Relevant bibliographies by topics / Pattern recognition applications

Academic literature on the topic 'Pattern recognition applications'

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Published: 4 June 2021
Last updated: 14 March 2023

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Journal articles on the topic "Pattern recognition applications"

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Mikhailov, Alexei. "Indexing-Based Pattern Recognition." Advanced Materials Research 403-408 (November 2011): 5254–59. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.5254.

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The paper discusses the mathematics of pattern indexing and its applications to recognition of visual patterns and classification of objects that are represented by objects-properties matrices. It is shown that (a) pattern indexes can be represented by collections of inverted patterns, (b) solutions to pattern classification problems can be found as intersections of inverted patterns and, thus, matching of original patterns avoided.
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Grabusts, Peter. "POTENTIAL FUNCTION METHOD APPROACH TO PATTERN RECOGNITION APPLICATIONS." Environment. Technology. Resources. Proceedings of the International Scientific and Practical Conference 2 (June 15, 2017): 30. http://dx.doi.org/10.17770/etr2017vol2.2512.

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Potential function method was originally offered to solve the pattern recognition tasks, then it was generalized to a wider range of tasks, which were associated with the function approximation. Potential function method algorithms are based on the hypothesis of the nature of the function that separates sets according to different classes of patterns. Geometrical interpretation of pattern recognition task includes display of patterns in the form of vector in the space of input signal that allows to perceive the learning as approximation task. The paper describes the essence of potential function method and the learning procedure is shown that is based on practical application of potential methods. Pattern recognition applications with the help of examples of potential functions and company bankruptcy data analysis with the help of potential functions are given.
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Nezhad, Mohammad Saber Fallah. "Applications of Pattern Recognition Techniques in Social Science." Studies in Social Science & Humanities 2, no. 1 (January 2023): 31–35. http://dx.doi.org/10.56397/sssh.2023.01.06.

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Pattern recognition is a branch of machine learning that focuses on the recognition of patterns and regularities in data, although it is in some cases considered to be nearly synonymous with machine learning. Pattern recognition algorithms can be effectively applied for analysis of big data obtained from society to predict possible chaos in future or to identify required changes needed for society or changing the public policies. This technical note reviews some method of pattern recognition in society.
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PAPAKOSTAS, G. A., Y. S. BOUTALIS, D. E. KOULOURIOTIS, and B. G. MERTZIOS. "FUZZY COGNITIVE MAPS FOR PATTERN RECOGNITION APPLICATIONS." International Journal of Pattern Recognition and Artificial Intelligence 22, no. 08 (December 2008): 1461–86. http://dx.doi.org/10.1142/s0218001408006910.

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A first attempt to incorporate Fuzzy Cognitive Maps (FCMs), in pattern classification applications is performed in this paper. Fuzzy Cognitive Maps, as an illustrative causative representation of modeling and manipulation of complex systems, can be used to model the behavior of any system. By transforming a pattern classification problem into a problem of discovering the way the sets of patterns interact with each other and with the classes that they belong to, we could describe the problem in terms of Fuzzy Cognitive Maps. More precisely, some FCM architectures are introduced and studied with respect to their pattern recognition abilities. An efficient novel hybrid classifier is proposed as an alternative classification structure, which exploits both neural networks and FCMs to ensure improved classification capabilities. Appropriate experiments with four well-known benchmark classification problems and a typical computer vision application establish the usefulness of the Fuzzy Cognitive Maps, in a pattern recognition research field. Moreover, the present paper introduces the use of more flexible FCMs by incorporating nodes with adaptively adjusted activation functions. This advanced feature gives more degrees of freedom in the FCM structure to learn and store knowledge, as needed in pattern recognition tasks.
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Kober, Vitaly, Tae Choi, Victor Diaz-Ramírez, and Pablo Aguilar-González. "Pattern Recognition: Recent Advances and Applications." Mathematical Problems in Engineering 2018 (November 15, 2018): 1–2. http://dx.doi.org/10.1155/2018/8510319.

