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Mobile User Interfaces for Efficient
Verification of Holograms
Andreas Hartl, Jens Grubert, Christian Reinbacher, Clemens Arth
and Dieter Schmalstieg
Graz University of Technology
How to identify fake money?
Hologram Verification Workflow
Pre-processing
1. Record reference images
and associated camera poses
under controlled lighting
Online
1. Capture relevant views
2. Compare reference view with current view
(automatic or manual)
Capture single views [1]:
guide users to individual
pre-selected views
Capturing Relevant Views
Capture many views [2]
[1] Hartl, A., Grubert, J., Schmalstieg, D., Reitmayr, G.: Mobile interactive hologram verifcation. ISMAR, pages, 2013
[2] T.-H. Park and H.-J. Kwon. Vision inspection system for holograms with mixed patterns. CASE, pages 563–567, 2010
Capture single views [1]:
naive impl. too slow
high workload
Capturing Relevant Views
Capture many views [1]:
stationary equipment or
too time consuming
[1] Hartl, A., Grubert, J., Schmalstieg, D., Reitmayr, G.: Mobile interactive hologram verifcation. ISMAR, pages, 2013
[2] T.-H. Park and H.-J. Kwon. Vision inspection system for holograms with mixed patterns. CASE, pages 563–567, 2010
?
Alignment (ALI)
Constrained Navigation (CON)
Hybrid Approach (HYB)
Summary View
User Evaluation
• 3x4 within-subjects desing
• 19 participants
• IV:
– 3 UIs
– 4 holograms (2 real, 2 fake)
• DV:
– Task completion time
– System + user decision perf.
– Workload (TLX)
– Usability (ASQ)
– UX (AttrakDiff, IMI)
Findings: Task Completion Time
0
10
20
30
40
50
60
70
80
90
Interface
Time(s)
HYB CON ALI
*
Findings: Performance + UX
Performance
 Correct: User (~80%), System (~73%)
 Including Neutral: User (~92%), System (~84%)
UX
 Nasa TLX: no sign. differences (overall: ~40/100)
 ASQ (ease-of-use, task duration), AttrakDiff (Pragmatic
and Hedonic qualities), Intrinsic Motivation  no sig.
differences
 User preference: CON (47%), ALI (26%), HYB (26%)
Summary
• New UIs for sampling regions rather than precisely
aligning 6 DOF poses can speed up the verification
process
• still too slow for most real-world applications 
• Users did not prefer the fastest UI
• Automatic matching performance poor
 better similarity metric

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Mobile User Interfaces for Efficient Verification of Holograms

  • 1. Teaser Image + Title Mobile User Interfaces for Efficient Verification of Holograms Andreas Hartl, Jens Grubert, Christian Reinbacher, Clemens Arth and Dieter Schmalstieg Graz University of Technology
  • 2. How to identify fake money?
  • 3.
  • 4. Hologram Verification Workflow Pre-processing 1. Record reference images and associated camera poses under controlled lighting Online 1. Capture relevant views 2. Compare reference view with current view (automatic or manual)
  • 5. Capture single views [1]: guide users to individual pre-selected views Capturing Relevant Views Capture many views [2] [1] Hartl, A., Grubert, J., Schmalstieg, D., Reitmayr, G.: Mobile interactive hologram verifcation. ISMAR, pages, 2013 [2] T.-H. Park and H.-J. Kwon. Vision inspection system for holograms with mixed patterns. CASE, pages 563–567, 2010
  • 6. Capture single views [1]: naive impl. too slow high workload Capturing Relevant Views Capture many views [1]: stationary equipment or too time consuming [1] Hartl, A., Grubert, J., Schmalstieg, D., Reitmayr, G.: Mobile interactive hologram verifcation. ISMAR, pages, 2013 [2] T.-H. Park and H.-J. Kwon. Vision inspection system for holograms with mixed patterns. CASE, pages 563–567, 2010 ?
  • 11. User Evaluation • 3x4 within-subjects desing • 19 participants • IV: – 3 UIs – 4 holograms (2 real, 2 fake) • DV: – Task completion time – System + user decision perf. – Workload (TLX) – Usability (ASQ) – UX (AttrakDiff, IMI)
  • 12. Findings: Task Completion Time 0 10 20 30 40 50 60 70 80 90 Interface Time(s) HYB CON ALI *
  • 13. Findings: Performance + UX Performance  Correct: User (~80%), System (~73%)  Including Neutral: User (~92%), System (~84%) UX  Nasa TLX: no sign. differences (overall: ~40/100)  ASQ (ease-of-use, task duration), AttrakDiff (Pragmatic and Hedonic qualities), Intrinsic Motivation  no sig. differences  User preference: CON (47%), ALI (26%), HYB (26%)
  • 14. Summary • New UIs for sampling regions rather than precisely aligning 6 DOF poses can speed up the verification process • still too slow for most real-world applications  • Users did not prefer the fastest UI • Automatic matching performance poor  better similarity metric