Continuous graph cuts for prior-based object segmentation

In this paper we propose a novel prior-based variational object segmentation method in a global minimization framework which unifies image segmentation and image denoising. The idea of the proposed method is to convexify the energy functional of the Chan-Vese method in order to find a global minimizer, so called continuous graph cuts. The method is extended by adding an additional shape constraint into the convex energy functional in order to segment an object using prior information. We show that the energy functional including a shape prior term can be relaxed from optimization over characteristic functions to optimization over arbitrary functions followed by a thresholding at

• Type: Conference
• Year: 2008
• Where: ICPR
• Authors: Anders Heyden, Christian Gosch, Christoph Schnörr, Ketut Fundana
• Keywords: Additional Shape Constraint, Computer Vision, ICPR 2008, Object Segmentation Method, Shape Prior Term