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A Study on Image Segmentations and Applications
License plate recognition (LPR)
cell segmentation scheme
|引用:|| C. N. E. Anagnostopoulos, I. E. Anagnostopoulos, V. Loumos, and E. Kayafas, “A License Plate-Recognition Algorithm for Intelligent Transportation System Applications,” IEEE Transactions on Intelligent Transportation Systems, Vol. 7, No. 3, pp.377-392, 2006.  S. Arivazhagan, S. Deivalakshmi, K. Kannan, B. N. Gajbhiye, C. Muralidhar, S. N. Lukose, and M. P. Subramanian, “Multi-resolution system for artifact removal and edge enhancement in computerized tomography images,” Pattern Recogn. Lett., Vol.28, pp.1769-1780, 2007.  H. Bai, J. Zhu, and C. Liu, “A Fast License Plate Extraction Method on Complex Background,” IEEE Proceedings of Intelligent Transportation Systems, Vol. 2 pp.985-987, 2003.  J. C. Bezdek, “Fuzzy Mathematics in Pattern Classification,” New York: Cornell University, 1973.  J. Canny, “A Computational Approach to Edge Detection,” IEEE Trans. Pattern Anal. Vol.8, pp.679-698, 1986.  K. R. Castleman, “Digital Image Processing,” Prentice Hall International, Inc., New Jersey, 1996.  N. D. Levine Chairman, J. O. Corliss, F. E. G. Cox, G. Deroux, J. Grain, B. M. Honigberg, G. F. Leedaile, A. R. Loeblich, III. J. Lom, D. Lynn, E. G. Merinfeld, F. C. Page, G. Poljansky, V. Sprague, J. Vavra, and F. G. Wallace, “A Newly Revised Classification of the Protozoa,” J. Eukaryot. Microbiol., Vol. 27 pp.37-58, 1980.  S. L. Chang, L. S. Chen, Y. C. Chung, and S. W. Chen, “Automatic License Plate Recognition,” IEEE Transactions on Intelligent Transportation Systems, Vol. 5, No. 1, pp.42-53, 2004.  T. S. Chen, H. C. Wu, and C. H. Lai, “Multi-target Car License Plate Detection from Complex Environment,” Proceedings of IEEE International Workshop on Nonlinear Signal and Image Processing, pp. 399-403, 2005.  E. Davies, Machine Vision: Theory, Algorithms and Practicalities, Academic Press, 1990.  O. Duda, P. E. Hart and D. G. Stork, “Pattern Classification,” 2nd Edition, Richard John Wiley & Sons, 2001.  R. C. Gonzalez and P. Wintz, “Digital Image Processing,” 3rd Ed., Addison-Wesley, 1992.  Rafael C. Gonzalez, Richard E. Woods, “Digital Image Processing,” 2nd edition, Prentice Hall, Inc., 2002.  R. C. Gonzalez, R. E. Woods, and S. L. Eddins, “Digital Image Processing using Matlab,” Prentice Hall Pub., 2004.  N. Guo, L. Zeng, and Q. Wu, “A Method Based on Multispectral Imaging Technique for White Blood Cell Segmentation,” Comput. Biol. Med., Vol.37, pp.70-76, 2006.  E. M. Haacke, M. Ayaz, A. Khan, E. S. Manova, B. Krishnamurthy, L. Gollapalli, C. Ciulla, I. Kim, F. Petersen, and W. Kirsch, “Establishing a baseline phase behavior in magnetic resonance imaging to determine normal vs. abnormal iron content in the brain,” J. Magn. Reson. Imaging, Vol. 26 pp.265-273, 2007.  J. W. Hsieh, S. H. Yu, and Y. S. Chen, “Morphology-based License Plate Detection from Complex Scenes,” roceedings of 16th International Conference on Pattern Recognition, Vol. 3, pp. 176-179, 2002.  K. Jiang, Q. -M. Liao, and Y. Xiong, “A Novel White Blood Cell Segmentation Scheme Based on Feature Space Clustering,” Soft Comput., Vol.10, pp.12-19, 2006.  J. Jones and L. Palemer, “An Evaluation of the Two Dimensional Gabor Filter Medel of the Two Dimensional Gabor Filter Medel of Simple Receptive Fields in Cat Striate Cortex,” Journal Neurophysiology, Vol. 58, pp.1233-1258, 1987.  S. J. Lord, W. Lei, P. Craft, J. N. Cawson, I. Morris, S. Walleser, A. Griffiths, S. Parker, and N. Houssami, “A systematic review of the effectiveness of magnetic resonance imaging (MRI) as an addition to mammography and ultrasound in screening young women at high risk of breast cancer,” Eur. J. Cancer 431905-1917, 2007.  J. B. MacQueen, “Some Methods for classification and Analysis of Multivariate Observations,” Proc. 5th Berkeley Symp. Math. Stati. Prob., University of California Press 1 pp.281-297, 1967.  K. Z. Mao, Peng Zhao, and Puay-Hoon Tan, “Supervised Learning-Based Cell Image Segmentation for P53 Immunohistochemistry,” IEEE Transactions on Biomedical Engineering, Vol. 53, No. 6, 2006.  E. K. Markell, D. T. John, W. A. Krotoski, “Medical Parasitology,” 8th edition, W.B. Saunders Company, 1999.  T. Naito, T. Tsukada, K. Uamada, K. Kozuka, and S. Yamamoto, “Robust License-Plate Recognition Method for Passing Vehicles Under Outside Environment,” IEEE Transactions on Vehicular Technology, Vol. 49, No. 6, pp.2309-2319, 2000.  Yasuo Nakagawa, Azriel Rosenfeld, “Some Experiments on Variable Thresholding,” Pattern Recognition, Vol. 11, No. 3, pp.191-204, 1979.  