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Fast GPU implementation of large scale dictionary and sparse representation based vision problems

Pradeep Nagesh, Rahul Gowda, Baoxin Li
Arizona State University, Tempe, AZ, USA
IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2010

@conference{nagesh2010fast,

   title={Fast GPU implementation of large scale dictionary and sparse representation based vision problems},

   author={Nagesh, P. and Gowda, R. and Li, B.},

   booktitle={Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on},

   pages={1570–1573},

   issn={1520-6149},

   year={2010},

   organization={IEEE}

}

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Recently, Computer Vision problems like Face Recognition and Super-Resolution solved using sparse representation based methods with large dictionaries have shown state-of-the-art results. However such methods are computationally prohibitive for typical CPUs, especially for a large dictionary size. We present fast implementation of these methods by exploiting the massively parallel processing capabilities of a GPU within a CUDA framework, owing to its easy off-the-shelf availability and programmer friendliness. We provide details of system level design, memory management and implementation strategies. Further, we integrate the solution to the preferred scientific computational platform – MATLAB.
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