This paper proposes a parallel image segmentation algorithm for Graphical Processing Units (GPU) that follows the region-growing paradigm. The proposal can be regarded as a parallel version of a sequential algorithm widely used by the Geographic Object Based Image Analysis community (GEOBIA). In relation to the sequential version this, work presents new attributes to characterize segments' morphological heterogeneity, whose computation can be performed by GPUs more efficiently than the original ones. Two variants of the parallel algorithm with distinct heuristics for the selection of adjacent segments to be merged in each iteration are described. Aiming at exploring the potential of GPUs for the parallel execution of fine grained threads, the new parallel method assigns a thread to each image pixel. This also contributes to better load balance among the GPU processors. A detailed experimental analysis using a simple GPU upon four different test images has shown that the parallel algorithm may run 8 times faster than its sequential counterpart or even more than that.
Parallel Segmentation; GPU; GEOBIA
