ADAPTIVE FUZZY COLOUR SEGMENTATION ON RGB RATIO SPACE FOR ROAD DETECTION

Chieh-Li Chen, Chung-Li Tai

2009

Abstract

In this paper, the RGB ratio is defined according to a reference colour such that the image can be transformed from a conventional colour space to the RGB ratio space. Different to distance measurement, a road colour segment is determined by an area in RGB ratio space enclosed by the estimated boundaries. Adaptive fuzzy logic, which fuzzy membership functions are defined according to estimated boundaries, is introduced to implement clustering rules. Low computation cost of the proposed segmentation method shows the feasibility to real time application. Experimental results for road detection demonstrate the robustness to intensity variation of the proposed approach.

References

  1. Al Aghbari, Z., Al-Haj, R., 2006. Hill-Manipulation: An Effective Algorithm for Colour Image Segmentation. Image Vis. Comput., Vol. 24, No. 8 pp. 894-903.
  2. Bascle, B., Bernier, O., Lemaire, V., 2007. Learning Invariants to Illumination Changes Typical of Indoor Environments: Application to Image Colour Correction. Int. J. Imaging Syst. Technol., Vol. 17, No. 3 pp. 132-142.
  3. Benedek, C., Sziranyi, T., 2007. Study on Colour Space Selection for Detecting Cast Shadows in Video Surveillance. Int. J. Imaging Syst. Technol., Vol. 17, No. 3 pp. 190-201.
  4. Bosch, A., Munoz, X., Freixenet, J., 2007. Segmentation and Description of Natural Outdoor Scenes. Image Vis. Comput., Vol. 25, No. 5 pp. 727-740.
  5. Do, H. C., You, J. Y., Chien, S. I., 2007. Skin Colour Detection through Estimation and Conversion of Illuminant Colour under Various Illuminations. IEEE Trans. Consum. Electron, Vol. 53, No. 3 pp. 1103- 1108.
  6. Gonzalez, R.C., Woods, R.E., 2002. Digital Image Processing, Prentice-Hall. New Jersey, 2nd edition.
  7. He, Y., Luo, Y. P., Hu, D. C., 2007. Automatic Seeded Region Growing Based on Gradient Vector Flow for Colour Image Segmentation. Opt. Eng., Vol. 46, No. 4 pp. 047003.
  8. Kato, Z., 2008. Segmentation of Colour Images via Reversible Jump MCMC Sampling. Image Vis. Comput., Vol. 26, No. 3 pp. 361-371.
  9. Kim, H. S., Sakamoto, R., Kitahara, I., Toriyama, T., 2007. Robust Foreground Extraction Technique Using Background Subtraction with Multiple Thresholds. Opt. Eng., Vol. 46, No. 9 pp. 097004.
  10. Kim, C., You, B. J., Jeong, M. H., Kim, H., 2008. Colour Segmentation Robust to Brightness Variations by Using B-Spline Curve Modeling. Pattern Recognit., Vol. 41, No. 1 pp. 22-37.
  11. Kuo, C. F. J., Shin, C. Y., Lee, J. Y., 2008. Separating Colour and Identifying Repeat Pattern through The Automatic Computerized Analysis System for Printed Fabrics. J. Inf. Sci. Eng., Vol. 24, No. 2 pp. 453-467.
  12. Lin, C., 2007. Face Detection in Complicated Backgrounds and Different Illumination Conditions by Using YCbCr Colour Space and Neural Network. Pattern Recognit. Lett., Vol. 28, No. 16 pp. 2190-2200.
  13. Luis-Garcia, R. de, Deriche, R., Alberola-Lopez, C., 2008. Texture and Colour Segmentation Based on The Combined Use of The Structure Tensor and The Image Components. Signal Process., Vol. 88, No. 4 pp. 776-795.
  14. Mikic, I., Cosman, P. C., Kogut, G. T., Trivedi, M. M., 2000. Moving Shadow and Object Detection in Traffic Scenes. Proceedings of the 15th international conference on pattern recognition, Barcelona, Spain pp. 321-324.
  15. Murshrif, M. M., Ray, A. K., 2008. Colour Image Segmentation: Rough-Set Theoretic Approach. Pattern Recognit. Lett., Vol. 29, No. 4 pp.483-493.
  16. Plataniotis, K. N., Venestsanopoulos, A. N., 2000. Colour Image Processing and Applications, Springer. Berlin.
  17. Tai, Y. H., Jia J. Y., Tang, C. K., 2007. Soft Colour Segmentation and Its Applications. IEEE Trans. Pattern Anal. Mach. Intell., Vol. 29, No. 9 pp. 1520- 1537.
  18. Wang, C. M., Huang, Y. H., 2006. A Novel Automatic Colour Transfer Algorithm between Images. J. Chin. Inst. Eng., Vol. 29, No. 6 pp. 1051-1060.
  19. Wang, Y. G., Yang, J., Zhou, Y., Wang Y. Z., 2007. Region Partition and Feature Matching Based Colour Recognition of Tongue Image. Pattern Recognit. Lett., Vol. 28, No. 1 pp. 11-19.
  20. Wangenheim, A. V., Bertoldi, R. F., Abdala, D. D., Richter, M. M., 2007. Colour Image Segmentation Guided by A Colour Gradient Network. Pattern Recognit. Lett., Vol. 28, No. 13 pp. 1795-1803.
  21. Weng, S. K., Kuo, C. M., Kang, W. C., 2007. Colour Texture Segmentation Using Colour Transform and Feature Distributions. IEICE Trans. Inf. Syst., Vol. E90D, No. 4 pp. 787-790.
  22. Yates, R. D., Goodman, D. J., 2005. Probability and Stochastic Processes, John Wiley & Sons. New Jersey.
  23. Zimmermann, H.J., 1991, Fuzzy Set Theory and Its Applications, Kluwer Academic Publishers. Boston, 2nd edition.
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Paper Citation


in Harvard Style

Chen C. and Tai C. (2009). ADAPTIVE FUZZY COLOUR SEGMENTATION ON RGB RATIO SPACE FOR ROAD DETECTION . In Proceedings of the First International Conference on Computer Imaging Theory and Applications - Volume 1: IMAGAPP, (VISIGRAPP 2009) ISBN 978-989-8111-68-5, pages 31-36. DOI: 10.5220/0001746300310036


in Bibtex Style

@conference{imagapp09,
author={Chieh-Li Chen and Chung-Li Tai},
title={ADAPTIVE FUZZY COLOUR SEGMENTATION ON RGB RATIO SPACE FOR ROAD DETECTION},
booktitle={Proceedings of the First International Conference on Computer Imaging Theory and Applications - Volume 1: IMAGAPP, (VISIGRAPP 2009)},
year={2009},
pages={31-36},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001746300310036},
isbn={978-989-8111-68-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the First International Conference on Computer Imaging Theory and Applications - Volume 1: IMAGAPP, (VISIGRAPP 2009)
TI - ADAPTIVE FUZZY COLOUR SEGMENTATION ON RGB RATIO SPACE FOR ROAD DETECTION
SN - 978-989-8111-68-5
AU - Chen C.
AU - Tai C.
PY - 2009
SP - 31
EP - 36
DO - 10.5220/0001746300310036