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University of Illinois at Urbana-Champaign
Graph Models and Shape Deformation for Image Segmentation
Abstract
dc:descriptionThe second part of this thesis is focused on using prior geometric information to improve the reliability and accuracy of image segmentation. Specifically, a new shape-deformation method is proposed to incorporate prior template shape information into image segmentation by deforming a given template shape to fit the detected low-level edge features in a target image. Combining the support vector machine and thin-plate splines, this method increases the robustness to the detection noise as well as the reliability to preserve the template shape topology. In addition, an efficient algorithm is developed to optimize the deformation cost function using quadratic programming.
Degree
thesis:*- Name thesis:degree_name
- Ph.D.
- Level thesis:degree_level
- Dissertation
- Discipline thesis:degree_discipline
- Electrical Engineering
- Grantor
- University of Illinois at Urbana-Champaign
- Year dc:date
- 2015
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Wang, Song
- Contributors dc:contributor
-
- Liang, Zhi-Pei
Subjects
dc:subject × 1Rights
- Language dc:language
- eng
Identifiers
dc:identifier.*- Identifier
- (MiAaPQ)AAI3070471
- OAI identifier oai:identifier
- oai:www.ideals.illinois.edu:2142/80807