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RISS 인기검색어
- 검색키워드
- 저자 : Ahmad Fakhri Ab. Nasir (검색결과 2 건)
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Image Segmentation Using OpenMP and Its Application in Plant Species Classification
M Nordin A Rahman,Ahmad Fakhri Ab. Nasir,Nashriyah Mat,A Rasid Mamat 보안공학연구지원센터 2015 International Journal of Software Engineering and Vol.9 No.5
Segmentation is very important in early stage of image processing pipelines. Final results of image processing are strongly depending on the initial image segmentation quality. A good quality result often comes at the price of high computational cost including computation speed. Image segmentation requires long computation task caused by sequential processing of huge sizes of image and complex tasks. Nowadays, multi-core architectures are emerging as an attractive platform for parallel processing because it has two or more independent cores in a single physical package and their comparatively low cost. In this paper, two parallelization strategies (fine-grain and coarse-grain approach) are proposed for computing leaf image segmentation. The Canny Edge Detector and Otsu thresholding methods are used due to their wide range of usage for leaf segmentation in plant classification. The implementation is developed under multi-core architecture with shared memory multiprocessors. The OpenMP (Open Multi-Processing), an API (Application Programming Interface) is utilized for writing multi-threaded applications in shared memory architecture. The comparative study with two parallelization strategies is discussed further in this paper.
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Evaluation of the Machine Learning Classifier in Wafer Defects Classification
Jessnor Arif Mat Jizat,Anwar P.P. Abdul Majeed,Ahmad Fakhri Ab. Nasir,Zahari Taha,Edmund Yuen 한국통신학회 2021 ICT Express Vol.7 No.4
In this paper, an evaluation of machine learning classifiers to be applied in wafer defect detection is described. The objective is to establish the best machine learning classifier for Wafer Defect Detection application. k-Nearest Neighbours (k-NN), Logistic Regression, Stochastic Gradient Descent, and Support Vector Machine were evaluated with 3 defects categories and one non-defect category. The key metrics for the evaluation are classification accuracy, classification precision and classification recall. 855 images were used to train, test and validate the classifier. Each image went through the embedding process by InceptionV3 algorithms before the evaluated classifier classifies the images.
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