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Automated separation of touching grains in digital images of thin sections - ScienceDirect
Source: sciencedirect.com
Article summary:
1. An algorithm is presented to separate touching grain sections in binary images of granular material.
2. 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.
3. The performance of the new algorithm is compared to that of the watershed segmentation method, showing improved preservation of size and shape characteristics of the granular material over the watershed segmentation method.
Article analysis:
The article provides a detailed description of an automated separation algorithm for thin section digital images, which can be used to accurately determine textural properties of granular material. The article presents a comparison between this new algorithm and existing methods such as erosion–dilation cycles or watershed segmentation, showing improved preservation of size and shape characteristics over these existing methods.
The article appears to be reliable and trustworthy, as it provides evidence for its claims in the form of laboratory laser particle sizer results which verify the grain-size distributions obtained with automated separation techniques. Furthermore, it provides a detailed description of both existing methods and its own proposed algorithm, allowing readers to understand how each works and why one may be better than another.
However, there are some potential biases that should be noted when considering this article's trustworthiness. For example, while it does provide evidence for its claims in terms of laboratory results, it does not provide any evidence from real-world applications or experiments using actual thin section digital images. Additionally, while it does compare its proposed algorithm to existing methods such as erosion–dilation cycles or watershed segmentation, it does not explore any other potential alternatives or counterarguments that could be made against its proposed solution. Finally, while it does provide a detailed description of its proposed algorithm, it does not discuss any potential risks associated with using this automated separation technique on thin section digital images (e.g., accuracy issues due to noise or other factors).