"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 for 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, IsraëlContents

1. Introduction
2. Detection and Recognition: Overview of Models
3. 1D Models: Deformable Contours
4. 1D Models: Deformable Curves
5. 2D Models: Deformable Images
6. Sparse Models: Formulation, Training, and Statistical Properties
7. Detection of Sparse Models: Dynamic Programming
8. Detection of Sparse Models: Counting
9. Object Recognition
10. Scene Analysis: Merging Detection and Recognition
11. Neural Network Implementations
12. Software