We formulate the shape localization problem in the Bayesian framework. In the learning stage, we propose the Constrained Rank-Boost approach to model the likelihood of local features associated with the key points of an object, like face, while preserve the prior ranking order between the ground truth position of a key point and its neighbors; in the inferring stage, a simple efficient iterative algorithm is proposed to uncover the MAP shape by locally modeling the likelihood distribution around each key point via our proposed variational locally weighted learning (VLWL) method. Our proposed framework has the following benefits: 1) compared to the classical PCA models, the likelihood presented by the ranking prior likelihood model has more discriminating power as to the optimal position and its neighbors, especially in the problem with ambiguity between the optimal positions and their neighbors; 2) the VLWL method guarantees that the posterior probability of the derived shape increases monotonously; and 3) the above two methods are both based on accurate probability formulation, which spontaneously leads to a robust confidence measure for the discovered shape. Moreover, we present a theoretical analysis for the convergence of the Constrained Rank-Boost. Extensive experiments compared with the active shape models demonstrate the accuracy, robustness, and stability of our proposed framework.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Ranking prior likelihood distributions for Bayesian shape localization framework


    Contributors:


    Publication date :

    2003-01-01


    Size :

    403203 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Ranking Prior Likelihood Distributions for Bayesian Shape Localization Framework

    Yan, S. / Li, M. / Zhang, H. et al. | British Library Conference Proceedings | 2003




    Bayesian Framework for Vehicle Localization Using Crowdsourced Data

    Verentsov, Sergey / Magerramov, Emil / Vinogradov, Vlad et al. | IEEE | 2018


    Bayesian Body Localization Using Mixture of Nonlinear Shape Models

    Zhang, J. / Collins, R. / Liu, Y. et al. | British Library Conference Proceedings | 2005