Wan Khairunizam Wan Ahmad
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Preferred name
Wan Khairunizam Wan Ahmad
Official Name
Wan Khairunizam, Wan Ahmad
Alternative Name
Wan, Khairunizam
Ahmad, Wan Khairunizam Wan
Khairunizam, W. A. N.
Main Affiliation
Scopus Author ID
57200576499
Researcher ID
E-6072-2011

Research Output

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  • Publication
    An improved defect classification algorithm for six printing defects and its implementation on real printed circuit board images
    ( 2012)
    Ismail. Ibrahim
    ;
    Zuwairie Ibrahim
    ;
    Kamal Khalil
    ;
    Musa Mohd Mokji
    ;
    Syed Abdul Rahman Syed Abu Bakar
    ;
    Norrima Mokhtar
    ;
    Wan Khairunizam Wan Ahmad
    Because decisions made by human inspectors often involve subjective judg- ment, in addition to being intensive and therefore costly, an automated approach for printed circuit board (PCB) inspection is preferred to eliminate subjective discrimination and thus provide fast, quantitative, and dimensional assessments. In this study, defect classi cation is essential to the identi cation of defect sources. Therefore, an algorithm for PCB defect classi cation is presented that consists of well-known conventional op- erations, including image difference, image subtraction, image addition, counted image comparator, ood- ll, and labeling for the classi cation of six different defects, namely, missing hole, pinhole, underetch, short-circuit, open-circuit, and mousebite. The de- fect classi cation algorithm is improved by incorporating proper image registration and thresholding techniques to solve the alignment and uneven illumination problem. The improved PCB defect classi cation algorithm has been applied to real PCB images to successfully classify all of the defects.
  • Publication
    No-reference quality assessment for imagebased assessment of economically important tropical woods
    ( 2020-05-01)
    Rajagopal H.
    ;
    Mokhtar N.
    ;
    Izam T.F.T.M.N.
    ;
    Wan Khairunizam Wan Ahmad
    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 WNRIQA could be seen from its independency from a "perfect" reference image for the image quality evaluation.