There are several projects and missions designed to strictly observe the Sun. These projects usually produce a large amount of information embedded in images. The analysis of such information is valuable for the study and monitoring of solar storms that can affect telecommunications, for instance. The databases sizes with sun image are huge. Several projects are producing images of the Sun and exists a considerable amount of stored images. Combining image processing algorithms with parallel programming techniques we can compute such information faster and a major volume. This paper describes our parallel OpenMP-MPI hybrid solutions for processing Sun images, and our results obtained in a hybrid system, i.e. a cluster with several multi-core nodes. Specifically, we present two methods to detect and categorize solar filaments in hybrid systems: Filament DiffusionDetection based on graphs and Morph Detection, based on morphological operators. The results show that the Filament Diffusion-Detection based on graphs detects approximately 80% of the filaments, with a 326-fold speed-up over. In turn, Morph Detection detects 58% of the objects with a 54-fold increase in speed. Overall, these results show that our OpenMP-MPI combination works well for hybrid architectures, but more optimizations are needed to improve accuracy.

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Abstract-Edition of natural images usually asks for considerable user involvement, being segmentation one of the main challenges. This paper describes an unified graph-based framework for fast, precise and accurate interactive image segmentation. The method divides segmentation into object recognition, enhancement and extraction. Recognition is done by the user when markers are selected inside and outside the object. Enhancement increases the dissimilarities between object and background and Extraction separates them. Enhancement is done by a fuzzy pixel classifier and it has a great impact in the number of markers required for extraction. In view of minimizing user involvement, we focus this paper on a comparative study among popular classifiers for enhancement, conducting experiments with several natural images and seven users.

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