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

Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/718

Title: Constrained Markov Random Field Model for Color and Texture Image Segmentation
Authors: Dey, R
Nanda, P K
Panda, S
Keywords: Markov processes
image colour analysis
image segmentation
image texture
simulated annealing
Issue Date: 2008
Publisher: IEEE
Citation: International Conference on Signal Processing, Communications and Networking, 2008. ICSCN '08. 4-6 Jan. 2008, Anna Unviersity, Chennai, P 317-322
Abstract: In this paper, the problem of color image segmentation is addressed as a pixel labeling problem. The observed color image is assumed to be the degraded version of the image labels. We have proposed a new Markov random field (MRF) model known as constrained MRF (CMRF) model to model the unknown image labels and Ohta (I1I2I3) model is used as the color model. The unique feature of the proposed CMRF model is found to posses a unifying feature of modeling scene and texture images as well. The labels are estimated using maximum a posteriori (MAP) estimation criterion. A hybrid algorithm is proposed to obtain the MAP estimate and the performance of the algorithm is found to be better than that of using simulated annealing (SA) algorithm. The performance of the proposed model is compared with JSEG method and the proposed model is found to be better than JSEG method.
Description: Copyright for the paper belongs to IEEE
URI: http://dx.doi.org/10.1109/ICSCN.2008.4447211
http://hdl.handle.net/2080/718
Appears in Collections:Conference Papers

Files in This Item:

File Description SizeFormat
pkn1.pdf1882KbAdobe PDFView/Open

Show full item record

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

 

Powered by DSpace Feedback