Accelerating Mean Shift Segmentation Algorithm on Hybrid CPU/GPU Platforms

Liang Men, Miaoqing Huang, John Gauch
Department of Computer Science and Computer Engineering, University of Arkansas
2012 International Workshop on Modern Accelerator Technologies for GIScience (MAT4GIScience 2012), 2012


   title={Accelerating Mean Shift Segmentation Algorithm on Hybrid CPU/GPU Platforms},

   author={Men, L. and Huang, M. and Gauch, J.},



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Image segmentation is a very important step in many GIS applications. Mean shift is an advanced and versatile technique for clustering-based segmentation, and is favored in many cases because it is non-parametric. However, mean shift is very computationally intensive compared with other simple methods such as k-means. In this work, we present a hybrid design of mean shift algorithm on a computer platform consisting of both CPUs and GPUs. By taking advantages of the massive parallelism and the advanced memory hierarchy on Nvidia’s Fermi GPU, the hybrid design achieves a 20x speedup compared with the pure CPU implementation when dealing with images bigger than 1024×1024 pixels.
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