Abstract
Random Walk (RW) is a popular algorithm and can be applied to many applications in computer vision. In this paper, a fast algorithm is proposed to solve the large linear system in RW based on adapting the Gauss-Seidel method on a multi-core embedded system. Two tables, TYPE and INDEX, are introduced to fast locate the required data for the close-form solution. The computational overhead, along with the memory requirement, to solve the linear system can be reduced greatly, thus making the RW algorithm feasible to many applications on an embedded system. In addition, the proposed fast method is parallelized for a heterogeneous multi-core embedded platform to make the most use of the benefits of the system architecture. Experimental results show that the computational overhead can be significantly reduced by the proposed algorithm.
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