ISBN 10: 160845133X / ISBN 13: 9781608451333
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Riassunto: 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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Zhang, Cha
Editore: Morgan & Claypool Publishers (2016)
ISBN 10: 160845133X ISBN 13: 9781608451333
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Descrizione libro Morgan & Claypool Publishers, 2016. Paperback. Condizione libro: New. PRINT ON DEMAND Book; New; Publication Year 2016; Not Signed; Fast Shipping from the UK. No. book. Codice libro della libreria ria9781608451333_lsuk

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Cha Zhang, Zhengyou Zhang, Gerard Medioni, Sven Dickinson
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Descrizione libro Morgan & Claypool Publishers. Paperback. Condizione libro: new. BRAND NEW, Boosting-Based Face Detection and Adaptation, Cha Zhang, Zhengyou Zhang, Gerard Medioni, Sven Dickinson. Codice libro della libreria B9781608451333

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Cha Zhang, Zhengyou Zhang
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Descrizione libro Morgan Claypool Publishers, United States, 2011. Paperback. Condizione libro: New. 234 x 188 mm. Language: English . Brand New Book. 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. We then present two multiple instance learning schemes for face detection, multiple instance learning boosting (MILBoost) and winner-take-all multiple category boosting (WTA-McBoost). MILBoost addresses the uncertainty in accurately pinpointing the location of the object being detected, while WTA-McBoost addresses the uncertainty in determining the most appropriate subcategory label for multiview object detection. Both schemes can resolve the ambiguity of the labeling process and reduce outliers during training, which leads to improved detector performances. In many applications, a detector trained with generic data sets may not perform optimally in a new environment. We propose detection adaption, which is a promising solution for this problem. We present an adaptation scheme based on the Taylor expansion of the boosting learning objective function, and we propose to store the second order statistics of the generic training data for future adaptation. We show that with a small amount of labeled data in the new environment, the detector s performance can be greatly improved. We also present two interesting applications where boosting learning was applied successfully. The first application is face verification for filtering and ranking image/video search results on celebrities. We present boosted multi-task learning (MTL), yet another boosting learning algorithm that extends MILBoost with a graphical model. Since the available number of training images for each celebrity may be limited, learning individual classifiers for each person may cause overfitting. MTL jointly learns classifiers for multiple people by sharing a few boosting classifiers in order to avoid overfitting. The second application addresses the need of speaker detection in conference rooms. The goal is to find who is speaking, given a microphone array and a panoramic video of the room. We show that by combining audio and visual features in a boosting framework, we can determine the speaker s position very accurately. Finally, we offer our thoughts on future directions for face detection. Codice libro della libreria AAS9781608451333

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Descrizione libro Morgan and Claypool Publishers, 2010. PAP. Condizione libro: New. New Book. Delivered from our UK warehouse in 3 to 5 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Codice libro della libreria I2-9781608451333

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Descrizione libro Morgan and Claypool Publishers, 2010. PAP. Condizione libro: New. New Book. Shipped from US within 10 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Codice libro della libreria I2-9781608451333

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Cha Zhang, Zhengyou Zhang
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Descrizione libro Morgan Claypool Publishers, United States, 2011. Paperback. Condizione libro: New. 234 x 188 mm. Language: English . Brand New Book. 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. We then present two multiple instance learning schemes for face detection, multiple instance learning boosting (MILBoost) and winner-take-all multiple category boosting (WTA-McBoost). MILBoost addresses the uncertainty in accurately pinpointing the location of the object being detected, while WTA-McBoost addresses the uncertainty in determining the most appropriate subcategory label for multiview object detection. Both schemes can resolve the ambiguity of the labeling process and reduce outliers during training, which leads to improved detector performances. In many applications, a detector trained with generic data sets may not perform optimally in a new environment. We propose detection adaption, which is a promising solution for this problem. We present an adaptation scheme based on the Taylor expansion of the boosting learning objective function, and we propose to store the second order statistics of the generic training data for future adaptation. We show that with a small amount of labeled data in the new environment, the detector s performance can be greatly improved. We also present two interesting applications where boosting learning was applied successfully. The first application is face verification for filtering and ranking image/video search results on celebrities. We present boosted multi-task learning (MTL), yet another boosting learning algorithm that extends MILBoost with a graphical model. Since the available number of training images for each celebrity may be limited, learning individual classifiers for each person may cause overfitting. MTL jointly learns classifiers for multiple people by sharing a few boosting classifiers in order to avoid overfitting. The second application addresses the need of speaker detection in conference rooms. The goal is to find who is speaking, given a microphone array and a panoramic video of the room. We show that by combining audio and visual features in a boosting framework, we can determine the speaker s position very accurately. Finally, we offer our thoughts on future directions for face detection. Codice libro della libreria AAS9781608451333

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Zhang, Cha; Zhang, Zhengyou
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Descrizione libro Morgan and Claypool Publishers. PAPERBACK. Condizione libro: New. 160845133X Brand New Book. Ships from the United States. 30 Day Satisfaction Guarantee!. Codice libro della libreria 14390233

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Descrizione libro Morgan and Claypool Publishers, 2010. PAP. Condizione libro: New. New Book. Shipped from UK in 4 to 14 days. Established seller since 2000. Codice libro della libreria CE-9781608451333

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Zhang, Cha/ Zhang, Zhengyou/ Medioni, Gerard (Editor)/ Dickinson, Sven (Editor)
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Descrizione libro Morgan & Claypool, 2009. Paperback. Condizione libro: Brand New. 100 pages. 9.25x7.50x0.32 inches. In Stock. Codice libro della libreria z-160845133X

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Zhang, Cha; Zhang, Zhengyou; Dickinson, Sven [Series Editor]; Medioni, Gerard [Series Editor];
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Descrizione libro Morgan and Claypool Publishers, 2010. Paperback. Condizione libro: New. Codice libro della libreria INGM9781608451333

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