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رمزنگاری تصاویر با استفاده از اتوماتای سلولی برگشت پذیر Image Encryption Algorithm based on Recursive Cellular Automata 1 1 در این مقاله یک ساختار جدید برای رمزنگاری تصویر با استفاده از اتوماتای سلولی برگشت­پذیر ارائه می­شود. رمزنگاری تصویر در روش پیشنهادی در سه مرحله جداگانه صورت می­گیرد. در مرحله اول تصویر بلوک­بندی می­شود و سپس مقادیر روشنایی پیکسل­ها با استفاده از اتوماتای سلولی برگشت­پذیر جایگزین می­شوند. درهم­ریزی تصویر در مرحله دوم توسط اتوماتای دیگری انجام می­شود و سپس در مرحله نهایی بلوک­های تصویر در کنار یک دیگر قرار می­گیرند و عمل جایگزینی پیکسل­ها با استفاده از اتوماتای سلولی برگشت­پذیر صورت می­پذیرد. بدیهی است مراحل رمزگشایی تصویر با توجه به برگشت­پذیری اتوماتای پیشنهادی به صورت معکوس قابل اجرا است. نتایج آزمایش­ها نشان می­دهد روش پیشنهادی می­تواند تصویر را به گونه­ای رمز کند که تصویر رمز شده از لحاظ بصری قابل درک نباشد. هم چنین ارزیابی با استفاده از معیارهای کمی نشان می­دهد روش پیشنهادی نتایج مطلوب­تری نسبت به روش­های مورد بررسی داشته است.                                                                                2 In this paper, a new structure for image encryption using recursive cellular automatais presented. The image encryption contains three recursive cellular automata in three steps, individually. At the first step, the image is blocked and the pixels are substituted. In the next step, pixels are scrambledby the second cellular automata and at the last step, the blocks are attachedtogether and the pixels substitute by the third cellular automata. Due to reversibility of cellular automata, the decryption of the image is possible by doing the steps reversely. The experimental results show that the encrypted image is not comprehend visually, also this algorithmhas satisfactory performance in terms of quantitative assessment from some other schemes. 3 14 2014/08/12 1393/5/21 2016/02/26 1394/12/7 زینب مهرنهاد دانشگاه یزد zmehrnahad@gmail.com محمد لطیف دانشگاه یزد alatif@yazd.ac.ir Cryptography Cellular Automata Recursive Cellular Automata رمزنگاری تصویر اتوماتای سلولی اتوماتای سلولی برگشت­پذیر Abdo, A., et al. (2013). "A cryptosystem based on elementary cellular automata." Communications in Nonlinear Science and Numerical Simulation 18(1): 136-147.##Asnaashari, M. and M. Meybodi (2007). "Irregular cellular learning automata and its application toclustering in sensor networks."Proceedings of 15th Conference on Electrical Engineering, Tehran, Iran.##Beigy, H. and M. R. Meybodi (2008). "Asynchronous cellular learning automata." Automatica 44(5): 1350-1357.##Eslami, Z., et al. (2010). "Secret image sharing based on cellular automata and steganography." Pattern Recognition 43(1): 397-404.##Eslami, Z. and J. Zarepour Ahmadabadi (2010). "A verifiable multi-secret sharing scheme based on cellular automata." Information Sciences 180(15): 2889-2894.##Guan, Z.H., et al. (2005). "Chaos-based image encryption algorithm." Physics Letters A 346(1): 153-157.##Guodong Ye, et al. (2007). "Image encryption algorithm of double scrambling based on ASCII code of matrix element." International Conference on Computational Intelligence and Security:843-847.##Jin, J. (2012). "An image encryption based on elementry cellular automata." Optics & Laser Technology50(12): 1836-1843.##Jin, J. and Z.h. Wu (2012). "A secret image sharing based on neighborhood configurations of 2-d cellular automata." Optics & Laser Technology 44(3): 538-548.##Jolfaei, A. and A. Mirghadri (2010). " Image encryption using salsa20." International Journal of Computer Science Issues 7(5).##Kanso, A. and M. Ghebleh (2012). "A novel image encryption algorithm based on a 3D chaotic map." Communications in Nonlinear Science and Numerical Simulation 17(7): 2943-2959.##Kauffmann, C. and N. Piché (2010). "Seeded ND medical image segmentation by cellular automaton on GPU." International Journal of Computer Assisted Radiology and Surgery 5(3): 251-262.##Mehrnahad, Z. and A. latif (2015). " A new image encryption method with Hybrid and reversible cellular automata." ADST Journal 5(4): 257-267.##Mohamed, F. K. (2014). "A parallel block-based encryption schema for digital images using reversible cellular automata”; an International Journal of Science and Technology 17(2): 85-94.##Pareek, N.K., et al. (2006). "Image encryption using chaotic logistic map." Image and Vision Computing 24(9): 926-934.##Rosin, P. L. (2010). "Image processing using 3-state cellular automata." Computer Vision and Image Understanding 114(7): 790-802.##Ruisong Ye. and L. Huiliang (2008). "A novel image scrambling and watermarking scheme based on cellular automata." International Symposium on Electronic Commerce and SecurityS938-941.##Toffoli, T. and N. H. Margolus (1990). "Invertible cellular automata: A review." Physica D: Nonlinear Phenomena 45(1): 229-253.##Ville, V. D., et al. (2004). "Image scrambling without bandwidth expansion." IEEE Transactions on Circuits and Systems for Video Technology14(6): 892-897.##Von Neumann, J. (1966). "Theory of self-reproducing automata." University of Illinois Press.##Wang, X. and D. Luan (2013). "A novel image encryption algorithm using chaos and reversible cellular automata."Communications in Nonlinear Science and Numerical Simulation 18(11): 3075-3085.##Zhenwei, S., et al. (2008). "A block location scrambling algorithm of digital image based on Arnold transformation." The 9th International Conference for Young Computer Scientists:2942-2947.##Abdo, A., et al. (2013). "A cryptosystem based on elementary cellular automata." Communications in Nonlinear Science and Numerical Simulation 18(1): 136-147.##Asnaashari, M. and M. Meybodi (2007). "Irregular cellular learning automata and its application toclustering in sensor networks."Proceedings of 15th Conference on Electrical Engineering, Tehran, Iran.##Beigy, H. and M. R. Meybodi (2008). "Asynchronous cellular learning automata." Automatica 44(5): 1350-1357.##Eslami, Z., et al. (2010). "Secret image sharing based on cellular automata and steganography." Pattern Recognition 43(1): 397-404.##Eslami, Z. and J. Zarepour Ahmadabadi (2010). "A verifiable multi-secret sharing scheme based on cellular automata." Information Sciences 180(15): 2889-2894.##Guan, Z.H., et al. (2005). "Chaos-based image encryption algorithm." Physics Letters A 346(1): 153-157.##Guodong Ye, et al. (2007). "Image encryption algorithm of double scrambling based on ASCII code of matrix element." International Conference on Computational Intelligence and Security:843-847.##Jin, J. (2012). "An image encryption based on elementry cellular automata." Optics & Laser Technology50(12): 1836-1843.##Jin, J. and Z.h. Wu (2012). "A secret image sharing based on neighborhood configurations of 2-d cellular automata." Optics & Laser Technology 44(3): 538-548.##Jolfaei, A. and A. Mirghadri (2010). " Image encryption using salsa20." International Journal of Computer Science Issues 7(5).##Kanso, A. and M. Ghebleh (2012). "A novel image encryption algorithm based on a 3D chaotic map." Communications in Nonlinear Science and Numerical Simulation 17(7): 2943-2959.##Kauffmann, C. and N. Piché (2010). "Seeded ND medical image segmentation by cellular automaton on GPU." International Journal of Computer Assisted Radiology and Surgery 5(3): 251-262.##Mehrnahad, Z. and A. latif (2015). " A new image encryption method with Hybrid and reversible cellular automata." ADST Journal 5(4): 257-267.##Mohamed, F. K. (2014). "A parallel block-based encryption schema for digital images using reversible cellular automata”; an International Journal of Science and Technology 17(2): 85-94.##Pareek, N.K., et al. (2006). "Image encryption using chaotic logistic map." Image and Vision Computing 24(9): 926-934.##Rosin, P. L. (2010). "Image processing using 3-state cellular automata." Computer Vision and Image Understanding 114(7): 790-802.##Ruisong Ye. and L. Huiliang (2008). "A novel image scrambling and watermarking scheme based on cellular automata." International Symposium on Electronic Commerce and SecurityS938-941.##Toffoli, T. and N. H. Margolus (1990). "Invertible cellular automata: A review." Physica D: Nonlinear Phenomena 45(1): 229-253.##Ville, V. D., et al. (2004). "Image scrambling without bandwidth expansion." IEEE Transactions on Circuits and Systems for Video Technology14(6): 892-897.##Von Neumann, J. (1966). "Theory of self-reproducing automata." University of Illinois Press.##Wang, X. and D. Luan (2013). "A novel image encryption algorithm using chaos and reversible cellular automata."Communications in Nonlinear Science and Numerical Simulation 18(11): 3075-3085.##Zhenwei, S., et al. (2008). "A block location scrambling algorithm of digital image based on Arnold transformation." The 9th International Conference for Young Computer Scientists:2942-2947. ##

