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Artykuły w czasopismach na temat "Pattern recognition applications"
Mikhailov, Alexei. "Indexing-Based Pattern Recognition". Advanced Materials Research 403-408 (listopad 2011): 5254–59. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.5254.
Pełny tekst źródłaGrabusts, Peter. "POTENTIAL FUNCTION METHOD APPROACH TO PATTERN RECOGNITION APPLICATIONS". Environment. Technology. Resources. Proceedings of the International Scientific and Practical Conference 2 (15.06.2017): 30. http://dx.doi.org/10.17770/etr2017vol2.2512.
Pełny tekst źródłaNezhad, Mohammad Saber Fallah. "Applications of Pattern Recognition Techniques in Social Science". Studies in Social Science & Humanities 2, nr 1 (styczeń 2023): 31–35. http://dx.doi.org/10.56397/sssh.2023.01.06.
Pełny tekst źródłaPAPAKOSTAS, G. A., Y. S. BOUTALIS, D. E. KOULOURIOTIS i B. G. MERTZIOS. "FUZZY COGNITIVE MAPS FOR PATTERN RECOGNITION APPLICATIONS". International Journal of Pattern Recognition and Artificial Intelligence 22, nr 08 (grudzień 2008): 1461–86. http://dx.doi.org/10.1142/s0218001408006910.
Pełny tekst źródłaKober, Vitaly, Tae Choi, Victor Diaz-Ramírez i Pablo Aguilar-González. "Pattern Recognition: Recent Advances and Applications". Mathematical Problems in Engineering 2018 (15.11.2018): 1–2. http://dx.doi.org/10.1155/2018/8510319.
Pełny tekst źródłaRen, Dong, i Simon X. Yang. "Intelligent Pattern Recognition Technology and Applications". Intelligent Automation & Soft Computing 19, nr 4 (grudzień 2013): 497–99. http://dx.doi.org/10.1080/10798587.2013.869107.
Pełny tekst źródłaPaolanti, Marina, i Emanuele Frontoni. "Multidisciplinary Pattern Recognition applications: A review". Computer Science Review 37 (sierpień 2020): 100276. http://dx.doi.org/10.1016/j.cosrev.2020.100276.
Pełny tekst źródłaLavine, Barry K. "Environmental applications of pattern recognition techniques". Chemometrics and Intelligent Laboratory Systems 15, nr 2-3 (sierpień 1992): 219–30. http://dx.doi.org/10.1016/0169-7439(92)85011-q.
Pełny tekst źródłaKrasnoproshin, V. V., i V. A. Obraztsov. "Pattern Recognition: Theoretical Research Experience and Applications". Pattern Recognition and Image Analysis 31, nr 1 (styczeń 2021): 163–71. http://dx.doi.org/10.1134/s1054661821010132.
Pełny tekst źródłaSilva, Francisco de Assis Tavares Ferreira da, Magno Prudêncio de Almeida Filho, Antonio Macilio Pereira de Lucena i Alexandre Guirland Nowosad. "Pattern recognition on FPGA for aerospace applications". Research, Society and Development 10, nr 12 (14.09.2021): e83101219181. http://dx.doi.org/10.33448/rsd-v10i12.19181.
Pełny tekst źródłaRozprawy doktorskie na temat "Pattern recognition applications"
Thompson, J. R. "Applications of pattern recognition in medicine". Thesis, Open University, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.377939.
Pełny tekst źródłaRobinson, Daniel D. "Applications of pattern recognition and pattern analysis to molecule design". Thesis, University of Oxford, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343465.
Pełny tekst źródłaPAOLANTI, MARINA. "Pattern Recognition for challenging Computer Vision Applications". Doctoral thesis, Università Politecnica delle Marche, 2018. http://hdl.handle.net/11566/252904.
