ISBN-10:
0262011948
ISBN-13:
9780262011945
Pub. Date:
08/02/2002
Publisher:
MIT Press
2D Object Detection and Recognition: Models, Algorithms, and Networks / Edition 1

2D Object Detection and Recognition: Models, Algorithms, and Networks / Edition 1

by Yali Amit

Hardcover

Current price is , Original price is $11.75. You
Select a Purchase Option
  • purchase options
    $10.90 $11.75 Save 7% Current price is $10.9, Original price is $11.75. You Save 7%.
  • purchase options

Product Details

ISBN-13: 9780262011945
Publisher: MIT Press
Publication date: 08/02/2002
Series: The MIT Press
Pages: 324
Product dimensions: 7.00(w) x 9.00(h) x 1.25(d)
Age Range: 18 Years

About the Author

Yali Amit is Professor of Statistics and Computer Science at the University of Chicago.

What People are Saying About This

Jitendra Malik

Modeling the human ability to identify objects in images has proved to be a significant challenge. While computer vision researchers have largely concentrated on the geometric aspects of the problem such as recognition under varying poses, researchers in statistics and machine learning typically have treated the problem as one of classifying feature vectors. In this important book, Yali Amit presents a novel synthesis of these strands of research. His approach to recognition based on learned configurations of sparse features provides a rare combination of efficient algorithms based on a solid statistical foundation. Amit's thorough and well-documented experimentation with examples ranging from medical images to handwritten digits should set a standard for the field. Highly recommended.

Shimon Ullman

The book develops a novel and elegant approach to the important problem of visual object recognition. The efficient and well motivated algorithms have fundamental theoretical as well as practical implications to the study of computer vision. The book will appeal to computer scientists as well as researchers modeling the functions of biological visual systems.

Endorsement

The book develops a novel and elegant approach to the important problem of visual object recognition. The efficient and well motivated algorithms have fundamental theoretical as well as practical implications to the study of computer vision. The book will appeal to computer scientists as well as researchers modeling the functions of biological visual systems.

Shimon Ullman, The Weizmann Institute of Science, Israel

From the Publisher

Modeling the human ability to identify objects in images has proved to be a significant challenge. While computer vision researchers have largely concentrated on the geometric aspects of the problem such as recognition under varying poses, researchers in statistics and machine learning typically have treated the problem as one of classifying feature vectors. In this important book, Yali Amit presents a novel synthesis of these strands of research. His approach to recognition based on learned configurations of sparse features provides a rare combination of efficient algorithms based on a solid statistical foundation. Amit's thorough and well-documented experimentation with examples ranging from medical images to handwritten digits should set a standard for the field. Highly recommended.

Jitendra Malik, Department of Computer Science, University of California at Berkeley

The book develops a novel and elegant approach to the important problem of visual object recognition. The efficient and well motivated algorithms have fundamental theoretical as well as practical implications to the study of computer vision. The book will appeal to computer scientists as well as researchers modeling the functions of biological visual systems.

Shimon Ullman, The Weizmann Institute of Science, Israel

Customer Reviews

Most Helpful Customer Reviews

See All Customer Reviews