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@PHDTHESIS{doretto05dissertation,
  author = {Doretto, G.},
  title = {{DYNAMIC TEXTURES}: modeling, learning, synthesis, animation, segmentation,
	and recognition},
  school = {University of California},
  year = {2005},
  address = {Los Angeles, CA},
  month = {March},
  note = {{C}ommittee: {A}dnan {D}arwiche, {P}etros {F}aloutsos, {D}emetri
	{T}erzopoulos, {Y}ing {N}ian {W}u, {S}tefano {S}oatto ({C}hair)},
  bib2html_pubtype = {Theses},
  bib2html_rescat = {Dynamic Textures, Visual Motion Analysis, Visual Motion Segmentation,
	Visual Motion Recognition, Shape and Appearance Modeling, Image Based
	Rendering},
  abstract = {Dynamic textures are sequences of images of dynamic scenes that exhibit
	some temporal regularity properties, intended in a statistical sense;
	these include, for example, ocean waves, smoke, whirlwind, fire,
	foliage, but also moving objects with a “defined shape,” for instance
	flowers, or flags in wind etc. This work presents a characterization
	of this class of video sequences, and poses the problems of modeling,
	learning, synthesis, animation, recognition, and segmentation of
	dynamic textures.
	Since, in absence of any additional prior knowledge, the visual reconstruction
	problem from images alone is ill-posed, in this work we give up trying
	to infer the physical model that generated the images, and analyze
	sequences of images solely as visual signals. We do so by building
	a statistical framework, and draw on disciplines like time series
	analysis, system, control, and identification theory.
	We derive three generative models, the simplest possible, that are
	able to capture, respectively, the temporal second-order statistics,
	the spatio-temporal second-order statistics, and the higher-order
	temporal statistics of dynamic textures. We propose to learn model
	parameters in the maximum-likelihood sense, or minimum prediction
	error variance. We derive efficient closed-form inference procedures
	for learning the second-order statistics, and revert to nonlinear
	optimization techniques for the higher-order ones. After learning
	a model, it can be used to extrapolate, or predict new image data
	both in the temporal and spatial domain. We analyze the meaning of
	the parameters of a model, and show how they can be manipulated to
	control, or animate the simulation. Using the geometry of subspaces,
	and statistical pattern recognition theory we derive a technique
	to discriminate between models, and assess the potential for building
	a recognition system. Finally, by combining these results with a
	variational framework, we design a region-based segmentation system
	able to partition a video sequence into regions characterized by
	different spatio-temporal statistics.},
  file = {doretto05dissertation.pdf:doretto\\thesis\\doretto05dissertation.pdf:PDF},
  owner = {doretto},
  pdf = {doretto\thesis\doretto05dissertation.pdf}
}