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Author Vineet, Vibhav ♦ Narayanan, P. J.
Source CiteSeerX
Content type Text
File Format PDF
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Multilabel Mrfs ♦ Incremental Expansion ♦ Fast Performance ♦ Energy Function ♦ Stereo Correspondence ♦ Atomic Operation ♦ Grid Structure ♦ Many Vision Problem ♦ Standard Datasets ♦ Tsukuba Image ♦ Discrete Mrf ♦ Incremental Expansion Algorithm ♦ Energy Minimization ♦ Various Vision Problem ♦ Basic Push-relabel Implementation ♦ Control Loop ♦ High-performance Multilabel Mrf Optimization ♦ Good Parallelism ♦ Graph Cut
Abstract Abstract. Many vision problems map to the minimization of an energy function over a discrete MRF. Fast performance is needed if the energy minimization is one step in a control loop. In this paper, we present the incremental α-expansion algorithm for high-performance multilabel MRF optimization on the GPU. Our algorithm utilizes the grid structure of the MRFs for good parallelism on the GPU. We improve the basic push-relabel implementation of graph cuts using the atomic operations of the GPU and by processing blocks stochastically. We also reuse the flow using reparametrization of the graph from cycle to cycle and iteration to iteration for fast performance. We show results on various vision problems on standard datasets. Our approach takes 950 milliseconds on the GPU for stereo correspondence on Tsukuba image with 16 labels compared to 5.4 seconds on the CPU. 1
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article