Plant species recognition based on bark patterns using novel Gabor filter banks

Zheru Chi; Houqiang Li; Chao Wang

doi:10.1109/ICNNSP.2003.1281045

Plant species recognition based on bark patterns using novel Gabor filter banks

Zheru Chi, Houqiang Li, Chao Wang

The Hong Kong Polytechnic University

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

32 Citations (Scopus)

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.

Original language	English
Title of host publication	Proceedings of 2003 International Conference on Neural Networks and Signal Processing, ICNNSP'03
Pages	1035-1038
Number of pages	4
Volume	2
DOIs	https://doi.org/10.1109/ICNNSP.2003.1281045
Publication status	Published - 1 Dec 2003
Event	2003 International Conference on Neural Networks and Signal Processing, ICNNSP'03 - Nanjing, China Duration: 14 Dec 2003 → 17 Dec 2003

Conference

Conference	2003 International Conference on Neural Networks and Signal Processing, ICNNSP'03
Country/Territory	China
City	Nanjing
Period	14/12/03 → 17/12/03

ASJC Scopus subject areas

Signal Processing
Computer Networks and Communications

More information

10.1109/ICNNSP.2003.1281045

Cite this

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title = "Plant species recognition based on bark patterns using novel Gabor filter banks",

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.",

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Chi, Z, Li, H & Wang, C 2003, Plant species recognition based on bark patterns using novel Gabor filter banks. in Proceedings of 2003 International Conference on Neural Networks and Signal Processing, ICNNSP'03. vol. 2, 1281045, pp. 1035-1038, 2003 International Conference on Neural Networks and Signal Processing, ICNNSP'03, Nanjing, China, 14/12/03. https://doi.org/10.1109/ICNNSP.2003.1281045

Plant species recognition based on bark patterns using novel Gabor filter banks. / Chi, Zheru; Li, Houqiang; Wang, Chao.
Proceedings of 2003 International Conference on Neural Networks and Signal Processing, ICNNSP'03. Vol. 2 2003. p. 1035-1038 1281045.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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Plant species recognition based on bark patterns using novel Gabor filter banks

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