حذف نویز ضربه تصاویر با استفاده از فیلتر تطبیقی سوئیچ کننده مبتنی بر ماشین یادگیر بیشینه image denoising using adaptive switching filter based on extreme learning machine 1 1 در این مقاله یک رویکرد کارآمد مبتنی بر ماشین یادگیر بیشینه برای تشخیص و شناسایی پیکسلهای آغشته به نویز فلفل نمک از تصاویر دیجیتال ارائه‌شده است. الگوریتم پیشنهادی با استفاده از یک طبقه بند ماشین یادگیر بیشینه با ورودی های پیکسل مرکزی، ROAD و چهار فاکتور تصمیم گیری فیلتر SD-ROM، ابتدا پیکسل های نویزی را تشخیص داده و سپس با استفاده از فیلتر میانه تطبیقی، مقدار پیکسل نویزی تخمین زده می شود. نتایج حاصل از ارزیابی عملکرد طبقه بند، نمایانگر قابلیت بالای ویژگی های ورودی در متمایزکردن پیکسل نویزی از پیکسل سالم است. برای ارزیابی، تصاویر بهبودیافته توسط الگوریتم پیشنهادی با تصاویر حاصل از چند فیلتر متداول دیگر مقایسه و از معیار نرخ ماکزیمم سیگنال به نویز استفاده شد. نتایج عددی حاصل از آزمایش‌ها حاکی از کارآمدی فیلتر پیشنهادی ازنظر معیارهای کمی و کیفی می باشند. 2 In this paper a new efficient method for detecting the impulse noise from the corrupted image using extreme learning machine (ELM) is proposed. An improved version of the standard median filter is suggested to remove the detected noisy pixel. The performance of proposed detector is evaluated using classification accuracy. The results show that our detector is robust even at higher noise density. Results illustrate that proposed filter provides better performance in terms of PSNR than many other median filter variants for Salt and pepper noise. . The suggested technique yields significantly good results both in objective and subjective judgments of image quality. 15 25 2014/08/122014/11/30 1393/9/9 2016/02/262015/04/21 1394/2/1 مجید خراشادیزاده majid khorashadizadeh دانشگاه یزد smkh1985@gmail.com علی محمد لطیف ali mohammad latif دانشگاه یزد alatif@yazduni.ac.ir حذف نویز ماشین یادگیر بیشینه فیلتر میانه تطبیقی نویز فلفل نمک فیلتر سوئیچ کننده Abreu, E. and S. Mitra. A signal-dependent rank ordered mean (SD-ROM) filter-a new approach for removal of impulses from highly corrupted images. in International Conference on Acoustics, Speech, and Signal Processing. 1995. IEEE.##Apalkov, I.V., P.S. Zvonarev, and V.V. Khryashchev. Neural network adaptive switching median filter for image denoising. in The International Conference on Computer as a Tool. 2005. IEEE.##Beşdok, E. and M.E. Yüksel, Impulsive noise suppression from images with Jarque-Bera test based median filter. AEU-International Journal of Electronics and Communications, 2005. 59(2): p. 105-110.##Brownrigg, D., The weighted median filter. Communications of the ACM, 1984. 27(8): p. 807-818.##Garnett, R., et al., A universal noise removal algorithm with an impulse detector. IEEE Transactions on Image Processing , 2005. 14(11): p. 1747-1754.##Huang, G.-B., An insight into extreme learning machines: random neurons, random features and kernels. Cognitive Computation, 2014: p. 1-15.##Hwang, H. and R. Haddad, Adaptive median filters: new algorithms and results. Image Processing, IEEE Transactions on, 1995. 4(4): p. 499-502.##Ibrahim, H., N.S.P. Kong, and T.F. Ng, Simple adaptive median filter for the removal of impulse noise from highly corrupted images. IEEE Transactions on Consumer Electronics, 2008. 54(4): p. 1920-1927.##Ko, S.-J. and Y.H. Lee, Center weighted median filters and their applications to image enhancement. IEEE Transactions on Circuits and Systems, 1991. 38(9): p. 984-993.##Kuykin, D., V. Khryashchev, and I. Apalkov. Modified progressive switched median filter for image enhancement. in Proceedings of the International Conference on Computer Graphics and Vision. 2009.##Schulte, S., et al., A fuzzy impulse noise detection and reduction method. IEEE Transactions on Image Processing, , 2006. 15(5): p. 1153-1162.##Srinivasan, K. and D. Ebenezer, A new fast and efficient decision-based algorithm for removal of high-density impulse noises. Signal Processing Letters, IEEE, 2007. 14(3): p. 189-192.##Sun, T. and Y. Neuvo, Detail-preserving median based filters in image processing. Pattern Recognition Letters, 1994. 15(4): p. 341-347.##Toh, K.K.V. and M. H Mahyuddin, Salt-and-pepper noise detection and reduction using fuzzy switching median filter. 2008.##Toh, K.K.V. and N.A.M. Isa, Noise adaptive fuzzy switching median filter for salt-and-pepper noise reduction. Signal Processing Letters, IEEE, 2010. 17(3): p. 281-284.##Tomasi, C. and R. Manduchi. Bilateral filtering for gray and color images. Sixth International Conference on Computer Vision. 1998. IEEE.##Tukey, J., Nonlinear (nonsuperposable) methods for smoothing data. Congr. Rec. 1974 EASCON, 1974. 673.##Wang, Z. and D. Zhang, Progressive switching median filter for the removal of impulse noise from highly corrupted images. Circuits and Systems II: IEEE Transactions on Analog and Digital Signal Processing, , 1999. 46(1): p. 78-80.##Xu, H., et al., Adaptive fuzzy switching filter for images corrupted by impulse noise. Pattern Recognition Letters, 2004. 25(15): p. 1657-1663.##Abreu, E. and S. Mitra. A signal-dependent rank ordered mean (SD-ROM) filter-a new approach for removal of impulses from highly corrupted images. in International Conference on Acoustics, Speech, and Signal Processing. 1995. IEEE.##Apalkov, I.V., P.S. Zvonarev, and V.V. Khryashchev. Neural network adaptive switching median filter for image denoising. in The International Conference on Computer as a Tool. 2005. IEEE.##Beşdok, E. and M.E. Yüksel, Impulsive noise suppression from images with Jarque-Bera test based median filter. AEU-International Journal of Electronics and Communications, 2005. 59(2): p. 105-110.##Brownrigg, D., The weighted median filter. Communications of the ACM, 1984. 27(8): p. 807-818.##Garnett, R., et al., A universal noise removal algorithm with an impulse detector. IEEE Transactions on Image Processing , 2005. 14(11): p. 1747-1754.##Huang, G.-B., An insight into extreme learning machines: random neurons, random features and kernels. Cognitive Computation, 2014: p. 1-15.##Hwang, H. and R. Haddad, Adaptive median filters: new algorithms and results. Image Processing, IEEE Transactions on, 1995. 4(4): p. 499-502.##Ibrahim, H., N.S.P. Kong, and T.F. Ng, Simple adaptive median filter for the removal of impulse noise from highly corrupted images. IEEE Transactions on Consumer Electronics, 2008. 54(4): p. 1920-1927.##Ko, S.-J. and Y.H. Lee, Center weighted median filters and their applications to image enhancement. IEEE Transactions on Circuits and Systems, 1991. 38(9): p. 984-993.##Kuykin, D., V. Khryashchev, and I. Apalkov. Modified progressive switched median filter for image enhancement. in Proceedings of the International Conference on Computer Graphics and Vision. 2009.##Schulte, S., et al., A fuzzy impulse noise detection and reduction method. IEEE Transactions on Image Processing, , 2006. 15(5): p. 1153-1162.##Srinivasan, K. and D. Ebenezer, A new fast and efficient decision-based algorithm for removal of high-density impulse noises. Signal Processing Letters, IEEE, 2007. 14(3): p. 189-192.##Sun, T. and Y. Neuvo, Detail-preserving median based filters in image processing. Pattern Recognition Letters, 1994. 15(4): p. 341-347.##Toh, K.K.V. and M. H Mahyuddin, Salt-and-pepper noise detection and reduction using fuzzy switching median filter. 2008.##Toh, K.K.V. and N.A.M. Isa, Noise adaptive fuzzy switching median filter for salt-and-pepper noise reduction. Signal Processing Letters, IEEE, 2010. 17(3): p. 281-284.##Tomasi, C. and R. Manduchi. Bilateral filtering for gray and color images. Sixth International Conference on Computer Vision. 1998. IEEE.##Tukey, J., Nonlinear (nonsuperposable) methods for smoothing data. Congr. Rec. 1974 EASCON, 1974. 673.##Wang, Z. and D. Zhang, Progressive switching median filter for the removal of impulse noise from highly corrupted images. Circuits and Systems II: IEEE Transactions on Analog and Digital Signal Processing, , 1999. 46(1): p. 78-80.##Xu, H., et al., Adaptive fuzzy switching filter for images corrupted by impulse noise. Pattern Recognition Letters, 2004. 25(15): p. 1657-1663. ##

برچسب‌زنی خودکار نقش‌های معنایی در جملات فارسی به کمک درخت‌های وابستگی Automatic Labeling of Semantic Roles in Persian Sentences using Dependency Trees 1 1 تتشخیص خودکار واژه‌های دارای نقش‌های معنایی (همچون کنش‌گر، کنش‌پذیر، منشأ، و ...) در جملات و اختصاص صحیح نقش‌های معنایی به آن‌ها توسط رایانه می‌تواند موجب بهبود کیفیت در بسیاری از کاربردهای پردازش زبان طبیعی همچون استخراج اطلاعات، پرسش و پاسخ، خلاصه‌سازی، و ترجمۀ ماشینی شود. در چنین پردازشی که برچسب‌زنی نقش معنایی و یا تجزیۀ معنایی سطحی خوانده می‌شود معمولاً از تجزیۀ نحوی جملات به منظور تعریف ویژگی‌های نحوی استفاده می‌شود و نوع بازنمایی نحوی مورد استفاده در دقت سامانۀ برچسب‌زنی نقش معنایی موثر است. در این پژوهش به ارائۀ برچسب‌زن نقش معنایی مبتنی بر تجزیۀ نحوی کامل می‌پردازیم. بدین منظور از تجزیه‌گر نحوی وابستگی و روش‌های یادگیری ماشینی استفاده می‌شود. در برچسب‌زن ارائه‌شده سعی شده است که مشکلات برچسب‌زن‌های قبلی ارائه‌شده برای زبان فارسی، که همگی مبتنی بر تجزیۀ نحوی سطحی بوده‌اند، رفع شود و معماری سیستم به برچسب‌زن‌های به‌روز دنیا نزدیک باشد. نتایج پژوهش نشان‌دهندۀ دقت مناسب سیستم ارائه‌شده است. 2 Automatic identification of words with semantic roles (such as Agent, Patient, Source, etc.) in sentences and attaching correct semantic roles to them, may lead to improvement in many natural language processing tasks including information extraction, question answering, text summarization and machine translation. Semantic role labeling systems usually take advantage of syntactic parsing and therefor the syntactic representation chosen affects the overall performance of the system. In this research, we present a semantic role labeling system based on full syntactic parsing. For this purpose, we use a dependency parser and machine learning methods. In our system, we have made an effort to overcome the problems of previous semantic role labelers for Persian, which all are based on shallow syntactic parsing. The outcome of the system is promising. 