T. W. Nattkemper, H. Wersing, W. Schubert, and H. Ritter, “A Neural Network Architecture for Automatic Segmentation of Fluorescence Micrographs,” Neurocomputing, Vol.48, pp.357-367, 2002.  T. V. Nguyen, V. P. Le, C. L. Huy, K. N. Gia, and A. Weintraub, “Detection and characterization of diarrheagenic Escherichia coli from young children in Hanoi, Vietnam,” J. Clin. Microbiol, Vol. 43, No. 2 pp.755-760, 2005.  Nobuyuki Otsu, “A Threshold Selection Method from Gray-Level Histograms,” IEEE Transactions on Systems. Man, and Cybernet Ics, Vol. Smc 9, NO.1, 1979.  E. Pietka, S. Popiech, A. Gertych, F. Cao, H. K. Huang, and V. Gilsanz, “Computer Automated Approach to the Extraction of Epiphyseal Regions in Hand Radiographs,” J. Digit. Imaging, Vol.14, pp.165-172, 2001.  N. P. Sherwood and L. Heathman, “Further Studies on the Antigenic Properties of Pathogenic and Free Living Amebas. II. Complement Fixation in Amebic Dysentery,” Am. J. Epidemiol., Vol.16, pp.124-136, 1932.  B. R Thapa, K. Ventkateswarlu, A. K. Malik and D. Panigrahi, “Shigellosis in Children from North India: A Clinicopathological Study,” J. Trop. Pediatr., Vol. 41, pp.303-307, 1995.  V. Vapnik, “The Nature of Statistical Learning Theory,” Springer-Verlag, 1995. L. Vincent and P. Soille, “Watersheds in digital spaces: an efficient algorithm based on immersion simulations,” IEEE Trans. Pattern Anal. Mach. Intell., Vol.13, pp.583-598, 1991.  L. Vincent and P. Soille, “Watersheds in digital spaces: an efficient algorithm based on immersion simulations,” IEEE Trans. Pattern Anal. Mach. Intell., Vol.13, pp.583-598, 1991.  C. Xu and J. L. Prince, “Snakes, Shapes, and Gradient Vector Flow,” IEEE Trans. Image Process., Vol.7, pp.359-369, 1998.  S. F. Yang-Mao, “Object Contour Detection in Medical Images,” Ph. D. thesis, National Chung-Hsing University, R.O.C., 2007.  D. Zheng, Y. Zhao, and J. Wang, “An Efficient Method of License Plate Location,” Pattern Recognition Letters, Vol. 26, pp. 2431-2438, 2005.  Center of Disease Control, R.O.C., Legal Infectious Diseases - Understanding Diseases, August 24, 2007, website available at http://www.cdc.gov.tw/.|
車牌偵測在車牌辨識系統中扮演著相當重要的角色。如果車牌能夠被準確的偵測，對於接下來的字元切割及字元辨識會有事半功倍的效果。在我們的觀察中，影像的照度(illumination)將會影響車牌偵測。因此本論文的目標之一是在複雜背景且拍攝於不同情境的影像中擷取一張以上的車牌。車牌偵測的方法包含六個步驟：(1) 將彩色影像轉換為灰階影像 (2)影像均化 (3)邊緣偵測 (4)檢查黑色象素比例(5)車牌確認(6)輸出車牌。實驗結果顯示，本論文提出的方法在GetRatio達到94%的正確率; 在GetRight擁有83%的正確率。
Digital image processing is a widely applied technology in recent thirty years. Geometric transformations, image compression, image enhancement, image segmentation are some of the most used digital image processing techniques. Image segmentation is employed to distinguish the region of interest (ROI) from background. However, segmenting image is a great challenge for computer vision because the accuracy of image segmentation highly affects the accuracy of followed image recognition. In this thesis, some image processing techniques are proposed to deal with license plate detection and cell segmentation. License plate detection plays an important role in license plate recognition (LPR) system. If license plates can be located exactly, the character segmentation and recognition can be implemented more precisely and efficiently. In our observation, the illumination affects the result of locating a license plate. One aim of this thesis is to extract more than one license plate from an image and to obtain the license plates in the image taken under different conditions. The proposed method includes six processes as follows: transferring color to grayscale, image equalization, edge detection, checking black pixel ratio, plate verification and output license plates. The experimental results show that the proposed method can gain 94% accuracy in GetRatio and 83% accuracy in GetRight. The pathogenic protozoan parasites can cause human to get many diseases, such as, cholera, typhoid fever, and amoebiasis, etc. Digital images are extensively applied to medical fields for doctors and pathologists to analyze pathological sections and further diagnose the disease. A digital image processing scheme is prosposed to segment protozoan parasites from protozoan parasite microscopic images. The proposed image processing scheme has precise segmentation ability even if the image is with poor quality or complex background. The experimental results show that the proposed scheme can gain 96.64% correct rate (TN+TP), and about 0.04, 0.45 and 0.06 of the average error rates: ME, RN and EFAE, respectively.
|Appears in Collections:||資訊科學與工程學系所|
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