Abstract
In this paper, a new method for bark classification based on textural and fractal dimension features using Artificial Neural Networks is presented. The approach involving the grey level co-occurrence matrices and fractal dimension is used for bark image analysis, which improves the accuracy of bark image classification by combining fractal dimension feature and structural texture features on bark image. Furthermore, we have investigated the relation between Artificial Neural Network (ANN) topologies and bark classification accuracy. Furthermore, the experimental results show the facts that this new approach can automaticly identify the Tplants categories and the classification accuracy of the new method is better than that of the method using the nearest neighbor classifier.
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© 2006 Springer-Verlag Berlin Heidelberg
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Huang, ZK., Zheng, CH., Du, JX., Wan, Yy. (2006). Bark Classification Based on Textural Features Using Artificial Neural Networks. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_52
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DOI: https://doi.org/10.1007/11760023_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34437-7
Online ISBN: 978-3-540-34438-4
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