27 38 2014/08/122014/11/302014/10/25 1393/8/3 2016/02/262015/04/212015/05/16 1394/2/26 مرتضی رضائی شریف آبادی Morteza Rezaei Sharifabadi دانشگاه صنعتی شریف mrezaeis@mehr.sharif.edu پروانه خسروی‌زاده Parvaneh Khosravizadeh دانشگاه صنعتی شریف khosravizadeh@sharif.edu semantic role labeling shallow semantic parsing dependency grammar Persian language natural language processing computational linguistics برچسب‌زنی نقش معنایی تجزیۀ معنایی سطحی دستور وابستگی زبان فارسی پردازش زبان طبیعی زبان‌شناسی رایانه‌ای اسلامی، م. شریفی آتشگاه، م. علیزاده لمجیری، ص. و زندی، ط. واژگان زایای زبان فارسی. مجموعه مقالات اولین کارگاه پژوهشی زبان فارسی و رایانه، ۱۳۸۳. ##رسولی، م. تجزیه نحوی با استفاده از دستور وابستگی. گزارش پژوهشی. تهران، مرکز تحقیقات کامپیوتری علوم اسلامی. ۱۳۸۹.##رضائی، م. برچسب‌زنی نقش‌های معنایی با استفاده از درخت‌های وابستگی جملات فارسی. پایان‌نامه کارشناسی ارشد، تهران، دانشگاه صنعتی شریف. اردیبهشت ۱۳۹۳.##شمس‌فرد، م. جعفری‌نژاد، ف. استخراج روابط معنایی میان فعل و وابسته‌های آن از متون زبان فارسی. فصلنامه پازند، ۱۳۹۱، شماره ۸ (۳۰).##صدر موسوی، م. شمس‌فرد، م. برچسب‌زنی نقش‌های معنایی با استفاده از تجزیه سطحی جملات فارسی. سیزدهمین کنفرانس سالانه انجمن کامپیوتر ایران، ۱۳۸۶، جزیره کیش.##طبیب‌زاده، م. ظرفیت فعل و ساخت‌های بنیادین جمله در فارسی امروز، پژوهشی بر اساس نظریه دستور وابستگی. تهران، نشر مرکز. ۱۳۸۵.##کامل قالیباف، آ. راحتی قوچانی، س. استاجی، ا. برچسب‌زنی نقش معنایی جملات فارسی با رویکرد یادگیری مبتنی بر حافظه. دوفصل‌نامه پردازش هوشمند علائم و داده‌ها، ۱۳۸۸، شماره ۱ (۱۱)، ۱۳-۲۲.##Gildea, D., & Jurafsky, D. Automatic Labeling of Semantic Roles. Computational linguistics, 2002, 28(3), 245-288.##Hacioglu, K. Semantic Role Labeling Using Dependency Trees. In Proceedings of the 20th Intern-ational Conference on Computational Linguistics, 2004, No.1273.##Jafarinejad, F., & Shamsfard, M. Extracting Gener-alized Semantic Roles from Corpus. International Journal of Computer Science Issues (IJCSI), 2002, 9(2), 200-206.##Johansson, R., & Nugues, P. The Effect of Syntactic Representation on Semantic Role Labeling. In Proceedings of the 22nd International Conference on Computational Linguistics, 2008a, Vol. 1, 393-400.##Johansson, R., & Nugues, P. Dependency-Based Syntactic-Semantic Analysis with PropBank and NomBank. In Proceedings of the Twelfth Conference on Computational Natural Language Learning, 2008b, 183-187.##Jurafsky, D., & Martin, J. H. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition (2nd ed.). Upper Saddle River, NJ: Prentice Hall, 2009.##Palmer, M., Gildea, D., & Xue, N. Semantic role labeling. Synthesis Lectures on Human Language Technologies, 2010, 3(1), 1-103.##Punyakanok, V., Roth, D., & Yih, W. T. The Nece-ssity of Syntactic Parsing for Semantic Role Label-ing. In Proceedings of the International Joint Confe-rence on Artificial Intelligence (IJCAI), 2005, Vol. 5, 1117-1123.##Rasooli, M. S., Kouhestani, M., & Moloodi, A. Deve-lopment of a Persian Syntactic Dependency Tree-bank. In Proceedings of NAACL-HLT, 2013, 306-314.##Rasooli, M. S., Moloodi, A., Kouhestani, M., & Minaei-Bidgoli, B. A Syntactic Valency Lexicon for Persian Verbs: The First Steps Towards Persian Dependency Treebank. In the 5th Language & Technology Conference (LTC): Human Language Technologies as a Challenge for Computer Science and Linguistics, 2011, 227-231.##Riemer, Nick. Introducing Semantics. Cambridge University Press, 2010.##Saeed, John I. Semantics (2nd ed.). 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معرفی شبکه های عصبی پیمانه ای عمیق با ساختار فضایی-زمانی دوگانه جهت بهبود بازشناسی گفتار پیوسته فارسی Deep Modular Neural Networks with Double Spatio-temporal َAssociation Structure for Persian Continuous Speech Recognition 1 1 در این مقاله به معرفی شبکه‌های عصبی پیمانه ای عمیق و قابل رشد به منظور بهبود بازشناسی گفتار پیوسته پرداخته می شود. ساختار این شبکه ها و روش‎های پیش‎تعلیم معرفی شده برای آنها بگونه ای است که درعین هماهنگی با ساختار گفتار، در حافظه و محاسبات لازم صرفه جویی میشود. بدلیل قابلیت رشد این ساختارها، می‌توان در تعلیم آنها اطلاعات فضایی-زمانی بردارهای بازنمایی در ورودی و اطلاعات فضایی-زمانی برچسب آوایی آنها را در خروجی شبکه عصبی انجمن کرد. شبکه تعلیم یافته با این ساختار انجمنگر فضایی-زمانی دوگانه، میتواند زیرفضای زنجیره های معتبر آوایی دادگان را یادبگیرد. بنابراین، در ساختار خود زنجیره های خروجی نامعتبر را پالایش کرده و زنجیره های درست را میدهد. جهت بررسی عملکرد این ساختارها، از دودسته دادگان گفتاری فارس دات و فارس دات بزرگ استفاده شد. نتایج آزمایش‎ها نشان می‌دهند که میتوان دقت بازشناسی آوا را برروی دادگان فارس دات تا 2.7% با استفاده از شبکه‌های عصبی پیمانه ای عمیق نسبت به مدل‌های مخفی مارکوف بالابرد. که با توسعه آنها به ساختار فضایی-زمانی دوگانه این نتیجه تا 5.1% بهبودمی یابد. بدلیل عدم وجود برچسب های آوایی برای دادگان بزرگ، یک روش تعلیم نیمه سرپرستی شده برای تعلیم شبکه های عصبی برروی این دادگان پیشنهاد شده است که میتواند به درصد بازشناسی قابل مقایسه ای با مدلهای مخفی مارکوف دست یابد. 2 In this article, growable deep modular neural networks for continuous speech recognition are introduced. These networks can be grown to implement the spatio-temporal information of the frame sequences at their input layer as well as their labels at the output layer at the same time. The trained neural network with such double spatio-temporal association structure can learn the phonetic sequence subspace. Therefore, it can filter out invalid phonetic sequences in its own structure and output valid sequences. To evaluate the performance of these growable neural networks, we used FARSDAT and BIG FARSDAT datasets. Experimental results on FARSDAT show that deep modular neural networks outperform the phone accuracy rate of GMM-HMM models with an absolute improvement of 2.7%. Moreover, developing deep modular neural networks to a double spatio-temporal association structure improves their result by 5.1%. As there is no phonetic labeling for BIG FARSDAT, a semi-supervised learning algorithm is proposed to fine-tune the neural network with double spatio-temporal structure on this dataset, which achieves a comparable result with HMMs. 39 56 2014/08/122014/11/302014/10/252014/10/19 1393/7/27 2016/02/262015/04/212015/05/162016/02/26 1394/12/7 زهره انصاری Zohreh Ansari دانشگاه صنعتی امیرکبیر z_ansari@aut.ac.ir علی سید صالحی Ali Seyyedsalehi دانشگاه صنعتی امیرکبیر ssalehi@aut.ac.ir Deep neural networks Modular neural networks Pre-training Semi-supervised learning Continuous speech recognition شبکه های عصبی عمیق شبکه های عصبی پیمانه ای پیش تعلیم تعلیم نیمه سرپرستی شده بازشناسی گفتار پیوسته انصاری، ل.، مدلسازی اثرات هم تولیدی آواها در یک مدل شبکه عصبی بازشناخت گفتار، پایان نامه کارشناسی ارشد، دانشکده مهندسی پزشکی، دانشگاه صنعتی امیرکبیر، 1382##رحیمی نژاد، م.، سیدصالحی، س.ع.، مقایسه و ارزیابی کارایی انواع روش‌های استخراج پاراکترهای بازنمایی و هنجارسازی در بازشناسی مستقل از گوینده گفتار، 1382، ش. 55##سیدصالحی، س.ز.، سیدصالحی، س.ع.، روش پیش تعلیم سریع برمبنای کمینه سازی خطا برای همگرایی یادگیری شبکه‌های عصبی با ساختار عمیق، دو فصل نامه پردازش علائم و داده‌ها، 1392، دوره 19، ش.1، ص. 13-26##سیدصالحی، س.ع.، بازشناخت گفتار پیوسته فارسی با استفاده از مدل عملکردی مغز انسان در درک گفتار، پایان نامه دکترا، دانشکده فنی و مهندسی، دانشگاه تربیت مدرس، 1374##سیدصالحی، س.ع.، طراحی سامانه بازشناسی گفتار بر اساس شبکه عصبی به منظور بازشناسی دادگان گفتاری فارسی با تعداد لغات زیاد، گزارش پژوهشی، پژوهشکده پرازش هوشمند علائم، 1384##شکفته، ی.، الماس گنج، ف.، بهبود بازشناسی گفتار با استفاده از شبکه‌های عصبی دربرگیرنده الگوهای زمانی، کنفرانس بین المللی فناوری اطلاعات (IKT 2007)، دانشگاه فردوسی مشهد، 1386##شیخ زادگان، ج.، بیجن خان، م.، دادگان‌های گفتاری زبان فارسی، دومین کارگاه پژوهشی زبان فارسی و رایانه، 1385، ص. 247-261##کرمی، ش.، به‌کارگیری اطلاعات موجود در نواحی گذرای مرز واج‌ها به منظور افزایش توان مدل‌های شبکه عصبی بازشناس گفتار مستقل از گوینده، پایان نامه کارشناسی ارشد، دانشکده مهندسی پزشکی، دانشگاه صنعتی امیرکبیر، 1379##یزدیان، م.، طراحی مدل بازشناسی گفتار توسط شبکه‌های عصبی برپایه پردازش وقایع گسسته سیگنال گفتار، پایان نامه کارشناسی ارشد، دانشکده مهندسی پزشکی، دانشگاه صنعتی امیرکبیر، 1380##Abdel-Hamid, O., Mohamed, A. 