dc.contributor.advisor | Li, Xianping | |
dc.contributor.author | Abbas, Karrar Kadhim | |
dc.date.issued | 2020 | |
dc.date.submitted | 2020 Fall | |
dc.description | Title from PDF of title page viewed January 12, 2021 | |
dc.description | Dissertation advisor: Xianping Li | |
dc.description | Vita | |
dc.description | Includes bibliographical references (pages 69-85) | |
dc.description | Thesis (Ph.D.)--Department of Mathematics and Statistics and School of Computing and Engineering. University of Missouri--Kansas City, 2020 | |
dc.description.abstract | As the resolution of digital images increases significantly, the processing of
images becomes more challenging in terms of accuracy and efficiency. In this dissertation,
we consider image segmentation by solving a partial differential equation
(PDE) model based on the Mumford-Shah functional. We first, develop a new
anisotropic mesh adaptation (AMA) framework to improve segmentation efficiency and accuracy. In the AMA framework, we incorporate an anisotropic mesh adaptation
for image representation and a nite element method for solving the PDE model.
Comparing to traditional algorithms solved by the finnite difference method, our AMA
framework provides faster and better results without the need for re-sizing the images
to lower quality. We also extend the algorithm to segment images with multiple
regions.
We also improve the well-known Chan-Vese model by developing a locally
enhanced Chan-Vese (LECV) model. Our LECV model incorporates a newly define
signed pressure force (SPF) function, which is built upon the local image information.
The SPF function helps to attract the contour curve to the object boundaries for images with inhomogeneous intensities. The proposed LECV model, together with the
AMA segmentation framework can successfully segment the image with or without
inhomogeneous intensities. While most other segmentation methods only work on low-resolution
images, our LECV model is successfully applied to high-resolution images,
with improved efficiency and accuracy. | |
dc.description.tableofcontents | Introduction -- PDE-Based Image Segmentation -- Background and Literature review -- AMA Segmentation Method -- LECV Model for Image Segmentation -- Conclusion and discussion | |
dc.format.extent | xii, 86 pages | |
dc.identifier.uri | https://hdl.handle.net/10355/79681 | |
dc.subject.lcsh | Digital images | |
dc.subject.lcsh | Image processing -- Digital techniques | |
dc.subject.lcsh | Image segmentation | |
dc.subject.other | Dissertation -- University of Missouri--Kansas City -- Mathematics | |
dc.subject.other | Dissertation -- University of Missouri--Kansas City -- Computer science | |
dc.title | Anisotropic Mesh Adaptation for Image Segmentation based on Partial Differential Equations | |
thesis.degree.discipline | Mathematics (UMKC) | |
thesis.degree.discipline | Telecommunications and Computer Networking (UMKC) | |
thesis.degree.grantor | University of Missouri--Kansas City | |
thesis.degree.level | Doctoral | |
thesis.degree.name | Ph.D. (Doctor of Philosophy) | |