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1860799836355297280
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INTELEK Repository
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IEEE Transactions on Magnetics
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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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2012-08-09 17:47:02
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Restricted Document
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7587
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UniSZA
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[1] J. P. Kerr and E. B. Bartlett, “Medical image processing utilizing neural networks trained on a massively parallel computer,” Computers in Biology and Medicine, vol. 25, no. 4, pp. 393–403, Feb. 1995. [2] G. E. Christensen, “MIMD vs. SIMD parallel processing: a case study in 3D medical image registration,” Parallel Computing – Special double issue on biomedical applications, vol. 24, no. 9, pp. 1369–1383, Sept. 1998. [3] A. Camargo and J.S. Smith, “Image pattern classification for the identification of disease causing agents in plants,” Computers and Electronis in Agriculture, vol. 66, no. 2, pp. 121–125, Feb. 2009. [4] A. Meunkaewjinda, P. Kumsawat, K. Attakitmongcol, and A. Srikaew, “Grape leaf disease detection from color imagery using hybrid intelligent system,” in 2008 Proc. of ECTI-CON, pp. 513– 516. [5] K. Singh, I. Gupta, and S. Gupta, S, “Classification of bamboo plant based on digital image processing by central moment,” in 2011 Int. Conf. on Image Information Processing. [6] J. C. Neto, G. E. Meyer, D. D. Jones, and A. K. Samal, “Plant species identification using elliptic Fourier leaf shape analysis,” Computers and Electronics in Agriculture, vol. 50, no. 2, pp. 121– 134, Feb. 2006. [7] H. B. Prajapati and S. K. Vij, “Analytical study of parallel and distributed image processing,” in 2011 Int. Conf. on Image Information Processing, pp. 1–6. [8] A. Wachsmann, “Concepts of parallel computing: clustering and shared memory,” unpublished. [9] M. J. Flynn and K. W. Rudd, “Parallel architectures,” ACM Computing Surveys, vol. 28, no. 1, pp. 67–70, Mar. 1996. [10] B. Barney. (2010, May 10). Introduction to parallel computing. [Online]. Available: https://computing.llnl.gov/tutorials/parallel_comp/ [11] T. Mattson and R. Eigenmann. (1999). OpenMP: an API for writing portable SMP application software. [Online]. Available: http://www.openmp.org/presentations/sc99/sc99_tutorial.pdf [12] W. Gropp, E. Lusk, and A. Skjellum, Using MPI: Portable Parallel Programming with the Message-Passing Interface. 1 st ed. Cambridge, MA, USA: MIT Press, 1999. [13] G. Sharma and J. Martin, “Matlab: A language for parallel computing,” International Journal of Parallel Computing, vol. 37, no. 1, pp. 3–36, 2009. [14] M. Baker, B. Carpenter, and A. Shafi, “Works in Progress: A pluggable architecture for high-performance Java messaging,” IEEE Distributed Systems Online, vol. 6, no. 10, 2005. [15] S. Annadurai and R. Shanmugalakshmi, Fundamentals of Digital Image Processing. India: Pearson Education, 2007, pp. 3–10. [16] X. Pengyun and L. Jigang, “Computer assistance image processing spores counting,” in 2009 Proc. Int. Asia Conf. on Informatics in Control, Automation and Robotics, pp. 203–206. [17] C. C. Yang, S. O. Prasher, J. A. Landry, H. S. Ramaswamy, and A. Ditommaso, “Application of artificial neural networks in image recognition and classification of crop and weeds,” Canadian Agricultural Engineering, vol. 42, no. 3, pp. 147 – 152, 2000. [18] X.-m. Suo, Y.-t. Jiang, M. Yang, S.-k. Li, K.-r. Wang, and C.