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تخمین سریع ضرایب پیچش در هنجارسازی طول مجرای صوتی با استفاده از امتیاز به دست آمده از مدلسازی تشخیص جنسیت Fast estimation of warping factor in the vocal tract length normalization using obtained scores of gender detection modeling 1 1 یکی از مشکلات عمده­ی سامانه­های خودکار بازشناسی گفتار (ASR)، تنوعات موجود در بین گویند­ه­ها، کانال انتقال داده و محیط است که به علت وجود این تنوعات، کارایی این سامانه‌ها در شرایط کاربردی مختلف به شدت تغییر می‌کند. مقاوم سازی سیستم­های بازشناسی جهت مقابله با این تغییرات از جمله مسائل حال حاضر در حوزه بازشناسی گفتار است. از جمله عواملی که باعث کاهش کارایی سیستم­ها می­شود، تمایز مشخصات صوتی آواهای یکسانِ تولید شده از گوینده­های مختلف است. یکی از عوامل اصلی این مشکل ناشی از تفاوت موجود در طول مجرای صوتی (VTL) بین گوینده­های مختلف می‌باشد. روش هنجارسازی طول مجرای صوتی (VTLN) از روش­های رایج برای رفع این مشکل است که در آن برای هر گوینده یک ضریب پیچش فرکانسی تعیین می­گردد. در این مقاله روش متداول تعیین ضریب پیچش با رویکرد مبتنی بر جستجو در یک سیستم بازشناسی گفتار پیوسته فارسی مبتنی بر مدل مخفی مارکوف معرفی و مشکلات محاسباتی استفاده از این روش شرح داده شده است. در نهایت روشی مبتنی بر رگرسیون خطی از روی امتیازِ محاسبه شده از مدلسازی تشخیص جنسیت جهت تخمین ضرایب پیچش پیشنهاد شده است که منجر به کاهش قابل ملاحظه هزینه محاسباتیِ روش مبتنی بر جستجو می­شود. علاوه بر این، نتایج آزمایشات بر روی دادگان آزمون گفتار تلفنی محاوره­ای، بیانگر بهبود 54/0 درصدی دقت تشخیص کلمه روش پیشنهادی نسبت به روش متداول مبتنی بر جستجو می­باشد.  2 The performance of automatic speech recognition (ASR) systems is adversely affected by the variations in speakers, audio channels and environmental conditions. Making these systems robust to these variations is still a big challenge. One of the main sources of variations in the speakers is the differences between their Vocal Tract Length (VTL). Vocal Tract Length Normalization (VTLN) is an effective method introduced to cope with this variation. In this method, the speech spectrum of each speaker is frequency warped according to a specific warping factor of that speaker. In this paper, we first developed the common search-based method to obtain the appropriate warping factor over a HMM-based Persian continuous speech recognition system. Then pointing out the computational cost of search-based method, we proposed a linear regression process for estimating warping factor based on the scores generated by our gender detection system. Experimental results over a Persian conversational speech database shown an improvement about 0.54 percent in word recognition accuracy as well as a significant reduction in computational cost of estimating warping factor, compared to search-based approach. 57 70 2014/08/122014/11/302014/10/252014/10/192014/06/30 1393/4/9 2016/02/262015/04/212015/05/162016/02/262016/02/26 1394/12/7 یاسر شکفته Yasser Shekofteh پژوهشگاه توسعه فناوری های پیشرفته خواجه نصیرالدین طوسی y_shekofteh@yahoo.com حسن قلی پور Hasan Gholipor پژوهشگاه توسعه فناوری های پیشرفته خواجه نصیرالدین طوسی y_shekofteh محمدمحسن گودرزی M.Mohsen Goodarzi پژوهشگاه توسعه فناوری های پیشرفته خواجه نصیرالدین طوسی y_shekofteh جهانشاه کبودیان Jahanshah kabudian پژوهشگاه توسعه فناوری های پیشرفته خواجه نصیرالدین طوسی y_shekofteh فرشاد الماس‌گنج Farshad Almasganj پژوهشگاه توسعه فناوری های پیشرفته خواجه نصیرالدین طوسی y_shekofteh شقایق رضا Shaghayegh Reza پژوهشگاه توسعه فناوری های پیشرفته خواجه نصیرالدین طوسی y_shekofteh ایمان صراف رضایی Iman Sarraf پژوهشگاه توسعه فناوری های پیشرفته خواجه نصیرالدین طوسی y_shekofteh speech recognition Vocal Tract Length Normalization gender detection linear regression warping factor بازشناسی گفتار هنجارسازی طول مجرای صوتی تشخیص جنسیت رگرسیون خطی ضریب پیچش فرکانسی. باقر باباعلی، حسین صامتی ، هادی ویسی، "بکارگیری نرمالسازی اثر طول مسیرصوتی گوینده ها (VTLN) در سیستم بازشناسی گفتار پیوسته فارسی مبتنی بر مدل مخفی مارکوف"، سیزدهمین کنفرانس سالانه انجمن کامپیوتر ایران، جزیره کیش، اسفند 1386. ##شهلا عزیزی، فرزاد توحیدخواه، فرشاد الماس گنج، "بررسی اثر استفاده از روش تطبیق هنجارسازی طول مسیر صوتی به منظور تشخیص اختلالات گفتاری رایج و گفتاردرمانی کودکان فارسی زبان"، فصلنامه مهندسی پزشکی زیستی، سال ششم، شماره 4، صص. 257-265، زمستان 1391. ##قمرناز تدین تبریزی، سعید ستایشی، "ارائه روشی مبتنی بر نرمالسازی اکوستیکی و خوشه بندی برای بهبود بازشناسی گفتار کودکان فارسی زبان"، مجله فنی مهندسی دانشگاه آزاد اسلامی مشهد، صص. 113-125 ، زمستان 1388. ##یاسر شکفته، جهانشاه کبودیان، محمدمحسن گودرزی و ایمان صراف رضائی، "بهبود کارایی سیستم کاوشگر کلمات تلفنی با استفاده از نرمالیزاسیون امتیاز اطمینان مبتنی بر روش برنامه‌ریزی خطی"، دوفصلنامه پردازش علائم و داده‌ها، سال چهارم، شماره پیاپی 14، صص. 37-48، زمستان 1389. ##Acero A. and Stern R. 1991, Robust speech reco-gnition by normalization of the acoustic space, in Proc. 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طراحی سامانۀ تشخیص دستبرد ادبی جمله‌بنیاد در متون فارسی به کمک هم‌جوشی گواه‌ها Design a Sentence Based Plagiarism Detection System by Evidences Fusion in Persian Text 1 1 در حال حاضر، افراد به راحتی می‌توانند سند جدیدی را با رونوشت‌برداری از منابع وسیع اینترنتی درست و به نام خود ثبت کنند که مصداقی از دستبرد ادبی است. سامانه‌های دستبرد ادبی موجود قابلیت شناسایی کامل انواع دستبرد را ندارند. چالش اساسی در این زمینه یافتن الگوریتمی مناسب برای بهبود میزان یافته‌های مشابه و زمان بررسی آنهاست. تاکنون سنجه‌های مختلفی برای ارزیابی مشابهت دو سند ارائه شده که کارایی آنها به محتوای متن و منابع مورد استفاده برای مقایسۀ بین واژه‌های دو سند محدود است. در این مقاله روشی ارائه شده است که با توجه به کیفی و ناکامل بودن عوامل اثرگذار بر سنجش شباهت بین دو متن، از نظریۀ گواه برای هم‌جوشی اطلاعات به منظور ارزیابی تشابه دو سند فارسی و کشف دستبرد ادبی استفاده می‌کند. سامانۀ طراحی‌شده در مرحلۀ اول جمله‌های موجود در سند را به دو بخش عمومی و تخصصی تقسیم کرده و سپس با استفاده از سنجه‌های متفاوت و استفاده از منابعی همانند «هستان‌نگار تخصصی» امتیاز تشابه برای هر بخش را محاسبه و در نهایت در دو سطح، میزان شباهت بین دو سند را استنتاج می‌کند؛ به طوری‌که در سطح اول نتایج سنجه‌های شباهت‌سنجی به عنوان گواه (با باور پایۀ مشخص) با قاعدۀ دمپستر-شفر با هم ترکیب شده و به عنوان گواهی جدید به سطح دوم منتقل می‌شوند. در سطح دوم نتیجۀ سطح اول ‌و‌گواه جدید از طریق قاعدۀ میانگین‌گیری ترکیب شده و توابع باور و مقبولیت نهایی محاسبه و شباهت بین دو جمله(سند) ارزیابی می‌شود. سامانۀ مذکور بر داده‌های واقعی در محیط زبان فارسی مورد ارزیابی قرار گرفته که با دقت بیش از 90% امکان شناسایی اسناد مشابه را داراست و به همین دلیل توانمندی لازم برای استفاده در حوزۀ شناسایی دستبرد ادبی را داراست. 2 Today, there are many documents on Internet, such that users can generate new documents by coping them and existing Plagiarism Detection systems (PDS) couldn't detect all kind of plagiarism. The main challenge is finding a suitable algorithm to improving the amount of similar documents and their assessing time. It’s difficult to do assessing similarity in Persian texts that different characteristics affect on it and also many of them are ambiguous. For this reason Dempster - Shefer (Evidence) theory has been used in this paper. The proposed system will assess in a two-level and in the first stage, sentences will divide in general and expert terms and then assessing by suitable measures and domain ontology. These results will be delivered to first level as "basic belief" and will be integrated by using a Dempster combination rule to create one of the second level inputs. In second level, the previous level result and another similarity measures will be weighted and combined belief and plausibility functions for final assessment will be distinguished. 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ارائه روشی جدید برای شاخص‌گذاری خودکار و استخراج کلمات کلیدی برای بازیابی اطلاعات و خوشه‌بندی متون Improved Clustering Persian Text Based on Keyword Using Linguistic and Thesaurus Knowledge 1 1 در زبان فارسی کلمات دارای صورت‌های نگارشی متنوعی هستند و پوشش کلیه حالات دستوری کلمات با به کارگیری یک سری قواعد معین ناممکن است به همین دلیل استخراج کلمات کلیدی به طور خودکار از متون فارسی دشوار و پیچیده است. در این مقاله سعی شده است با استفاده از اطلاعات زبان شناختی و اصطلاح‌نامه ، کلمات کلیدی بامعناتری ارائه شود. با استفاده از اصطلاح‌نامه که از نظامی ساختارمند برخوردار است می‌توان شبکه کلمات کلیدی، شامل کلمات هم ارز، کلمات سلسله مراتبی و وا‌بسته را تکمیل کرده و افزایش داد. بنابراین می‌توان توافق بین جستجوی کاربران و کلمات کلیدی متنی را بیشتر نمود و جامعیت جستجو را افزایش داد. در مرحله اول کلمات غیر مهم و عمومی حذف می‌شوند. سپس کلمات متن ریشه‌یابی می‌شوند و در ادامه برای مشخص شدن اهمیت نسبی کلمات با استفاده از روش‌های وزن‌دهی یک وزن عددی به هر کلمه منسوب می‌گردد که بیانگر میزان تاثیر کلمه در ارتباط با موضوع متن و درمقایسه با سایر کلمات بکار رفته در متن است‌. مجموعه عملیات فوق خصوصاً استفاده از اصطلاح‌نامه باعث می‌شود که دسته‌بندی متون دقیق‌تر انجام گیرد و به نوعی رده علمی سلسله مراتبی متون در حوزه بازیابی اطلاعات نیز مشخص می‌شود. نتایج آزمایش‌ها روی چندین متن در موضوعات مختلف نشان دهنده دقت و توانایی روش پیشنهادی در استخراج کلمات کلیدی منطبق با خواست کاربر است و در نتیجه خوشه‌بندی دقیق‌تر متون می‌باشد. 2 Persian words in writing with a diverse and cover all modes of grammatical words with the recruitment of a series of specific rules because it is impossible to extract keywords automatically from Persian texts difficult and complex. This thesis has attempted to use linguistic information and thesaurus, keywords Mnatry be provided. Using the symbol system is structured network can be keywords, including the exchange of words, words and words with hierarchical relationships complete the package has increased. Therefore the agreement between users and search keywords text search and recall is increased. In the first stage non-important words are removed and the public. Supervision in the text are words and more words to clarify the relative importance of using a blower numerical weight is assigned to each word that indicates the effectiveness of the word in connection with the subject and compared with the other words used in the text. Particularly complex operation that makes use of thesaurus keywords are extracted Mnytry that kind of hierarchical category scientific literature in the field of information retrieval is indicated. Test results on different topics several text accurately represents the proposed method and the ability to extract the keywords in accordance with user demand. 