KEYWORDS: Image processing, Image filtering, Data processing, Distributed computing, Real time image processing, Data modeling, Multimedia, Time metrology, Cameras, Applied physics
This paper presents a framework to add data and task parallelism to a sequential image processing library. The library contains 3 modules, one for low-level operators, the second for intermediate-level operators and the third for high-level operators. We parallelize the low-level operators by data decomposition and we are working at adding task parallelism at the image processing application level. We validate our data parallel approach by testing it with the geometric mean filter and the multibaseline stereo vision algorithm. Experiments on a cluster of workstations show very good speedup.
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