Abstract
ABSTRACT. Image processing algorithms present a necessary tool for various domains relatedto computer vision. These algorithms are hampered by their high consumption of computingtimes when processing large sets of high resolution images. In this work, we propose a deve-lopment scheme enabling an efficient exploitation of parallel (GPU) and heterogeneous (Multi-CPU/Multi-GPU) platforms, in order to improve performance of image processing algorithms.The proposed scheme enables an efficient scheduling of hybrid tasks and an effective manage-ment of heterogeneous memories. We present also parallel and hybrid implementations of edgeand corner detection methods. Experimental results showed a global speedup ranging from 5to 25, when processing different sets of images, by comparison with CPU implementations. MOTS-CLES : calcul heterogene, GPU, traitement d’images, detection des coins et contours. KEYWORDS: heterogeneous computing, GPU, image processing, corner and edge detection. DOI:10.3166/TSI.31.1183-1203 c 2012 Lavoisier
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