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2013 | OriginalPaper | Chapter

19. Simultaneous Convex Optimization of Regions and Region Parameters in Image Segmentation Models

Authors : Egil Bae, Jing Yuan, Xue-Cheng Tai

Published in: Innovations for Shape Analysis

Publisher: Springer Berlin Heidelberg

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Abstract

This work develops a convex optimization framework for image segmentation models, where both the unknown regions and parameters describing each region are part of the optimization process. Convex relaxations and optimization algorithms are proposed, which produce results that are independent from the initializations and closely approximate global minima. We focus especially on problems where the data fitting term depends on the mean or median image intensity within each region. We also develop a convex relaxation for the piecewise constant Mumford-Shah model, where additionally the number of regions is unknown. The approach is based on optimizing a convex energy potential over functions defined over a space of one higher dimension than the image domain.

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previous chapter Incremental Level Set Tracking

next chapter Fluctuating Distance Fields, Parts, Three-Partite Skeletons

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Title: Simultaneous Convex Optimization of Regions and Region Parameters in Image Segmentation Models
Authors: Egil Bae
Jing Yuan
Xue-Cheng Tai
Publisher: Springer Berlin Heidelberg
Book: Innovations for Shape Analysis
Print ISBN: 978-3-642-34140-3

Electronic ISBN: 978-3-642-34141-0

Copyright Year: 2013
DOI: https://doi.org/10.1007/978-3-642-34141-0_19

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