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RISS 인기검색어
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- 저자 : A Rasid Mamat (검색결과 2 건)
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Abd Rasid Mamat,Mohd Khalid Awang,Norkhairani Abdul Rawi,Mohd. Isa Awang,Mohd Fadzil Abdul Kadir 보안공학연구지원센터 2016 International Journal of Multimedia and Ubiquitous Vol.11 No.2
Many textures based image retrieval researchers use global texture features for representing and retrieval of images from an image database. However, this leads to misrepresentation of local information leading to the inefficient image retrieval performance. This paper presents an approach to overcome the problem. The approach focuses on extracting local Haralick’s texture feature based on a predetermined region using the color co-occurrence matrix method, the selection of the ‘significant’ Haralik’s texture features and evaluation of the performance of the combination of the ‘significant’ features. The proposed method which is an Average Analysis and a well known method, Principal Component Analysis were applied to obtain ‘significant’ features. In order to compare the performance, a series of experiments were carried out for both methods, which is the proposed Average Analysis and the Principal Component Analysis. Experiments were performed on a 1000 selected images from the Coral image database which were divided into ten categories. Based on the experimental results, it is interesting to note that for the combination ‘significant’ features obtained from the proposed Average Analysis showed better retrieval performance compared to the Principal Component Analysis for almost all categories. This finding has an important implication in deciding the correct combination of ‘significant’ features for certain image properties. It has shown that the proposed method is able to produce less computational processing time due to a reduced amount of processing involved. The result is also compared to the previous researches and has shown an increase of an average precision from 8.5% to 26%.
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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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