87 100 2014/08/122014/11/302014/10/252014/10/192014/06/302014/10/162013/07/3 1392/4/12 2016/02/262015/04/212015/05/162016/02/262016/02/262015/09/272016/05/2 1395/2/13 حمید پروین دانشگاه آزاد اسلامی، parvin@iust.ac.ir Keyword Extraction Thesaurus Computational Linguistic Information Retrieval استخراج کلمات کلیدی اصطلاح‌نامه زبان‌شناختی بازیابی اطلاعات بی‌جن‌خان، م. ، 1383. نقش پیکره‌های زبانی در نوشتن دستور زبان: معرفی یک نرم‌افزار رایانه‌ای، مجله زبان‌شناسی، سال 19، شماره 2.##حری، ع.، 1383. راهنمای تهیه و گسترش اصطلاح‌نامه یک زبانه، مرکز اسناد و مدارک علمی.##خسروی، ف.، 1379. اصطلاحنامه فرهنگی فارسی 'اصفا'، کتابخانه ملی جمهوری اسلامی ایران.##علاقه‌بند م.ر.، سعیدی محمدی، م.ر.، دزفولیان، م.ح.، 1391. خوشه‌بندی متون مبتنی بر مرکز دسته با استفاده از روش SVD و بهره‌گیری از نقاط همسایگی، نخستین کنفرانس بین‌المللی پردازش خط و زبان فارسی، شهریور.##یغمایی، ف.، تعبدی، س.، 1391. بهبود دسته بندی متون فارسی در روش همسایگی وزن دار، نخستین کنفرانس بین‌المللی پردازش خط و زبان فارسی، شهریور.##Arasteh, A., Elahimanesh, M.H., Sharif, A., Minaei-Bidgoli, B., 2012. Semantically Clustering of Persian Words, The first international conference on Persian language processing, September.## Deegan, M., 2004. Keyword Extraction with Thesauri and Content Analysis, URL: http://ww-w.rlg.org/en/page.php?Page_ID=17068 ##Faceli, K., Marcilio, C.P., 2006. Multi-objective Clustering Ensemble, Sixth International Conference on Hybrid Intelligent Systems (HIS'06), April.## Frank, E., 1999. Domain-Based Extraction of Tech-nical Keyphrases, International Joint Conference on Artificial Intelligence, April.##Frantzi, K., Ananiadou, S., Mima, H., 2002. Auto-matic Recognition of Multi-word Terms: the C-value/NC-value Method, Digital Libraries, 3(2), pp. 115-130.##Freitas, N., Kaestner, A., 2005. Automatic text sum-arization using a machine learning approach, Brazi-lian Symposium on Artificial Intelligence (SBIA), April.##Hult, A., 2003. Improved automatic keyword extra-ction given more linguistic knowledge, Conference on Empirical Methods in Natural Language Proc-essing (EMNLP), April. ## Hyun, D., 2006. Automatic Keyword Extraction Usi-ng Category Correlation of Data, Heidelberg, pp. 224-230.##Jain, A. Murty, M.N., Flynn, P., 1999. Data clust-ering: A review, ACM Computing Surveys, 31(3), pp. 264–323.##Klein, M., Steenbergen, W.V., 2006. Thesaurus-based Retrieval of Case Law, International JURIX confe-rence, April.##Liu, Y., Ciliax, B.J., Borges, K., Dasigi, V., Ram, A., Navathe S.B., Ingledine, R., 2005. Comparison of two schemes for automatic keyword extraction from MEDLINE for functional gene clustering, IEEE Computational Systems Bioinformatics Conference, April.##Maron, M.E., 1961. Automatic indexing: an exper-imental enquiry, Journal of the ACM, 8(1), pp. 404-417.##Martinez, J.L., 2008. Automatic Keyword Extraction for News Finder, Heidelberg, pp. 405-427.##Renz, I., 2003. Keyword Extraction for Text Chara-cterization, Information Processing and Management, 31(5), pp. 226-237.##Romero, A., Nino, F., 2007. Keyword Extraction Using an Artificial Immune System, Information Retrieval, 5(2), pp. 216-231.##Salton, G., Hill, G., 1983. Introduction to Modern Information Retrival, MC Graw Hill, 1983.## Salton, G., Yang, C.S., 1973. On the specification of term values in automatic indexing, Journal of Doc-umentation, 29, pp. 351-372.##Samiian, V., 1983. Origins of phrasal categories in Persian, an X-bar analysis, Ph.D dissertation, UCLA, 1983.##Strehl, A., Ghosh, J., 2002. Cluster ensembles - a knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research, 3, pp. 583–617.##Turney, P.D., 1999. Learning Algorithms for Keyp-hrase Extraction, Information Retrieval, 2(4), pp. 306-336.##Witten, W., Medley, I.H., 2006. Thesaurus based autom-atic keyphrase indexing, ACM/IEEE-CS JCDL '06, April.##Zhang, Y., Heywood N.Z., Milios, E., 2006. World Wide Web Site Summarization Web Intelligence and Agent Systems, Technical Report, CS-2002-8.##بی‌جن‌خان، م. ، 1383. نقش پیکره‌های زبانی در نوشتن دستور زبان: معرفی یک نرم‌افزار رایانه‌ای، مجله زبان‌شناسی، سال 19، شماره 2.##حری، ع.، 1383. راهنمای تهیه و گسترش اصطلاح‌نامه یک زبانه، مرکز اسناد و مدارک علمی.##خسروی، ف.، 1379. اصطلاحنامه فرهنگی فارسی 'اصفا'، کتابخانه ملی جمهوری اسلامی ایران.##علاقه‌بند م.ر.، سعیدی محمدی، م.ر.، دزفولیان، م.ح.، 1391. خوشه‌بندی متون مبتنی بر مرکز دسته با استفاده از روش SVD و بهره‌گیری از نقاط همسایگی، نخستین کنفرانس بین‌المللی پردازش خط و زبان فارسی، شهریور.##یغمایی، ف.، تعبدی، س.، 1391. بهبود دسته بندی متون فارسی در روش همسایگی وزن دار، نخستین کنفرانس بین‌المللی پردازش خط و زبان فارسی، شهریور.##Arasteh, A., Elahimanesh, M.H., Sharif, A., Minaei-Bidgoli, B., 2012. Semantically Clustering of Persian Words, The first international conference on Persian language processing, September.## Deegan, M., 2004. Keyword Extraction with Thesauri and Content Analysis, URL: http://ww-w.rlg.org/en/page.php?Page_ID=17068 ##Faceli, K., Marcilio, C.P., 2006. Multi-objective Clustering Ensemble, Sixth International Conference on Hybrid Intelligent Systems (HIS'06), April.## Frank, E., 1999. Domain-Based Extraction of Tech-nical Keyphrases, International Joint Conference on Artificial Intelligence, April.##Frantzi, K., Ananiadou, S., Mima, H., 2002. Auto-matic Recognition of Multi-word Terms: the C-value/NC-value Method, Digital Libraries, 3(2), pp. 115-130.##Freitas, N., Kaestner, A., 2005. Automatic text sum-arization using a machine learning approach, Brazi-lian Symposium on Artificial Intelligence (SBIA), April.##Hult, A., 2003. Improved automatic keyword extra-ction given more linguistic knowledge, Conference on Empirical Methods in Natural Language Proc-essing (EMNLP), April. ## Hyun, D., 2006. Automatic Keyword Extraction Usi-ng Category Correlation of Data, Heidelberg, pp. 224-230.##Jain, A. Murty, M.N., Flynn, P., 1999. Data clust-ering: A review, ACM Computing Surveys, 31(3), pp. 264–323.##Klein, M., Steenbergen, W.V., 2006. Thesaurus-based Retrieval of Case Law, International JURIX confe-rence, April.##Liu, Y., Ciliax, B.J., Borges, K., Dasigi, V., Ram, A., Navathe S.B., Ingledine, R., 2005. Comparison of two schemes for automatic keyword extraction from MEDLINE for functional gene clustering, IEEE Computational Systems Bioinformatics Conference, April.##Maron, M.E., 1961. Automatic indexing: an exper-imental enquiry, Journal of the ACM, 8(1), pp. 404-417.##Martinez, J.L., 2008. Automatic Keyword Extraction for News Finder, Heidelberg, pp. 405-427.##Renz, I., 2003. Keyword Extraction for Text Chara-cterization, Information Processing and Management, 31(5), pp. 226-237.##Romero, A., Nino, F., 2007. Keyword Extraction Using an Artificial Immune System, Information Retrieval, 5(2), pp. 216-231.##Salton, G., Hill, G., 1983. Introduction to Modern Information Retrival, MC Graw Hill, 1983.## Salton, G., Yang, C.S., 1973. On the specification of term values in automatic indexing, Journal of Doc-umentation, 29, pp. 351-372.##Samiian, V., 1983. Origins of phrasal categories in Persian, an X-bar analysis, Ph.D dissertation, UCLA, 1983.##Strehl, A., Ghosh, J., 2002. Cluster ensembles - a knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research, 3, pp. 583–617.##Turney, P.D., 1999. Learning Algorithms for Keyp-hrase Extraction, Information Retrieval, 2(4), pp. 306-336.##Witten, W., Medley, I.H., 2006. Thesaurus based autom-atic keyphrase indexing, ACM/IEEE-CS JCDL '06, April.##Zhang, Y., Heywood N.Z., Milios, E., 2006. World Wide Web Site Summarization Web Intelligence and Agent Systems, Technical Report, CS-2002-8. ##

