Decision Forests for Computer Vision and Medical Image Analysis

Antonio Criminisi (Curatore)

J Shotton (Curatore)

Springer London Ltd, 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 ina hands-on manner.

• Curatore: Antonio Criminisi, J Shotton
• Editore: Springer London Ltd
• Collana: Advances in Computer Vision and Pattern Recognition
• Anno: 2013
• Rilegatura: Hardback
• Pagine: 368 p.
• Dimensioni: 235 x 155 mm
• EAN: 9781447149286

Testo in English