Pełny tekst źródłaPattern Recognition is the study of how machines can observe the environment, learn to distinguish patterns of interest from their background, and make sound and reasonable decisions about the patterns categories. Nowadays, the application of Pattern Recognition algorithms and techniques is ubiquitous and transversal. With the recent advances in computer vision, we now have the ability to mine such massive visual data to obtain valuable insight about what is happening in the world. The availability of affordable and high resolution sensors (e.g., RGB-D cameras, microphones and scanners) and data sharing have resulted in huge repositories of digitized documents (text, speech, image and video). Starting from such a premise, this thesis addresses the topic of developing next generation Pattern Recognition systems for real applications such as Biology, Retail, Surveillance, Social Media Intelligence and Digital Cultural Heritage. The main goal is to develop computer vision applications in which Pattern Recognition is the key core in their design, starting from general methods, that can be exploited in more fields, and then passing to methods and techniques addressing specific problems. The privileged focus is on up-to-date applications of Pattern Recognition techniques to real-world problems, and on interdisciplinary research, experimental and/or theoretical studies yielding new insights that advance Pattern Recognition methods. The final ambition is to spur new research lines, especially within interdisciplinary research scenarios. Faced with many types of data, such as images, biological data and trajectories, a key difficulty was to nd relevant vectorial representations. While this problem had been often handled in an ad-hoc way by domain experts, it has proved useful to learn these representations directly from data, and Machine Learning algorithms, statistical methods and Deep Learning techniques have been particularly successful. The representations are then based on compositions of simple parameterized processing units, the depth coming from the large number of such compositions. It was desirable to develop new, efficient data representation or feature learning/indexing techniques, which can achieve promising performance in the related tasks. The overarching goal of this work consists of presenting a pipeline to select the model that best explains the given observations; nevertheless, it does not prioritize in memory and time complexity when matching models to observations. For the Pattern Recognition system design, the following steps are performed: data collection, features extraction, tailored learning approach and comparative analysis and assessment. The proposed applications open up a wealth of novel and important opportunities for the machine vision community. The newly dataset collected as well as the complex areas taken into exam, make the research challenging. In fact, it is crucial to evaluate the performance of state of the art methods to demonstrate their strength and weakness and help identify future research for designing more robust algorithms. For comprehensive performance evaluation, it is of great importance developing a library and benchmark to gauge the state of the art because the methods design that are tuned to a specic problem do not work properly on other problems. Furthermore, the dataset selection is needed from different application domains in order to offer the user the opportunity to prove the broad validity of methods. Intensive attention has been drawn to the exploration of tailored learning models and algorithms, and their extension to more application areas. The tailored methods, adopted for the development of the proposed applications, have shown to be capable of extracting complex statistical features and efficiently learning their representations, allowing it to generalize well across a wide variety of computer vision tasks, including image classication, text recognition and so on.
Hayes, William S. "Pattern recognition and signal detection in gene finding". Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/25420.
Pełny tekst źródłaPrendergast, David Jeremy. "Applications of statistical pattern recognition in medical imaging". Thesis, University of Manchester, 1993. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.629772.
Pełny tekst źródłaEvans, Fiona H. "Syntactic models with applications in image analysis /". [Perth, W.A.] : [University of W.A.], 2006. http://theses.library.uwa.edu.au/adt-WU2007.0001.
Pełny tekst źródłaYan, Wing-fai. "Eye movement measurement for clinical applications using pattern recognition /". [Hong Kong : University of Hong Kong], 1988. http://sunzi.lib.hku.hk/hkuto/record.jsp?B12434024.
Pełny tekst źródłaMa, Chengyuan. "A detection-based pattern recognition framework and its applications". Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/33889.
Pełny tekst źródła甄榮輝 i Wing-fai Yan. "Eye movement measurement for clinical applications using pattern recognition". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1988. http://hub.hku.hk/bib/B31209026.
Pełny tekst źródłaLopez-Bonilla, Roman Ernesto. "Object recognition in three-dimensions for robotic applications". Thesis, University of Bradford, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.305752.
Pełny tekst źródłaKsiążki na temat "Pattern recognition applications"
Devijver, Pierre A. Pattern Recognition Theory and Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987.
Znajdź pełny tekst źródłaFred, Ana, Maria De Marsico i Mário Figueiredo, red. Pattern Recognition: Applications and Methods. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27677-9.
