2005/07/27 by Tony F. Chan, Wei Zhu · 1 citation
Computer Science · Engineering · Mathematics · #Medical Image Segmentation Techniques #3D Shape Modeling and Analysis #Image and Object Detection Techniques #Segmentation #Level set (data structures) #Prior probability #Image segmentation #Scale-space segmentation #Level set method #Artificial intelligence #Segmentation-based object categorization #Set (abstract data type) #Computer science #Active shape model #Minification #Set function #Function (biology) #Mathematics #Object (grammar) #Pattern recognition (psychology) #Term (time) #Computer vision #Mathematical optimization #Bayesian probability
paper · doi:10.1109/cvpr.2005.212
openalex publication_date 2005/07/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
We propose a level set based variational approach that incorporates shape priors into Chan-Vese's model for the shape prior segmentation problem. In our model, besides the level set function for segmentation, as in Cremers' work, we introduce another labelling level set function to indicate the regions on which the prior shape should be compared. Our model can segment an object, whose shape is similar to the given prior shape, from a background where there are several objects. Moreover, we provide a proof for a fast solution principle, which was mentioned by F. Gibou et al., and similar to the one proposed in [B. Song et al., (2002)], for minimizing Chan-Vese's segmentation model without length term. We extend the principle to the minimization of our prescribed functionals.