We consider the problem of modeling a scene containing multiple dynamic textures undergoing multiple rigid-body motions, e.g., a video sequence of water taken by a rigidly moving camera. We propose to model each moving dynamic texture with a time varying linear dynamical system (LDS) plus a 2D translational motion model. We first consider a scene with a single moving dynamic texture and show how to simultaneously learn the parameters of the time varying LDS as well as the optical flow of the scene using the so-called dynamic texture constancy constraint (DTCC). We then consider a scene with multiple non-moving dynamic textures and show that learning the parameters of each time invariant LDS as well as their region of support is equivalent to clustering data living in multiple subspaces. We solve this problem with a combination of PCA and GPCA. Finally, we consider a scene with multiple moving dynamic textures, and show how to simultaneously learn the parameters of multiple time varying LDS and multiple 2D translational models, by clustering data living in multiple dynamically evolving subspaces. We test our approach on sequences of flowers, water, grass, and a beating heart.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Optical flow estimation & segmentation of multiple moving dynamic textures


    Contributors:


    Publication date :

    2005-01-01


    Size :

    305871 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Dynamic textures

    Soatto, S. / Doretto, G. / Ying Nian Wu, | IEEE | 2001


    Dynamic Textures

    Soatto, S. / Doretto, G. / Wu, Y. et al. | British Library Conference Proceedings | 2001


    Dynamic Textures

    Doretto, G. / Chiuso, A. / Wu, Y. N. et al. | British Library Online Contents | 2003