An Improved Algorithm for the Piecewise-Smooth Mumford and Shah Model in Image Segmentation

An Improved Algorithm for the Piecewise-Smooth Mumford and Shah Model in Image Segmentation

An improved algorithm for the piecewise-smooth Mumford and Shah functional is presented. Compared to the previous work of Chan and Vese, and Choi et al., extensions of the key functions u(+/-) are replaced by updating the level set function based on an artificial image that is composed of the diffused image and the original image. The low convergence problem of the classical algorithm is effici...

Fast Segmentation for the Piecewise Smooth Mumford-Shah Functional

This paper is concerned with an improved algorithm based on the piecewise-smooth Mumford and Shah (MS) functional for an efficient and reliable segmentation. In order to speed up convergence, an additional force, at each time step, is introduced further to drive the evolution of the curves instead of only driven by the extensions of the complementary functions + u and − u . In our scheme, furth...

The Piecewise Smooth Mumford-Shah Functional on an Arbitrary Graph

The Mumford-Shah functional has had a major impact on a variety of image analysis problems, including image segmentation and filtering, and, despite being introduced over two decades ago, it is still in widespread use. Present day optimization of the Mumford-Shah functional is predominated by active contour methods. Until recently, these formulations necessitated optimization of the contour by ...

A Multiscale Algorithm for Mumford-Shah Image Segmentation

We propose a new multiscale procedure for image segmentation that is based on the algorithm introduced by Song and Chan in [11]. After pointing out the presence of spurious stationary states for the original algorithm on certain types of images, we explain how the proposed procedure abates this issue and leads to a faster segmentation method.

Image segmentation using level sets and the piecewise-constant Mumford-Shah model