-t. Wang, “Artificial neural network to predict leaf population chlorophyll content from cotton plant images,” Agricultural Sciences in China, vol. 9, no. 1, pp. 38–45, Jan. 2010. [19] S. Phadikar and J. Sil, “Rice disease identification using pattern recognition techniques,” in Proc. 11th Int. Conf. on Computer and Information Technology, 2008, Khulna, Bangladesh, pp. 420–423. [20] J. I. Arribas, G. V. Sanchez-Ferrero, G. Ruiz-Ruiz, and J. Gomez-Gil, “Leaf classification in sunflower crops by computer vision and neural networks,” Computers and Electronics in Agriculture, vol. 78, no.1, pp. 9–18, Aug. 2011. [21] Y. Chen and X. Zhou, “Plant root image processing and analysis based on 2D scanner,” in Proc. IEEE 5 th Int. Conf. on Bio-Inspired Computing: Theories and Application, 2010, Ghangsha, China, pp. 1216–1220. [22] S. Thaiparnit and J. Jakkree Srinonchat, “Apply image processing technique to determine the correlation chlorophyll,” presented at the 6 th Int. Conf. on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, Thailand, May. 6–9, 2009, pp. 1116–1119. [23] J. S. Cope, D. Corney, J. Y. Clark, P. Remagnino, and P. Wilkin, “Plant species identification using digital morphometrics: a review,” Experts Systems with Applications, vol. 39, pp. 7562–7573, 2012. [24] Z. Wang, Z. Chi, and D. Feng, “Shape based leaf image retrieval,” Vision, Image & Signal Processing, vol. 150, no. 1, pp. 34–43, Feb. 2003. [25] Y. Nam, E. Hwang, and D. Kim, “A similarity-based leaf image retrieval scheme: joining shape and venation features,” Computer Vision and Image Understanding, vol. 110, no. 2, pp. 245–249, May 2008. [26] J.-X. Du, X.-F. Wang, and G.-J. Zhang, “Leaf shape based plant species recognition,” Applied Mathematics and Computation, vol. 185, no. 2, pp. 883–893, Feb. 2007. [27] O. M. Bruno, R. d. O. Plotze, R. M. Falvo, and M. d. Castro, “Fractal dimension applied to plant identification,” Information Sciences, vol. 178, no. 12, pp. 2722–2733, June 2008. [28] R. Fageth, W. G. Allen, U. and Jager, U. “Fuzzy logic classification in image processing,” Fuzzy Sets and Systems, vol. 82, no. 3, pp. 265–278, Sept. 1996. [29] H. Shirgahi and N. Danesh, “Presented a fuzzy system to determine barberry product quality based on average color spectra by image processing,” Journal of King Saud University, vol. 23, no. 1, pp. 105– 109, July 2010. [30] S. G. Wu, F. S. Bao, E. Y. Xu, Y-X. Wang, Y.-F. Chang, and Q.-L. Xiang, “A leaf recognition algorithm for plant classification using probabilistic neural network,” presented at the IEEE International Symposium on Signal Processing and Information Technology, Cairo, Egypt, Dec. 15–18, 2007, pp. 11–16. [31] T. Braunl, S. Feyrer, W. Rapf, and M. Reinhardt, Parallel Image Processing, 6 th ed. New York: Springer, 2001, pp. 1–2. [32] C. Nicolescu and P. Jonker, “Parallel low-level image processing on distributed-memory system,” in 2000 Proc. IPDPS Workshops on Parallel and Distributed Processing, pp. 226–233. [33] M. Baker, B. Carpenter, and A. Shafi, “MPJ Express: Towards thread safe Java HPC,” presented at the IEEE Int. Conf. on Cluster Computing, Barcelona, Spain, Sep. 25–28, 2006. [34] C. Ferri, J. Hernandez-Orallo, and R. Modroiu, “An experimental comparison of performance measures for classification,” Pattern Recognition Letters, vol. 30, no. 1, pp. 27–38, Jan. 2009. [35] X.-F. Wang, D.-S. Huang, J.-X. Du, H. Hu, and L. Heutte, “Classification of plant leaf images with complicated background,” Applied Mathematics and Computation, vol. 205, no. 2, pp. 916– 926, Nov. 2008. [36] A. A. Al-Janobi, “Date inspection by color machine vision,” Journal of King Saud University, vol. 12, no. 1, pp. 66–79, 2000. [37] Y. Al-Ohali, “Computer vision based date fruit grading system: design and implementation,” Journal of King Saud University, vol. 23, pp. 29–36, 2011. [38] R. Fellegari and H. Navid, “Determining the orange volume using image processing,” in 2011 Int. Conf. on Food Engineering and Biotechnology, pp. 180–184. [39] E. Koffman and U. Wolz, “A simple Java package for GUI-like interactivity,” in 2001 Proc. of the Third-Second SIGCSE Technical Symposium on Computer Science Education, pp. 11–15. [40] V. Getov, G. V. Laszewski, M. Philippsen, and I. Foster, “Multiparadigm communications in Java for grid computing,” Communications of the ACM, vol. 44, no. 10, pp. 118–125, Oct. 2001. [41] N. Mohamed, J. Al-Jaroodi, H. Jiang, and D. Swanson, “JOPI: A Java object-passing interface,” Concurrency and Computation: Practice & Experience, vol. 17, no. 7–8, pp. 775–795, June 2005.
