Detecting Non-lambertian Materials in Video

Seyed Mahdi Javadi, Yongmin Li, Xiaohui Liu

2017

Abstract

This paper describes a novel method to identify and distinguish shiny and glossy materials in videos automatically. The proposed solution works by analyzing the logarithm of chromaticity of sample pixels from various materials over a period of time to differentiate between shiny and matt textures. The Lambertian materials have different reflectance model and the distribution of their chromaticity is not the same as non-Lambertian texture. We will use this to detect shiny materials. This system has many application in texture and object recognition, water leakage and oil spillage detection systems.

References

  1. Amini, H. and Karasfi, B. (2016). New approach to road detection in challenging outdoor environment for autonomous vehicle. In 2016 Artificial Intelligence and Robotics (IRANOPEN), pages 7-11.
  2. Arora, C. and Werman, M. (2014). Optical flow for non lambertian surfaces by cancelling illuminant chromaticity. In 2014 IEEE International Conference on Image Processing (ICIP), pages 1977-1981.
  3. Basri, R. and Jacobs, D. W. (2003). Lambertian reflectance and linear subspaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(2):218-233.
  4. Ged, G., Obein, G., Silvestri, Z., Rohellec, J., and Vinot, F. (2010). Recognizing real materials from their glossy appearance. Journal of Vision, 10(18):451-465.
  5. Hariyono, J. and Jo, K. H. (2015). Detection of pedestrian crossing road using action classification model. In 2015 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), pages 21-24.
  6. Hertzmann, A. and Seitz, S. M. (2005). Example-based photometric stereo: shape reconstruction with general, varying brdfs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(8):1254-1264.
  7. Kahily, H. M. and Sudheer, A. P. (2016). Real-time human detection and tracking from a mobile armed robot using rgb-d sensor. In 2016 World Conference on Futuristic Trends in Research and Innovation for Social Welfare (Startup Conclave), pages 1-6.
  8. Kim, Y. S., Park, J. I., Lee, D. J., and Chun, M. G. (2007). Real time detection of moving human based on digital image processing. In SICE, 2007 Annual Conference.
  9. Knoblauch, K. and Viot, F. (2010). Difference scaling of gloss: Nonlinearity, binocularity, and constancy. Journal of Vision, 10:189-203.
  10. Li, G., Liu, Y., and Dai, Q. (2011). Multi-view photometric stereo of non-lambertian surface under general illuminations. In 2011 International Conference on 3D Imaging (IC3D), pages 1-6.
  11. Malik, J. and T.Leung (2001). Representing and recognizing the visual appearance of materials using threedimensional textons. International JOurnal of Computer Vision, pages 29-43.
  12. Maloney, L. and Yang, J. (2003). Maximum likelihood difference scaling. Journal of Vision, 6:573-568.
  13. Munajat, M. D. E., Widyantoro, D. H., and Munir, R. (2015). Road detection system based on rgb histogram filterization and boundary classifier. In 2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS), pages 195-200.
  14. Prinet, V., Lischinski, D., and Werman, M. (2014). Illuminant chromaticity from image sequences. The IEEE International Conference on Computer Vision (ICCV), 3:311-319.
  15. Schechner, Y. Y., Nayar, S. K., and Belhumeur, P. N. (2007). Multiplexing for optimal lighting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(8):1339-1354.
  16. Sharma, A., Singh, A., and Rohilla, R. (2016). Color based human detection and tracking algorithm using a nongaussian adaptive particle filter. In 2016 3rd International Conference on Recent Advances in Information Technology (RAIT), pages 439-442.
  17. Yang, Q., Wang, S., Ahuja, N., and Yang, R. (2011). A uniform framework for estimating illumination chromaticity, correspondence, and specular reflection. IEEE Transactions on Image Processing, 20(1):53- 63.
  18. Yoon, K. and Kweon, I. (2006). Correspondence search in the presence of specular highlights using specular-free two-band images. Computer Vision ACCV, 7:761- 770.
  19. Zhang, Y., Meng, H., Hu, C., Liu, Y., and Du, Z. (2015). Road and vehicle detection in highway scene for automotive fmcw antenna array radar. In IET International Radar Conference 2015, pages 1-5.
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Paper Citation


in Harvard Style

Javadi S., Li Y. and Liu X. (2017). Detecting Non-lambertian Materials in Video . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-225-7, pages 254-259. DOI: 10.5220/0006185002540259


in Bibtex Style

@conference{visapp17,
author={Seyed Mahdi Javadi and Yongmin Li and Xiaohui Liu},
title={Detecting Non-lambertian Materials in Video},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={254-259},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006185002540259},
isbn={978-989-758-225-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)
TI - Detecting Non-lambertian Materials in Video
SN - 978-989-758-225-7
AU - Javadi S.
AU - Li Y.
AU - Liu X.
PY - 2017
SP - 254
EP - 259
DO - 10.5220/0006185002540259