Statistical shape-and-texture appearance models employ image metamorphosis to form a rich, compact representation of object appearance. They achieve their efficiency by decomposing appearance into simpler shape-and-texture representations. In general, the shape and texture of an object can vary nonlinearly and in this case the conventional shape-and-texture mappings using principle component analysis (PCA) may poorly approximate the true space. In this paper we propose two nonlinear techniques for modelling shape-and-texture appearance manifolds. Our first method uses a mixture of Gaussians in image space to separate the different parts of the shape and texture spaces. A linear shape-and-texture model is defined at each component to form the overall model. Our second approach employs a nearest-neighbor method to find a local set of shapes and images that can be morphed to explain a new input. We test each approach using a speaking-mouth video sequence and compare both approaches to a conventional active appearance model (AAM).


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    On modelling nonlinear shape-and-texture appearance manifolds


    Contributors:


    Publication date :

    2005-01-01


    Size :

    2459233 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Texture enhanced appearance models

    Larsen, R. / Stegmann, M. B. / Darkner, S. et al. | British Library Online Contents | 2007


    Learning appearance manifolds from video

    Rahimi, A. / Darrell, T. / Recht, B. | IEEE | 2005


    Nonlinear manifold learning for dynamic shape and dynamic appearance

    Elgammal, A. / Lee, C. S. | British Library Online Contents | 2007


    Video-Based Face Recognition Using Probabilistic Appearance Manifolds

    Lee, K.-C. / Ho, J. / Yang, M.-H. et al. | British Library Conference Proceedings | 2003


    Visual tracking and recognition using probabilistic appearance manifolds

    Lee, K. C. / Ho, J. / Yang, M. H. et al. | British Library Online Contents | 2005