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

    1. A brief survey of the face detection literature -- Introduction -- The Viola-Jones face detector -- The integral image -- AdaBoost learning -- The attentional cascade structure -- Recent advances in face detection -- Feature extraction -- Variations of the boosting learning algorithm -- Other learning schemes -- Book overview --

    2. Cascade-based real-time face detection -- Soft-cascade training -- Fat stumps -- Multiple instance pruning -- Pruning using the final classification -- Multiple instance pruning -- Experimental results --

    3. Multiple instance learning for face detection -- MILboost -- Noisy-or MILboost -- ISR MILboost -- Application of MILboost to low resolution face detection -- Multiple category boosting -- Probabilistic McBoost -- Winner-take-all McBoost -- Experimental results -- A practical multi-view face detector --

    4. Detector adaptation -- Problem formulation -- Parametric learning -- Detector adaptation -- Taylor-expansion-based adaptation -- Adaptation of logistic regression classifiers -- Logistic regression -- Adaptation of logistic regression classifier -- Direct labels -- Similarity labels -- Adaptation of boosting classifiers -- Discussions and related work -- Experimental results -- Results on direct labels -- Results on similarity labels --

    5. Other applications -- Face verification with boosted multi-task learning -- Introduction -- AdaBoosting LBP -- Boosted multi-task learning -- Experimental results -- Boosting-based multimodal speaker detection -- Introduction -- Related works -- Sound source localization -- Boosting-based multimodal speaker detection -- Merge of detected windows -- Alternative speaker detection algorithms -- Experimental results --

    6. Conclusions and future work -- Bibliography -- Authors' biographies


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    Titel :

    Boosting-Based Face Detection and Adaptation : Cha Zhang and Zhengyou Zhang (Microsoft Research)


    Beteiligte:
    Zhang, Cha (Autor:in)


    Erscheinungsdatum :

    2010


    Format / Umfang :

    1 Online-Ressource (140 Seiten)


    Anmerkungen:

    Campusweiter Zugriff (Universität Hannover) - Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots.
    Description based upon print version of record




    Medientyp :

    Buch


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    006.42 / 006.37



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