Decision Forests for Computer Vision and Medical Image Analysis (Advances in Computer Vision and Pattern Recognition)

Criminisi, Antonio, Shotton, J

ISBN 10: 1447149289 / ISBN 13: 9781447149286

Edité par Springer, 2013

A propos de cet article

368 pp., hardcover, new. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country.

N° de réf. du vendeur ZB1315400

Détails bibliographiques

• Titre: Decision Forests for Computer Vision and Medical Image Analysis (Advances in Computer Vision and Pattern Recognition)
• Éditeur: Springer
• Date de parution: 2013
• Langue: anglais
• ISBN à 10 chiffres: 1447149289
• ISBN à 13 chiffres: 9781447149286
• Reliure: Couverture rigide
• État de l'article: New

A propos de ce titre

Synopsis

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.

Présentation de l'éditeur

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.