skip to main content
10.1145/3563137.3563167acmotherconferencesArticle/Chapter ViewAbstractPublication PagesdsaiConference Proceedingsconference-collections
research-article

“Hungarian” Image (Differencing) Descriptor

Published:25 May 2023Publication History

ABSTRACT

Cultural or natural heritage digitization has become an essential part of the knowledge economy in the EU. We propose a tool to improve the accessibility of hi-tech devices and the sustainability of cooperation in multidisciplinary teams in the first scanning session. The CRUSE scanning beginners may take into account a novel image descriptor of moderate size for practical applications, e.g. differencing similar CRUSE scans. Our approach combines a set of Harris corners with the Hungarian algorithm to achieve an informative visual representation in the form of a planar subgraph. We compare, on selected use-cases, the solution quality and/or disadvantages. The key practical contribution of our research is an original approach for an alternative way of image structure understanding, named "polygon shape descriptor". Our innovation is about transforming the image differencing using Harris corners and Hungarian edges for support and/or speed-up of visual comparison of very similar scans. Methodologically, we interrelate computer vision and computational geometry to enhance hi-tech accessibility in virtual reality classes and student projects.

References

  1. Otakar Borůvka. 1926. O jistém problému minimálním (About a certain minimal problem). Práce Moravské přírodovědecké společnosti.Google ScholarGoogle Scholar
  2. Paula Budzáková. 2016. Lokálne príznaky vo farebných obrazoch. (Local features in color images). Master’s thesis. Comenius University.Google ScholarGoogle Scholar
  3. Yi Cao. 2011. Hungarian algorithm for linear assignment problems (V2. 3). MATLAB Central File Exchange(2011).Google ScholarGoogle Scholar
  4. Mike Chambers. 2018. Picture element, About Our Scans. http://www.pictureelement.com/aboutcruse.phpGoogle ScholarGoogle Scholar
  5. CRUSE Spezialmaschinen GmbH 2018. Cruse software CSx 3.9. CRUSE Spezialmaschinen GmbH.Google ScholarGoogle Scholar
  6. Malcolm Daniel. 2004. Daguerre (1787–1851) and the Invention of Photography. https://www.metmuseum.org/toah/hd/dagu/hd_dagu.htmGoogle ScholarGoogle Scholar
  7. Bohdal et al.2019. Adaptive Scanning of Diverse Heritage Originals like Synagogue Interior, Empty Rare Papers or Herbarium Items from the 19 th Century. In Proceedings of the 18th Conference on Applied Mathematics (APLIMAT 2019). 72–82.Google ScholarGoogle Scholar
  8. Andrej Ferko, Jerguš Moravčík, and Ivana Kolingerová. 2016. Souhvězdí jako podgrafy triangulací. Pokroky matematiky, fyziky a astronomie 61, 1 (2016), 14–20.Google ScholarGoogle Scholar
  9. Rajiv Gupta and Richard I Hartley. 1997. Linear pushbroom cameras. IEEE Transactions on pattern analysis and machine intelligence 19, 9(1997), 963–975.Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Chris Harris, Mike Stephens, 1988. A combined corner and edge detector. In Alvey vision conference, Vol. 15. Citeseer, 10–5244.Google ScholarGoogle Scholar
  11. Jana Hojstričová. 2014. Renesancia fotografie 19. storočia. VSVU Bratislava.Google ScholarGoogle Scholar
  12. Image Permanent Institute. 2022. Graphic Atlas, Ambrotype. http://www.graphicsatlas.org/identification/?process_id=283Google ScholarGoogle Scholar
  13. Harold W Kuhn. 1955. The Hungarian method for the assignment problem. Naval research logistics quarterly 2, 1-2 (1955), 83–97.Google ScholarGoogle Scholar
  14. Andrej Kulhány. 2014. Digitalizačné centrum. Pracovné postupy. Technológie 2D. https://sites.google.com/site/andrejkulhany/digitalizacnecentrum/2d-technologieGoogle ScholarGoogle Scholar
  15. Bertrand Lavédrine, Michel Frizot, Jean-Paul Gandolfo, and Sibylle Monod. 2009. Photographs of the past: process and preservation. Getty Publications.Google ScholarGoogle Scholar
  16. Franco P Preparata and Michael I Shamos. 2012. Computational geometry: an introduction. Springer Science & Business Media.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Sarthak Sharma, Junaid Ahmed Ansari, J Krishna Murthy, and K Madhava Krishna. 2018. Beyond pixels: Leveraging geometry and shape cues for online multi-object tracking. In 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 3508–3515.Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Tinne Tuytelaars, Krystian Mikolajczyk, 2008. Local invariant feature detectors: a survey. Foundations and trends® in computer graphics and vision 3, 3(2008), 177–280.Google ScholarGoogle Scholar
  19. Chee Sun Won, Dong Kwon Park, and Soo-Jun Park. 2002. Efficient use of MPEG-7 edge histogram descriptor. ETRI journal 24, 1 (2002), 23–30.Google ScholarGoogle ScholarCross RefCross Ref
  20. Wang Zhou. 2004. Image quality assessment: from error measurement to structural similarity. IEEE transactions on image processing 13 (2004), 600–613.Google ScholarGoogle Scholar

Index Terms

  1. “Hungarian” Image (Differencing) Descriptor

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in
        • Published in

          cover image ACM Other conferences
          DSAI '22: Proceedings of the 10th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion
          August 2022
          237 pages
          ISBN:9781450398077
          DOI:10.1145/3563137

          Copyright © 2022 ACM

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 25 May 2023

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article
          • Research
          • Refereed limited

          Acceptance Rates

          Overall Acceptance Rate17of23submissions,74%
        • Article Metrics

          • Downloads (Last 12 months)19
          • Downloads (Last 6 weeks)2

          Other Metrics

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        HTML Format

        View this article in HTML Format .

        View HTML Format