@article{(Open Science Index):https://publications.waset.org/pdf/2586,
	  title     = {Multiple Regression based Graphical Modeling for Images },
	  author    = {Pavan S. and  Sridhar G. and  Sridhar V.},
	  country	= {},
	  institution	= {},
	  abstract     = {Super resolution is one of the commonly referred inference problems in computer vision. In the case of images, this problem is generally addressed using a graphical model framework wherein each node represents a portion of the image and the edges between the nodes represent the statistical dependencies. However, the large dimensionality of images along with the large number of possible states for a node makes the inference problem computationally intractable. In this paper, we propose a representation wherein each node can be represented as acombination of multiple regression functions. The proposed approach achieves a tradeoff between the computational complexity and inference accuracy by varying the number of regression functions for a node.
},
	    journal   = {International Journal of Aerospace and Mechanical Engineering},
	  volume    = {1},
	  number    = {2},
	  year      = {2007},
	  pages     = {469 - 472},
	  ee        = {https://publications.waset.org/pdf/2586},
	  url   	= {https://publications.waset.org/vol/2},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 2, 2007},
	}