Dynamic texture segmentation

Doretto, G., Cremers, D., Favaro, P., and Soatto, S.
Dynamic texture segmentation. In Proceedings of IEEE International Conference on Computer Vision, pp. 1236–1242, Nice, France, October 2003.

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Abstract

We address the problem of segmenting a sequence of images of natural scenes into disjoint regions that are characterized by constant spatio-temporal statistics. We model the spatio-temporal dynamics in each region by Gauss-Markov models, and infer the model parameters as well as the boundary of the regions in a variational optimization framework. Numerical results demonstrate that – in contrast to purely texture-based segmentation schemes – our method is effective in segmenting regions that differ in their dynamics even when spatial statistics are identical.

@INPROCEEDINGS{dorettoCFS03iccv,
  author = {Doretto, G. and Cremers, D. and Favaro, P. and Soatto, S.},
  title = {Dynamic texture segmentation},
  booktitle = iccv,
  year = {2003},
  volume = {2},
  pages = {1236--1242},
  address = {Nice, France},
  month = oct,
  note = {\btohremove{\textsf{\textbf{SCC: 7, GSCC: 15, AR: 20.6\%}}}},
  bib2html_pubtype = {Refereed Conferences},
  bib2html_rescat = {Dynamic Textures, Visual Motion Segmentation},
  abstract = {We address the problem of segmenting a sequence of images of natural
	scenes into disjoint regions that are characterized by constant spatio-temporal
	statistics. We model the spatio-temporal dynamics in each region
	by Gauss-Markov models, and infer the model parameters as well as
	the boundary of the regions in a variational optimization framework.
	Numerical results demonstrate that – in contrast to purely texture-based
	segmentation schemes – our method is effective in segmenting regions
	that differ in their dynamics even when spatial statistics are identical.},
  file = {dorettoCFS03iccv.pdf:doretto\\conference\\dorettoCFS03iccv.pdf:PDF},
  owner = {doretto},
  pdf = {doretto\conference\dorettoCFS03iccv.pdf}
}

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