Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/37728
Title: Plant species recognition based on bark patterns using novel Gabor filter banks
Authors: Chi, Z 
Li, HQ
Wang, C
Keywords: Eature extraction
Filtering theory
Image segmentation
Image texture
Issue Date: 2003
Source: Proceedings of the International Conference on Neural Networks and Signal Processing (ICNNSP'2003), Nanjing, China, 14-17 Dec. 2003, p. 1035-1038 How to cite?
Abstract: This paper presents a novel style of Gabor filter banks designed for plant species recognition using their bark texture features. In this paper, texture is modeled as multiple narrowband signals that are characterized by their central frequencies and normalized ratios of amplitudes. The normalized ratio of amplitudes is employed as an energy weight for combining narrowband signals. Based on this texture model, a set of texture features can be extracted from each kind of plant bark that is used to characterize the plant and to design the corresponding Gabor filter bank. A classifier is constructed by these Gabor filter banks. Plant recognition experiments on a small database of bark images have been conducted and the effectiveness of our approach is confirmed by the experimental results.
URI: http://hdl.handle.net/10397/37728
ISBN: 0-7803-7702-8
ISSN: 1098-7576
DOI: 10.1109/ICNNSP.2003.1281045
Appears in Collections:Conference Paper

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