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Ren, Dong, and Simon X. Yang. "Intelligent Pattern Recognition Technology and Applications." Intelligent Automation & Soft Computing 19, no. 4 (December 2013): 497–99. http://dx.doi.org/10.1080/10798587.2013.869107.

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Paolanti, Marina, and Emanuele Frontoni. "Multidisciplinary Pattern Recognition applications: A review." Computer Science Review 37 (August 2020): 100276. http://dx.doi.org/10.1016/j.cosrev.2020.100276.

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Lavine, Barry K. "Environmental applications of pattern recognition techniques." Chemometrics and Intelligent Laboratory Systems 15, no. 2-3 (August 1992): 219–30. http://dx.doi.org/10.1016/0169-7439(92)85011-q.

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Krasnoproshin, V. V., and V. A. Obraztsov. "Pattern Recognition: Theoretical Research Experience and Applications." Pattern Recognition and Image Analysis 31, no. 1 (January 2021): 163–71. http://dx.doi.org/10.1134/s1054661821010132.

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Silva, Francisco de Assis Tavares Ferreira da, Magno Prudêncio de Almeida Filho, Antonio Macilio Pereira de Lucena, and Alexandre Guirland Nowosad. "Pattern recognition on FPGA for aerospace applications." Research, Society and Development 10, no. 12 (September 14, 2021): e83101219181. http://dx.doi.org/10.33448/rsd-v10i12.19181.

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This paper presents a low power near real-time pattern recognition technique based on Mathematical Morphology-MM implemented on FPGA (Field Programmable Gate Array). The key to the success of this approach concerns the advantages of machine learning paradigm applied to the translation invariant template-matching operators from MM. The paper shows that compositions of simple elementary operators from Mathematical Morphology based on ELUTs (Elementary Look-Up Tables) are very suitable to embed in FPGA hardware. The paper also shows the development techniques regarding all mathematical modeling for computer simulation and system generating models applied for hardware implementation using FPGA chip. In general, image processing on FPGAs requires low-level description of desired operations through Hardware Description Language-HDL, which uses high complexity to describe image operations at pixel level. However, this work presents a reconfiguring pattern recognition device implemented directly in FPGA from mathematical modeling simulation under Matlab/Simulink/System Generator environment. This strategy has reduced the hardware development complexity. The device will be useful mainly when applied on remote sensing tasks for aerospace missions using passive or active sensors.
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Dissertations / Theses on the topic "Pattern recognition applications"

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Thompson, J. R. "Applications of pattern recognition in medicine." Thesis, Open University, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.377939.

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Robinson, 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.

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PAOLANTI, MARINA. "Pattern Recognition for challenging Computer Vision Applications." Doctoral thesis, Università Politecnica delle Marche, 2018. http://hdl.handle.net/11566/252904.

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La Pattern Recognition è lo studio di come le macchine osservano l'ambiente, imparano a distinguere i pattern di interesse dal loro background e prendono decisioni valide e ragionevoli sulle categorie di modelli. Oggi l'applicazione degli algoritmi e delle tecniche di Pattern Recognition è trasversale. Con i recenti progressi nella computer vision, abbiamo la capacità di estrarre dati multimediali per ottenere informazioni preziose su ciò che sta accadendo nel mondo. Partendo da questa premessa, questa tesi affronta il tema dello sviluppo di sistemi di Pattern Recognition per applicazioni reali come la biologia, il retail, la sorveglianza, social media intelligence e i beni culturali. L'obiettivo principale è sviluppare applicazioni di computer vision in cui la Pattern Recognition è il nucleo centrale della loro progettazione, a partire dai metodi generali, che possono essere sfruttati in più campi di ricerca, per poi passare a metodi e tecniche che affrontano problemi specifici. Di fronte a molti tipi di dati, come immagini, dati biologici e traiettorie, una difficoltà fondamentale è trovare rappresentazioni vettoriali rilevanti. Per la progettazione del sistema di riconoscimento dei modelli vengono eseguiti i seguenti passaggi: raccolta dati, estrazione delle caratteristiche, approccio di apprendimento personalizzato e analisi e valutazione comparativa. Per una valutazione completa delle prestazioni, è di grande importanza collezionare un dataset specifico perché i metodi di progettazione che sono adattati a un problema non funzionano correttamente su altri tipi di problemi. I metodi su misura, adottati per lo sviluppo delle applicazioni proposte, hanno dimostrato di essere in grado di estrarre caratteristiche statistiche complesse e di imparare in modo efficiente le loro rappresentazioni, permettendogli di generalizzare bene attraverso una vasta gamma di compiti di visione computerizzata.
Pattern 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.
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Hayes, William S. "Pattern recognition and signal detection in gene finding." Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/25420.

