Articles liés à Boosting-Based Face Detection and Adaptation
Synopsis
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
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Présentation de l'éditeur
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
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
- ÉditeurMorgan and Claypool Publishers
- Date d'édition2010
- ISBN 10 160845133X
- ISBN 13 9781608451333
- ReliureBroché
- Langueanglais
- Nombre de pages140
- Coordonnées du fabricantnon disponible
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Boosting-Based Face Detection and Adaptation
Edité par
Morgan & Claypool, 2010
ISBN 10 : 160845133X
ISBN 13 : 9781608451333
Ancien ou d'occasion
Soft Cover
Vendeur : BookOrders, Russell, IA, Etats-Unis
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Soft Cover. Etat : Good. Ex-library with the usual features. Library label on front cover. The interior is clean and tight. Binding is good. Cover shows light wear. Ex-Library. N° de réf. du vendeur 122154x
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Boosting-Based Face Detection and Adaptation (Synthesis Lectures on Computer Vision, 2)
Edité par
Morgan and Claypool Publishers, 2010
ISBN 10 : 160845133X
ISBN 13 : 9781608451333
Ancien ou d'occasion
paperback
Vendeur : dsmbooks, Liverpool, Royaume-Uni
Évaluation du vendeur 4 sur 5 étoiles
paperback. Etat : Good. Good. book. N° de réf. du vendeur D8S0-3-M-160845133X-6
Quantité disponible : 1 disponible(s)