ROBUST HAND-DRAWN SQUARE-ROI CONTOUR DETECTOR BASED ON ADAPTIVE THRESHOLDING

Authors

  • Rechard Lee Real Time Graphics and Visualization Research Group (GRAVS), Mathematics with Computer Graphics, School of Science and Technology, Universiti Malaysia Sabah Jalan UMS, 88400 Kota Kinabalu, Sabah, Malaysia
  • Abdullah Bade Real Time Graphics and Visualization Research Group (GRAVS), Mathematics with Computer Graphics, School of Science and Technology, Universiti Malaysia Sabah Jalan UMS, 88400 Kota Kinabalu, Sabah, Malaysia
  • Salina Sulaiman Real Time Graphics and Visualization Research Group (GRAVS), Mathematics with Computer Graphics, School of Science and Technology, Universiti Malaysia Sabah Jalan UMS, 88400 Kota Kinabalu, Sabah, Malaysia
  • Siti Hasnah Tanalol Real Time Graphics and Visualization Research Group (GRAVS), Mathematics with Computer Graphics, School of Science and Technology, Universiti Malaysia Sabah Jalan UMS, 88400 Kota Kinabalu, Sabah, Malaysia

DOI:

https://doi.org/10.11113/jt.v75.4991

Keywords:

Augmented reality, contour, feature, ROI, thresholding,

Abstract

Hand-drawn square-ROI detector was developed as one of the vital components in Real-Time Pre-Placed Markerless Square-ROI (RPMS) recognition technique. It aims to; 1. To verify hand-drawn Square-ROI (Region of Interest) as a square, and 2. To create a robust and flexible square-ROI detector technique which can be applied in uneven lighting condition. In this paper, we aim to detect only the desired ROI and handle the uneven lighting condition which is one of the primary disturbance sources that may generate false results. This may lead to error in registration in Augmented Reality application due to inability to correctly define a marker. As a solution, our technique applies adaptive thresholding in order to address this issue and to create a robust and flexible technique. To verify our proposed technique, two kinds of square is used in the testing and evaluation phase. In this experiment, two influencing factors; viewing distance, and detection accuracy were used to validate our aim. The results of the experiments show that the proposed technique efficiently detects and defines the desired square-ROI and also robust to illumination changes.  

References

Milgram, P. and F. Kishino. 1994. A Taxonomy of Mixed Reality Visual Displays. IEICE Transactions on Information and Systems. E77-D(12): 1-26.

Azuma, R. 1997. A Survey of Augmented Reality. Presence-Teleoperators and Virtual Environments. 6(4): 355-385.

Klein, G. 2006. Visual Tracking for Augmented Reality. Ph.D. Thesis. University of Cambridge.

Xu, K., S. J. D. Prince, A. D. Cheok, Y. Qiu and K. G. Kumar. 2003. Visual Registration for Unprepared Augmented Reality Environments. Personal and Ubiquitous Computing. 7(5): 287-298.

Blackwell, M., C. Nikou, A. M. DiGioia and T. Kanade. 2000. An Image Overlay System for Medical Data Visualization. Medical Image Analysis. 4(1): 67-72.

William, S. G. D., Progress Report: Adaptive Hybrid Tracking and Registration for Mobile Outdoor Augmented Reality. 1-16.

Carmigniani, J., B. Furht, M. Anisetti, P. Ceravolo, E. Damiani and M. Ivkovic. 2010. Augmented Reality Technologies, Systems and Applications. Multimedia Tools and Applications. 51(1): 341-377.

Canny, J. 1986. A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence. 8(6): 679-698.

Baggio, D.L., S. Emami, D.M. Escriva, K. Ievgen, N. Mahmood, J. Saragih and R. Shilkrot. 2012. Mastering OpenCV with Practical Computer Vision Projects. First Packt Publishing Ltd.

Wyk, C.V. 2011. Markerless Augmented Reality on Ubiquitous Mobile Devices with Integrated Sensors. Master Thesis. Stellenbosch University.

Tresaco, A. 2009. Markers Recognition in A Camera-based Calibration System for Immersive Applications. Master Thesis. Universitat Politècnica de Catalunya.

Sonka, M., V. Hlavac and R. Boyle. 1993. Image Processing, Analysis and Machine Vision. Springer US.

Faille, F. 2003. Adapting Interest Point Detection to Illumination Conditions. Proceeding of the VIIth Digital Image Computing: Techniques and Applications. Sydney. 10-12 Dec, 2003. 10-12.

Nixon, M. and A. Aguado. 2002. Feature Extraction and Image Processing. Second Edition. Elsevier.

Qureshi, S. 2005. Chapter 5: Edge Detection and Segmentation. from Embedded Image Processing on the TMS320C600 DSP: Examples in Code Composer Studio and MATLAB. US: Springer.

Idris, M. Y. I., H. Arof, E. M. Tamil, N. M. Noor and Z. Razak. 2009. Review of Feature Detection Techniques for Simultaneous Localization and Mapping and System on Chip Approach. Information Technology Journal. 8(3): 250-262.

Bansal, B., J. S. Saini, V. Bansal and G. Kaur. 2012. Comparison of Various Edge Detection Techniques. Journal of Information and Operation Management. 3(1): 103-106.

Bin L. and M. S. Yeganeh. 2012. Comparison for Image Edge Detection Algorithms. IOSR Journal of Computer Engineering (IOSRJCE). 2(6): 1-4.

Downloads

Published

2015-07-13

Issue

Section

Science and Engineering

How to Cite

ROBUST HAND-DRAWN SQUARE-ROI CONTOUR DETECTOR BASED ON ADAPTIVE THRESHOLDING. (2015). Jurnal Teknologi (Sciences & Engineering), 75(2). https://doi.org/10.11113/jt.v75.4991