Bibliografías temáticas / Pattern recognition

Literatura académica sobre el tema "Pattern recognition"

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Publicado: 4 de junio de 2021
Última modificación: 7 de julio de 2024

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Artículos de revistas sobre el tema "Pattern recognition"

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Mikhailov, Alexei. "Indexing-Based Pattern Recognition". Advanced Materials Research 403-408 (noviembre de 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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Klein, Freada Kapor y Ana Díaz-Hernández. "Pattern recognition". XRDS: Crossroads, The ACM Magazine for Students 20, n.º 4 (junio de 2014): 20–23. http://dx.doi.org/10.1145/2604991.

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Rutkove, S. B. "Pattern recognition". Neurology 61, n.º 4 (25 de agosto de 2003): 585–86. http://dx.doi.org/10.1212/01.wnl.0000078930.98769.11.

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Heidelbaugh, Joel J. "Pattern Recognition". Primary Care: Clinics in Office Practice 42, n.º 4 (diciembre de 2015): xiii—xiv. http://dx.doi.org/10.1016/j.pop.2015.09.002.

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Lavine, Barry K. "Pattern Recognition". Critical Reviews in Analytical Chemistry 36, n.º 3-4 (diciembre de 2006): 153–61. http://dx.doi.org/10.1080/10408340600969411.

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Dixon, Jennifer. "Pattern recognition". Lancet 360, n.º 9343 (noviembre de 2002): 1418. http://dx.doi.org/10.1016/s0140-6736(02)11396-1.

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Holmström, Lasse y Petri Koistinen. "Pattern recognition". Wiley Interdisciplinary Reviews: Computational Statistics 2, n.º 4 (3 de junio de 2010): 404–13. http://dx.doi.org/10.1002/wics.99.

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Narayan, Ritushree y Puja Mishra. "Pattern Recognition of Jharkhand Tribal Language". International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (30 de abril de 2018): 267–71. http://dx.doi.org/10.31142/ijtsrd10854.

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Dutta Majumder, D. "Fuzzy sets in pattern recognition, image analysis and automatic speech recognition". Applications of Mathematics 30, n.º 4 (1985): 237–54. http://dx.doi.org/10.21136/am.1985.104148.

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Cheeney, R. F. "Pattern recognition and pattern analysis". Geological Magazine 122, n.º 2 (marzo de 1985): 203–4. http://dx.doi.org/10.1017/s0016756800031150.

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Tesis sobre el tema "Pattern recognition"

1

An, Kyung Hee. "Concurrent Pattern Recognition and Optical Character Recognition". Thesis, University of North Texas, 1991. https://digital.library.unt.edu/ark:/67531/metadc332598/.

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The problem of interest as indicated is to develop a general purpose technique that is a combination of the structural approach, and an extension of the Finite Inductive Sequence (FI) technique. FI technology is pre-algebra, and deals with patterns for which an alphabet can be formulated.
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Yao, Xiaoqiang. "Pattern-recognition scheduling". Ohio : Ohio University, 1996. http://www.ohiolink.edu/etd/view.cgi?ohiou1177698616.

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Petheram, R. J. "Automatic pattern recognition". Thesis, University of Nottingham, 1989. http://eprints.nottingham.ac.uk/28974/.

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In this thesis the author presents a new method for the location, extraction and normalisation of discrete objects found in digital images. The extraction is by means of sub-pixcel contour following around the object. The normalisation obtains and removes the information concerning size, orientation and location of the object within an image. Analyses of the results are carried out to determine the confidence in recognition of patterns, and methods of cross correlation of object descriptions using Fourier transforms are demonstrated.
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Choakjarernwanit, Naruetep. "Statistical pattern recognition". Thesis, University of Surrey, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.306586.

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Placide, Eustache. "Hybrid pattern recognition". DigitalCommons@Robert W. Woodruff Library, Atlanta University Center, 1987. http://digitalcommons.auctr.edu/dissertations/3018.

