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Accelerating Computer Vision Algorithms Using OpenCL on Mobile GPU – A Case Study

Guohui Wang, Yingen Xiong, Jay Yun, Joseph R. Cavallaro
ECE Department, Rice University, Houston, Texas
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013), 2013
@INPROCEEDINGS{ICASSP2013_Wang,

   author={Guohui Wang and Yingen Xiong and Jay Yun and Joseph R. Cavallaro},

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

   title={Accelerating Computer Vision Algorithms Using {OpenCL} on the Mobile {GPU} – A Case Study},

   year={2013},

   month={may}

}

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Recently, general-purpose computing on graphics processing units (GPGPU) has been enabled on mobile devices thanks to the emerging heterogeneous programming models such as OpenCL. The capability of GPGPU on mobile devices opens a new era for mobile computing and can enable many computationally demanding computer vision algorithms on mobile devices. As a case study, this paper proposes to accelerate an exemplar-based inpainting algorithm for object removal on a mobile GPU using OpenCL. We discuss the methodology of exploring the parallelism in the algorithm as well as several optimization techniques. Experimental results demonstrate that our optimization strategies for mobile GPUs have significantly reduced the processing time and make computationally intensive computer vision algorithms feasible for a mobile device. To the best of the authors’ knowledge, this work is the first published implementation of general-purpose computing using OpenCL on mobile GPUs.
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