Interpreting images of polyhedral objects in the presence of uncertainty
type: CreativeWork
description: This thesis addresses various problems in computer vision which are related to the interpretation of images of polyhedral objects with a special emphasis on the effects of uncertainty. Its contributions span three areas: we first present an approach to the recovery of 3D shape from a single image using line-drawing analysis and complex reflectance models. The algorithm deals explicitly with uncertainty in vertex position. We then propose an algorithm for computing the finite-resolution aspect graph of polyhedral objects. For each region of the aspect graph a representative finite-resolution aspect is computed. Neighboring regions with identical finite-resolution aspects are merged producing the finite-resolution aspect graph. Finally, we develop a probabilistic approach to object recognition. Match hypotheses are ranked by the probability that they are correct. For each hypothesis the pose of the object is recovered and the region of the pose space compatible with the image uncertainty is computed. Hypotheses which match different features of the same model reinforce each other when the corresponding uncertainty regions in the pose space have a non-empty intersection. Sets of consistent hypotheses are ranked by probability that they are the correct interpretation of the features, producing an ordering of the possible interpretations. The three algorithms have been fully implemented and examples are presented.
abstract: This thesis addresses various problems in computer vision which are related to the interpretation of images of polyhedral objects with a special emphasis on the effects of uncertainty. Its contributions span three areas: we first present an approach to the recovery of 3D shape from a single image using line-drawing analysis and complex reflectance models. The algorithm deals explicitly with uncertainty in vertex position. We then propose an algorithm for computing the finite-resolution aspect graph of polyhedral objects. For each region of the aspect graph a representative finite-resolution aspect is computed. Neighboring regions with identical finite-resolution aspects are merged producing the finite-resolution aspect graph. Finally, we develop a probabilistic approach to object recognition. Match hypotheses are ranked by the probability that they are correct. For each hypothesis the pose of the object is recovered and the region of the pose space compatible with the image uncertainty is computed. Hypotheses which match different features of the same model reinforce each other when the corresponding uncertainty regions in the pose space have a non-empty intersection. Sets of consistent hypotheses are ranked by probability that they are the correct interpretation of the features, producing an ordering of the possible interpretations. The three algorithms have been fully implemented and examples are presented.
author: Shimshoni, Ilan Moshe
language: eng
keywords: Artificial Intelligence, Computer Science
date published: 2011-05-07T11:56:03Z
date modified: 2026-07-22T22:25:12Z
provider: University of Illinois - Urbana-Champaign
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Illinois at Urbana-Champaign

PropertyValue
property ID: Handle
value: http://hdl.handle.net/2142/19070