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3154-01-FH02-FIK-14-00662.pdf
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oai_dc
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https://intelek.unisza.edu.my/intelek/pages/view.php?ref=7587
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7587 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=7587 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Article Journal application/pdf 9 1.6 Adobe Acrobat Pro DC 20 Paper Capture Plug-in IEEE Transactions on Magnetics IEEE Transactions on Magnetics 2012-08-09 17:47:02 3154-01-FH02-FIK-14-00662.pdf UniSZA Private Access A Study of Image Processing in Agriculture Application under High Performance Computing Environment International Journal of Computer Science and Telecommunications The available literatures on image processing in agriculture application under high performance computing (HPC) are limited and sometimes are not discussed in details. This paper reviewed the steps of image analysis done in some image processing focusing on agriculture application and also the details analysis of parallel and distributed image processing. The memory architecture in parallel and distributed image processing and some suitable application programming interface (API) in parallel and distributed image processing are examined. In general, this study provides basic understanding of parallel and distributed image processing for agriculture application. 3 8 16-24 [1] J. P. Kerr and E. B. Bartlett, “Medical image processing utilizing neural networks trained on a massively parallel computer,” Computers in Biology and Medicine, vol. 25, no. 4, pp. 393–403, Feb. 1995. [2] G. E. Christensen, “MIMD vs. SIMD parallel processing: a case study in 3D medical image registration,” Parallel Computing – Special double issue on biomedical applications, vol. 24, no. 9, pp. 1369–1383, Sept. 1998. [3] A. Camargo and J.S. Smith, “Image pattern classification for the identification of disease causing agents in plants,” Computers and Electronis in Agriculture, vol. 66, no. 2, pp. 121–125, Feb. 2009. [4] A. Meunkaewjinda, P. Kumsawat, K. Attakitmongcol, and A. Srikaew, “Grape leaf disease detection from color imagery using hybrid intelligent system,” in 2008 Proc. of ECTI-CON, pp. 513– 516. [5] K. Singh, I. Gupta, and S. Gupta, S, “Classification of bamboo plant based on digital image processing by central moment,” in 2011 Int. Conf. on Image Information Processing. [6] J. C. Neto, G. E. Meyer, D. D. Jones, and A. K. Samal, “Plant species identification using elliptic Fourier leaf shape analysis,” Computers and Electronics in Agriculture, vol. 50, no. 2, pp. 121– 134, Feb. 2006. [7] H. B. Prajapati and S. K. Vij, “Analytical study of parallel and distributed image processing,” in 2011 Int. Conf. on Image Information Processing, pp. 1–6. [8] A. Wachsmann, “Concepts of parallel computing: clustering and shared memory,” unpublished. [9] M. J. Flynn and K. W. Rudd, “Parallel architectures,” ACM Computing Surveys, vol. 28, no. 1, pp. 67–70, Mar. 1996. [10] B. Barney. (2010, May 10). Introduction to parallel computing. [Online]. Available: https://computing.llnl.gov/tutorials/parallel_comp/ [11] T. Mattson and R. Eigenmann. (1999). OpenMP: an API for writing portable SMP application software. [Online]. Available: http://www.openmp.org/presentations/sc99/sc99_tutorial.pdf [12] W. Gropp, E. Lusk, and A. Skjellum, Using MPI: Portable Parallel Programming with the Message-Passing Interface. 