Modeling dynamic scenes: An overview of dynamic textures

Doretto, G. and Soatto, S.
Modeling dynamic scenes: An overview of dynamic textures. In Paragios, N., Chen, Y., and Faugeras, O., editors, Handbook of mathematical models in computer vision, pp. 343–357, Springer-Verlag, 2005.

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@INCOLLECTION{dorettoS05chapter,
  author = {Doretto, G. and Soatto, S.},
  title = {Modeling dynamic scenes: {A}n overview of dynamic textures},
  booktitle = {Handbook of mathematical models in computer vision},
  publisher = {Springer-Verlag},
  year = {2005},
  editor = {Paragios, N. and Chen, Y. and Faugeras, O.},
  chapter = {21},
  pages = {343--357},
  bib2html_pubtype = {Book Chapters},
  bib2html_rescat = {Dynamic Textures, Visual Motion Analysis, Shape and Appearance Modeling},
  file = {dorettoS05chapter.pdf:doretto\\chapter\\dorettoS05chapter.pdf:PDF},
  owner = {doretto},
  pdf = {doretto\chapter\dorettoS05chapter.pdf},
  review = {Dynamic scenes with arbitrary radiometry and geometry present a challenge
	in that a physical model of their motion, shape, and reflectance
	cannot be inferred. Therefore, the issue of representation becomes
	crucial, and while there is no right or wrong representation, the
	task at hand should guide the modeling process.For instance, if the
	task is three-dimensional reconstruction, one can make assumptions
	on reflectance and illumination in order to recover shape and motion.
	If the task is synthesis, or reprojection, the correct shape is unimportant,
	as long as the model supports the generation of a valid view of the
	scene. If the task is detection or recognition, a physical model
	is not necessary as long as one can infer a statistical model that
	can be used to perform classification. We concentrate our attention
	on the two latter cases, and describe a modeling framework for dynamic
	scenes for the purpose of synthesis, detection and recognition. In
	particular, we restrict our attention to sequences of images of moving
	scenes that exhibit certain statistical stationarity properties,
	which have been called Dynamic Textures. They include sea-waves,
	smoke, foliage, whirlwind etc. In this chapter we describe a characterization
	of dynamic textures and pose the problems of modeling, learning,
	recognition and segmentation of dynamic textures using tools from
	time series analysis, and system identification theory.},
  timestamp = {2007.01.19}
}

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