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QR Code Augmented Reality Tracking with Merging on
Conventional Marker based Backpropagation Neural
Network
GIA MUHAMAD AGUSTA, KHODIJAH HULLIYAH, ARINI, RIZAL BROER BAHAWERES
DEPARTMENT OF COMPUTER SCIENCE
• Introduction about Augmented Reality and QR Code
• A simple approach to handle QR Code as Marker
• Marker Detection Algorithm
• Perspective Projection
• Feature Extraction
• Backpropagation Approach
• Experimental Result
• Conclusion and Future Work
PRESENTATIONSUMMARY
GiaMuhammad | Depok, December 1st – 2nd, 2012
International Conference on Advanced Computer Science and Information Systems (ICACSIS) 2012 | Crystal of Knowledge, University of Indonesia
• Augmented Reality
• QR Code
INTRODUCTION
GiaMuhammad | Depok, December 1st – 2nd, 2012
International Conference on Advanced Computer Science and Information Systems (ICACSIS) 2012 | Crystal of Knowledge, University of Indonesia
INTRODUCTION
GiaMuhammad | Depok, December 1st – 2nd, 2012
International Conference on Advanced Computer Science and Information Systems (ICACSIS) 2012 | Crystal of Knowledge, University of Indonesia
ARToolKitPlus
4096 Combination ID
ARTag
4x10^12 Combination ID
QRAR
10^7089 Combination ID
But have limited distance and angle (6 DOF) while tracking
MERGING QR CODE ON CONVENTIONAL MARKER
GiaMuhammad | Depok, December 1st – 2nd, 2012
International Conference on Advanced Computer Science and Information Systems (ICACSIS) 2012 | Crystal of Knowledge, University of Indonesia
QR Code Marker
MARKER DETECTION ALGORITHM
GiaMuhammad | Depok, December 1st – 2nd, 2012
International Conference on Advanced Computer Science and Information Systems (ICACSIS) 2012 | Crystal of Knowledge, University of Indonesia
PERSPECTIVE PROJECTION
GiaMuhammad | Depok, December 1st – 2nd, 2012
International Conference on Advanced Computer Science and Information Systems (ICACSIS) 2012 | Crystal of Knowledge, University of Indonesia
𝑢𝑖
𝑣𝑖
𝑤𝑖
=
𝑎 𝑏 𝑐
𝑑 𝑒 𝑓
𝑔 ℎ 1
𝑥𝑖
𝑦𝑖
1
FEATURE EXTRACTION
GiaMuhammad | Depok, December 1st – 2nd, 2012
International Conference on Advanced Computer Science and Information Systems (ICACSIS) 2012 | Crystal of Knowledge, University of Indonesia
Listed Feature
1) White color percentage,
2) Black color percenage,
3) Number of Contour,
4) Contour Graylevel,
5) x coordinate, and
6) y coodinate graylevel location.
BACKPROPAGATION
GiaMuhammad | Depok, December 1st – 2nd, 2012
International Conference on Advanced Computer Science and Information Systems (ICACSIS) 2012 | Crystal of Knowledge, University of Indonesia
• ArchitectureandParameter
o 3 Layer (Input, Hidden dan Output)
o 24 Neuron Input
o 105 Neuron Hidden
o 2 Neuron Output
o Sigmoid Activation
o Nguyen Widrow Weight
Initialitation
o MSETarget5x10-5
o 5000 Epoch
o Learning Rate (α) =0.06
EXPERIMENTAL RESULT
GiaMuhammad | Depok, December 1st – 2nd, 2012
International Conference on Advanced Computer Science and Information Systems (ICACSIS) 2012 | Crystal of Knowledge, University of Indonesia
Marker Sample Number MSE
QR Encode Length 78 105 0.003715
QR Encode Length 53 110 0.070988
QR Encode Length 32 90 0.087193
QR Encode Length 16 90 0.351523
Microsoft Tag 85 0.155362
Tradisional Marker 85 0.000149
ARToolKitPlus Marker 60 0.000069
ARTag Marker 60 0.000050
EXPERIMENTAL RESULT
GiaMuhammad | Depok, December 1st – 2nd, 2012
International Conference on Advanced Computer Science and Information Systems (ICACSIS) 2012 | Crystal of Knowledge, University of Indonesia
(Castro & Figueroa, 2007)
Pitching
Yawing
Surging
EXPERIMENTAL RESULT
GiaMuhammad | Depok, December 1st – 2nd, 2012
International Conference on Advanced Computer Science and Information Systems (ICACSIS) 2012 | Crystal of Knowledge, University of Indonesia
6 DOF
Accurate Maximum
Previous Method (Kan,
Teng, & Chou, 2009)
Current Method
pitching ±43°
±10.65°
yawing ±58°
±15.03°
surging ±374 ±408.07
EXPERIMENTAL RESULT
GiaMuhammad | Depok, December 1st – 2nd, 2012
International Conference on Advanced Computer Science and Information Systems (ICACSIS) 2012 | Crystal of Knowledge, University of Indonesia
CONCLUSION AND FUTURE WORK
GiaMuhammad | Depok, December 1st – 2nd, 2012
International Conference on Advanced Computer Science and Information Systems (ICACSIS) 2012 | Crystal of Knowledge, University of Indonesia

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QR Code Augmented Reality Tracking with Merging on Conventional Marker based Backpropagation Neural Network

