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Bark classification by combining grayscale and binary texture features

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4 Author(s)
Jiatao Song ; Dept. of Electron. & Inf. Eng., Hong Kong Polytech. Univ., China ; Zheru Chi ; Jilin Liu ; Hong Fu

In this paper, a texture feature based bark classification method is presented. Our method uses two types of texture features: the co-occurrence matrix feature and the long connection length emphasis (LCLE) feature, which is extracted from the binary bark image. For the extraction of binary texture maps, an improved wavelet-based edge detection algorithm is proposed. It includes two binarization steps and a post-processing step. The paper also presents an approach to combine two feature sets. Experiments on 18 different tree species, and in total 90 bark images, show that a combination of these two feature sets can achieve a much higher bark classification rate than that when each feature set is utilized individually.

Published in:

Intelligent Multimedia, Video and Speech Processing, 2004. Proceedings of 2004 International Symposium on

Date of Conference:

20-22 Oct. 2004