Similarity Measures for Visual Comparison and Retrieval of Test Data in Aluminum Production

Nikolina Jekic, Belgin Mutlu, Manuela Schreyer, Steffen Neubert, Tobias Schreck

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


Monitoring, analyzing and determining the production quality in a complex and long-running process such as in the aluminum production is a challenging task. The domain experts are often overwhelmed by the flood of data being generated and collected and have difficulties to analyze and interpret the results. Likewise, experts find it difficult to identify patterns in their data that may indicate deviations and anomalies that lead to unstable processes and lower product quality. We aim to support domain experts in the production data exploration and identifying meaningful patterns. The existing research covers a broad spectrum of pattern recognition methodologies that can be potentially applied to elicit patterns in data collected from industrial production. Hence, in this paper, we further analyze the applicability of different similarity measures to effectively recognize specific ultrasonic patterns which may indicate critical process deviations in aluminum production.

Original languageEnglish
Title of host publicationIVAPP
EditorsChristophe Hurter, Helen C. Purchase, José Braz, Kadi Bouatouch
PublisherSciTePress 2013
Number of pages9
ISBN (Electronic)9789897584886
Publication statusPublished - 2021

Publication series

NameVISIGRAPP 2021 - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications


  • Aluminum Casting
  • Similarity Measures
  • Visual Analysis

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

Fields of Expertise

  • Information, Communication & Computing


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