Determinants of Occupational Accidents in the Woodworking Sector: The Case of the Malayasian Wooden Furniture Industry

Ratnasinga, J.; Ioraş, Florin; Swan, Taryn; Yoon, C. Y.; Thanasegar, G.

doi:10.3923/jas.2011.561.566

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…These two types of distortions usually occur in the industrial setting. Generally, the wood images are exposed to Gaussian white noise due to the poor illumination and heat in the lumber mill while acquiring the wood images [8,27].…”

Section: Wood Imagesmentioning

confidence: 99%

“…Nevertheless, the image enhancement processes consume extra computational time, and could cause a checkerboard artefact to the wood images [7]. In addition, the dusty and dark environment in timber factories could degrade the quality of the image acquired [8]. Thus, an appropriate Image Quality Assessment (IQA) algorithm is required to assess the acquired wood images prior to feeding it to any automatic wood recognition system.…”

Section: Introductionmentioning

confidence: 99%

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A No-Reference Image Quality Assessment Metric for Wood Images

Rajagopal¹,

Mokhtar²,

Khairuddin³

et al. 2021

JRNAL

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Image Quality Assessment (IQA) is a vital element in improving the efficiency of an automatic recognition system of various wood species. There is a need to develop a No-Reference IQA (NR-IQA) system as a perfect and distortion free wood images may be impossible to be acquired in the dusty environment in timber factories. To the best of our knowledge, there is no NR-IQA developed for wood images specifically. Therefore, a Gray Level Co-Occurrence Matrix (GLCM) and Gabor features-based NR-IQA, GGNR-IQA metric is proposed to assess the quality of wood images. The proposed metric is developed by training the support vector machine regression with GLCM and Gabor features calculated for wood images together with scores obtained from subjective evaluation. The proposed IQA metric is compared with a widely used NR-IQA metric, Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) and Full Reference-IQA (FR-IQA) metrics. Results shows that the proposed NR-IQA metric outperforms the BRISQUE and the FR-IQA metrics. Moreover, the proposed NR-IQA metric is beneficial in wood industry as a distortion free reference image is not needed to evaluate the wood images.

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Section: Wood Imagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A No-Reference Image Quality Assessment Metric for Wood Images

Rajagopal¹,

Mokhtar²,

Khairuddin³

et al. 2021

JRNAL

View full text Add to dashboard Cite

show abstract

“…The ten reference wood images were then distorted by Gaussian white noise and motion blur to represent the image distortions, which were encountered in the industrial setting. To be specific, Gaussian white noise often arises during the acquisition of wood images due to the sensor noise [22] caused by poor illumination and high ambient temperature in the lumber mill [8]. Meanwhile, wood images were subjected to motion blur upon the presence of relative motion between the camera and the wood slice [6].…”

Section: Training and Testing Databasementioning

confidence: 99%

“…However, the processes of image enhancement require more time and may impart a checkerboard artefact to the wood images [7]. Besides, the environment of timber factories is surrounded by dust, poor illumination, and heat [8], which lead to the degradation of the image quality. Therefore, a suitable Image Quality Assessment (IQA) metric is essential to evaluate the captured images before proceeding to the pipeline for recognition algorithms.…”

Section: Introductionmentioning

confidence: 99%

No-reference quality assessment for image-based assessment of economically important tropical woods

et al. 2020

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Image Quality Assessment (IQA) is essential for the accuracy of systems for automatic recognition of tree species for wood samples. In this study, a No-Reference IQA (NR-IQA), wood NR-IQA (WNR-IQA) metric was proposed to assess the quality of wood images. Support Vector Regression (SVR) was trained using Generalized Gaussian Distribution (GGD) and Asymmetric Generalized Gaussian Distribution (AGGD) features, which were measured for wood images. Meanwhile, the Mean Opinion Score (MOS) was obtained from the subjective evaluation. This was followed by a comparison between the proposed IQA metric, WNR-IQA, and three established NR-IQA metrics, namely Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE), deepIQA, Deep Bilinear Convolutional Neural Networks (DB-CNN), and five Full Reference-IQA (FR-IQA) metrics known as MSSIM, SSIM, FSIM, IWSSIM, and GMSD. The proposed WNR-IQA metric, BRISQUE, deepIQA, DB-CNN, and FR-IQAs were then compared with MOS values to evaluate the performance of the automatic IQA metrics. As a result, the WNR-IQA metric exhibited a higher performance compared to BRISQUE, deepIQA, DB-CNN, and FR-IQA metrics. Highest quality images may not be routinely available due to logistic factors, such as dust, poor illumination, and hot environment present in the timber industry. Moreover, motion blur could occur due to the relative motion between the camera and the wood slice. Therefore, the advantage of WNR-IQA could be seen from its independency from a "perfect" reference image for the image quality evaluation.

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