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Application of image quality assessment module to motion-blurred wood images for wood species identification system

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Abstract

Despite tighter conservation regulations, demand for timber products has continued to increase due to growing population. Normally, experts identify the wood species based on the pattern of the wood surface texture. However, manual inspection on wood texture is tedious, time-consuming, impractical and cost-ineffective for a human to analyze a large number of timber species. Therefore, a reliable automatic wood recognition system is needed in order to classify the wood species efficiently. The proposed system includes image acquisition, image quality assessment module (IQA), image deblurring, feature extraction and classification. In this research, the wood images are motion-blurred due to imperfections in the imaging and capturing process. Hence, an IQA module is proposed to monitor the quality of images before proceeding to the next stage which is the feature extraction process. The IQA module will determine whether the image has to undergo the image deblurring process based on the image quality value. If the image is of low quality based on the image quality value obtained, then the image will be deblurred before the feature extraction procedure. A reliable motion deblurring technique, which is based on Lucy–Richardson algorithm, is employed to enhance the motion-blurred images before proceeding to the next stage, which is the feature extraction process. Then, a statistical feature extraction technique is proposed to extract 24 features from each wood image. Finally, a support vector machine is used to classify the 20 tropical wood species.

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Acknowledgements

Research funding was provided by RU Grant—Faculty Programme by Faculty of Engineering, University of Malaya with Project Number RF001A-2018.

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Authors and Affiliations

  1. Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia

    Heshalini Rajagopal, Anis Salwa Mohd Khairuddin & Norrima Mokhtar

  2. Malaysia Japan International Institute of Technology, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia

    Rubiyah Yusof

  3. Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam, Malaysia

    Azlin Ahmad

Authors
  1. Heshalini Rajagopal
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  2. Anis Salwa Mohd Khairuddin
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  3. Norrima Mokhtar
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  4. Azlin Ahmad
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  5. Rubiyah Yusof
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Correspondence to Anis Salwa Mohd Khairuddin.

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Rajagopal, H., Khairuddin, A.S.M., Mokhtar, N. et al. Application of image quality assessment module to motion-blurred wood images for wood species identification system. Wood Sci Technol 53, 967–981 (2019). https://doi.org/10.1007/s00226-019-01110-2

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  • DOI: https://doi.org/10.1007/s00226-019-01110-2

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