Decision Forests for Computer Vision and Medical Image Analysis [Paperback]

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.This practical, easy-to-follow book reviews the theoretical underpinnings of decision forests, organizing the existing literature in a new, general-purpose forest model. Includes exercises and experiments; slides, videos and more reside at a companion website.

Decision forests (also known as random forests) are an indispensable tool for automatic image analysis.

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. A number of exercises encourage the reader to practice their skills with the aid of the provided free software library. An international selection of leading researchers from both academia and industry then contribute their own perspectives on the use of decision forests in real-world applications such as pedestrian tracking, human body pose estimation, pixel-wise semantic segmentation of iml#2