<mods:mods xmlns:mods="http://www.loc.gov/mods/v3" xmlns:METS="http://www.loc.gov/METS/" xmlns:fits="http://hul.harvard.edu/ois/xml/ns/fits/fits_output" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:IR="http://dl.lib.brown.edu/md/irdata" xmlns:xs="http://www.w3.org/2001/XMLSchema" xmlns:rights="http://cosimo.stanford.edu/sdr/metsrights/" ID="etd240">
     <mods:titleInfo>
      <mods:title>Statistical Inference and Probabilistic Modeling in Compositional Vision</mods:title>
     </mods:titleInfo>
     <mods:name type="personal">
      <mods:namePart>Zhang, Wei</mods:namePart>
      <mods:role>
       <mods:roleTerm type="text">creator</mods:roleTerm>
      </mods:role>
     </mods:name>
     <mods:originInfo>
      <mods:copyrightDate keyDate="yes" encoding="w3cdtf">2009</mods:copyrightDate>
     </mods:originInfo>
     <mods:physicalDescription>
      <mods:extent>xiii, 138 p.</mods:extent>
      <mods:digitalOrigin>born digital</mods:digitalOrigin>
     </mods:physicalDescription>
     <mods:note>Thesis (Ph.D.) -- Brown University (2009)</mods:note>
     <mods:name type="personal">
      <mods:namePart>Geman, Stuart</mods:namePart>
      <mods:role>
       <mods:roleTerm type="text">director</mods:roleTerm>
      </mods:role>
     </mods:name>
     <mods:name type="personal">
      <mods:namePart>Bienenstock, Elli</mods:namePart>
      <mods:role>
       <mods:roleTerm type="text">reader</mods:roleTerm>
      </mods:role>
     </mods:name>
     <mods:name type="personal">
      <mods:namePart>Gidas, Basilis</mods:namePart>
      <mods:role>
       <mods:roleTerm type="text">reader</mods:roleTerm>
      </mods:role>
     </mods:name>
     <mods:name type="corporate">
      <mods:namePart>Brown University. Applied Mathematics</mods:namePart>
      <mods:role>
       <mods:roleTerm type="text">sponsor</mods:roleTerm>
      </mods:role>
     </mods:name>
     <mods:genre authority="aat">theses</mods:genre>
     <mods:abstract>This thesis is a mathematical and computational study of compositional vision. Three topics are covered: (1) ROC performance in a compositional world; (2) the construction of a
      probabilistic model for compositional structure; and (3) the construction of probabilistic model of image gray levels for a given vocabulary of elementary parts. Chapter 1 introduces
      compositional vision and a probabilistic framework for modeling hierarchy, reusability, and conditional data models. Chapter 2 focuses on theoretical questions about the ROC performance of
      various approaches to recognition in hypothetical compositional worlds. The results suggest that even sub-optimal decisions within a hierarchical framework will substantially outperform a
      decision process that does not explicitly allow for part-based decomposition. Chapter 3 focuses on the first component of the Bayesian approach to compositional vision: a prior probability model
      on hierarchical image interpretations. Non-Markovian (context-sensitive) distributions are investigated, and two theoretical questions are addressed. The existence of a class of non-Markovian
      distributions is established, and the convergence of an iterative perturbation scheme for achieving these distributions is proven. Chapter 4 focuses on the second component of the Bayesian
      approach to compositional vision: a probability model on pixel intensities conditioned on a given hierarchical structure. In particular, a generative approach to modeling object parts is
      developed through a probabilistic extension of the idea of fragment-based templates. Chapter 5 makes some conclusions and suggests future directions.</mods:abstract>
     <mods:subject authority="local">
      <mods:topic>generative</mods:topic>
     </mods:subject>
     <mods:subject authority="local">
      <mods:topic>probabilistic</mods:topic>
     </mods:subject>
     <mods:subject authority="local">
      <mods:topic>hierarchical</mods:topic>
     </mods:subject>
     <mods:subject authority="local">
      <mods:topic>pattern recognition</mods:topic>
     </mods:subject>
     <mods:subject authority="local">
      <mods:topic>template-based</mods:topic>
     </mods:subject>
     <mods:subject authority="FAST" authorityURI="http://id.worldcat.org/fast" valueURI="http://id.worldcat.org/fast/1055254"><mods:topic>Pattern perception</mods:topic></mods:subject><mods:subject authority="FAST" authorityURI="http://id.worldcat.org/fast" valueURI="http://id.worldcat.org/fast/872687"><mods:topic>Computer vision</mods:topic></mods:subject><mods:recordInfo>
      <mods:recordContentSource authority="marcorg">RPB</mods:recordContentSource>
      <mods:recordCreationDate encoding="iso8601">20091218</mods:recordCreationDate>
     </mods:recordInfo>
    <mods:language><mods:languageTerm type="code" authority="iso639-2b">eng</mods:languageTerm><mods:languageTerm type="text">English</mods:languageTerm></mods:language><mods:identifier type="doi">10.7301/Z04F1P1W</mods:identifier><mods:accessCondition type="rights statement" xlink:href="http://rightsstatements.org/vocab/InC/1.0/">In Copyright</mods:accessCondition><mods:accessCondition type="restriction on access">Collection is open for research.</mods:accessCondition><mods:typeOfResource authority="primo">dissertations</mods:typeOfResource></mods:mods>