REAL-TIME ADAPTIVE LEARNING SYSTEM USING OBJECT COLOR PROBABILITY FOR VIRTUAL REALITY APPLICATIONS

Chutisant Kerdvibulvech

Abstract

Segmentation is not a trivial task, especially in challenging situations such as outdoor area. In this paper, we develop an adaptive learning system to segment an object robustly. By using the on-line adaptation of color probabilities, the proposed method presents several specific features: it is able to cope with illumination changes even in the outdoor area, and also it can be done in real-time. Bayes’ rule and Bayesian classifier is employed to calculate the probability of an object color. Representative experimental results are also presented and discussed. The system presented can be further used to develop the real-time game of augmented reality in virtual spaces.

References

  1. Argyros, A. A., Lourakis, M. I. A., 2004. Real time Tracking of Multiple Skin-Colored Objects with a Possibly Moving Camera. European Conference on Computer Vision.
  2. Cabrol, A. D., Bonnin, P., Costis, T., Hugel, V., Blazevic, P. (2005). A New Video Rate Region Color Segmentation and Classication for Sony Legged RoboCup Application. Lecture Notes in Computer Science. RoboCup.
  3. Chai, D., Ngan, K. N., 1998. Locating facial region of a head-and-shoulders color image. 3rd IEEE International Conference on Automatic Face and Gesture Recognition (FG'98).
  4. Gonzalez, R., Woods, R. E., 2002. Digital Image Processing. 2 ed, Prentice Hall Press., ISBN 0-201- 18075-8.
  5. Hua, R. C. K., Silva, L. C. D., Vadakkepat, P., 2002. Detection and Tracking of Faces in Real-Time Environments. International Conference on Imaging Science, Systems, and Technology.
  6. Jack, K., 2004. Video demystified, Elsevier science, 4th edition.
  7. Jebara, T. S., Pentland, A., 1997. Parameterized structure from motion for 3D adaptive feedback tracking of faces,” IEEE Conference on Computer Vision and Pattern Recognition.
  8. Kim, S. H., Kim, N. K., Ahn, S. C. and Kim, H. G., 1998. Object oriented face detection using range and color information. 3rd IEEE International Conference on Automatic Face and Gesture Recognition.
  9. Shi, S., Wang, L., Jin, W., Zhao, Y., 2007. Color night vision based on color transfer in YUV color space. International Symposium on Photoelectronic Detection and Imaging.
  10. Zabulis, X., Baltzakis, H., Argyros, A. A., 2009. Visionbased Hand Gesture Recognition for HumanComputer Interaction, In "The Universal Access Handbook", Lawrence Erlbaum Associates, Inc. (LEA), Series on "Human Factors and Ergonomics", ISBN: 978-0-8058-6280-5.
Download


Paper Citation


in Harvard Style

Kerdvibulvech C. (2011). REAL-TIME ADAPTIVE LEARNING SYSTEM USING OBJECT COLOR PROBABILITY FOR VIRTUAL REALITY APPLICATIONS . In Proceedings of 1st International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH, ISBN 978-989-8425-78-2, pages 200-204. DOI: 10.5220/0003622802000204


in Bibtex Style

@conference{simultech11,
author={Chutisant Kerdvibulvech},
title={REAL-TIME ADAPTIVE LEARNING SYSTEM USING OBJECT COLOR PROBABILITY FOR VIRTUAL REALITY APPLICATIONS},
booktitle={Proceedings of 1st International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH,},
year={2011},
pages={200-204},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003622802000204},
isbn={978-989-8425-78-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of 1st International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH,
TI - REAL-TIME ADAPTIVE LEARNING SYSTEM USING OBJECT COLOR PROBABILITY FOR VIRTUAL REALITY APPLICATIONS
SN - 978-989-8425-78-2
AU - Kerdvibulvech C.
PY - 2011
SP - 200
EP - 204
DO - 10.5220/0003622802000204