Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/14582
Title: Regularizing variational methods for robust object boundary detection
Authors: Fang, Wen
Keywords: DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Issue Date: 2008
Source: Fang, W. (2008). Regularizing variational methods for robust object boundary detection. Doctoral thesis, Nanyang Technological University, Singapore.
Abstract: In this thesis, robust object boundary detection by the variational methods is studied. The variational methods fall into two categories: boundary-based and region based. This thesis focuses on the boundary-based variational methods. However, as the boundary-based variational methods depend only on the boundary information to locate the object, they are very sensitive to the disruptions to the object boundary. If the object is partially occluded by other objects or is interfered by cluttered background, the evolving curve of the variational methods may be pulled away from the real boundary and converge to the wrong place. This is regarded as the “missing boundary” problem in this thesis. In order to achieve a robust object detection, additional boundary constraints need to be incorporated into the variational methods to regularize the curve evolution. In this thesis, two new boundary constraints are proposed. The first method incorporates the temporal information into the variational methods, while the second incorporates the shape prior information.
URI: https://hdl.handle.net/10356/14582
DOI: 10.32657/10356/14582
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

Files in This Item:
File Description SizeFormat 
Teeeg0301926l.pdf3.27 MBAdobe PDFThumbnail
View/Open

Page view(s) 50

290
Updated on Aug 2, 2021

Download(s) 20

148
Updated on Aug 2, 2021

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.