A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parallelizing and optimizing LIP-canny using NVIDIA CUDA
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part III
International Journal of High Performance Computing Applications
Image and video processing on CUDA: state of the art and future directions
MACMESE'11 Proceedings of the 13th WSEAS international conference on Mathematical and computational methods in science and engineering
Performance models for asynchronous data transfers on consumer Graphics Processing Units
Journal of Parallel and Distributed Computing
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Nowadays, Graphics Processing Units (GPU) are emerging as SIMD coprocessors for general purpose computations, specially after the launch of nVIDIA CUDA. Since then, some libraries have been implemented for matrix computation and image processing. However, in real video applications some stages need irregular data distributions and the parallelism is not so inherent. This paper presents the parallelization of a video segmentation application on GPU hardware, which implements an algorithm for abrupt and gradual transitions detection. A critical part of the algorithm requires highly intensive computation for video frames features calculation. Results on three CUDA-enabled GPUs are encouraging, because of the significant speedup achieved. They are also compared with an OpenMP version of the algorithm, running on two platforms with multiples cores.