Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/37692
Title: Bark classification by combining grayscale and binary texture features
Authors: Song, J
Chi, Z 
Liu, J
Fu, H
Keywords: Edge detection
Feature extraction
Image classification
Image texture
Wavelet transforms
Issue Date: 2004
Source: Proceedings of the International Symposium on Intelligent Multimedia, Video and Speech Processing (ISIMP’2004), Hong Kong, 20-22 Oct. 2004, p. 450-453 How to cite?
Abstract: 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.
URI: http://hdl.handle.net/10397/37692
ISBN: 0-7803-8687-6
DOI: 10.1109/ISIMP.2004.1434097
Appears in Collections:Conference Paper

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