Date on Master's Thesis/Doctoral Dissertation
8-2016
Document Type
Doctoral Dissertation
Degree Name
Ph. D.
Department
Mathematics
Degree Program
Applied and Industrial Mathematics, PhD
Committee Chair
Xu, Yongzhi Steve
Committee Co-Chair (if applicable)
Sahoo, Prasanna
Committee Member
Sahoo, Prasanna
Committee Member
Hu, Changbing
Committee Member
Lorenz, Douglas
Author's Keywords
partial; differential; equations; image; segmentation
Abstract
Two hybrid image segmentation models that are able to process a wide variety of images are proposed. The models take advantage of global (region) and local (edge) data of the image to be segmented. The first one is a region-based PDE model that incorporates a combination of global and local statistics. The influence of each statistic is controlled using weights obtained via an asymptotically stable exponential function. Through incorporation of edge information, the second model extends the capabilities of a strictly region-based variational formulation, making it able to process more general images. Several examples are provided showing the improvements of the proposed models over recent methods along with an application to dermoscopy imaging. A number of avenues for future research are also discussed.
Recommended Citation
Paniagua Mejia, Carlos M., "Mathematical hybrid models for image segmentation." (2016). Electronic Theses and Dissertations. Paper 2534.
https://doi.org/10.18297/etd/2534
