---
res:
  bibo_abstract:
  - Complexity is a fundamental part of product design and manufacturing today, owing
    to increased demands for customization and advances in digital design techniques.
    Assembling and repairing such an enormous variety of components means that workers
    are cognitively challenged, take longer to search for the relevant information
    and are prone to making mistakes. Although in recent years deep learning approaches
    to object recognition have seen rapid advances, the combined potential of deep
    learning and augmented reality in the industrial domain remains relatively under
    explored. In this paper we introduce AR-ProMO, a combined hardware/software solution
    that provides a generalizable assistance system for identifying mistakes during
    product assembly and repair.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Hitesh
      foaf_name: Dhiman, Hitesh
      foaf_surname: Dhiman
      foaf_workInfoHomepage: http://www.librecat.org/personId=71767
  - foaf_Person:
      foaf_givenName: Sebastian
      foaf_name: Büttner, Sebastian
      foaf_surname: Büttner
      foaf_workInfoHomepage: http://www.librecat.org/personId=61868
  - foaf_Person:
      foaf_givenName: Carsten
      foaf_name: Röcker, Carsten
      foaf_surname: Röcker
      foaf_workInfoHomepage: http://www.librecat.org/personId=61525
  - foaf_Person:
      foaf_givenName: Raphael
      foaf_name: Reisch, Raphael
      foaf_surname: Reisch
  bibo_doi: 10.1145/3369457.3370919
  dct_date: 2019^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/978-1-4503-7696-9
  dct_language: eng
  dct_publisher: ACM@
  dct_subject:
  - Augmented Reality
  - Deep Learning
  dct_title: Handling Work Complexity with AR/Deep Learning@
...