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2D Object Detection and Recognition: Models, Algorithms, and Networks
Contributor(s): Amit, Yali (Author)

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 ISBN: 0262011948     ISBN-13: 9780262011945
Publisher: MIT Press
OUR PRICE: $9.49  

Binding Type: Hardcover - See All Available Formats & Editions
Published: August 2002
* Out of Print *

Annotation: Two important subproblems of computer vision are the detection and recognition of 2D objects in gray-level images. This book discusses the construction and training of models, computational approaches to efficient implementation, and parallel implementations in biologically plausible neural network architectures. The approach is based on statistical modeling and estimation, with an emphasis on simplicity, transparency, and computational efficiency. The book describes a range of deformable template models, from coarse sparse models involving discrete, fast computations to more finely detailed models based on continuum formulations, involving intensive optimization. Each model is defined in terms of a subset of points on a reference grid (the template), a set of admissible instantiations of these points (deformations), and a statistical model for the data given a particular instantiation of the object present in the image. A recurring theme is a coarse to fine approach to the solution of vision problems. The book provides detailed descriptions of the algorithms used as well as the code, and the software and data sets are available on the Web.

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Additional Information
BISAC Categories:
- Technology & Engineering | Robotics
- Computers | Computer Vision & Pattern Recognition
- Computers | Computer Science
Dewey: 006.37
LCCN: 2002016508
Age Level: 18-UP
Grade Level: 13-UP
Series: Mit Press
Physical Information: 0.86" H x 7.18" W x 9.32" L (1.50 lbs) 324 pages
Features: Bibliography, Illustrated, Index
 
Descriptions, Reviews, Etc.
Publisher Description:

A guide to the computer detection and recognition of 2D objects in gray-level images.

Two important subproblems of computer vision are the detection and recognition of 2D objects in gray-level images. This book discusses the construction and training of models, computational approaches to efficient implementation, and parallel implementations in biologically plausible neural network architectures. The approach is based on statistical modeling and estimation, with an emphasis on simplicity, transparency, and computational efficiency.

The book describes a range of deformable template models, from coarse sparse models involving discrete, fast computations to more finely detailed models based on continuum formulations, involving intensive optimization. Each model is defined in terms of a subset of points on a reference grid (the template), a set of admissible instantiations of these points (deformations), and a statistical model for the data given a particular instantiation of the object present in the image. A recurring theme is a coarse to fine approach to the solution of vision problems. The book provides detailed descriptions of the algorithms used as well as the code, and the software and data sets are available on the Web.

Contributor Bio(s): Amit, Yali: - Yali Amit is Professor of Statistics and Computer Science at the University of Chicago.
 
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