@COMMENT This file was generated by bib2html.pl <https://sourceforge.net/projects/bib2html/> version 0.94
@COMMENT written by Patrick Riley <http://sourceforge.net/users/patstg/>
@COMMENT This file came from Patrick Riley's publication pages at
@COMMENT http://www.cs.cmu.edu/~pfr/publications
@MASTERSTHESIS{doretto02thesis,
  author = {Doretto, G.},
  title = {Dynamic texture modeling},
  school = {University of California},
  year = {2002},
  address = {Los Angeles, CA},
  month = {June},
  note = {{C}ommittee: {A}dnan {D}arwiche, {M}ichael {D}yer, {S}tefano {S}oatto
	({C}hair). \btohremove{\textsf{\textbf{GSCC: 2}}}},
  bib2html_pubtype = {Theses},
  bib2html_rescat = {Dynamic Textures, Visual Motion Analysis, Image Based Rendering},
  abstract = {Dynamic textures are sequences of images of moving scenes that exhibit
	certain stationarity properties in time; these include, for example,
	sea-waves, smoke, foliage, whirlwind etc. This work presents a novel
	characterization of dynamic textures that poses the problems of modeling,
	learning, recognizing, classifying, synthesizing, and editing dynamic
	textures on a firm analytical footing. By means of system identification
	tools it is possible to capture the “essence” of dynamic textures;
	this is done 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 second-order stationary processes, a model
	can be identified sub-optimally in closed-form. Once learned, a model
	has predictive power and can be used for extrapolating, and editing
	(i.e. modifying the temporal and spatial behavior of) synthetic sequences.
	It is presented experimental evidence that, within this framework,
	even low-dimensional models can capture very complex visual phenomena.
	Furthermore, it is shown the possibility to map the manipulation
	of model parameters into sensible changes of visual appearance in
	extrapolated sequences. The uniqueness of the model allows to pose
	the problem of recognition and classification in the space of models.
	Since the space is non-linear, a distance between models must be
	defined. This work examines three different distances in the space
	of autoregressive models and assess their power.},
  file = {doretto02thesis.pdf:doretto\\thesis\\doretto02thesis.pdf:PDF},
  owner = {doretto},
  pdf = {doretto\thesis\doretto02thesis.pdf},
  timestamp = {2007.01.19}
}