Accelerating Mean Shift Segmentation Algorithm on Hybrid CPU/GPU Platforms
Department of Computer Science and Computer Engineering, University of Arkansas
2012 International Workshop on Modern Accelerator Technologies for GIScience (MAT4GIScience 2012), 2012
@article{men2012accelerating,
title={Accelerating Mean Shift Segmentation Algorithm on Hybrid CPU/GPU Platforms},
author={Men, L. and Huang, M. and Gauch, J.},
year={2012}
}
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.
October 9, 2012 by hgpu
