Exploiting Data Parallelism in the yConvex Hypergraph Algorithm for Image Representation using GPGPUs
VIT University, Chennai, India
arXiv:1307.2560 [cs.DC], (23 Jun 2013)
@article{2013arXiv1307.2560J,
author={Jha}, S. and {Agarwal}, T. and {Kanna}, B.~R.},
title={"{Exploiting Data Parallelism in the yConvex Hypergraph Algorithm for Image Representation using GPGPUs}"},
journal={ArXiv e-prints},
archivePrefix={"arXiv"},
eprint={1307.2560},
primaryClass={"cs.DC"},
keywords={Computer Science – Distributed, Parallel, and Cluster Computing, Computer Science – Computer Vision and Pattern Recognition, I.3},
year={2013},
month={jun},
adsurl={http://adsabs.harvard.edu/abs/2013arXiv1307.2560J},
adsnote={Provided by the SAO/NASA Astrophysics Data System}
}
To define and identify a region-of-interest (ROI) in a digital image, the shape descriptor of the ROI has to be described in terms of its boundary characteristics. To address the generic issues of contour tracking, the yConvex Hypergraph (yCHG) model was proposed by Kanna et al [1]. In this work, we propose a parallel approach to implement the yCHG model by exploiting massively parallel cores of NVIDIA’s Compute Unified Device Architecture (CUDA). We perform our experiments on the MODIS satellite image database by NASA, and based on our analysis we observe that the performance of the serial implementation is better on smaller images, but once the threshold is achieved in terms of image resolution, the parallel implementation outperforms its sequential counterpart by 2 to 10 times (2x-10x). We also conclude that an increase in the number of hyperedges in the ROI of a given size does not impact the performance of the overall algorithm.
July 10, 2013 by hgpu
