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2018 | OriginalPaper | Chapter

Discretized Convex Relaxations for the Piecewise Smooth Mumford-Shah Model

Authors : Christopher Zach, Christian Häne

Published in: Energy Minimization Methods in Computer Vision and Pattern Recognition

Publisher: Springer International Publishing

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Abstract

The Mumford-Shah model for image formation is an important, but also difficult energy functional. In this work we focus on several approaches based on convex relaxation operating on a discretized image domain. Existing methods typically use discretized intensity labels, but in this work we propose to retain the continuous label structure. To this end we employ a recently proposed framework for a new convex relaxation of the Mumford-Shah functional. Numerical results illustrate the performance of the various approaches.

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previous chapter Geometric Image Labeling with Global Convex Labeling Constraints
next chapter A Projected Gradient Descent Method for CRF Inference Allowing End-to-End Training of Arbitrary Pairwise Potentials
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Metadata
Title
Discretized Convex Relaxations for the Piecewise Smooth Mumford-Shah Model
Authors
Christopher Zach
Christian Häne
Copyright Year
2018
Book
Energy Minimization Methods in Computer Vision and Pattern Recognition

Print ISBN: 978-3-319-78198-3

Electronic ISBN: 978-3-319-78199-0

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-78199-0_36

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