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    A Bayesian approach to object detection using probabilistic appearance-based models

    Dahyot, Rozenn and Charbonnier, Pierre and Heitz, Fabrice (2004) A Bayesian approach to object detection using probabilistic appearance-based models. Pattern Analysis and Applications, 7 (3). pp. 317-332. ISSN 1433-7541

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    In this paper, we introduce a Bayesian approach, inspired by probabilistic principal component analysis (PPCA) (Tipping and Bishop in J Royal Stat Soc Ser B 61(3):611–622, 1999), to detect objects in complex scenes using appearance-based models. The originality of the proposed framework is to explicitly take into account general forms of the underlying distributions, both for the in-eigenspace distribution and for the observation model. The approach combines linear data reduction techniques (to preserve computational efficiency), nonlinear constraints on the in-eigenspace distribution (to model complex variabilities) and non-linear (robust) observation models (to cope with clutter, outliers and occlusions). The resulting statistical representation generalises most existing PCA-based models (Tipping and Bishop in J Royal Stat Soc Ser B 61(3):611–622, 1999; Black and Jepson in Int J Comput Vis 26(1):63–84, 1998; Moghaddam and Pentland in IEEE Trans Pattern Anal Machine Intell 19(7):696–710, 1997) and leads to the definition of a new family of non-linear probabilistic detectors. The performance of the approach is assessed using receiver operating characteristic (ROC) analysis on several representative databases, showing a major improvement in detection performances with respect to the standard methods that have been the references up to now.

    Item Type: Article
    Keywords: Eigenspace representation; Probabilistic PCA; Bayesian approach; Non-Gaussian models; M-estimators; Half-quadratic algorithms;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Item ID: 15128
    Identification Number:
    Depositing User: Rozenn Dahyot
    Date Deposited: 14 Dec 2021 16:20
    Journal or Publication Title: Pattern Analysis and Applications
    Publisher: Springer
    Refereed: Yes
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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