Automated scanning and individual identification system for parts without marking or tagging

Kengo Makino, Wen Jie Duan, Rui Ishiyama, Toru Takahashi, Yuta Kudo, Pieter Jonker

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

This paper presents a fully automated system for detecting, classifying, microscopic imaging, and individually identifying multiple parts without ID-marking or tagging. The system is beneficial for product assemblers, who handle multiple types of parts simultaneously. They can ensure traceability quite easily by only placing the parts freely on the system platform. The system captures microscopic images of parts as their "fingerprints," which are matched with pre-registered images in a database to identify an individual part's information such as its serial number. We demonstrate a working prototype and interaction scenario.
Original languageEnglish
Title of host publicationProceedings of the 2018 ACM on International Conference on Multimedia Retrieval (ICMR 2018)
EditorsKiyoharu Aizawa, Michael Lew, Shin'ichi Satoh
Place of PublicationNew York, NY, USA
PublisherAssociation for Computing Machinery (ACM)
Pages509-512
ISBN (Print)978-1-4503-5046-4
DOIs
Publication statusPublished - 2018
EventICMR 2018: ACM International Conference on Multimedia Retrieval - Yokohama, Japan
Duration: 11 Jun 201814 Jun 2018

Conference

ConferenceICMR 2018: ACM International Conference on Multimedia Retrieval
CountryJapan
CityYokohama
Period11/06/1814/06/18

Keywords

  • IoT
  • Traceability
  • Image recognition
  • Individual identification
  • Classification
  • Objects localization
  • Fingerprint of Things

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  • Cite this

    Makino, K., Duan, W. J., Ishiyama, R., Takahashi, T., Kudo, Y., & Jonker, P. (2018). Automated scanning and individual identification system for parts without marking or tagging. In K. Aizawa, M. Lew, & S. Satoh (Eds.), Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval (ICMR 2018) (pp. 509-512). Association for Computing Machinery (ACM). https://doi.org/10.1145/3206025.3206088