Decision Forests for Computer Vision and Medical Image Analysis (Paperback, Softcover reprint of the original 1st ed. 2013)

This practical and easy-to-follow text explores the theoretical underpinnings of decision forests, organizing the vast existing literature on the field within a new, general-purpose forest model. Topics and features: with a foreword by Prof. Y. Amit and Prof. D. Geman, recounting their participation in the development of decision forests; introduces a flexible decision forest model, capable of addressing a large and diverse set of image and video analysis tasks; investigates both the theoretical foundations and the practical implementation of decision forests; discusses the use of decision forests for such tasks as classification, regression, density estimation, manifold learning, active learning and semi-supervised classification; includes exercises and experiments throughout the text, with solutions, slides, demo videos and other supplementary material provided at an associated website; provides a free, user-friendly software library, enabling the reader to experiment with forests in a hands-on manner.

General

Imprint:	Springer London
Country of origin:	United Kingdom
Series:	Advances in Computer Vision and Pattern Recognition
Release date:	August 2016
First published:	2013
Editors:	Antonio Criminisi • J. Shotton
Dimensions:	235 x 155 x 21mm (L x W x T)
Format:	Paperback
Pages:	368
Edition:	Softcover reprint of the original 1st ed. 2013
ISBN-13:	978-1-4471-6962-8
Categories:	Books > Computing & IT > Applications of computing > Pattern recognition Books > Computing & IT > Applications of computing > Artificial intelligence > General
LSN:	1-4471-6962-X
Barcode:	9781447169628

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