@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}, }