Exploiting Data Parallelism in the yConvex Hypergraph Algorithm for Image Representation using GPGPUs
This work provides a domain-specific speedup for image processing tasks, particularly in satellite imagery analysis, but is incremental as it applies an existing parallel computing method to an existing algorithm.
The authors tackled the problem of accelerating the yConvex Hypergraph algorithm for image region-of-interest shape description by implementing a parallel version using CUDA on GPGPUs, achieving a 2x to 10x speedup over the serial implementation for high-resolution images.
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.