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IMAGE SEGMENTATION BY PIECEWISE CONSTANT MUMFORD-SHAH MODEL WITHOUT ESTIMATING THE CONSTANTS

Xue-cheng Tai and Chang-hui Yao
Journal of Computational Mathematics
Vol. 24, No. 3, SPECIAL ISSUE DEDICATED TO THE 70TH BIRTHDAY OF PROFESSOR LIN QUN (MAY 2006), pp. 435-443
Stable URL: http://www.jstor.org/stable/43693303
Page Count: 9
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IMAGE SEGMENTATION BY PIECEWISE CONSTANT MUMFORD-SHAH MODEL WITHOUT ESTIMATING THE CONSTANTS
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Abstract

In this work, we try to use the so-called Piecewise Constant Level Set Method (PCLSM) for the Mumford-Shah segmentation model. For image segmentation, the Mumford-Shah model needs to find the regions and the constant values inside the regions for the segmentation. In order to use PCLSM for this purpose, we need to solve a minimization problem using the level set function and the constant values as minimization variables. In this work, we test on a model such that we only need to minimize with respect to the level set function, i.e., we do not need to minimize with respect to the constant values. Gradient descent method and Newton method are used to solve the Euler-Lagrange equation for the minimization problem. Numerical experiments are given to show the efficiency and advantages of the new model and algorithms.

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