Pełny tekst źródłaFred, Ana, Maria De Marsico i Gabriella Sanniti di Baja, red. Pattern Recognition Applications and Methods. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53375-9.
Pełny tekst źródłaDe Marsico, Maria, Gabriella Sanniti di Baja i Ana Fred, red. Pattern Recognition Applications and Methods. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05499-1.
Pełny tekst źródłaDe Marsico, Maria, Gabriella Sanniti di Baja i Ana Fred, red. Pattern Recognition Applications and Methods. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66125-0.
Pełny tekst źródłaFred, Ana, i Maria De Marsico, red. Pattern Recognition Applications and Methods. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-12610-4.
Pełny tekst źródłaDe Marsico, Maria, Gabriella Sanniti di Baja i Ana Fred, red. Pattern Recognition Applications and Methods. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93647-5.
Pełny tekst źródłaDevijver, Pierre A., i Josef Kittler, red. Pattern Recognition Theory and Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-83069-3.
Pełny tekst źródłaLatorre Carmona, Pedro, J. Salvador Sánchez i Ana L. N. Fred, red. Pattern Recognition - Applications and Methods. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36530-0.
Pełny tekst źródłaDe Marsico, Maria, Gabriella Sanniti di Baja i Ana Fred, red. Pattern Recognition Applications and Methods. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40014-9.
Pełny tekst źródłaCzęści książek na temat "Pattern recognition applications"
Croall, Ian F., i John P. Mason. "Pattern Recognition". W Industrial Applications of Neural Networks, 55–117. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-84837-7_4.
Pełny tekst źródłaZimmermann, H. J. "Pattern Recognition". W Fuzzy Set Theory — and Its Applications, 217–40. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-015-7949-0_11.
Pełny tekst źródłaZimmermann, H. J. "Pattern Recognition". W Fuzzy Set Theory — and Its Applications, 187–212. Dordrecht: Springer Netherlands, 1985. http://dx.doi.org/10.1007/978-94-015-7153-1_11.
Pełny tekst źródłaBunke, Horst, Peter Dickinson, Miro Kraetzl, Michel Neuhaus i Marc Stettler. "Matching of Hypergraphs — Algorithms, Applications, and Experiments". W Applied Pattern Recognition, 131–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-76831-9_6.
Pełny tekst źródłaCristescu, Gabriela, i Liana Lupşa. "Applications in pattern recognition". W Non-Connected Convexities and Applications, 227–46. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0003-2_9.
Pełny tekst źródłaGökberk, Berk, Albert Ali Salah, Neşe Alyüz i Lale Akarun. "3D Face Recognition: Technology and Applications". W Advances in Pattern Recognition, 217–46. London: Springer London, 2009. http://dx.doi.org/10.1007/978-1-84882-385-3_9.
Pełny tekst źródłaYin, Xiaoxia, Brian W. H. Ng i Derek Abbott. "THz Pattern Recognition Experiments". W Terahertz Imaging for Biomedical Applications, 133–77. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-1821-4_9.
Pełny tekst źródłaFerrans, James C., i Jonathan Engelsma. "Software Architectures for Networked Mobile Speech Applications". W Advances in Pattern Recognition, 279–99. London: Springer London, 2008. http://dx.doi.org/10.1007/978-1-84800-143-5_13.
Pełny tekst źródłaWang, P. S. P. "Intelligent pattern recognition and applications". W Lecture Notes in Computer Science, 34–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/3-540-56346-6_28.
Pełny tekst źródłaEvans, Brian J. "Pattern Recognition and Its Applications". W A Simple Guide to Technology and Analytics, 49–72. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003108443-3.
Pełny tekst źródłaStreszczenia konferencji na temat "Pattern recognition applications"
Wang, Patrick. "Intelligent pattern recognition and applications". W the First ACM International Conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1854776.1854782.
Pełny tekst źródłaMartínez-Díaz, Saúl, i Vitaly Kober. "Pattern recognition with adaptive nonlinear filters". W Optical Engineering + Applications, redaktor Andrew G. Tescher. SPIE, 2007. http://dx.doi.org/10.1117/12.734240.
