Efficient Classification Method for Autonomous Driving Application

  • Pangyu Jeong
  • Sergiu Nedevschi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3211)


This paper intends to propose a real-time and robust classification method against noise facts for extracting the road region in complex environments. A new approach based on the probability is presented aiming the reduction of the classification area and time. The process starts from initial seed inside sampled road region and stops when the seeds identify the road region borders. In order to increase accuracy of classification, a more powerful discrimination function is proposed based on the local difference probability. This method behaves like a supervised classification. However, it extracts a priori information from each processed image providing better tuning of the discrimination threshold to the image features.


Initial Seed Discrimination Threshold Classification Ability Efficient Classification Road Area 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Reed, T.R., Wechsler, H.: Segmentation of textured images and Gestalt organization using spatial/spatial-frequency representations. IEEE Trans. Pattern analysis and Machine Intelligent 12, 1–12 (1990)CrossRefGoogle Scholar
  2. 2.
    Nikias, C.: High Order Spectral Analysis. In: Haykin, S. (ed.) Advances in Spectrum Analysis and Array Processing, pp. 326–365. Prentice Hall, Englewood Cliffs (1991)Google Scholar
  3. 3.
    Rioul, O., Vetterli, M.: Wavelet and signal processing. IEEE SP mag., 14–38 (october 1991)Google Scholar
  4. 4.
    Strang, G.: Wavelet and dilation equation: a brief introduction. SIAM Rev. 31, 614–627 (1989)zbMATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Wiskott, L., Fellous, J.-M., Kruger, N., von der Malsburg, C.: Face recognition by elastic graph matching. IEEE Trans. Pattern Analysis and Machine Intelligence 9(7) (July 1997)Google Scholar
  6. 6.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern classification (2001)Google Scholar
  7. 7.
    Krishnamachari, S., Chellappa, R.: Multiresolution Gauss-Markov Random Field Models for Texture Segmentation. IEEE Trans. Image Processing 6(2) (February 1997)Google Scholar
  8. 8.
    Theiler, J., Gisler, G.: A contiguity-enhanced K-Means clustering algorithm for unsupervised multispectral image segmentaion. In: Processing SPIE, vol. 3159, pp. 108–118 (1997)Google Scholar
  9. 9.
    Jeong, P., Nedevschi, S.: Intelligent Road Detection Based on Local Averaging Classifier in Real-Time Environments. In: 12th IEEE International Conference on Image Analysis and Processing, Mantova, September 17-19, pp. 245–249 (2003)Google Scholar
  10. 10.
    Jeong, P., Nedevschi, S.: Unsupervised Muliti-classification for Lane detection using the combination of Color-Texture and Gray-Texture. In: CCCT 2003, August 2003, vol. 1, pp. 216–221 (2003)Google Scholar
  11. 11.
    PARAGIOS, N., DERICHE, R.: Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects. IEEE Transactions on pattern analysis and machine intelligent 22(3) ( March 2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Pangyu Jeong
    • 1
  • Sergiu Nedevschi
    • 1
  1. 1.Computer Science DepartmentTechnical University of Cluj-NapocaCluj-NapocaRomania

Personalised recommendations