طراحی و آموزش شبکه‏ های عصبی مصنوعی به وسیله استراتژی تکاملی با جمعیت‏ های موازی Construction and Training of Artificial Neural Networks using Evolution Strategy with Parallel Populations 1 1 کاربرد شبکه‏‏ های عصبی مصنوعی در حوزه‏ هایی از قبیل دسته‏ بندی تصاویر و سیگنال های صوتی مؤید توانایی این ابزار قدرتمند هوش مصنوعی در حل مسائل دنیای امروز است. طراحی و آموزش شبکه‏ های عصبی همواره یک فرآیند زمان‏بر و مشکل بوده است. یک مدل عصبی مناسب باید بتواند الگوی داده‏ های آموزشی را فراگرفته و نیز قابلیت تعمیم داشته باشد. در این مقاله، از جمعیت‏ های موازی برای طراحی معماری شبکه عصبی و همچنین از استراتژی تکاملی برای آموزش آن استفاده شده است، به‏ طوریکه در هر جمعیت شبکه ای با معماری خاصی تکامل می‏ یابد. با کمک یک روش انتخاب دومعیاره مبتنی بر میزان خطا و پیچیدگی شبکه‏ ها، الگوریتم ارائه شده قادر است شبکه‏ های ساده با قابلیت تعمیم بالا تولید کند. برای ارزیابی کارایی الگوریتم پیشنهادی از 7 مسأله استاندارد دسته‏ بندی استفاده شده است. روش ارائه شده با روش‏های تکامل اوزان، تکامل معماری و نیز الگوریتم‏های تکامل همزمان معماری و اوزان مورد مقایسه قرار گرفته است. نتایج آزمایش‏ها کارایی و پایداری این روش را نسبت به روش‏های مورد مقایسه نشان می‏دهد. در این مقاله، همچنین تأثیر وجود جمعیت‏های موازی، روش انتخاب دومعیاره و نیز عملگر ادغام در الگوریتم ارائه شده مورد بررسی قرار گرفته است. از مزایای اصلی این روش بهره‏ گیری از پردازش موازی به وسیله جمعیت‏های مستقل است. 2 Application of artificial neural networks (ANN) in areas such as classification of images and audio signals shows the ability of this artificial intelligence technique for solving practical problems. Construction and training of ANNs is usually a time-consuming and hard process. A suitable neural model must be able to learn the training data and also have the generalization ability. In this paper, multiple parallel populations are used for construction of ANN and evolution strategy for its training, so that in each population a particular ANN architecture is evolved. By using a bi-criteria selection method based on error and complexity of ANNs, the proposed algorithm can produce simple ANNs that have high generalization ability. To assess the performance of the algorithm, 7 benchmark classification problems have been used. It has then been compared against the existing evolutionary algorithms that train and/or construct ANNs. Experimental results show the efficiency and robustness of the proposed algorithm compared to the other methods. In this paper, the impact of parallel populations, the bi-criteria selection method, and the crossover operator on the algorithm performance has been analyzed. A key advantage of the proposed algorithm is the use of parallel computing by means of multiple populations. 101 114 2014/08/122014/11/302014/10/252014/10/192014/06/302014/10/162013/07/32013/06/8 1392/3/18 2016/02/262015/04/212015/05/162016/02/262016/02/262015/09/272016/05/22016/02/26 1394/12/7 فردین احمدی زر Fardin Ahmadizar دانشگاه کردستان f.ahmadizar@uok.ac.ir خه‏ بات سلطانیان Khabat Soltanian دانشگاه کردستان فردین اخلاقیان‏ طاب Fardin AkhlaghianTab دانشگاه کردستان Artificial Neural Networks Evolution Strategy Parallel Populations شبکه‏ های عصبی مصنوعی استراتژی تکاملی جمعیت‏ های موازی سعیدی، ساره، توحیدخواه، فرزاد، مدل شبکۀ عصبی از نگاشت سلول‏های شبکه به سلول‏های مکانی، پردازش علائم و داده‏ها، 1388، (2) 12، 53-62.##محمدزاده، جواد، مسعودنیا، سعید، آرانی، علی، افزایش نرخ طبقه‏بندی با استفاده از تجمیع ویژگی‏های موثر روش‏های مختلف ترکیب شبکه‏های عصبی، پردازش علائم و داده‏ها، 1390، (2) 16، 101-114.##Angeline, P.J., Saunders, G.M., Pollack, J.B., An evolutionary algorithm that constructs recurrent neural networks, IEEE Transactions on Neural Net-works, 1994, 5, 54-65.##Blake, C.L., Merz., C.J., UCI Repository of Machine Learning Databases, 1998.##Cantu-Paz, E., Kamath, C., An empirical comparison of combinations of evolutionary algorithms and neu-ral networks for classification problems, IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, 2005, 35, 915-927.##Castellani, M., Evolutionary generation of neural net-work classifiers – An empirical comparison, Neurocomputing, 2013, 99, 214-229.##Deb, K., Anand, A., Joshi, D., A computationally efficient evolutionary algorithm for real-parameter optimization, Evolutionary Computation, 2002, 10, 371-395.##Haflidason, S., On the significance of the permutation problem in neuroevolution, in the School of Com-puter Science, The University of Manchester, 2010.##Hancock, P.J.B., Genetic Algorithms and permutation problems: a comparison of recombination operators for neural net structure specification, In: Proceedings of the International Workshop on Combination of Genetic Algorithms and Neural Networks, IEEE Computer Society Press, 1992, pp. 108-122.##Haykin, S., Neural Networks: A Comprehensive Foundation, Macmillan, 1994.##Hertz, J., Introduction to the theory of neural comp-utation (Santa Fe Institute Studies in the Sciences of Complexity), Westview Press, 1991.##Kitano, H., Designing neural networks using genetic algorithms with graph generation system, Complex Systems Journal, 1990, 4, 461-476.##Lang, K.J., Waibel, A.H., Hinton, G.E., A time-delay neural network architecture for isolated word recognition, Neural Networks, 1990, 3, 23-43.##Mitchell, M., An introduction to genetic algorithms (Complex Adaptive Systems), The MIT Press, 1998.##Montana, D. Davis, L., Training feedforward neural networks using genetic algorithms, In: Proceedings of the 11th International Joint Conference on Artificial Intelligence, 1989, pp. 762-767.##Rivero, D., Dorado, J., Rabuñal, J., Pazos, A., Gene-ration and simplification of artificial neural networks by means of genetic programming, Neurocomputing, 2010, 73, 3200-3223.##Siddiqi, A.A., Lucas, S.M., A comparison of matrix rewriting versus direct encoding for evolving neural networks, In: Proceedings of the IEEE International Conference on Evolutionary Computation, 1998, pp. 392-397.##Stanley, K.O., Efficient evolution of neural networks through complexification, The University of Texas at Austin, 2004.##Stanley, K.O., D' Ambrosio, D.B., Gauci, J., A hyper-cube-based indirect encoding for evolving large-scale neural networks, Artificial Life, 2009, 15, 185-212.##Stanley, K.O., Miikkulainen, R., Evolving neural networks through augmenting topologies, Evolut-ionary Computation, 2002, 10, 99-127.##Tsoulos, I., Gavrilis, D., Glavas, E., Neural network construction and training using grammatical evolu-tion, Neurocomputing, 2008, 72, 269-277.##Whitley, D., The GENITOR algorithm and selection pressure: why rank-based allocation of reproductive trials is best, In: Proceedings of the Third Inter-national Conference on Genetic Algorithms, San Mateo, CA, 1989, pp. 116-123.##Whitley, D., Starkweather, T., Bogart, C., Genetic algorithms and neural networks: optimizing conn-ections and connectivity, Parallel Computing, 1990, 14, 347-361.##Yao, X., Evolving artificial neural networks, In: Proceedings of the IEEE, 87, 1999, pp. 1423-1447.##Yao, X., Islam, Md.M., Evolving artificial neural network ensembles, IEEE Computational Intelligence Magazine, 2008, 3, 31-42.##سعیدی، ساره، توحیدخواه، فرزاد، مدل شبکۀ عصبی از نگاشت سلول‏های شبکه به سلول‏های مکانی، پردازش علائم و داده‏ها، 1388، (2) 12، 53-62.##محمدزاده، جواد، مسعودنیا، سعید، آرانی، علی، افزایش نرخ طبقه‏بندی با استفاده از تجمیع ویژگی‏های موثر روش‏های مختلف ترکیب شبکه‏های عصبی، پردازش علائم و داده‏ها، 1390، (2) 16، 101-114.##Angeline, P.J., Saunders, G.M., Pollack, J.B., An evolutionary algorithm that constructs recurrent neural networks, IEEE Transactions on Neural Net-works, 1994, 5, 54-65.##Blake, C.L., Merz., C.J., UCI Repository of Machine Learning Databases, 1998.##Cantu-Paz, E., Kamath, C., An empirical comparison of combinations of evolutionary algorithms and neu-ral networks for classification problems, IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, 2005, 35, 915-927.##Castellani, M., Evolutionary generation of neural net-work classifiers – An empirical comparison, Neurocomputing, 2013, 99, 214-229.##Deb, K., Anand, A., Joshi, D., A computationally efficient evolutionary algorithm for real-parameter optimization, Evolutionary Computation, 2002, 10, 371-395.##Haflidason, S., On the significance of the permutation problem in neuroevolution, in the School of Com-puter Science, The University of Manchester, 2010.##Hancock, P.J.B., Genetic Algorithms and permutation problems: a comparison of recombination operators for neural net structure specification, In: Proceedings of the International Workshop on Combination of Genetic Algorithms and Neural Networks, IEEE Computer Society Press, 1992, pp. 108-122.##Haykin, S., Neural Networks: A Comprehensive Foundation, Macmillan, 1994.##Hertz, J., Introduction to the theory of neural comp-utation (Santa Fe Institute Studies in the Sciences of Complexity), Westview Press, 1991.##Kitano, H., Designing neural networks using genetic algorithms with graph generation system, Complex Systems Journal, 1990, 4, 461-476.##Lang, K.J., Waibel, A.H., Hinton, G.E., A time-delay neural network architecture for isolated word recognition, Neural Networks, 1990, 3, 23-43.##Mitchell, M., An introduction to genetic algorithms (Complex Adaptive Systems), The MIT Press, 1998.##Montana, D. Davis, L., Training feedforward neural networks using genetic algorithms, In: Proceedings of the 11th International Joint Conference on Artificial Intelligence, 1989, pp. 762-767.##Rivero, D., Dorado, J., Rabuñal, J., Pazos, A., Gene-ration and simplification of artificial neural networks by means of genetic programming, Neurocomputing, 2010, 73, 3200-3223.##Siddiqi, A.A., Lucas, S.M., A comparison of matrix rewriting versus direct encoding for evolving neural networks, In: Proceedings of the IEEE International Conference on Evolutionary Computation, 1998, pp. 392-397.##Stanley, K.O., Efficient evolution of neural networks through complexification, The University of Texas at Austin, 2004.##Stanley, K.O., D' Ambrosio, D.B., Gauci, J., A hyper-cube-based indirect encoding for evolving large-scale neural networks, Artificial Life, 2009, 15, 185-212.##Stanley, K.O., Miikkulainen, R., Evolving neural networks through augmenting topologies, Evolut-ionary Computation, 2002, 10, 99-127.##Tsoulos, I., Gavrilis, D., Glavas, E., Neural network construction and training using grammatical evolu-tion, Neurocomputing, 2008, 72, 269-277.##Whitley, D., The GENITOR algorithm and selection pressure: why rank-based allocation of reproductive trials is best, In: Proceedings of the Third Inter-national Conference on Genetic Algorithms, San Mateo, CA, 1989, pp. 116-123.##Whitley, D., Starkweather, T., Bogart, C., Genetic algorithms and neural networks: optimizing conn-ections and connectivity, Parallel Computing, 1990, 14, 347-361.##Yao, X., Evolving artificial neural networks, In: Proceedings of the IEEE, 87, 1999, pp. 1423-1447.##Yao, X., Islam, Md.M., Evolving artificial neural network ensembles, IEEE Computational Intelligence Magazine, 2008, 3, 31-42. ##

