Biometrics and Bioinformatics

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Unsupervised Segmentation Using Map-ML Estimation


Affiliations
1 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tirunelveli, India
     

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Segmentation is an important problem of Image processing. It has attracted much attention due to its potential value for applications and its theoretical challenges. In this proposed System MAP-ML estimation is used to segment the objects in the images, The Proposed Method contains a Graph cut Algorithm to overcome computation complexity and over segmentation. This is an unsupervised algorithm which automatically segments the given input image into regions according to relevant texture and colors.

Keywords

Graph Cuts, Markov Random Fields, Maximum a Posteriori, Maximum Likelihood.
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  • Unsupervised Segmentation Using Map-ML Estimation

Abstract Views: 143  |  PDF Views: 3

Authors

M. Sorna Selvi
Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tirunelveli, India
S. Antelin Vijila
Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tirunelveli, India
M. Mohammed Ashiqa
Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tirunelveli, India

Abstract


Segmentation is an important problem of Image processing. It has attracted much attention due to its potential value for applications and its theoretical challenges. In this proposed System MAP-ML estimation is used to segment the objects in the images, The Proposed Method contains a Graph cut Algorithm to overcome computation complexity and over segmentation. This is an unsupervised algorithm which automatically segments the given input image into regions according to relevant texture and colors.

Keywords


Graph Cuts, Markov Random Fields, Maximum a Posteriori, Maximum Likelihood.