Shadow-light images of simulated 25 classes of surface roughness for automatic classification

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<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Kozubal, Janusz V.</dc:creator>
  <dc:creator>Hassanat, Ahmad</dc:creator>
  <dc:creator>Tarawneh, Ahmad S.</dc:creator>
  <dc:creator>Wróblewski, Roman J.</dc:creator>
  <dc:creator>Anysz, Hubert</dc:creator>
  <dc:creator>Valença, Jónatas</dc:creator>
  <dc:creator>Júlio, Eduardo</dc:creator>
  <dc:date>2022-06-03</dc:date>
  <dc:description>Many relationships important to civil engineering depend on surface roughness (morphology). Examples are the bond strength between concrete layers, the adhesion of a wheel to the pavement, the angle of friction in the soil in contact with a wall surface, and many other cases when we deal with a material with a surface having the characteristics of a Gaussian field. Based on scans of the natural concrete surfaces subjected to different smoothing processes, theoretical models were made. The observed features of the models were grouped into 25 categories belonging to the spherical semivariogram model. Each category is described by two parameters: range (with discrete domain 0.01, 0.04, 0.08, 0.16, 0.32) and upper limits (also with discrete domain 1, 2, 4 , 8, 16) with zero trend. For all combinations of range-limit pairs, homogeneous Gaussian random fields satisfying the spatial dependence of the category were generated in R software by using the RandomFields library. In the final step, the Blender rendering software was used. 1,000 images were taken for each category type using constant angle lighting and moving the camera over the generated surface. Users can download a set of 25,000 images to build or verify the surface classifier. The generated 25 types of surfaces are also provided.</dc:description>
  <dc:identifier>https://staging-archive.materialscloud.org/record/2022.68</dc:identifier>
  <dc:identifier>doi:10.24435/materialscloud:y1-jq</dc:identifier>
  <dc:identifier>mcid:2022.68</dc:identifier>
  <dc:identifier>oai:materialscloud.org:1361</dc:identifier>
  <dc:language>en</dc:language>
  <dc:publisher>Materials Cloud</dc:publisher>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:subject>concrete</dc:subject>
  <dc:subject>Shadow-light images</dc:subject>
  <dc:subject>virtual modeling</dc:subject>
  <dc:subject>machine learning</dc:subject>
  <dc:subject>Classification</dc:subject>
  <dc:subject>Deep learning</dc:subject>
  <dc:subject>civil engineering</dc:subject>
  <dc:title>Shadow-light images of simulated 25 classes of surface roughness for automatic classification</dc:title>
  <dc:type>Dataset</dc:type>
</oai_dc:dc>