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

Rajagopal, Heshalini; Khairuddin, Anis Salwa Mohd; Mokhtar, Norrima; Ahmad, Azlin; Yusof, Rubiyah

doi:10.1007/s00226-019-01110-2

Cited by 26 publications

(17 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where i is the image index in the pyramid. For the purpose to simulate the conduction equation, AKAZE maps the σ to the t which is displayed by formula (13).…”

Section: ) Key Pointmentioning

confidence: 99%

Wood Product Tracking Using an Improved AKAZE Method in Wood Traceability System

Sun

Cao

et al. 2021

IEEE Access

View full text Add to dashboard Cite

The tracking of the wood product is an important technology in the trade activity of rare plants. Normally, factories use Quick Response (QR) and Radio-Frequency Identification (RFID) to identify the individual wood product, but these technologies are not safe enough because they can be easily falsified. The traditional methods are hard to catch the detail of the slim wood texture from the wood product. In this study, a novel method is employed to resolve these problems using a biometric feature on the surface of the real wood product to distinguish the individual wood product. AKAZE is used to extract the keypoint of wood texture. A sub-area detection technique along with a serialization method is then developed to improve the rate of identification. The sub-area detection technique deals with picking out a sub-region in which there are enough AKAZE points as small as possible. The serialization method is also utilized to reduce the redundant process of feature extraction. The experimental results demonstrate that the values of accuracy, recall, and F 1 reach 0.98, 0.96, and 0.96, respectively. The match time that uses serialized function is reduced to 1/3 of which has no application in the original image. Validated results also reveal that our proposed methodology improve the robustness of the wood product identification, and it can be used in Wood Traceability System (WTS) with the blockchain to resolve the digital trust problem and the fast distinction issues of the real wood product.

show abstract

“…where i is the image index in the pyramid. For the purpose to simulate the conduction equation, AKAZE maps the σ to the t which is displayed by formula (13).…”

Section: ) Key Pointmentioning

confidence: 99%

Wood Product Tracking Using an Improved AKAZE Method in Wood Traceability System

Sun

Cao

et al. 2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…These distortions degrade the quality of the wood images where the features of the pores on the wood texture may not be discerned. Hence, this may lead to misclassification of the wood genus as the feature extractor may not obtain distinctive features from the wood images efficiently [28]. Nine modulations of Gaussian white noise with standard deviation, s GN and motion blur with standard deviation, s MB were added to the reference images, i.e.…”

Section: Wood Imagesmentioning

confidence: 99%

“…The proposed GGNR-IQA is compared with a well-known NR-IQA algorithm, BRISQUE and five FR-IQAs [28]: SSIM [10], MS-SSIM [10], FSIM [11], IW-SSIM [12] and GMSD [13].…”

Section: Performance Evaluationmentioning

confidence: 99%

A No-Reference Image Quality Assessment Metric for Wood Images

Rajagopal¹,

Mokhtar²,

Khairuddin³

et al. 2021

JRNAL

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Provided that these distortions resulted in a low quality of the wood image, the features of the pores on the wood texture could not be distinguished from one another. As a result, misclassification of the wood species occurred as the feature extractor would not be able to effectively extract distinctive features from the wood texture images [23].…”

Section: Training and Testing Databasementioning

confidence: 99%

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

et al. 2020

Self Cite

View full text Add to dashboard Cite

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.

show abstract

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

Cited by 26 publications

References 27 publications

Wood Product Tracking Using an Improved AKAZE Method in Wood Traceability System

Wood Product Tracking Using an Improved AKAZE Method in Wood Traceability System

A No-Reference Image Quality Assessment Metric for Wood Images

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

Contact Info

Product

Resources

About