Distributed Agreement Based Ml Approximation

In this manuscript we suggest a fast adaptive distributed method for maximumlikelihood approximation (MLA) in multiple view object localization problem. Forthis purpose, we use up to scale property of projective geometry and by definingcoefficients for convergence criterion, we increase the convergence speed of theconsensus algorithm. We try to present a mathematical model for the problem. Weuse two types of error function. The proposed method uses maximum likelihood forobtaining its best parameters. Our approach utilizes up to scale property inprojective geometry to reach the consensus quickly. The difference between nodes values and meanwhile consensus values are evaluated by two error functions. Toestimate consensus value in the second error function, we used local weightedaverage of each node. At the last of the paper, we prove our claims by experimentalresults

Distributed Agreement Based Ml Approximation Keywords:

Maximum Likelihood Approximation , Data Fusion , Consensus Algorithm , Homography