Parallelizing Image Analysis Algorithms: ANET Solution and Performances.

Several hard problems have to be addressed in order to parallelize image analysis algorithms. Indeed, at the region level, these algorithms handle irregular (and sometimes strongly dynamic) data-structures. Moreover, they often lead to an unbalanced amount of computations, which is quite impossible to foresee offline. This paper focus on the parallelization of the ANET image analysis programming environment. Thanks to graph related data structures and efficient computing primitives, ANET allows rapid image algorithm prototyping. But in return, these primitives are difficult to parallelize. We present a solution for powerful implicit parallelization of the ANET environment, without any change in the application programming interface. The ANET API is summarized and illustrated with some examples. Several parallelization experimentations are reported. The solution we propose is detailed, and results are given on complete image analysis applications. ANET appears as a powerful environment, both for its expressiveness that allows rapid prototyping and for its implicit parallelization that allows good computation time.