DSpace@nitr >
National Institue of Technology- Rourkela >
Conference Papers >

Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/838

Title: Constrained Compound Markov Random Field Model for Segmentation of Color Texture and Scene Images
Authors: Panda, S
Nanda, P K
Dey, R
Keywords: Gaussian processes
Markov processes
image colour analysis
image segmentation
image texture
maximum likelihood estimation
Issue Date: 2008
Publisher: IEEE
Citation: IEEE Region 10 Conference TENCON, Hyderabad, November 19-21, 2008
Abstract: In this paper, we propose a constrained compound Markov random field model (MRF) to model color texture as well as scene images. Ohta (I1, I2, I3) color model is used as the color model for segmentation. Besides, intra plane model, the constrained model is modified to take care of inter-plane interaction as well. Hence, the model is called as double constrained compound MRF (DCCMRF) model. The problem is formulated as pixel labelling problem and the pixel labels are estimated using maximum a posteriori (MAP) criterion.The MAP estimates are obtained using hybrid algorithm. The DCCMRF model exhibited improved segmentation accuracy as compared to DCMRF, MRF, double MRF (DMRF), double Gauss MRF(DGMRF) and JSEG method. The proposed models have been successfully tested for two, four and five class problem.
URI: http://dx.doi.org/10.1109/TENCON.2008.4766604
http://hdl.handle.net/2080/838
Appears in Collections:Conference Papers

Files in This Item:

File Description SizeFormat
panda.pdf494KbAdobe PDFView/Open

Show full item record

All items in DSpace are protected by copyright, with all rights reserved.

 

Powered by DSpace Feedback