Shot boundary detection using Zernike moments in multi-GPU multi-CPU architectures
Journal of Parallel and Distributed Computing
Efficient data partitioning for the GPU computation of moment functions
Journal of Parallel and Distributed Computing
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Zernike moments are trascendental digital image descriptors used in many application areas like biomedical image processing and computer vision due to their good properties of orthogonality and rotation invariance. However, their computation is too expensive and limits its application in practice, overall when real-time constraints are imposed. This work introduces a novel approach to the high-performance computation of Zernike moments using CUDA on graphics processors. The proposed method is applicable to the computation of an individual Zernike moment as well as a set of Zernike moments of a given order, and it is compared against three of the fastest implementations performed on CPUs over the last decade. Our experimental results on a commodity PC reveal up to 5脳 faster execution times on a GeForce 8800 GTX against the best existing implementation on a Pentium 4 CPU.