Bhusnurmath, Arvind

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Now showing 1 - 4 of 4
  • Publication
    Solving Stereo Matching Problems Using Interior Point Methods
    (2008-06-01) Taylor, Camillo J; Bhusnurmath, Arvind
    This paper describes an approach to reformulating the stereo matching problem as a large scale Linear Program. The approach proceeds by approximating the match cost function associated with each pixel with a piecewise linear convex function. Regularization terms related to the first and second derivative of the disparity field are also captured with piecewise linear penalty terms. The resulting large scale linear program can be tackled using interior point methods and the associated Newton Steps involve matrices that reflect the structure of the underlying pixel grid. The proposed scheme effectively exploits the structure of these matrices to solve these linear systems efficiently.
  • Publication
    Solving Image Registration Problems Using Interior Point Methods
    (2008-10-12) Taylor, Camillo J; Bhusnurmath, Arvind
    This paper describes a novel approach to recovering a parametric deformation that optimally registers one image to another. The method proceeds by constructing a global convex approximation to the match function which can be optimized using interior point methods. The paper also describes how one can exploit the structure of the resulting optimization problem to develop efficient and effective matching algorithms. Results obtained by applying the proposed scheme to a variety of images are presented.
  • Publication
    Graph Cuts via l1 Norm Minimization
    (2008-10-01) Bhusnurmath, Arvind; Taylor, Camillo J
    Graph cuts have become an increasingly important tool for solving a number of energy minimization problems in computer vision and other fields. In this paper, the graph cut problem is reformulated as an unconstrained l1 norm minimization that can be solved effectively using interior point methods. This reformulation exposes connections between graph cuts and other related continuous optimization problems. Eventually, the problem is reduced to solving a sequence of sparse linear systems involving the Laplacian of the underlying graph. The proposed procedure exploits the structure of these linear systems in a manner that is easily amenable to parallel implementations. Experimental results obtained by applying the procedure to graphs derived from image processing problems are provided.
  • Publication
    Solving the Graph Cut Problem via l1 Norm Minimization
    (2007-01-01) Bhusnurmath, Arvind; Taylor, Camillo J
    Graph cuts have become an increasingly important tool for solving a number of energy minimization problems in computer vision and other fields. In this paper, the graph cut problem is reformulated as an unconstrained l1 norm minimization. This l1 norm minimization can then be tackled by solving a sequence of sparse linear systems involving the Laplacian of the underlying graph. The proposed procedure exploits the structure of these linear systems and can be implemented effectively on modern parallel architectures. The paper describes an implementation of the algorithm on a GPU and discusses experimental results obtained by applying the procedure to graphs derived from image processing problems.