image segmentation in computer vision, covering Jensen-Shannon divergence, the maximum entropy principle, the minimum description length (MDL) principle, and discriminative-generative approaches to segmentation Explores problems in image and pattern clustering, discussing Gaussian mixtures, information bottleneck, robust information clustering, and IT-based mean-shift, as well as strategies to form clustering ensembles Includes a selection of problems at the end of each chapter, to both consolidate what has been learnt and to test the ability of generalizing the concepts discussed Investigates the application of IT to interest points, edge detection and grouping in computer vision, including the concept of Shannon’s entropy, Chernoff information and mutual information, Sanov’s theorem, and the theory of types Reviews methods of registration, matching and recognition of images and patterns, considering measures related to the concept of mutual information, alternative derivations of Jensen-Shannon divergence, the Fisher-Rao metric tensor, and the application of the MDL principle to tree registration Supplies additional material, including sketched solutions and additional references, at http://www.rvg.ua.es/ITinCVPR Examines the main approaches to feature selection and feature transform, describing the methods of principal component analysis and its generalization, and independent component analysis, together with filter, wrapper and on-line methods Explores the IT approach for classifier design including classifiers ensembles and connections with information projection and information geometry.

Pages: 406

Publisher: CreateSpace Independent Publishing Platform (November 27, 2009)

ISBN: 1449599540

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