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There are two basic approaches to pattern recognition: decision-theoretic and syntactic. However, in actual applications, a combination of both may be needed. One such hybrid technique consists of syntactic method coupled with stochasticity in its grammar. Randomness in the syntactic case is caused due to noise and insufficient information about characteristics of pattern classes. To absorb the effect of this randomness, the grammar must be generalized to include the probabilities of production rules. In this paper, a preliminary discussion of issues involved with hybrid techniques, in general, and stochastic grammars, in particular, is provided. An efficient algorithm for an automatic learning of production probabilities is devised. Concepts are illustrated via examples.
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Smeraldi, Fabrizio. "Attention-driven pattern recognition /". [S.l.] : [s.n.], 2000. http://library.epfl.ch/theses/?nr=2153.

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Angstenberger, Larisa. "Dynamic fuzzy pattern recognition". [S.l.] : [s.n.], 2000. http://deposit.ddb.de/cgi-bin/dokserv?idn=962701106.

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Weir, D. K. "Pattern recognition of electrocardiograms". Thesis, University of Ulster, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.355922.

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Lindén, Fredrik. "Fractal pattern recognition and recreation". Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-181224.

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It speaks by itself that in order to find oil, one must know where to look for it. In this thesis I have investigated and created new tools to find salt in the bedrock, and to recreate images according to some parameters, (fractal dimension and lacunarity). The oil prospecting company Schlumberger gathers nowadays a huge amount of seismic information. It is very time consuming to interpret the seismic data by hand. My task is to find a good way to detect salt in the seismic images of the underworld, that can then be used to classify the seismic data. The theory indicates that the salt behaves as fractals, and by studying the fractal dimension and lacunarity we can make a prediction of where the salt can be located. I have also investigated three different recreation techniques, so that one can go from parameters values (fractal dimension and lacunarity) back to a possible recreation. It speaks by itself that in order to find oil, one must know where to look for it. In this thesis I have investigated and created new tools to find salt in the bedrock, and to recreate images according to some parameters, (fractal dimension and lacunarity). The oil prospecting company Schlumberger gathers nowadays a huge amount of seismic information. It is very time consuming to interpret the seismic data by hand. My task is to find a good way to detect salt in the seismic images of the underworld, that can then be used to classify the seismic data. The theory indicates that the salt behaves as fractals, and by studying the fractal dimension and lacunarity we can make a prediction of where the salt can be located. I have also investigated three different recreation techniques, so that one can go from parameters values (fractal dimension and lacunarity) back to a possible recreation.
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Sun, Te-Wei. "DEPARS, design pattern recognition system". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq28464.pdf.

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Libros sobre el tema "Pattern recognition"

1

William, Gibson. Pattern recognition. New York: G.P. Putnam's Sons, 2003.

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1967-, Koutroumbas Konstantinos y ScienceDirect (Online service), eds. Pattern recognition. 4a ed. Amsterdam: Elsevier/Academic Press, 2009.

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Akata, Zeynep, Andreas Geiger y Torsten Sattler, eds. Pattern Recognition. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71278-5.

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Roman-Rangel, Edgar, Ángel Fernando Kuri-Morales, José Francisco Martínez-Trinidad, Jesús Ariel Carrasco-Ochoa y José Arturo Olvera-López, eds. Pattern Recognition. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77004-4.

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Bauckhage, Christian, Juergen Gall y Alexander Schwing, eds. Pattern Recognition. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92659-5.

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Wallraven, Christian, Qingshan Liu y Hajime Nagahara, eds. Pattern Recognition. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-02444-3.

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Wallraven, Christian, Qingshan Liu y Hajime Nagahara, eds. Pattern Recognition. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-02375-0.

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Vergara-Villegas, Osslan Osiris, Vianey Guadalupe Cruz-Sánchez, Juan Humberto Sossa-Azuela, Jesús Ariel Carrasco-Ochoa, José Francisco Martínez-Trinidad y José Arturo Olvera-López, eds. Pattern Recognition. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07750-0.

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Andres, Björn, Florian Bernard, Daniel Cremers, Simone Frintrop, Bastian Goldlücke y Ivo Ihrke, eds. Pattern Recognition. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16788-1.

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Theodoridis, S. Pattern recognition. 2a ed. Amsterdam: Academic Press, 2003.

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Capítulos de libros sobre el tema "Pattern recognition"

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Marques de Sá, Joaquim P. "Pattern Discrimination". En Pattern Recognition, 21–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56651-6_2.

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Marques de Sá, Joaquim P. "Basic Notions". En Pattern Recognition, 1–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56651-6_1.