Pełny tekst źródłaKovács, Levente, i Tamás Szirányi. "Recognition of hidden pattern with background". W Optical Engineering + Applications, redaktorzy Oliver E. Drummond i Richard D. Teichgraeber. SPIE, 2007. http://dx.doi.org/10.1117/12.738512.
Pełny tekst źródłaCasacuberta, F., i A. Sanfeliu. "Advances in Pattern Recognition and Applications". W Vth Spanish Symposium on Pattern Recognition and Image Analysis. WORLD SCIENTIFIC, 1994. http://dx.doi.org/10.1142/9789814533928.
Pełny tekst źródłaKabasakal, Burak, i Emre Sumer. "Gender recognition using innovative pattern recognition techniques". W 2018 26th Signal Processing and Communications Applications Conference (SIU). IEEE, 2018. http://dx.doi.org/10.1109/siu.2018.8404306.
Pełny tekst źródłaSlack, Marc G. "Autonomous navigation of mobile robots for real-world applications". W Applied Imaging Pattern Recognition, redaktor Jane Harmon. SPIE, 1993. http://dx.doi.org/10.1117/12.142786.
Pełny tekst źródłaLiu, Ying. "Pattern recognition using Hilbert space". W Applications in Optical Science and Engineering, redaktor David P. Casasent. SPIE, 1992. http://dx.doi.org/10.1117/12.131517.
Pełny tekst źródłaSchmalz, Mark S., Gerhard X. Ritter, Eric Hayden i Gary Key. "Algorithms for adaptive nonlinear pattern recognition". W SPIE Optical Engineering + Applications, redaktorzy Mark S. Schmalz, Gerhard X. Ritter, Junior Barrera i Jaakko T. Astola. SPIE, 2011. http://dx.doi.org/10.1117/12.896561.
Pełny tekst źródłaHill, Robert E., Donald K. Fronek, Calton S. Faller i Richard A. Lane. "Pattern Recognition Utilizing Binary Light Modulators". W Spatial Light Modulators and Applications. Washington, D.C.: Optica Publishing Group, 1988. http://dx.doi.org/10.1364/slma.1988.the3.
Pełny tekst źródłaAguilar-González, Pablo Mario, i Vitaly Kober. "Pattern recognition of an implicitly given target". W Optical Engineering + Applications, redaktor Andrew G. Tescher. SPIE, 2008. http://dx.doi.org/10.1117/12.793912.
Pełny tekst źródłaRaporty organizacyjne na temat "Pattern recognition applications"
Howell, J. A., G. W. Eccleston, R. Whiteson, H. O. Menlove, C. C. Fuyat, J. K. Halbig, S. F. Klosterbuer i M. F. Mullen. Safeguards applications of pattern recognition and neural networks. Office of Scientific and Technical Information (OSTI), wrzesień 1993. http://dx.doi.org/10.2172/10102590.
Pełny tekst źródłaVarastehpour, Soheil, Hamid Sharifzadeh, Iman Ardekani i Abdolhossein Sarrafzadeh. Human Biometric Traits: A Systematic Review Focusing on Vascular Patterns. Unitec ePress, grudzień 2020. http://dx.doi.org/10.34074/ocds.086.
Pełny tekst źródłaRohatgi, Upendra, i Michael Furey. Development of Application of Pattern Recognition System. Office of Scientific and Technical Information (OSTI), marzec 2010. http://dx.doi.org/10.2172/1012395.
Pełny tekst źródłaMarkova, Oksana, Serhiy Semerikov i Maiia Popel. СoCalc as a Learning Tool for Neural Network Simulation in the Special Course “Foundations of Mathematic Informatics”. Sun SITE Central Europe, maj 2018. http://dx.doi.org/10.31812/0564/2250.
Pełny tekst źródłaFoster, Thomas. Application of Pattern Recognition Techniques for Early Warning Radar (EWR) Discrimination. Fort Belvoir, VA: Defense Technical Information Center, styczeń 1995. http://dx.doi.org/10.21236/ada298895.
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