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Prendergast, 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.

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Evans, 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.

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Yan, 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.

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Ma, Chengyuan. "A detection-based pattern recognition framework and its applications." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/33889.

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The objective of this dissertation is to present a detection-based pattern recognition framework and demonstrate its applications in automatic speech recognition and broadcast news video story segmentation. Inspired by the studies of modern cognitive psychology and real-world pattern recognition systems, a detection-based pattern recognition framework is proposed to provide an alternative solution for some complicated pattern recognition problems. The primitive features are first detected and the task-specific knowledge hierarchy is constructed level by level; then a variety of heterogeneous information sources are combined together and the high-level context is incorporated as additional information at certain stages. A detection-based framework is a â divide-and-conquerâ design paradigm for pattern recognition problems, which will decompose a conceptually difficult problem into many elementary sub-problems that can be handled directly and reliably. Some information fusion strategies will be employed to integrate the evidence from a lower level to form the evidence at a higher level. Such a fusion procedure continues until reaching the top level. Generally, a detection-based framework has many advantages: (1) more flexibility in both detector design and fusion strategies, as these two parts can be optimized separately; (2) parallel and distributed computational components in primitive feature detection. In such a component-based framework, any primitive component can be replaced by a new one while other components remain unchanged; (3) incremental information integration; (4) high level context information as additional information sources, which can be combined with bottom-up processing at any stage. This dissertation presents the basic principles, criteria, and techniques for detector design and hypothesis verification based on the statistical detection and decision theory. In addition, evidence fusion strategies were investigated in this dissertation. Several novel detection algorithms and evidence fusion methods were proposed and their effectiveness was justified in automatic speech recognition and broadcast news video segmentation system. We believe such a detection-based framework can be employed in more applications in the future.
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甄榮輝 and 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.

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Lopez-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.

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Books on the topic "Pattern recognition applications"

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Devijver, Pierre A. Pattern Recognition Theory and Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987.

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Fred, Ana, Maria De Marsico, and Mário Figueiredo, eds. Pattern Recognition: Applications and Methods. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27677-9.

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Fred, Ana, Maria De Marsico, and Gabriella Sanniti di Baja, eds. Pattern Recognition Applications and Methods. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53375-9.

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De Marsico, Maria, Gabriella Sanniti di Baja, and Ana Fred, eds. Pattern Recognition Applications and Methods. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05499-1.

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De Marsico, Maria, Gabriella Sanniti di Baja, and Ana Fred, eds. Pattern Recognition Applications and Methods. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66125-0.

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Fred, Ana, and Maria De Marsico, eds. Pattern Recognition Applications and Methods. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-12610-4.

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De Marsico, Maria, Gabriella Sanniti di Baja, and Ana Fred, eds. Pattern Recognition Applications and Methods. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93647-5.

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Devijver, Pierre A., and Josef Kittler, eds. Pattern Recognition Theory and Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-83069-3.

