Automated evaluation of volumetric grain-size distribution from thin-section images
Computers & Geosciences
The image processing handbook (2nd ed.)
The image processing handbook (2nd ed.)
Interactive characterization of granulated materials
Computers & Geosciences
Detecting grain boundaries in deformed rocks using a cellular automata approach
Computers & Geosciences
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The determination of textural properties of granular material with image analysis is generally troubled by the fact that touching grain sections merge into single features. Without separation of these touching grain sections, the textural properties derived from the images contain substantial bias. Existing methods for separating touching grains, like erosion-dilation cycles or watershed segmentation, are time-consuming and/or alter the textural properties of the grain sections analyzed. An alternative computer algorithm is presented to separate touching grain sections in binary images of granular material. The algorithm detects characteristic sharp contact wedges in the outline of touching grain sections and creates an intersection after checking if the angle of the contact wedge is smaller than a user-defined threshold value. The performance of the new algorithm is compared to that of the watershed segmentation method. The resulting grain-size distributions after applying automated separation techniques, were verified with the size distribution obtained with a laboratory laser particle sizer. The algorithm shows improved preservation of size and shape characteristics of the granular material over the watershed segmentation method.