On the evaluation of photometric stereo applications testing using image modifications

Franz Wotawa (Speaker), Ledio Jehaj, Nicole Brosch

Research output: Chapter in Book or Conference ProceedingsConference Proceedings with Oral Presentationpeer-review

Abstract

Computer vision is vital for various applications like object tracking for autonomous driving or quality assurance. Hence, assuring that computer vision fulfills given quality criteria is essential and requires sufficient testing. In previous work, authors introduced a testing method relying on image modifications for a photometric stereo application. Image modifications include pixel errors or the rotation of images to be analyzed, revealing a substantial impact on the computed outcome of the photometric stereo application, depending on the applied modification. This paper focuses on whether we can reproduce the impact of image modifications in a real-world setup. In particular, we compare the impact of the rotation of the analyzed sample with the rotation modification applied to the image of the sample. The comparison indicates a similar effect when using rotation, showing that testing based on image modifications is valuable for verifying computer vision applications.
Original languageEnglish
Title of host publicationICTSS 2023: Testing Software and Systems
EditorsS. Bonfanti, A. Gargantini, P. Salvaneschi
Pages40-51
Number of pages12
Volume14131
ISBN (Electronic)978-3-031-43240-8
DOIs
Publication statusPublished - 19 Sept 2023
EventICTSS 2023 : The 34th IFIP International Conference on Testing Software and Systems - Bergamo, Italy
Duration: 18 Sept 202320 Sept 2023

Publication series

NameLecture Notes in Computer Science
PublisherSpringer, Cham
Volume14131
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceICTSS 2023 : The 34th IFIP International Conference on Testing Software and Systems
Abbreviated titleICTS 2023
Country/TerritoryItaly
CityBergamo
Period18/09/2320/09/23

Research Field

  • High-Performance Vision Systems

Keywords

  • Testing computer vision applications
  • Test automation
  • Test case generation

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