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Graph Cuts Method for Unsupervised Multiphase Segmentation


Affiliations
1 Karunya University, India
2 Department of Computer Science at Karunya University, Coimbatore, India
     

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Here we introduce a novel approach for multiphase image segmentation. The approach presents a unified framework that unifies three basic segmentation approaches; level set methods, graph cut algorithms and unsupervised multiphase segmentation. Here we have presented an image segmentation approach that have the advantages of segmenting the image into multiple phases where the favorable number of segments the image to be segmented will be automatically chooses by the algorithm itself. The unsupervised multiphase segmentation will provide the separation of image into favorable number of multiple phases. Our main objective is to segment the image robust to noise, blurred edges and topology changes and to achieve global optimization and speed. Here we are segmenting the image by constructing graph on the basis of the intensity values of each pixel in the image. The graph cuts method will provide robustness towards noise and the brute force numerical algorithm for unsupervised multiphase segmentation will provide fast and efficient segmentation and global optimization.

Keywords

Phase, Balance, Graph Cuts.
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  • Graph Cuts Method for Unsupervised Multiphase Segmentation

Abstract Views: 214  |  PDF Views: 1

Authors

Lydia Liz Lukose
Karunya University, India
Niya Joseph
Karunya University, India
Kethsy Prabavathy
Department of Computer Science at Karunya University, Coimbatore, India

Abstract


Here we introduce a novel approach for multiphase image segmentation. The approach presents a unified framework that unifies three basic segmentation approaches; level set methods, graph cut algorithms and unsupervised multiphase segmentation. Here we have presented an image segmentation approach that have the advantages of segmenting the image into multiple phases where the favorable number of segments the image to be segmented will be automatically chooses by the algorithm itself. The unsupervised multiphase segmentation will provide the separation of image into favorable number of multiple phases. Our main objective is to segment the image robust to noise, blurred edges and topology changes and to achieve global optimization and speed. Here we are segmenting the image by constructing graph on the basis of the intensity values of each pixel in the image. The graph cuts method will provide robustness towards noise and the brute force numerical algorithm for unsupervised multiphase segmentation will provide fast and efficient segmentation and global optimization.

Keywords


Phase, Balance, Graph Cuts.