Efficient Exploitation of Heterogeneous Platforms for Images Features Extraction

Image processing algorithms present a necessary
tool for various domains related to computer vision, such as video
surveillance, medical imaging, pattern recognition, etc. However,
these algorithms are hampered by their high consumption of
both computing power and memory, which increase significantly
when processing large sets of images. In this work, we propose a
development scheme enabling an efficient exploitation of parallel
(GPU) and heterogeneous platforms (Multi-CPU/Multi-GPU), for
improving performance of single and multiple image processing
algorithms. The proposed scheme allows a full exploitation of
hybrid platforms based on efficient scheduling strategies. It
enables also overlapping data transfers by kernels executions
using CUDA streaming technique within multiple GPUs. We
present also parallel and heterogeneous implementations of
several features extraction algorithms such as edge and corner
detection. Experimentations have been conducted using a set of
high resolution images, showing a global speedup ranging from
5 to 30, by comparison with CPU implementations