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INTELEK Repository
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Online Access
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https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072
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2024-08-26 18:32:50
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Restricted Document
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12090
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[1] L. Huang, “Extending OpenMP on distributed memory Systems via global arrays”, (Thesis, University of Houston, (2006). [2] R. B. Batista, A. Boukerche and A. C. M. A. de Melo, “A parallel strategy for biological sequence alignment in restricted memory space”, Journal of Parallel and Distributed Computing, vol. 68, (2008), pp. 548-561. [3] M. Nordin A. Rahman, M. Yazid, MSaman, A. Ahmad and A. Osman M Tap, “Parallel Guided Dynamic Programming Approach for DNA Sequence Similarity Search”, International Journal of Computer and Electrical Engineering, vol. 1, no. 4, (2009), pp. 402-409. [4] N. Otsu, “A threshold selection method from gray level histograms”, IEEE Trans. on Systems, Man and Cybernetics, vol. 9, (1979), pp. 62-66. [5] J. Canny, “A computational approach to edge detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, no. 6, 1986, 679-698. [6] J. Adams, C. Nevison and N. C. Schaller, “Parallel computing to start the millennium”, Association for Computing Machinery (ACM) SIGCSE Bulletin, vol. 32, no. 1, 2000, 65-69. [7] R. Yang, J. Cai, A. P. Rendell and V. Ganesh, “Cluster OpenMP and the Gaussian code: a preliminary performance analysis”, 5 th International Workshop on OpenMP IWOMP, Dresden, Germany, (2009). [8] M Nordin A Rahman, Utilizing MPJ Express Software in Parallel DNA Sequence Alignment. In the Proc. of the IEEE International Conference on Future Computer and Communication, (2009), pp. 567- 571. [9] J. K. Park, E. J. Hwang and Y. Nam, “Utilizing venation features for efficient leaf image retrieval”, Journal of Systems and Software, vol. 81, (2008), pp. 71-82. [10] S. D. Noble and R. B. Brown, “Spectral band selection and testing of edge-subtraction leaf segmentation”, Canadian Biosystems Engineering, vol. 50, (2008), pp. 2.1-2.8. [11] X. Li, H.-H. Lee and K.-S. Hong, “Leaf contour extraction based on an intelligent scissor algorithm with complex background”, In the International Conference on Future Computers in Education, (2012), pp. 215-220. [12] X.-F. Wang, D.-S. Huang, J.-X. Du, H. Xu and L. Heutte, “Classification of plant leaf images with complicated background”, Applied Mathematics and Computation, vol. 205, (2008), pp. 916-926. [13] X. Zheng, “Leaf vein extraction based on gray-scale morphology”, International Journal of Image, Graphics and Signal Processing, vol. 2, (2010), pp. 25-31. [14] T. H. Jaware, R. D. Badgujar and P. G. Patil, “Crop disease detection using image segmentation”, World Journal of Science and Technology, vol. 2, no. 4, (2012), pp. 190-194. [15] A. Fakhri, A. Nasir, M. Nordin, A. Rahman and A. Rasid Mamat, “A study of image processing in agriculture application under high performance computing environment”, International Journal of Computer Science and Telecommunications, vol. 3, no. 8, (2012), pp. 16-24. [16] B. Barney, “Introduction to parallel computing”, form: https://computing.llnl.gov/tutorials/ parallel_comp, Retrieved (2013). [17] K. Sun, Q. Zhou, K. Mohanram and D. C. Sorensen, “Parallel domain decomposition for simulation of large-scale power grids”, IEEE/ACM International Conference on Computer Aided Design, (2007), pp. 54-59. [18] C. Terboven, D. An Mey and S. Sarholz, “Openmp in multicore architectures”, International Workshop on OpenMP A Practical Programming Model for the MultiCore Era, (2008), 1-15. [19] B.M. Singh, R. Sharma, A. Mittal and D. Ghosh, “Parallel implementation of Otsu’s binarization approach on GPU”, International Journal of Computer Applications, vol. 32, no. 2, (2010), pp. 16-21. [20] K. Ogawa, Y. Ito and K. Nakano, “Efficient Canny edge detection using a GPU”, In the International Conference on Networking and Computing, (2010), pp. 279-280. [21] N. S. Chadrashekar and K. R. Nataraj, “A distributed Canny edge detector and its implementation of FPGA”, International Journal of Computational Engineering Research, vol. 2, no. 7, (2012), pp. 177- 181. [22] Y. M. Luo and R. Duraiswami, “Canny edge detection on NVIDIA CUDA”, In the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, (2008), pp. 1-8.
