Integration of Local Chan Vase Along with Optimization Techniques for Segmentation

A local region-based Chan-Vese model for image segmentation

In this paper, a new region-based active contour model, namely local region-based Chan–Vese (LRCV) model, is proposed for image segmentation. By considering the image local characteristics, the proposed model can effectively and efficiently segment images with intensity inhomogeneity. To reduce the dependency on manual initialization in many active contour models and for an automatic segmentati...

An efficient local Chan-Vese model for image segmentation

Article history: Received 28 September 2008 Received in revised form 14 May 2009 Accepted 2 August 2009

Chan-Vese Segmentation

While many segmentation methods rely heavily in some way on edge detection, the “Active Contours Without Edges” method by Chan and Vese [7, 9] ignores edges completely. Instead, the method optimally fits a two-phase piecewise constant model to the given image. The segmentation boundary is represented implicitly with a level set function, which allows the segmentation to handle topological chang...

Image Segmentation Using the Chan-Vese Algorithm

Mean Shift Segmentation - Evaluation of Optimization Techniques

The mean shift algorithm is a powerful clustering technique, which is based on an iterative scheme to detect modes in a probability density function. It has been utilized for image segmentation by seeking the modes in a feature space composed of spatial and color information. Although the modes of the feature space can be efficiently calculated in that scheme, different optimization techniques ...