- Title Information
- Title
- Statistical Inference and Probabilistic Modeling in Compositional Vision
- Name:
Personal
- Name Part
- Zhang, Wei
- Role
- Role Term:
Text
- creator
- Origin Information
- Copyright Date
(keyDate="yes", encoding="w3cdtf")
- 2009
- Physical Description
- Extent
- xiii, 138 p.
- digitalOrigin
- born digital
- Note
- Thesis (Ph.D.) -- Brown University (2009)
- Name:
Personal
- Name Part
- Geman, Stuart
- Role
- Role Term:
Text
- director
- Name:
Personal
- Name Part
- Bienenstock, Elli
- Role
- Role Term:
Text
- reader
- Name:
Personal
- Name Part
- Gidas, Basilis
- Role
- Role Term:
Text
- reader
- Name:
Corporate
- Name Part
- Brown University. Applied Mathematics
- Role
- Role Term:
Text
- sponsor
- Genre (aat)
- theses
- 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.
- Subject (Local)
- Topic
- generative
- Subject (Local)
- Topic
- probabilistic
- Subject (Local)
- Topic
- hierarchical
- Subject (Local)
- Topic
- pattern recognition
- Subject (Local)
- Topic
- template-based
- Subject (FAST)
(authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/1055254")
- Topic
- Pattern perception
- Subject (FAST)
(authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/872687")
- Topic
- Computer vision
- Record Information
- Record Content Source (marcorg)
- RPB
- Record Creation Date
(encoding="iso8601")
- 20091218
- Language
- Language Term:
Code (ISO639-2B)
- eng
- Language Term:
Text
- English
- Identifier:
DOI
- 10.7301/Z04F1P1W
- Access Condition:
rights statement
(href="http://rightsstatements.org/vocab/InC/1.0/")
- In Copyright
- Access Condition:
restriction on access
- Collection is open for research.
- Type of Resource (primo)
- dissertations