Publication 21-CNA-013

Higher Order Ambrosio-Tortorelli Scheme With Non-Negative Spatially Dependent Parameters

Irene Fonseca
Department of Mathematical Sciences
Carnegie Mellon University
Pittsburgh, PA 15213
fonseca@andrew.cmu.edu

Pan Liu
Department of Radiology
First Medical Center of Chinese PLA General Hospital
Beijing 100853, China
dragonrider.liupan@gmail.com

Xin Yang Lu
Department of Mathematical Sciences
Lakehead University
Thunder Bay, ON, Canada
xlu8@lakeheadu.ca

Abstract: The Ambrosio-Tortorelli approximation scheme with weighted underlying metric is investigated. It is shown that it $\Gamma$-converges to a Mumford-Shah image segmentation functional depending on the weight $\omega$ dx, where $\omega$ is a special function of bounded variation, and on its values at the jumps.

Get the paper in its entirety as  21-CNA-013.pdf

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