Hardcover. Zustand: Very Good. 2012. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.
Anbieter: Zubal-Books, Since 1961, Cleveland, OH, USA
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In den WarenkorbPAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.
Anbieter: BookOrders, Russell, IA, USA
Soft Cover. Zustand: 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.
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Zustand: As New. Expédié soigneusement dans un emballage adapté depuis la France. 22x16x1cm. 2008. Broché. 128 pages. As New.
Zustand: As New. Expedié soigneusement dans un emballage adapté depuis la France. 22x16x1cm. 2008. Broché. 128 pages. As New.
Zustand: As New. SOUS BLISTER- envoi rapide et soigné dans une enveloppe à bulle depuis France. 22x16x1cm. 2008. Broché. 128 pages. As New.
Verlag: Springer International Publishing AG, CH, 2010
ISBN 10: 303100681X ISBN 13: 9783031006814
Sprache: Englisch
Anbieter: Rarewaves.com USA, London, LONDO, Vereinigtes Königreich
EUR 39,79
Anzahl: 1 verfügbar
In den WarenkorbPaperback. Zustand: New. 1°. 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 schemesfor 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'sperformance 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 futur.
ISBN 10: 1598293907 ISBN 13: 9781598293906
Sprache: Englisch
Anbieter: Basi6 International, Irving, TX, USA
Zustand: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.
Zustand: good. Photo non contractuelle. Envoi rapide et soigné.
Zustand: very good. Envoi rapide et soigné.