طبقه‌بندی زیرپیکسلی تصاویر ابرطیفی براساس تعمیم الگوریتم معاوضه پیکسلی و ارزیابی آن Hyperspectral Images Sub-Pixel Classification Based on Pixel-Swapping Algorithm Extension and Its Evaluation 1 1 قابلیت شناسایی ماهیتی پوشش‌های سطح در تصاویر ابرطیفی به‌نحو قابل‌توجهی فراهم شده‌است. دراین تصاویر طیف بازتابی سطح در محدوده مرئی و مادون‌قرمزنزدیک طیف الکترومغناطیس در باندهای بسیار باریک و پیوسته ثبت می‌گردد. لیکن بدلایلی ازجمله وجود پیکسل‏های مخلوط و پایین بودن قدرت تفکیک‌مکانی این تصاویر، شناسایی دقیق موقعیتی پوشش‌های سطح در آنها دشوار است. روش‏های طبقه‏بندی‌نرم امکان برآورد سهم کلاس‏ها رادر داخل پیکسل‏های مخلوط فراهم می‌آورد. اما بااستفاده ازاین روش‏ها تنها اطلاعات ماهیتی درسطح زیرپیکسل تولید شده و آرایش مکانی کلاس‌ها نامعلوم باقی می‏ماند. جهت حل این مشکل، روش‏هایی بانام SRM ارائه شده که مقادیر عضویت حاصل از طبقه‏بندی‌نرم رادر زیرپیکسل‌ها جانمایی نموده و نقشه پوششی با توان‌تفکیک بالاتری تولید می‏نمایند. دراین مقاله، ازروش معاوضه‌پیکسلی بعنوان یکی‌از الگوریتم‏های SRM استفاده وبا تکرار آن‌برای هرکلاس‏، حالت چندکلاسه آن ایجاد شد. نکته اساسی که‌در طبقه‌بندی زیرپیکسلی مطرح می‌شود، ارزیابی این نوع طبقه‌بندی‌کنندهها است، که بدلیل اثرگذاری پارامترهای متنوع در طبقه‌بندی زیرپیکسلی، پیچیده است. از اینرو بعنوان فعالیتی اصلی و نوآورانه اثرگذاری دو پارامتر سطح‌همسایگی و ضریب‌بزرگنمایی درروش معاوضه‌پیکسلی تعمیم‌داده‌شده، شبیه‌سازی و تحلیل شده‌است. برای این‌منظور چارچوبی برای ارزیابی عملکرد طبقه‌بندی زیرپیکسلی به‌دو صورت مستقل از خطای طبقه‏بندی‌نرم و وابسته به‌آن، پیشنهاد گردید. 2 The capability of the matter identification is developed considerably in hyperspectral images. The spectral reflectance of surfaces in these imaging systems in the visible and near infrared range of the electromagnetic spectrum is recorded in extremely narrow and continuous bands. But for some reasons, such as existence the mixed pixels and low spatial resolution of these images, is difficult to land cover accurate position identify. The soft classification methods provide the estimation of the membership value of various classes within mixed pixels. But, by using these methods, the matter information extraction is possible only and position information extraction in sub-pixel level is impossible. In recent years, in order to solve this problem, some methods that are called SRM, have been developed for positioning the extracted membership values by soft classification process in sub-pixels for producing a higher spatial resolution land use map. In this paper, pixel-swapping method is used as the latest SRM algorithms, and with repetition the binary case of this algorithm for each class, this algorithm has been generalized and developed for multi-class. Another main point in sub-pixel classification is the performance evaluation of these classifiers. Because of the influence of various parameters in the sub-pixel classification, the evaluation of this process is very complex. Hence, as a main and innovative activity in this paper, the Influence of the neighborhood level and the zoom factor as two important parameters in the extension pixel-swapping method has been simulated and analyzed. For this purpose, in this paper a framework for evaluating the sub-pixel classification performance based on dependent on and independent on soft classification error is proposed. 115 125 2014/08/122014/11/302014/10/252014/10/192014/06/302014/10/162013/07/32013/06/82013/04/27 1392/2/7 2016/02/262015/04/212015/05/162016/02/262016/02/262015/09/272016/05/22016/02/262016/03/5 1394/12/15 حمید دهقانی دانشگاه صنعتی مالک اشتر hamid_dehyahoo.com حسین رجایی دانشگاه صنعتی مالک اشتر ahmad.madanchi@gmail.com Hyperspectral image Sub-pixel classification Sub-pixel classification evaluation Pixel-swapping method SRM algorithm تصویر ابرطیفی طبقه بندی زیرپیکسل ارزیابی طبقه بندی ریزپیکسلی روش معاوضه پیکسلی الگوریتم SRM خزائی، صفا، همایونی، سعید و عبدالرضا صفری، "تصویربرداری ابرطیفی و ملاحظات «آفا» در برابر تهدیدات آن" نشریه علمی- پژوهشی علوم و فناوری‏های پدافند غیرعامل،1389، سال اول، شماره دوم، صفجه 63-74،.##رامک، راضیه، مجردی، برات و محمدجواد ولدان‏زوج، "مقایسه خروجی حاصل از مدل تجزیه خطی (LMM) و طبقه‏بندی کننده‏های نرم در تصاویر ابرطیفی" همایش ژئوماتیک، 1390.##Atkinson, P. 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آشکارسازی عروق شبکیه چشم بر اساس مدل محاسباتی سلول ساده کورتکس اولیه بینایی A New Approach to Retinal Vessel Segmentation by Using Computational Model of Simple Cells in Primary Visual Cortex 1 1 در این مقاله یک الگوریتم جدید برای آشکارسازی خودکار و بدون نظارت عروق خونی شبکیه چشم ارائه شده است. برای آشکارسازی عروق خونی شبکیه، ابتدا یک مرحله پیش‌پردازش برای حذف لکه زرد و شدت روشنایی‌های نویزگونه پس‌زمینه تصویر انجام شده است. برجسته‌سازی رگ‌های خونی تصویر در جهت‌های مختلف با استفاده از یک الگوریتم جدید مبتنی بر عملکرد سلول ساده سیستم بینایی انسان انجام شده است. یک مقدار آستانه تطبیقی نیز به عنوان تابع فعالیت این سلول ساده در نظر گرفته شده است. در انتها برای حذف ترشحات آشکارشده به عنوان رگ‌های خونی، یک مرحله پس‌پردازش روی خروجی‌های سلول‌های ساده انجام می‌شود. نتایج به‌دست آمده نشان می‌دهد که روش پیشنهادی تقریباً در تمامی تصاویر پایگاه داده DRIVE عروق شبکیه چشم را با درصد بالاتری نسبت به روش‌های موجود آشکارسازی کرده و مدت زمان استخراج عروق بسیار بهبود یافته است. 2 In this paper, a new unsupervised algorithm for automatic retinal blood vessel extraction is presented. A pre-processing step is introduced to eliminating optic disk and back ground noise. Blood vessel highlighting is prepared by a new method inspired by simple cells in human visual system. An adaptive threshold is introduced as an activation function of simple cells. Post-processing step is used as a final stage at the output of simple cells to eliminating exudates which is detected as blood vessels. The results on DRIVE database demonstrate that the performance of the proposed algorithm is comparable with state-of-the-art techniques in terms of execution time and extracted vessels. 127 138 2014/08/122014/11/302014/10/252014/10/192014/06/302014/10/162013/07/32013/06/82013/04/272014/04/12 1393/1/23 2016/02/262015/04/212015/05/162016/02/262016/02/262015/09/272016/05/22016/02/262016/03/52016/04/11 1395/1/23 محسن زردادی Mohsen Zardadi دانشگاه بیرجند zardadi@birjand.ac.ir ناصر مهرشاد Naser Mehrshad دانشکده مهندسی دانشگاه بیرجند nmehrshad@birjand.ac.ir retinal vessel segmentation medical assistance systems retinal simple cell model DRIVE database آشکارسازی عروق شبکیه سیستم‌های تصمیم‌یار پزشکی مدل سلول ساده شبکیه بانک داده DRIVE Al-Diri, B., Hunter, A., & Steel, D. (2009). An active contour model for segmenting and measuring retinal vessels. Medical Imaging, IEEE Transactions on, 28(9), 1488-1497. ##America, P. B., & Tielsch, J. M. (1994). Vision Problems in the US: A Report on Blindness & Vision Impairment in Adults Age 40 and Older: The Society.##Amin, M. A., & Yan, H. (2011). High speed detec-tion of retinal blood vessels in fundus image using phase congruency. Soft Computing, 15(6), 1217-1230. ##Anitha, J., Vijila, C. K. S., & Hemanth, D. J. (2009). An Overview Of Computational Intelligence Tec-hniques For Retinal Disease Identification Applic-ations. International Journal of Reviews in Comp-uting, 5, 29-46. ##Fowler, M. J. (2008). Microvascular and macro-vascular complications of diabetes. Clinical Diabetes, 26(2), 77-82. ##Frangi, A. F., Niessen, W. J., Vincken, K. L., & Viergever, M. A. (1998). Multiscale vessel enhance-ment filtering Medical Image Computing and Comp-uter-Assisted Interventation—MICCAI’98 (pp. 130-137): Springer.##Fraz, M., Barman, S., Remagnino, P., Hoppe, A., Basit, A., Uyyanonvara, B., . . . Owen, C. G. (2011). An approach to localize the retinal blood vessels using bit planes and centerline detection. Computer methods and programs in biomedicine. ##Fraz, M. M., Remagnino, P., Hoppe, A., Uyyan-onvara, B., Rudnicka, A. R., Owen, C. G., & Barman, S. A. (2012a). Blood vessel segmentation meth-odologies in retinal images–A survey. Computer methods and programs in biomedicine, 108(1), 407-433. ##Fraz, M. M., Remagnino, P., Hoppe, A., Uyyan-onvara, B., Rudnicka, A. R., Owen, C. G., & Barman, S. A. (2012b). An ensemble classification-based approach applied to retinal blood vessel segmentation. Biomedical Engineering, IEEE Transactions on, 59(9), 2538-2548. ##Gang, L., Chutatape, O., & Krishnan, S. M. (2002). Detection and measurement of retinal vessels in fundus images using amplitude modified second-order Gaussian filter. Biomedical Engineering, IEEE Transactions on, 49(2), 168-172. ##Grigorescu, C., Petkov, N., & Westenberg, M. A. (2003). Contour detection based on nonclassical receptive field inhibition. Image Processing, IEEE Transactions on, 12(7), 729-739. ##Hoover, A., Kouznetsova, V., & Goldbaum, M. (2000). Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. Medical Imaging, IEEE Transactions on, 19(3), 203-210. ##Jiang, X., & Mojon, D. (2003). Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 25(1), 131-137.##Klein, R., Klein, B. E., Moss, S. E., & Wong, T. Y. (2007). Retinal vessel caliber and microvascular and macrovascular disease in type 2 diabetes: XXI: the Wisconsin Epidemiologic Study of Diabetic Retin-opathy. Ophthalmology, 114(10), 1884-1892. ##Lam, B. S., Gao, Y., & Liew, A.-C. (2010). General retinal vessel segmentation using regularization-based multiconcavity modeling. Medical Imaging, IEEE Transactions on, 29(7), 1369-1381. ##Lam, B. S., & Yan, H. (2008). A novel vessel segmentation algorithm for pathological retina images based on the divergence of vector fields. Medical Imaging, IEEE Transactions on, 27(2), 237-246. ##Lupascu, C. A., Tegolo, D., & Trucco, E. (2010). FABC: retinal vessel segmentation using AdaBoost. Information Technology in Biomedicine, IEEE Transactions on, 14(5), 1267-1274.##Marín, D., Aquino, A., Gegúndez-Arias, M. E., & Bravo, J. M. (2011). A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features. Medical Imaging, IEEE Transactions on, 30(1), 146-158. ##Mendonca, A. M., & Campilho, A. (2006). Segme-ntation of retinal blood vessels by combining the detection of centerlines and morphological recon-struction. Medical Imaging, IEEE Transactions on, 25(9), 1200-1213. ##Miri, M. S., & Mahloojifar, A. (2011). Retinal image analysis using curvelet transform and multistructure elements morphology by reconstruction. Biomedical Engineering, IEEE Transactions on, 58(5), 1183-1192.##Niemeijer, M., Staal, J., van Ginneken, B., Loog, M., & Abramoff, M. D. (2004). Comparative study of retinal vessel segmentation methods on a new publicly available database. Paper presented at the Medical Imaging 2004.##Owen, C. G., Rudnicka, A. R., Mullen, R., Barman, S. A., Monekosso, D., Whincup, P. H., . . . Paterson, C. (2009). Measuring retinal vessel tortuosity in 10-year-old children: validation of the Computer-Assisted Image Analysis of the Retina (CAIAR) program. Investigative ophthalmology & visual science, 50(5), 2004-2010. ##Quek, F. K., & Kirbas, C. (2001). Vessel extraction in medical images by wave-propagation and traceb-ack. Medical Imaging, IEEE Transactions on, 20(2), 117-131. ##Ricci, E., & Perfetti, R. (2007). Retinal blood vessel segmentation using line operators and support vector classification. Medical Imaging, IEEE Transactions on, 26(10), 1357-1365. ##Rossi, A. F., Desimone, R., & Ungerleider, L. G. (2001). Contextual modulation in primary visual cortex of macaques. the Journal of Neuroscience, 21(5), 1698-1709. ##Soares, J. V., Leandro, J. J., Cesar, R. M., Jelinek, H. F., & Cree, M. J. (2006). Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classif-ication. Medical Imaging, IEEE Transactions on, 25(9), 1214-1222. ##Staal, J., Abràmoff, M. D., Niemeijer, M., Viergever, M. A., & van Ginneken, B. (2004). Ridge-based vess-el segmentation in color images of the retina. Medical Imaging, IEEE Transactions on, 23(4), 501-509. ##Sum, K., & Cheung, P. Y. (2008). Vessel extraction under non-uniform illumination: a level set approach. Biomedical Engineering, IEEE Transactions on, 55(1), 358-360. ##Zana, F., & Klein, J.-C. (2001). Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. Image Processing, IEEE Transactions on, 10(7), 1010-1019.##Al-Diri, B., Hunter, A., & Steel, D. (2009). An active contour model for segmenting and measuring retinal vessels. Medical Imaging, IEEE Transactions on, 28(9), 1488-1497. ##America, P. B., & Tielsch, J. M. (1994). Vision Problems in the US: A Report on Blindness & Vision Impairment in Adults Age 40 and Older: The Society.##Amin, M. A., & Yan, H. (2011). High speed detec-tion of retinal blood vessels in fundus image using phase congruency. Soft Computing, 15(6), 1217-1230. ##Anitha, J., Vijila, C. K. S., & Hemanth, D. J. (2009). An Overview Of Computational Intelligence Tec-hniques For Retinal Disease Identification Applic-ations. International Journal of Reviews in Comp-uting, 5, 29-46. ##Fowler, M. J. (2008). Microvascular and macro-vascular complications of diabetes. Clinical Diabetes, 26(2), 77-82. ##Frangi, A. F., Niessen, W. J., Vincken, K. L., & Viergever, M. A. (1998). Multiscale vessel enhance-ment filtering Medical Image Computing and Comp-uter-Assisted Interventation—MICCAI’98 (pp. 130-137): Springer.##Fraz, M., Barman, S., Remagnino, P., Hoppe, A., Basit, A., Uyyanonvara, B., . . . Owen, C. G. (2011). An approach to localize the retinal blood vessels using bit planes and centerline detection. Computer methods and programs in biomedicine. ##Fraz, M. M., Remagnino, P., Hoppe, A., Uyyan-onvara, B., Rudnicka, A. R., Owen, C. G., & Barman, S. A. (2012a). Blood vessel segmentation meth-odologies in retinal images–A survey. Computer methods and programs in biomedicine, 108(1), 407-433. ##Fraz, M. M., Remagnino, P., Hoppe, A., Uyyan-onvara, B., Rudnicka, A. R., Owen, C. G., & Barman, S. A. (2012b). An ensemble classification-based approach applied to retinal blood vessel segmentation. Biomedical Engineering, IEEE Transactions on, 59(9), 2538-2548. ##Gang, L., Chutatape, O., & Krishnan, S. M. (2002). Detection and measurement of retinal vessels in fundus images using amplitude modified second-order Gaussian filter. Biomedical Engineering, IEEE Transactions on, 49(2), 168-172. ##Grigorescu, C., Petkov, N., & Westenberg, M. A. (2003). Contour detection based on nonclassical receptive field inhibition. Image Processing, IEEE Transactions on, 12(7), 729-739. ##Hoover, A., Kouznetsova, V., & Goldbaum, M. (2000). Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. Medical Imaging, IEEE Transactions on, 19(3), 203-210. ##Jiang, X., & Mojon, D. (2003). Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 25(1), 131-137.##Klein, R., Klein, B. E., Moss, S. E., & Wong, T. Y. (2007). Retinal vessel caliber and microvascular and macrovascular disease in type 2 diabetes: XXI: the Wisconsin Epidemiologic Study of Diabetic Retin-opathy. Ophthalmology, 114(10), 1884-1892. ##Lam, B. S., Gao, Y., & Liew, A.-C. (2010). General retinal vessel segmentation using regularization-based multiconcavity modeling. Medical Imaging, IEEE Transactions on, 29(7), 1369-1381. ##Lam, B. S., & Yan, H. (2008). A novel vessel segmentation algorithm for pathological retina images based on the divergence of vector fields. Medical Imaging, IEEE Transactions on, 27(2), 237-246. ##Lupascu, C. A., Tegolo, D., & Trucco, E. (2010). FABC: retinal vessel segmentation using AdaBoost. Information Technology in Biomedicine, IEEE Transactions on, 14(5), 1267-1274.##Marín, D., Aquino, A., Gegúndez-Arias, M. E., & Bravo, J. M. (2011). A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features. Medical Imaging, IEEE Transactions on, 30(1), 146-158. ##Mendonca, A. M., & Campilho, A. (2006). Segme-ntation of retinal blood vessels by combining the detection of centerlines and morphological recon-struction. Medical Imaging, IEEE Transactions on, 25(9), 1200-1213. ##Miri, M. S., & Mahloojifar, A. (2011). Retinal image analysis using curvelet transform and multistructure elements morphology by reconstruction. Biomedical Engineering, IEEE Transactions on, 58(5), 1183-1192.##Niemeijer, M., Staal, J., van Ginneken, B., Loog, M., & Abramoff, M. D. (2004). Comparative study of retinal vessel segmentation methods on a new publicly available database. Paper presented at the Medical Imaging 2004.##Owen, C. G., Rudnicka, A. R., Mullen, R., Barman, S. A., Monekosso, D., Whincup, P. H., . . . Paterson, C. (2009). Measuring retinal vessel tortuosity in 10-year-old children: validation of the Computer-Assisted Image Analysis of the Retina (CAIAR) program. Investigative ophthalmology & visual science, 50(5), 2004-2010. ##Quek, F. K., & Kirbas, C. (2001). Vessel extraction in medical images by wave-propagation and traceb-ack. Medical Imaging, IEEE Transactions on, 20(2), 117-131. ##Ricci, E., & Perfetti, R. (2007). Retinal blood vessel segmentation using line operators and support vector classification. Medical Imaging, IEEE Transactions on, 26(10), 1357-1365. ##Rossi, A. F., Desimone, R., & Ungerleider, L. G. (2001). Contextual modulation in primary visual cortex of macaques. the Journal of Neuroscience, 21(5), 1698-1709. ##Soares, J. V., Leandro, J. J., Cesar, R. M., Jelinek, H. F., & Cree, M. J. (2006). Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classif-ication. Medical Imaging, IEEE Transactions on, 25(9), 1214-1222. ##Staal, J., Abràmoff, M. D., Niemeijer, M., Viergever, M. A., & van Ginneken, B. (2004). Ridge-based vess-el segmentation in color images of the retina. Medical Imaging, IEEE Transactions on, 23(4), 501-509. ##Sum, K., & Cheung, P. Y. (2008). Vessel extraction under non-uniform illumination: a level set approach. Biomedical Engineering, IEEE Transactions on, 55(1), 358-360. ##Zana, F., & Klein, J.-C. (2001). Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. Image Processing, IEEE Transactions on, 10(7), 1010-1019. ##