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عنوان
Face Image Analysis by Unsupervised Learning

پدید آورنده
by Marian Stewart Bartlett.

موضوع
Artificial intelligence.,Computer science.,Computer vision.,Information theory.,Statistics.

رده

کتابخانه
Center and Library of Islamic Studies in European Languages

محل استقرار
استان: Qom ـ شهر: Qom

Center and Library of Islamic Studies in European Languages

تماس با کتابخانه : 32910706-025

INTERNATIONAL STANDARD BOOK NUMBER

(Number (ISBN
9781461356530
(Number (ISBN
9781461516378

NATIONAL BIBLIOGRAPHY NUMBER

Number
b403190

TITLE AND STATEMENT OF RESPONSIBILITY

Title Proper
Face Image Analysis by Unsupervised Learning
General Material Designation
[Book]
First Statement of Responsibility
by Marian Stewart Bartlett.

.PUBLICATION, DISTRIBUTION, ETC

Place of Publication, Distribution, etc.
Boston, MA :
Name of Publisher, Distributor, etc.
Imprint: Springer,
Date of Publication, Distribution, etc.
2001.

SERIES

Series Title
Springer International Series in Engineering and Computer Science,
Volume Designation
612
ISSN of Series
0893-3405 ;

SUMMARY OR ABSTRACT

Text of Note
Face Image Analysis by Unsupervised Learning explores adaptive approaches to image analysis. It draws upon principles of unsupervised learning and information theory to adapt processing to the immediate task environment. In contrast to more traditional approaches to image analysis in which relevant structure is determined in advance and extracted using hand-engineered techniques, Face Image Analysis by Unsupervised Learning explores methods that have roots in biological vision and/or learn about the image structure directly from the image ensemble. Particular attention is paid to unsupervised learning techniques for encoding the statistical dependencies in the image ensemble. The first part of this volume reviews unsupervised learning, information theory, independent component analysis, and their relation to biological vision. Next, a face image representation using independent component analysis (ICA) is developed, which is an unsupervised learning technique based on optimal information transfer between neurons. The ICA representation is compared to a number of other face representations including eigenfaces and Gabor wavelets on tasks of identity recognition and expression analysis. Finally, methods for learning features that are robust to changes in viewpoint and lighting are presented. These studies provide evidence that encoding input dependencies through unsupervised learning is an effective strategy for face recognition. Face Image Analysis by Unsupervised Learning is suitable as a secondary text for a graduate-level course, and as a reference for researchers and practitioners in industry.

OTHER EDITION IN ANOTHER MEDIUM

International Standard Book Number
9781461356530

PIECE

Title
Springer eBooks

TOPICAL NAME USED AS SUBJECT

Artificial intelligence.
Computer science.
Computer vision.
Information theory.
Statistics.

PERSONAL NAME - PRIMARY RESPONSIBILITY

Bartlett, Marian Stewart.

CORPORATE BODY NAME - ALTERNATIVE RESPONSIBILITY

SpringerLink (Online service)

ORIGINATING SOURCE

Date of Transaction
20190301081800.0

ELECTRONIC LOCATION AND ACCESS

Electronic name
 مطالعه متن کتاب 

[Book]

Y

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