Bibliothèque de L'IMIST/CNRST

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Boosting-based face detection and adaptation par Zhang,, Cha. Publication : [S.l.] Morgan and Claypool Publishers 2010 . 140 p. , 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. 24 cm. Date : 2010 Disponibilité : Exemplaires disponibles: La bibliothèque des sciences de l'ingénieur (1),

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Face image analysis by unsupervised learning par Bartlett, Marian Stewart. Publication : Boston Kluwer Academic Publishers 2001 . xv, 173 pages 25 cm. Date : 2001 Disponibilité : Exemplaires disponibles: La bibliothèque des sciences de l'ingénieur (1),

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Reliable face recognition methods system design, implementation and evaluation par Wechsler, Harry Publication : New York Springer 2007 . xiii, 329 pages 25 cm. Date : 2007 Disponibilité : Exemplaires disponibles: La bibliothèque des sciences de l'ingénieur (1),

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