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Latorre Carmona, Pedro, J. Salvador Sánchez, and Ana L. N. Fred, eds. Pattern Recognition - Applications and Methods. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36530-0.

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De Marsico, Maria, Gabriella Sanniti di Baja, and Ana Fred, eds. Pattern Recognition Applications and Methods. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40014-9.

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Book chapters on the topic "Pattern recognition applications"

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Croall, Ian F., and John P. Mason. "Pattern Recognition." In 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.

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Zimmermann, H. J. "Pattern Recognition." In Fuzzy Set Theory — and Its Applications, 217–40. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-015-7949-0_11.

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Zimmermann, H. J. "Pattern Recognition." In Fuzzy Set Theory — and Its Applications, 187–212. Dordrecht: Springer Netherlands, 1985. http://dx.doi.org/10.1007/978-94-015-7153-1_11.

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Bunke, Horst, Peter Dickinson, Miro Kraetzl, Michel Neuhaus, and Marc Stettler. "Matching of Hypergraphs — Algorithms, Applications, and Experiments." In Applied Pattern Recognition, 131–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-76831-9_6.

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Cristescu, Gabriela, and Liana Lupşa. "Applications in pattern recognition." In Non-Connected Convexities and Applications, 227–46. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0003-2_9.

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Gökberk, Berk, Albert Ali Salah, Neşe Alyüz, and Lale Akarun. "3D Face Recognition: Technology and Applications." In Advances in Pattern Recognition, 217–46. London: Springer London, 2009. http://dx.doi.org/10.1007/978-1-84882-385-3_9.

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Yin, Xiaoxia, Brian W. H. Ng, and Derek Abbott. "THz Pattern Recognition Experiments." In 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.

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Ferrans, James C., and Jonathan Engelsma. "Software Architectures for Networked Mobile Speech Applications." In Advances in Pattern Recognition, 279–99. London: Springer London, 2008. http://dx.doi.org/10.1007/978-1-84800-143-5_13.

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Wang, P. S. P. "Intelligent pattern recognition and applications." In Lecture Notes in Computer Science, 34–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/3-540-56346-6_28.

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Evans, Brian J. "Pattern Recognition and Its Applications." In A Simple Guide to Technology and Analytics, 49–72. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003108443-3.

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Conference papers on the topic "Pattern recognition applications"

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Wang, Patrick. "Intelligent pattern recognition and applications." In the First ACM International Conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1854776.1854782.

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Martínez-Díaz, Saúl, and Vitaly Kober. "Pattern recognition with adaptive nonlinear filters." In Optical Engineering + Applications, edited by Andrew G. Tescher. SPIE, 2007. http://dx.doi.org/10.1117/12.734240.

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Kovács, Levente, and Tamás Szirányi. "Recognition of hidden pattern with background." In Optical Engineering + Applications, edited by Oliver E. Drummond and Richard D. Teichgraeber. SPIE, 2007. http://dx.doi.org/10.1117/12.738512.

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Casacuberta, F., and A. Sanfeliu. "Advances in Pattern Recognition and Applications." In Vth Spanish Symposium on Pattern Recognition and Image Analysis. WORLD SCIENTIFIC, 1994. http://dx.doi.org/10.1142/9789814533928.

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Kabasakal, Burak, and Emre Sumer. "Gender recognition using innovative pattern recognition techniques." In 2018 26th Signal Processing and Communications Applications Conference (SIU). IEEE, 2018. http://dx.doi.org/10.1109/siu.2018.8404306.

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Slack, Marc G. "Autonomous navigation of mobile robots for real-world applications." In Applied Imaging Pattern Recognition, edited by Jane Harmon. SPIE, 1993. http://dx.doi.org/10.1117/12.142786.

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Liu, Ying. "Pattern recognition using Hilbert space." In Applications in Optical Science and Engineering, edited by David P. Casasent. SPIE, 1992. http://dx.doi.org/10.1117/12.131517.

