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SWIR_volume_estimation - MaRDI portal

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SWIR_volume_estimation (Q6720281)

From MaRDI portal





Dataset published at Zenodo repository.
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English
SWIR_volume_estimation
Dataset published at Zenodo repository.

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    The dataset consists of grayscale images containing up to four circular objects (samples) per image. The dataset follows a structured naming convention, where filenames encode the thickness of the material or the depth of holes present in the samples. Each image file follows the naming pattern cc-cc-cc-cc_0001.jpeg, where cc represents a two-digit number specifying either the material thickness or the hole depth in millimeters. The four cc values correspond to specific positions in the image: the first value represents the top-left object, the second value represents the top-right object, the third value represents the bottom-right object, and the fourth value represents the bottom-left object. If no object is present in a specific position, XX is used instead of a numerical value. The final _0001 is an incrementing counter for multiple images of the same set of objects, increasing with each occurrence. The numbers in the filenames correspond to sample specifications listed in an accompanying dataset index. The handwritten notes provided define the measurements as follows: 21 corresponds to 0.58 mm, 22 corresponds to 1.20 mm, 23 corresponds to 1.58 mm, 24 corresponds to 2.17 mm, 25 corresponds to 2.57 mm, 26 corresponds to 3.22 mm, 27 corresponds to 3.58 mm, 28 corresponds to 4.16 mm, and 29 corresponds to 4.57 mm. Similarly, hole specifications include 00 corresponding to a 5 mm hole, 01 corresponding to a 4.5 mm hole, going lineary by 0.5 mm decrease to vanule 09 corresponding to a 0.5 mm hole, and 10 corresponding to no hole. Each image displays up to four dark circular shapes representing material samples, some of which may have central holes of varying diameters. The grayscale shading might indicate variations in depth, surface texture, or material density. This dataset can be used for image processing and segmentation tasks, optical analysis of material properties, and machine learning applications related to material classification. If any additional metadata is required, it can be extracted from the naming conventions.
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    17 January 2025
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