Abstract
This paper suggests 3D co-occurrence texture features by extending the concept of co-occurrence feature to the 3D world. The suggested 3D features are described as a 3D co-occurrence matrix by using a co-occurrence histogram of digital elevations at two contiguous positions. With the addition of 3D co-occurrence features, we encounter the high dimensionality problem in the classification process. Since these ANN (Artificial Neural Networks) clustering algorithms are known as robust in this situation, FCM (Fuzzy C-mean) and GBFCM (Gradient Based Fuzzy C-mean) clustering algorithms are employed to implement the terrain classifier. Experimental results show that the classification accuracy with the addition of 3D co-occurrence features is significantly improved over the conventional classification method only with 2D features.
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References
Tamura, H., Mori, S., Yamawaki, T.: Textural Features Corresponding to Visual Perception. IEEE Trans. Systems, Man and Cybernetics 8, 460–473 (1978)
Haralick, R.: Statistical and Structural Approaches to Texture. Proceeding IEEE 67, 786–804 (1979)
Haralick, R., Shanmugam, K., Dinstein, I.: Texture Features for Image Classification. IEEE Trans. Systems, Man and Cybernetics 3, 610–621 (1973)
Dubes, R., Jain, A.: Random Field Models in Image Analysis. Journal of Applied Statistics 6, 131–164 (1989)
Campbell, F., Robson, J.: Application to Fourier Analysis to the Visibility of Gratings. Journal of Physiology 197, 551–566 (1968)
Kube, P., Pentland, A.: On the Imaging of Fractal Surfaces. IEEE Trans. Pattern Analysis and Machine Intelligence 10, 704–707 (1988)
Ohanian, P., Dubes, R.: Performance Evaluation for Four Classes of Textural Features. Pattern Recognition 25, 819–833 (1992)
Dana, K., Nayar, S., Van Ginneken, B., Koenderink, J.: Reflectance and Texture of Real-World Surfaces. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 151–157 (1997)
Wang, X., Stolle, F., Schultz, H., Riseman, E., Hanson, A.: Using Three-Dimensional Features to Improve Terrain Classification. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 915-920 (1997)
Park, D.C., Dagher, I.: Gradient Based Fuzzy c-means (GBFCM) Algorithm. IEEE Int. Conf. on Neural Networks, ICNN-94 3, 1626–1631 (1994)
Bezdek, J.C.: A convergence theorem for the fuzzy ISODATA clustering algorithms. IEEE Trans. Pattern Anal. Mach. Int 2, 1–8 (1980)
Bezdek, J.C.: Pattern recognition with fuzzy objective function algorithms. Plenum, NewYork (1981)
Looney, C.: Pattern Recognition Using Neural Networks, pp. 252–254. Oxford University press, New York (1997)
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Woo, DM., Park, DC., Song, YS., Nguyen, QD., Tran, QD.N. (2007). Terrain Classification Based on 3D Co-occurrence Features. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues. ICIC 2007. Lecture Notes in Computer Science, vol 4681. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74171-8_129
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DOI: https://doi.org/10.1007/978-3-540-74171-8_129
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74170-1
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