Sentence examples for grain segmentation from inspiring English sources

In this paper, we take advantage of multi-angle cross-polarized microscopic images and propose a method for grain segmentation with high accuracy.

Automatic grain segmentation of sandstone is to partition mineral grains into separate regions in the thin section, which is the first step for computer aided mineral identification and sandstone classification.

The sandstone microscopic images contain a large number of mixed mineral grains where differences among adjacent grains, i.e., quartz, feldspar and lithic grains, are usually ambiguous, which make grain segmentation difficult.

Because our goal in grain segmentation was to find reasonable contrast to recognize the grain contour, we merged the hue and saturation images to reconstruct an intermediate image that displayed better separation of grains from background using the following equation.

A decisive image-merging algorithm is also developed for improving the quality of image segmentation in grain-size measurements.

Detailed results of the tomographic analysis performed on the deepest sample (667 m) from Blake Ridge are presented as 2-D and 3-D images which show several mineral constituents, the internal grain/pore microstructure, and, following segmentation into pore and grain space, a visualization of the connecting pathways through the pore-space of the sediment.

To achieve computational efficiency, the algorithm employs a fuzzy data segmentation scheme, which coarse-grains the data while retaining most of the correlation information (see Experimental Procedures, Supplemental Experimental Procedures, and Figure S1 for details).

A segmentation of the image is produced, where regions represent grains and colonies, from which morphological features such as; grain size, volume fraction of globular alpha grains and alpha colony size can be measured.

The color segmentation method needs the user to define the grain and background colors.

However, most of the grain samples in this study minimally overlapped with debris, and efforts were made to confirm accurate segmentation.

This allows the segmentation of each sample into four distinct entities, consisting of pores, organic matter, shale grains and minerals.