Decision Forests for Computer Vision and Medical Image Analysis
Antonio Criminisi, J Shotton, "Decision Forests for Computer Vision and Medical Image Analysis"
2013 | ISBN-10: 1447149289 | 400 pages | PDF | 18,3 MB
2013 | ISBN-10: 1447149289 | 400 pages | PDF | 18,3 MB
is 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.