Logo - springer
Slogan - springer

Computer Science - Image Processing | Decision Forests for Computer Vision and Medical Image Analysis

Decision Forests for Computer Vision and Medical Image Analysis

Criminisi, Antonio, Shotton, J (Eds.)

2013, XIX, 368 p. 143 illus., 136 illus. in color.

Available Formats:
eBook
Information

Springer eBooks may be purchased by end-customers only and are sold without copy protection (DRM free). Instead, all eBooks include personalized watermarks. This means you can read the Springer eBooks across numerous devices such as Laptops, eReaders, and tablets.

You can pay for Springer eBooks with Visa, Mastercard, American Express or Paypal.

After the purchase you can directly download the eBook file or read it online in our Springer eBook Reader. Furthermore your eBook will be stored in your MySpringer account. So you can always re-download your eBooks.

 
$99.00

(net) price for USA

ISBN 978-1-4471-4929-3

digitally watermarked, no DRM

Included Format: PDF and EPUB

download immediately after purchase


learn more about Springer eBooks

add to marked items

Hardcover
Information

Hardcover version

You can pay for Springer Books with Visa, Mastercard, American Express or Paypal.

Standard shipping is free of charge for individual customers.

 
$129.00

(net) price for USA

ISBN 978-1-4471-4928-6

free shipping for individuals worldwide

usually dispatched within 3 to 5 business days


add to marked items

  • Introduces a flexible decision forest model capable of addressing a large and diverse set of image and video analysis tasks, covering both theoretical foundations and practical implementation
  • 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

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 images and videos, automatic parsing of medical 3D scans, and detection of tumors. The book concludes with a detailed discussion on the efficient implementation of decision forests.

Topics and features:

  • With a foreword by Prof. Yali Amit and Prof. Donald 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

With its clear, tutorial structure and supporting exercises, this text will be of great value to students wishing to learn the basics of decision forests, researchers wanting to become more familiar with forest-based learning, and practitioners interested in exploring modern and efficient image analysis techniques.

Dr. A. Criminisi and Dr. J. Shotton are Senior Researchers in the Computer Vision Group at Microsoft Research Cambridge, UK.

Content Level » Research

Keywords » Decision Forest - Kinect - Random Decision Forest - Random Forests - Randomized Trees

Related subjects » Artificial Intelligence - Image Processing

Table of contents / Preface / Sample pages 

Popular Content within this publication 

 

Articles

Read this Book on Springerlink

Services for this book

New Book Alert

Get alerted on new Springer publications in the subject area of Pattern Recognition.

Additional information