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Schmalz, Mark S., Gerhard X. Ritter, Eric Hayden, and Gary Key. "Algorithms for adaptive nonlinear pattern recognition." In SPIE Optical Engineering + Applications, edited by Mark S. Schmalz, Gerhard X. Ritter, Junior Barrera, and Jaakko T. Astola. SPIE, 2011. http://dx.doi.org/10.1117/12.896561.

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Hill, Robert E., Donald K. Fronek, Calton S. Faller, and Richard A. Lane. "Pattern Recognition Utilizing Binary Light Modulators." In Spatial Light Modulators and Applications. Washington, D.C.: Optica Publishing Group, 1988. http://dx.doi.org/10.1364/slma.1988.the3.

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Aguilar-González, Pablo Mario, and Vitaly Kober. "Pattern recognition of an implicitly given target." In Optical Engineering + Applications, edited by Andrew G. Tescher. SPIE, 2008. http://dx.doi.org/10.1117/12.793912.

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Reports on the topic "Pattern recognition applications"

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Howell, J. A., G. W. Eccleston, R. Whiteson, H. O. Menlove, C. C. Fuyat, J. K. Halbig, S. F. Klosterbuer, and M. F. Mullen. Safeguards applications of pattern recognition and neural networks. Office of Scientific and Technical Information (OSTI), September 1993. http://dx.doi.org/10.2172/10102590.

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Varastehpour, Soheil, Hamid Sharifzadeh, Iman Ardekani, and Abdolhossein Sarrafzadeh. Human Biometric Traits: A Systematic Review Focusing on Vascular Patterns. Unitec ePress, December 2020. http://dx.doi.org/10.34074/ocds.086.

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Authentication methods based on human traits, including fingerprint, face, iris, and palm print, have developed significantly, and currently they are mature enough to be reliably considered for human identification purposes. Recently, as a new research area, a few methods based on non-facial skin features such as vein patterns have been developed. This literature review paper explores some key biometric systems such as face recognition, iris recognition, fingerprint, and palm print, and discusses their respective advantages and disadvantages; then by providing a comprehensive analysis of these traits, and their applications, vein pattern recognition is reviewed.
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Rohatgi, Upendra, and Michael Furey. Development of Application of Pattern Recognition System. Office of Scientific and Technical Information (OSTI), March 2010. http://dx.doi.org/10.2172/1012395.

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Markova, Oksana, Serhiy Semerikov, and Maiia Popel. СoCalc as a Learning Tool for Neural Network Simulation in the Special Course “Foundations of Mathematic Informatics”. Sun SITE Central Europe, May 2018. http://dx.doi.org/10.31812/0564/2250.

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The role of neural network modeling in the learning сontent of special course “Foundations of Mathematic Informatics” was discussed. The course was developed for the students of technical universities – future IT-specialists and directed to breaking the gap between theoretic computer science and it’s applied applications: software, system and computing engineering. CoCalc was justified as a learning tool of mathematical informatics in general and neural network modeling in particular. The elements of technique of using CoCalc at studying topic “Neural network and pattern recognition” of the special course “Foundations of Mathematic Informatics” are shown. The program code was presented in a CofeeScript language, which implements the basic components of artificial neural network: neurons, synaptic connections, functions of activations (tangential, sigmoid, stepped) and their derivatives, methods of calculating the network`s weights, etc. The features of the Kolmogorov–Arnold representation theorem application were discussed for determination the architecture of multilayer neural networks. The implementation of the disjunctive logical element and approximation of an arbitrary function using a three-layer neural network were given as an examples. According to the simulation results, a conclusion was made as for the limits of the use of constructed networks, in which they retain their adequacy. The framework topics of individual research of the artificial neural networks is proposed.
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Foster, Thomas. Application of Pattern Recognition Techniques for Early Warning Radar (EWR) Discrimination. Fort Belvoir, VA: Defense Technical Information Center, January 1995. http://dx.doi.org/10.21236/ada298895.

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