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Texture image classification using improved image enhancement and adaptive SVM

The development of a robust and accurate wood species recognition system based on wood texture images is important to guarantee the quality of the wood merchandise. Wood species can be classified according to distinctive texture features such as the positioning of pores or vessel, fibres, rays paren...

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Main Authors: Abdul Hamid, Lydia, Mohd. Khairuddin, Anis Salwa, Khairuddin, Uswah, Rosli, Nenny Ruthfalydia, Mokhtar, Norrima
Format: Article
Published: Springer Science and Business Media Deutschland GmbH 2022
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utm.my/104117/
http://dx.doi.org/10.1007/s11760-021-02113-y
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spelling my.utm.1041172024-01-17T01:14:43Z http://eprints.utm.my/104117/ Texture image classification using improved image enhancement and adaptive SVM Abdul Hamid, Lydia Mohd. Khairuddin, Anis Salwa Khairuddin, Uswah Rosli, Nenny Ruthfalydia Mokhtar, Norrima TK Electrical engineering. Electronics Nuclear engineering The development of a robust and accurate wood species recognition system based on wood texture images is important to guarantee the quality of the wood merchandise. Wood species can be classified according to distinctive texture features such as the positioning of pores or vessel, fibres, rays parenchyma, phloem, soft tissue, intercellular canals and latex traces. Since the quality of wood texture images obtained at the inspection site might not present at its optimum quality, blurry texture images captured during image acquisition process has been a challenging issue in designing accurate wood species recognition system. Therefore, a modified image enhancement method and an adaptive classifier are proposed in this study to overcome the above-mentioned problem. Firstly, an improved image enhancement method is proposed by fusing an unsharp masking with the conventional constrained least squares filter (CLSF) to enhance the blurry texture images. Secondly, an adaptive support vector machine is proposed for final classification. The wood texture images are classified based on the deep features extracted using a convolutional neural network model. The proposed system is also benchmarked with several image enhancement methods for comparison purposes. Investigation results proved that the proposed wood species recognition system is feasible in classifying blurry wood texture images. Springer Science and Business Media Deutschland GmbH 2022-09 Article PeerReviewed Abdul Hamid, Lydia and Mohd. Khairuddin, Anis Salwa and Khairuddin, Uswah and Rosli, Nenny Ruthfalydia and Mokhtar, Norrima (2022) Texture image classification using improved image enhancement and adaptive SVM. Signal, Image and Video Processing, 16 (6). pp. 1587-1594. ISSN 1863-1703 http://dx.doi.org/10.1007/s11760-021-02113-y DOI:10.1007/s11760-021-02113-y
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Abdul Hamid, Lydia
Mohd. Khairuddin, Anis Salwa
Khairuddin, Uswah
Rosli, Nenny Ruthfalydia
Mokhtar, Norrima
Texture image classification using improved image enhancement and adaptive SVM
description The development of a robust and accurate wood species recognition system based on wood texture images is important to guarantee the quality of the wood merchandise. Wood species can be classified according to distinctive texture features such as the positioning of pores or vessel, fibres, rays parenchyma, phloem, soft tissue, intercellular canals and latex traces. Since the quality of wood texture images obtained at the inspection site might not present at its optimum quality, blurry texture images captured during image acquisition process has been a challenging issue in designing accurate wood species recognition system. Therefore, a modified image enhancement method and an adaptive classifier are proposed in this study to overcome the above-mentioned problem. Firstly, an improved image enhancement method is proposed by fusing an unsharp masking with the conventional constrained least squares filter (CLSF) to enhance the blurry texture images. Secondly, an adaptive support vector machine is proposed for final classification. The wood texture images are classified based on the deep features extracted using a convolutional neural network model. The proposed system is also benchmarked with several image enhancement methods for comparison purposes. Investigation results proved that the proposed wood species recognition system is feasible in classifying blurry wood texture images.
format Article
author Abdul Hamid, Lydia
Mohd. Khairuddin, Anis Salwa
Khairuddin, Uswah
Rosli, Nenny Ruthfalydia
Mokhtar, Norrima
author_facet Abdul Hamid, Lydia
Mohd. Khairuddin, Anis Salwa
Khairuddin, Uswah
Rosli, Nenny Ruthfalydia
Mokhtar, Norrima
author_sort Abdul Hamid, Lydia
title Texture image classification using improved image enhancement and adaptive SVM
title_short Texture image classification using improved image enhancement and adaptive SVM
title_full Texture image classification using improved image enhancement and adaptive SVM
title_fullStr Texture image classification using improved image enhancement and adaptive SVM
title_full_unstemmed Texture image classification using improved image enhancement and adaptive SVM
title_sort texture image classification using improved image enhancement and adaptive svm
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2022
url http://eprints.utm.my/104117/
http://dx.doi.org/10.1007/s11760-021-02113-y
_version_ 1789424381555900416
score 13.211869

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