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6393-01-FH02-FIK-15-03356.pdf
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12090 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=12090 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Article Journal application/pdf Adobe Acrobat Pro DC 20 Paper Capture Plug-in with ClearScan 11 1.6 SERSC 2024-08-26 18:32:50 6393-01-FH02-FIK-15-03356.pdf UniSZA Private Access Image Segmentation Using OpenMP and Its Application in Plant Species Classification International Journal of Software Engineering and Its Applications 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 multicore architecture with shared memory multiprocessors. The OpenMP (Open MultiProcessing), an API (Application Programming Interface) is utilized for writing multithreaded applications in shared memory architecture. The comparative study with two parallelization strategies is discussed further in this paper. 9 5 Science and Engineering Research Support Society Science and Engineering Research Support Society 135-144 [1] L. Huang, “Extending OpenMP on distributed memory Systems via global arrays”, (Thesis, University of Houston, (2006). [2] R. B. Batista, A. Boukerche and A. C. M. A. de Melo, “A parallel strategy for biological sequence alignment in restricted memory space”, Journal of Parallel and Distributed Computing, vol. 68, (2008), pp. 548-561. [3] M. Nordin A. Rahman, M. Yazid, MSaman, A. Ahmad and A. Osman M Tap, “Parallel Guided Dynamic Programming Approach for DNA Sequence Similarity Search”, International Journal of Computer and Electrical Engineering, vol. 1, no. 4, (2009), pp. 402-409. [4] N. Otsu, “A threshold selection method from gray level histograms”, IEEE Trans. on Systems, Man and Cybernetics, vol. 9, (1979), pp. 62-66. [5] J. Canny, “A computational approach to edge detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, no. 6, 1986, 679-698. [6] J. Adams, C. Nevison and N. C. Schaller, “Parallel computing to start the millennium”, Association for Computing Machinery (ACM) SIGCSE Bulletin, vol. 32, no. 1, 2000, 65-69. [7] R. Yang, J. Cai, A. P. Rendell and V. Ganesh, “Cluster OpenMP and the Gaussian code: a preliminary performance analysis”, 5 th International Workshop on OpenMP IWOMP, Dresden, Germany, (2009). [8] M Nordin A Rahman, Utilizing MPJ Express Software in Parallel DNA Sequence Alignment. In the Proc. of the IEEE International Conference on Future Computer and Communication, (2009), pp. 567- 571. [9] J. K. Park, E. J. Hwang and Y. Nam, “Utilizing venation features for efficient leaf image retrieval”, Journal of Systems and Software, vol. 81, (2008), pp. 71-82. [10] S. D. Noble and R. B. Brown, “Spectral band selection and testing of edge-subtraction leaf segmentation”, Canadian Biosystems Engineering, vol. 50, (2008), pp. 2.1-2.8. [11] X. Li, H.-H. Lee and K.-S. Hong, “Leaf contour extraction based on an intelligent scissor algorithm with complex background”, In the International Conference on Future Computers in Education, (2012), pp. 215-220. [12] X.-F. Wang, D.-S. Huang, J.-X. Du, H. Xu and L. Heutte, “Classification of plant leaf images with complicated background”, Applied Mathematics and Computation, vol. 205, (2008), pp. 916-926. [13] X. Zheng, “Leaf vein extraction based on gray-scale morphology”, International Journal of Image, Graphics and Signal Processing, vol. 2, (2010), pp. 25-31. [14] T. H. Jaware, R. D. Badgujar and P. G. Patil, “Crop disease detection using image segmentation”, World Journal of Science and Technology, vol. 2, no. 4, (2012), pp. 190-194. [15] A. Fakhri, A. Nasir, M. Nordin, A. Rahman and A. Rasid Mamat, “A study of image processing in agriculture application under high performance computing environment”, International Journal of Computer Science and Telecommunications, vol. 3, no. 8, (2012), pp. 16-24. [16] B. Barney, “Introduction to parallel computing”, form: https://computing.llnl.gov/tutorials/ parallel_comp, Retrieved (2013). [17] K. Sun, Q. Zhou, K. Mohanram and D. C. Sorensen, “Parallel domain decomposition for simulation of large-scale power grids”, IEEE/ACM International Conference on Computer Aided Design, (2007), pp. 54-59. [18] C. Terboven, D. An Mey and S. Sarholz, “Openmp in multicore architectures”, International Workshop on OpenMP A Practical Programming Model for the MultiCore Era, (2008), 1-15. [19] B.M. Singh, R. Sharma, A. Mittal and D. Ghosh, “Parallel implementation of Otsu’s binarization approach on GPU”, International Journal of Computer Applications, vol. 32, no. 2, (2010), pp. 16-21. [20] K. Ogawa, Y. Ito and K. Nakano, “Efficient Canny edge detection using a GPU”, In the International Conference on Networking and Computing, (2010), pp. 279-280. [21] N. S. Chadrashekar and K. R. Nataraj, “A distributed Canny edge detector and its implementation of FPGA”, International Journal of Computational Engineering Research, vol. 2, no. 7, (2012), pp. 177- 181. [22] Y. M. Luo and R. Duraiswami, “Canny edge detection on NVIDIA CUDA”, In the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, (2008), pp. 1-8.
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| spellingShingle |
Image Segmentation Using OpenMP and Its Application in Plant Species Classification
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| summary |
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 multicore architecture with shared memory multiprocessors. The OpenMP (Open MultiProcessing), an API (Application Programming Interface) is utilized for writing multithreaded applications in shared memory architecture. The comparative study with two parallelization strategies is discussed further in this paper.
|
| title |
Image Segmentation Using OpenMP and Its Application in Plant Species Classification
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| title_full |
Image Segmentation Using OpenMP and Its Application in Plant Species Classification
|
| title_fullStr |
Image Segmentation Using OpenMP and Its Application in Plant Species Classification
|
| title_full_unstemmed |
Image Segmentation Using OpenMP and Its Application in Plant Species Classification
|
| title_short |
Image Segmentation Using OpenMP and Its Application in Plant Species Classification
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| title_sort |
image segmentation using openmp and its application in plant species classification
|