Multi-camera Scene Reconstruction via Graph Cuts
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Interactive Graph Cut Based Segmentation with Shape Priors
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Semiautomatic segmentation with compact shape prior
Image and Vision Computing
Star Shape Prior for Graph-Cut Image Segmentation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Image Segmentation by Branch-and-Mincut
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
Graph Cuts Segmentation with Geometric Shape Priors for Medical Images
SSIAI '08 Proceedings of the 2008 IEEE Southwest Symposium on Image Analysis and Interpretation
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This paper presents a novel method to apply shape priors adaptively in graph cut image segmentation. By incorporating shape priors adaptively, we provide a flexible way to impose the shape priors selectively at pixels where image labels are difficult to determine during the graph cut segmentation. This is in contrast to the use of shape priors indiscriminatively at all pixels in existing image segmentation approaches, which may fail if the parameters for the shape prior term are not chosen appropriately. We integrate the proposed method in two existing graph cut image segmentation algorithms, one with shape template and the other with the star shape prior. To determine the need for a shape prior at each pixel, our experiments make use of either the original image or an enhanced version of the original image by smoothing. Experimental results in multiple application domains demonstrate the generality and superior performance of our adaptive shape prior method.