We have proposed algebraic methods and modified the level
sets based approach for segmenting dynamic textures.
dynamic textures can be modeled as linear systems,
if we consider a scene containing multiple dynamic
textures, each dynamic texture will live in its own
observability subspace. We can now use any subspace
clustering methods such as GPCA to segment
these subspaces. But since subspace clustering
algorithms do not take into account the spatial
coherence in images, we proposed a variation of the
standard GPCA, called Spatial GPCA. However algebraic
segmentation is sensitive to noise and does not
impose any temporal coherence, to over come this we
use the level set approach which is known to work
very well in the rigid body case. Level set cost
functions usually contain 2 terms, one for the data
smoothness and the other for the contour length. In
addition we add to this cost function, either a term
for texture or a term to account for the fact that
the image sequence must be an ARX model.
Video Registration using Dynamic Textures.
IEEE Transaction on Pattern Analysis and Machine Intelligence, January 2011.
A Unified Approach to Segmentation and Categorization of Dynamic Textures.
Asian Conference on Computer Vision, November 2010.
IEEE International Conference on Computer Vision and Pattern Recognition, June 2009.
European Conference on Computer Vision, October 2008.
IEEE International Conference on Computer Vision, October 2007.
International Workshop on Dynamical Vision, October 2007.
Neural Information and Processing Systems, December 2006.
International Workshop on Dynamical Vision, May 2006.
IEEE International Symposium on Biomedical Imaging, pages 634-637, April 2006.
IEEE International Conference on Computer Vision and Pattern Recognition, volume 2, pages 516-521, June 2005