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. Copyright © 2004 by the Institute of Electrical and Electronics Engineers, Inc.
|Title of host publication||Proceedings of 2004 International Symposium on Intelligent Multimedia, Video and Speech processing, ISIMP 2004|
|Place of Publication||Piscataway, NJ|
|Publication status||Published - 2004|