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Proceedings Paper

Fast Mumford-Shah segmentation using image scale space bases
Author(s): Christopher V. Alvino; Anthony J. Yezzi
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Paper Abstract

Image segmentation using the piecewise smooth variational model proposed by Mumford and Shah is both robust and computationally expensive. Fortunately, both the intermediate segmentations computed in the process of the evolution, and the final segmentation itself have a common structure. They typically resemble a linear combination of blurred versions of the original image. In this paper, we present methods for fast approximations to Mumford-Shah segmentation using reduced image bases. We show that the majority of the robustness of Mumford-Shah segmentation can be obtained without allowing each pixel to vary independently in the implementation. We illustrate segmentations of real images that show how the proposed segmentation method is both computationally inexpensive, and has comparable performance to Mumford-Shah segmentations where each pixel is allowed to vary freely.

Paper Details

Date Published: 28 February 2007
PDF: 10 pages
Proc. SPIE 6498, Computational Imaging V, 64980F (28 February 2007); doi: 10.1117/12.715201
Show Author Affiliations
Christopher V. Alvino, Georgia Institute of Technology (United States)
Anthony J. Yezzi, Georgia Institute of Technology (United States)

Published in SPIE Proceedings Vol. 6498:
Computational Imaging V
Charles A. Bouman; Eric L. Miller; Ilya Pollak, Editor(s)

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