Budai, Ádám

Csorba, Kristóf

Budai Ádám

Csorba Kristóf

University of Szeged

RJMCMC Optimization of marble thin section image segmentationsÁdám Budai, Kristóf CsorbaThe project GrainAutLine [1] addresses the issue of easing the analysis procedure of marblethin section images which usually involves time consuming manual work. The traditionaland the actually widespread method to extract valuable information about the images startsby drawing the borders of the grains by hand. These are then used to produce grain sizehistograms and further statistics about the grains and their neighborhood. Experts use theseresults to draw conclusions about the provenance of the sample, the quality, and a lot of otherproperties. The project is about creating a software especially for geologists which enables themto access the required information in an automatic way (further endeavor) or at least semi–automatic way (closer purpose). The automation involves the following features: identifyingthe grains using image segmentation methods, and extracting statistic related information. Thefull automation is difficult due to the fact that marble grains are not artificial formations andthey can show anomalies even an expert may be confused about. To eliminate uncertaintycaused by the difficult cases the program lets the user make some interventions and correctmistakes. In this work a sub–project of GrainAutLine is shown which deals with only theimage segmentation.The proposed algorithm presented in this paper is for the segmentation of the grains in aspecial situation when a lot of twin crystals pose a challenge for the traditional algorithms likeadaptive thresholding. A twin crystal is the result of more small crystals when they melt intoa bigger one under special circumstances, as this leads to very characteristic patterns insidethe grains which need to be distinguished from the true grain boundary lines. The basic ideais the over–segmentation of the image with another algorithm which is already available inthe software. The over–segmented picture contains many little segments called blobs and thealgorithm structures the blobs into bigger groups called superblobs. The concrete superblobsgive a possible configuration of the system. The goal is to change this configuration until thesuperblobs match the real grains. The change occurs according to an energy function whichdepends on the configuration. The better the configuration, the smaller the total energy. [2] Thecapability of the algorithm revolves around the good choice of the energy function. Finally, theremaining task is the minimization of the energy function.The energy function is the sum of four energy terms. Each term grabs a concrete character-istic of the shape of the grains: (1) the fact that grains tend to be convex, that (2) the straightparallel lines indicate twin crystals. (3) The actual technique relies on the user’s interventionin some extent because the user is required to mark every grain in the input picture, and that(4) grains do not entirely surround other grains. The proposed algorithm uses the Reversible-jump Markov Chain Monte Carlo (RJMCMC) optimization method [3] in order to change theconfiguration of the superblobs until the energy function reaches its minimum point. The algo-rithm was tested on both artificial and real images and the profile of its performance in terms ofprocessing time, accuracy and convergence speed. The results have proven that the concept ofthis algorithm can solve the segmentation of marble thin sections even in the presence of twincrystals.AcknowledgementsThis work has been supported by the Department of Automation and Applied Informatics atBudapest University of Technology and Economics.14References[1] Kristóf Csorba, Lilla Barancsuk, Balázs Székely, Judit Zoldfoldi (2015). A Supervised GrainBoundary Extraction Tool Supported by Image Processing and Pattern Recognition, AS-MOSIA XI. International Conference .[2] Yann Lecun et. al. (2006). A tutorial on energy based learning, MIT Press.[3] C. M. Bishop (2006). Pattern Recognition and Machine Learning, Springer.15

RJMCMC optimization of marble thin section image segmentations

http://acta.bibl.u-szeged.hu/61707/1/cscs_2016_015-016.pdf

RJMCMC optimization of marble thin section image segmentations

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