Abstract
Image and video processing algorithms present an important tool for various domains related to computer vision such as pattern recognition, video surveillance, medical imaging, etc. Due to the fast turning towards high definition on images and videos, the performances of these algorithms have been so hampered. Indeed, they require more resources and memory to achieve their computations. In this work, we propose a development scheme enabling an efficient exploitation of parallel (GPU) and heterogeneous (Multi-CPU/Multi-GPU) platforms, in order to improve performance of both image and video processing algorithms. This scheme enables an efficient scheduling of hybrid tasks and an effective management of heterogeneous memories. Based on this scheme, we developed heterogeneous implementations of
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