Feature quarrels: The dempster-shafer evidence theory for image segmentation using a variational framework

Abstract

Image segmentation is the process of partitioning an image into at least two regions. Usually, active contours or level set based image segmentation methods combine different feature channels, arising from the color distribution, texture or scale information, in an energy minimization approach. In this paper, we integrate the Dempster-Shafer evidence theory in level set based image segmentation to fuse the information (and resolve conflicts) arising from different feature channels. They are further combined with a smoothing term and applied to the signed distance function of an evolving contour. In several experiments we demonstrate the properties and advantages of using the Dempster-Shafer evidence theory in level set based image segmentation. © 2011 Springer-Verlag Berlin Heidelberg.