1 st ed. Cambridge, MA, USA: MIT Press, 1999. [13] G. Sharma and J. Martin, “Matlab: A language for parallel computing,” International Journal of Parallel Computing, vol. 37, no. 1, pp. 3–36, 2009. [14] M. Baker, B. Carpenter, and A. Shafi, “Works in Progress: A pluggable architecture for high-performance Java messaging,” IEEE Distributed Systems Online, vol. 6, no. 10, 2005. [15] S. Annadurai and R. Shanmugalakshmi, Fundamentals of Digital Image Processing. India: Pearson Education, 2007, pp. 3–10. [16] X. Pengyun and L. Jigang, “Computer assistance image processing spores counting,” in 2009 Proc. Int. Asia Conf. on Informatics in Control, Automation and Robotics, pp. 203–206. [17] C. C. Yang, S. O. Prasher, J. A. Landry, H. S. Ramaswamy, and A. Ditommaso, “Application of artificial neural networks in image recognition and classification of crop and weeds,” Canadian Agricultural Engineering, vol. 42, no. 3, pp. 147 – 152, 2000. [18] X.-m. Suo, Y.-t. Jiang, M. Yang, S.-k. Li, K.-r. Wang, and C.-t. Wang, “Artificial neural network to predict leaf population chlorophyll content from cotton plant images,” Agricultural Sciences in China, vol. 9, no. 1, pp. 38–45, Jan. 2010. [19] S. Phadikar and J. Sil, “Rice disease identification using pattern recognition techniques,” in Proc. 11th Int. Conf. on Computer and Information Technology, 2008, Khulna, Bangladesh, pp. 420–423. [20] J. I. Arribas, G. V. Sanchez-Ferrero, G. Ruiz-Ruiz, and J. Gomez-Gil, “Leaf classification in sunflower crops by computer vision and neural networks,” Computers and Electronics in Agriculture, vol. 78, no.1, pp. 9–18, Aug. 2011. [21] Y. Chen and X. Zhou, “Plant root image processing and analysis based on 2D scanner,” in Proc. IEEE 5 th Int. Conf. on Bio-Inspired Computing: Theories and Application, 2010, Ghangsha, China, pp. 1216–1220. [22] S. Thaiparnit and J. Jakkree Srinonchat, “Apply image processing technique to determine the correlation chlorophyll,” presented at the 6 th Int. Conf. on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, Thailand, May. 6–9, 2009, pp. 1116–1119. [23] J. S. Cope, D. Corney, J. Y. Clark, P. Remagnino, and P. Wilkin, “Plant species identification using digital morphometrics: a review,” Experts Systems with Applications, vol. 39, pp. 7562–7573, 2012. [24] Z. Wang, Z. Chi, and D. Feng, “Shape based leaf image retrieval,” Vision, Image & Signal Processing, vol. 150, no. 1, pp. 34–43, Feb. 2003. [25] Y. Nam, E. Hwang, and D. Kim, “A similarity-based leaf image retrieval scheme: joining shape and venation features,” Computer Vision and Image Understanding, vol. 110, no. 2, pp. 245–249, May 2008. [26] J.-X. Du, X.-F. Wang, and G.-J. Zhang, “Leaf shape based plant species recognition,” Applied Mathematics and Computation, vol. 185, no. 2, pp. 883–893, Feb. 2007. [27] O. M. Bruno, R. d. O. Plotze, R. M. Falvo, and M. d. Castro, “Fractal dimension applied to plant identification,” Information Sciences, vol. 178, no. 12, pp. 2722–2733, June 2008. [28] R. Fageth, W. G. Allen, U. and Jager, U. “Fuzzy logic classification in image processing,” Fuzzy Sets and Systems, vol. 82, no. 3, pp. 265–278, Sept. 1996. [29] H. Shirgahi and N. Danesh, “Presented a fuzzy system to determine barberry product quality based on average color spectra by image processing,” Journal of King Saud University, vol. 23, no. 1, pp. 105– 109, July 2010. [30] S. G. Wu, F. S. Bao, E. Y. Xu, Y-X. Wang, Y.-F. Chang, and Q.