  • 1. QR Code Augmented Reality Tracking with Merging on Conventional Marker based Backpropagation Neural Network GIA MUHAMAD AGUSTA, KHODIJAH HULLIYAH, ARINI, RIZAL BROER BAHAWERES DEPARTMENT OF COMPUTER SCIENCE
  • 2. • Introduction about Augmented Reality and QR Code • A simple approach to handle QR Code as Marker • Marker Detection Algorithm • Perspective Projection • Feature Extraction • Backpropagation Approach • Experimental Result • Conclusion and Future Work PRESENTATIONSUMMARY GiaMuhammad | Depok, December 1st – 2nd, 2012 International Conference on Advanced Computer Science and Information Systems (ICACSIS) 2012 | Crystal of Knowledge, University of Indonesia
  • 3. • Augmented Reality • QR Code INTRODUCTION GiaMuhammad | Depok, December 1st – 2nd, 2012 International Conference on Advanced Computer Science and Information Systems (ICACSIS) 2012 | Crystal of Knowledge, University of Indonesia
  • 4. INTRODUCTION GiaMuhammad | Depok, December 1st – 2nd, 2012 International Conference on Advanced Computer Science and Information Systems (ICACSIS) 2012 | Crystal of Knowledge, University of Indonesia ARToolKitPlus 4096 Combination ID ARTag 4x10^12 Combination ID QRAR 10^7089 Combination ID But have limited distance and angle (6 DOF) while tracking
  • 5. MERGING QR CODE ON CONVENTIONAL MARKER GiaMuhammad | Depok, December 1st – 2nd, 2012 International Conference on Advanced Computer Science and Information Systems (ICACSIS) 2012 | Crystal of Knowledge, University of Indonesia QR Code Marker
  • 6. MARKER DETECTION ALGORITHM GiaMuhammad | Depok, December 1st – 2nd, 2012 International Conference on Advanced Computer Science and Information Systems (ICACSIS) 2012 | Crystal of Knowledge, University of Indonesia
  • 7. PERSPECTIVE PROJECTION GiaMuhammad | Depok, December 1st – 2nd, 2012 International Conference on Advanced Computer Science and Information Systems (ICACSIS) 2012 | Crystal of Knowledge, University of Indonesia 𝑢𝑖 𝑣𝑖 𝑤𝑖 = 𝑎 𝑏 𝑐 𝑑 𝑒 𝑓 𝑔 ℎ 1 𝑥𝑖 𝑦𝑖 1
  • 8. FEATURE EXTRACTION GiaMuhammad | Depok, December 1st – 2nd, 2012 International Conference on Advanced Computer Science and Information Systems (ICACSIS) 2012 | Crystal of Knowledge, University of Indonesia Listed Feature 1) White color percentage, 2) Black color percenage, 3) Number of Contour, 4) Contour Graylevel, 5) x coordinate, and 6) y coodinate graylevel location.
  • 9. BACKPROPAGATION GiaMuhammad | Depok, December 1st – 2nd, 2012 International Conference on Advanced Computer Science and Information Systems (ICACSIS) 2012 | Crystal of Knowledge, University of Indonesia • ArchitectureandParameter o 3 Layer (Input, Hidden dan Output) o 24 Neuron Input o 105 Neuron Hidden o 2 Neuron Output o Sigmoid Activation o Nguyen Widrow Weight Initialitation o MSETarget5x10-5 o 5000 Epoch o Learning Rate (α) =0.06
  • 10. EXPERIMENTAL RESULT GiaMuhammad | Depok, December 1st – 2nd, 2012 International Conference on Advanced Computer Science and Information Systems (ICACSIS) 2012 | Crystal of Knowledge, University of Indonesia Marker Sample Number MSE QR Encode Length 78 105 0.003715 QR Encode Length 53 110 0.070988 QR Encode Length 32 90 0.087193 QR Encode Length 16 90 0.351523 Microsoft Tag 85 0.155362 Tradisional Marker 85 0.000149 ARToolKitPlus Marker 60 0.000069 ARTag Marker 60 0.000050
  • 11. EXPERIMENTAL RESULT GiaMuhammad | Depok, December 1st – 2nd, 2012 International Conference on Advanced Computer Science and Information Systems (ICACSIS) 2012 | Crystal of Knowledge, University of Indonesia (Castro & Figueroa, 2007) Pitching Yawing Surging
  • 12. EXPERIMENTAL RESULT GiaMuhammad | Depok, December 1st – 2nd, 2012 International Conference on Advanced Computer Science and Information Systems (ICACSIS) 2012 | Crystal of Knowledge, University of Indonesia 6 DOF Accurate Maximum Previous Method (Kan, Teng, & Chou, 2009) Current Method pitching ±43° ±10.65° yawing ±58° ±15.03° surging ±374 ±408.07
  • 13. EXPERIMENTAL RESULT GiaMuhammad | Depok, December 1st – 2nd, 2012 International Conference on Advanced Computer Science and Information Systems (ICACSIS) 2012 | Crystal of Knowledge, University of Indonesia
  • 14. CONCLUSION AND FUTURE WORK GiaMuhammad | Depok, December 1st – 2nd, 2012 International Conference on Advanced Computer Science and Information Systems (ICACSIS) 2012 | Crystal of Knowledge, University of Indonesia