Abstract
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.
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References
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)
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)
Rioul, O., Vetterli, M.: Wavelet and signal processing. IEEE SP mag., 14–38 (october 1991)
Strang, G.: Wavelet and dilation equation: a brief introduction. SIAM Rev. 31, 614–627 (1989)
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)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern classification (2001)
Krishnamachari, S., Chellappa, R.: Multiresolution Gauss-Markov Random Field Models for Texture Segmentation. IEEE Trans. Image Processing 6(2) (February 1997)
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)
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)
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)
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)
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© 2004 Springer-Verlag Berlin Heidelberg
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Jeong, P., Nedevschi, S. (2004). Efficient Classification Method for Autonomous Driving Application. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2004. Lecture Notes in Computer Science, vol 3211. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30125-7_29
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DOI: https://doi.org/10.1007/978-3-540-30125-7_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23223-0
Online ISBN: 978-3-540-30125-7
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