-L. Xiang, “A leaf recognition algorithm for plant classification using probabilistic neural network,” presented at the IEEE International Symposium on Signal Processing and Information Technology, Cairo, Egypt, Dec. 15–18, 2007, pp. 11–16. [31] T. Braunl, S. Feyrer, W. Rapf, and M. Reinhardt, Parallel Image Processing, 6 th ed. New York: Springer, 2001, pp. 1–2. [32] C. Nicolescu and P. Jonker, “Parallel low-level image processing on distributed-memory system,” in 2000 Proc. IPDPS Workshops on Parallel and Distributed Processing, pp. 226–233. [33] M. Baker, B. Carpenter, and A. Shafi, “MPJ Express: Towards thread safe Java HPC,” presented at the IEEE Int. Conf. on Cluster Computing, Barcelona, Spain, Sep. 25–28, 2006. [34] C. Ferri, J. Hernandez-Orallo, and R. Modroiu, “An experimental comparison of performance measures for classification,” Pattern Recognition Letters, vol. 30, no. 1, pp. 27–38, Jan. 2009. [35] X.-F. Wang, D.-S. Huang, J.-X. Du, H. Hu, and L. Heutte, “Classification of plant leaf images with complicated background,” Applied Mathematics and Computation, vol. 205, no. 2, pp. 916– 926, Nov. 2008. [36] A. A. Al-Janobi, “Date inspection by color machine vision,” Journal of King Saud University, vol. 12, no. 1, pp. 66–79, 2000. [37] Y. Al-Ohali, “Computer vision based date fruit grading system: design and implementation,” Journal of King Saud University, vol. 23, pp. 29–36, 2011. [38] R. Fellegari and H. Navid, “Determining the orange volume using image processing,” in 2011 Int. Conf. on Food Engineering and Biotechnology, pp. 180–184. [39] E. Koffman and U. Wolz, “A simple Java package for GUI-like interactivity,” in 2001 Proc. of the Third-Second SIGCSE Technical Symposium on Computer Science Education, pp. 11–15. [40] V. Getov, G. V. Laszewski, M. Philippsen, and I. Foster, “Multiparadigm communications in Java for grid computing,” Communications of the ACM, vol. 44, no. 10, pp. 118–125, Oct. 2001. [41] N. Mohamed, J. Al-Jaroodi, H. Jiang, and D. Swanson, “JOPI: A Java object-passing interface,” Concurrency and Computation: Practice & Experience, vol. 17, no. 7–8, pp. 775–795, June 2005.
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| spellingShingle |
A Study of Image Processing in Agriculture Application under High Performance Computing Environment
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| subject |
IEEE Transactions on Magnetics
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| summary |
The available literatures on image processing in agriculture application under high performance computing (HPC) are limited and sometimes are not discussed in details. This paper reviewed the steps of image analysis done in some image processing focusing on agriculture application and also the details analysis of parallel and distributed image processing. The memory architecture in parallel and distributed image processing and some suitable application programming interface (API) in parallel and distributed image processing are examined. In general, this study provides basic understanding of parallel and distributed image processing for agriculture application.
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| title |
A Study of Image Processing in Agriculture Application under High Performance Computing Environment
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| title_full |
A Study of Image Processing in Agriculture Application under High Performance Computing Environment
|
| title_fullStr |
A Study of Image Processing in Agriculture Application under High Performance Computing Environment
|
| title_full_unstemmed |
A Study of Image Processing in Agriculture Application under High Performance Computing Environment
|
| title_short |
A Study of Image Processing in Agriculture Application under High Performance Computing Environment
|
| title_sort |
study of image processing in agriculture application under high performance computing environment
|