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Boosting-based face detection and adaptation (notice n° 3025)

000 -LEADER
fixed length control field 02280nam a2200349 u 4500
001 - CONTROL NUMBER
control field UNI0000315
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20161122155617.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 130710s2010 XX eng
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 160845133X (paperback)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781608451333 (paperback)
040 ## - CATALOGING SOURCE
Original cataloging agency DCLC
040 ## - CATALOGING SOURCE
Modifying agency IMIST
Description conventions AFNOR
041 1# - LANGUAGE CODE
Language code of text/sound track or separate title eng
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.42
Edition number 22
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Zhang,, Cha.
245 #0 - TITLE STATEMENT
Title Boosting-based face detection and adaptation
Statement of responsibility, etc Cha Zhang, Zhengyou Zhang, Sven Dickinson, Gerard Medioni.
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc [S.l.]
Name of publisher, distributor, etc Morgan and Claypool Publishers
Date of publication, distribution, etc 2010.
300 ## - PHYSICAL DESCRIPTION
Extent 140 p.
Dimensions 24 cm.
490 1# - SERIES STATEMENT
Series statement Synthesis lectures on computer vision.
500 ## - GENERAL NOTE
General note Face detection, because of its vast array of applications, is one of the most active research areas in computer vision. In this book, we review various approaches to face detection developed in the past decade, with more emphasis on boosting-based learning algorithms. We then present a series of algorithms that are empowered by the statistical view of boosting and the concept of multiple instance learning. We start by describing a boosting learning framework that is capable to handle billions of training examples. It differs from traditional bootstrapping schemes in that no intermediate thresholds need to be set during training, yet the total number of negative examples used for feature selection remains constant and focused (on the poor performing ones). A multiple instance pruning scheme is then adopted to set the intermediate thresholds after boosting learning. This algorithm generates detectors that are both fast and accurate. Table of Contents: A Brief Survey of the Face Detection Literature / Cascade-based Real-Time Face Detection / Multiple Instance Learning for Face Detection / Detector Adaptation / Other Applications / Conclusions and Future Work.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer vision
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer vision
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Human face recognition (Computer science)
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Human face recognition (Computer science)
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Image processing--Digital techniques
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Image processing--Digital techniques
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine learning
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine learning
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Dickinson,, Sven.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Medioni,, Gerard.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Zhang,, Zhengyou.
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        La bibliothèque des sciences de l'ingénieur La bibliothèque des sciences de l'ingénieur   18886   006.42 ZHA 0000000018992 11/22/2016 11/22/2016 Livre
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