Dynamic textures

Soatto, S., Doretto, G., and Wu, Y. N.
Dynamic textures. In Proceedings of IEEE International Conference on Computer Vision, pp. 439–446, Vancouver, BC, Canada, July 2001.
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Abstract

Dynamic textures are sequences of images of moving scenes that exhibit certain stationarity properties in time; these include sea-waves, smoke, foliage, whirlwind but also talking faces, traffic scenes etc. We present a novel characterization of dynamic textures that poses the problems of modelling, learning, recognizing and synthesizing dynamic textures on a firm analytical footing. We borrow tools from system identification to capture the “essence” of dynamic textures; we do so by learning (i.e. identifying) models that are optimal in the sense of maximum likelihood or minimum prediction error variance. For the special case of secondorder stationary processes we identify the model in closed form. Once learned, a model has predictive power and can be used for extrapolating synthetic sequences to infinite length with negligible computational cost. We present experimental evidence that, within our framework, even low dimensional models can capture very complex visual phenomena.

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  author = {Soatto, S. and Doretto, G. and Wu, Y. N.},
  title = {Dynamic textures},
  booktitle = iccv,
  year = {2001},
  volume = {2},
  pages = {439--446},
  address = {Vancouver, BC, Canada},
  month = jul,
  note = {\btohremove{\textsf{\textbf{SCC: 34, GSCC: 85, ARO: 7.6\%}}}},
  wwwnote = {<span class="wwwnote">Oral Presentation</span>},
  bib2html_pubtype = {Refereed Conferences},
  bib2html_rescat = {Dynamic Textures, Visual Motion Analysis},
  abstract = {Dynamic textures are sequences of images of moving scenes that exhibit
	certain stationarity properties in time; these include sea-waves,
	smoke, foliage, whirlwind but also talking faces, traffic scenes
	etc. We present a novel characterization of dynamic textures that
	poses the problems of modelling, learning, recognizing and synthesizing
	dynamic textures on a firm analytical footing. We borrow tools from
	system identification to capture the “essence” of dynamic textures;
	we do so by learning (i.e. identifying) models that are optimal
	in the sense of maximum likelihood or minimum prediction error variance.
	For the special case of secondorder stationary processes we identify
	the model in closed form. Once learned, a model has predictive power
	and can be used for extrapolating synthetic sequences to infinite
	length with negligible computational cost. We present experimental
	evidence that, within our framework, even low dimensional models
	can capture very complex visual phenomena.},
  pdf = {doretto\conference\soattoDW01iccv.pdf},
}

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