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Marques de Sá, Joaquim P. "Data Clustering". En Pattern Recognition, 53–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56651-6_3.

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Marques de Sá, Joaquim P. "Statistical Classification". En Pattern Recognition, 79–145. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56651-6_4.

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Marques de Sá, Joaquim P. "Neural Networks". En Pattern Recognition, 147–242. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56651-6_5.

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Marques de Sá, Joaquim P. "Structural Pattern Recognition". En Pattern Recognition, 243–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56651-6_6.

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"PATTERN RECOGNITION". En Pattern Recognition, 1–52. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2018. http://dx.doi.org/10.1002/9781119302872.ch1.

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"PATTERN RECOGNITION". En Pattern Recognition, 53–99. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2018. http://dx.doi.org/10.1002/9781119302872.ch2.

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"CLASSIFICATION WITH REJECTION PROBLEM FORMULATION AND AN OVERVIEW". En Pattern Recognition, 101–31. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2018. http://dx.doi.org/10.1002/9781119302872.ch3.

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"EVALUATING PATTERN RECOGNITION PROBLEM". En Pattern Recognition, 133–57. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2018. http://dx.doi.org/10.1002/9781119302872.ch4.

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Actas de conferencias sobre el tema "Pattern recognition"

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Lu, Jianyi y T. William Lin. "Adaptive pattern recognition for binary images". En OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1991. http://dx.doi.org/10.1364/oam.1991.thx4.

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It is understood that the correlation process between similar patterns generally introduces ambiguity in decision making. This problem is much more profound in the binary pattern recognition process. For example, the cross-correlation between letters E and F exhibits the same main peak intensity as that in the autocorrelation of letter F. Many attempts have been made to overcome this difficulty, including the recognition technique based on a comparison of pattern’s perimeters, and the recognition of patterns in moment spaces or feature spaces. However, the phenomenon that causes the ambiguity can be converted and utilized as a feedback parameter when an adaptive process scheme is chosen in pattern recognition. In this paper, the relationship between pattern shapes and correlation results is analyzed first, followed by a proposed hybrid optical-electronic adaptive joint transform correlator. The adaptive capability of the system is achieved through interfacing between an optical correlator and a computer. The intensity distribution of correlation peaks detected in the optical correlator serves as a feedback to update reference images in the input plane of the correlator, so that an optimal decision can be made for the recognition process through adaptive iterations. In the iteration process, the saturating phenomenon in the intensity of correlation peaks is used as a guideline in designing a proper feedback scheme. Several uses in binary pattern recognition are demonstrated with results obtained from computer simulations as well as experimentally.
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Mikhailov, Alexei M. y Mikhail F. Karavay. "Pattern recognition by pattern inversion". En The Second International Conference on Image, Video Processing and Artificial Intelligence, editado por Ruidan Su. SPIE, 2019. http://dx.doi.org/10.1117/12.2539069.

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Kim, B. F. y J. Bohandy. "Partial Matching in a Hierarchical Pattern Recognition System". En Machine Vision. Washington, D.C.: Optica Publishing Group, 1987. http://dx.doi.org/10.1364/mv.1987.thb3.

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A pattern recognition system has recently been described,(1) in which images are represented by a hierarchical sequence of patterns of decreasing size, forming a type of pyramid structure (similar in some respects to resolution pyramids). Pattern recognition occurs by comparison of sub-patterns of images in each level with array patterns stored in a memory associated with each level. In these comparisons, a matching criterion determines, at each level, the degree to which the patterns being compared must match in order to be considered equivalent. The purpose of this report is to present some results on how the matching criteria affect the performance of this type of system.
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Morris, G. Michael. "Photon-Limited Pattern Recognition". En Quantum-Limited Imaging and Image Processing. Washington, D.C.: Optica Publishing Group, 1989. http://dx.doi.org/10.1364/qlip.1989.mc1.

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Often input scenes contain a vast amount of information, which tends to make pattern-recognition decisions laborious and time consuming. In traditional digital pattern recognition methods, one digitizes the input scene using a two-dimensional detector, e.g. a solid-state photodiode array and a frame store. If the detector consists of, say, a 1000 x 1000 array of detection elements, then one has to process a million points of data. This is too much information for even very large computers to process in real time, so one generally transforms the input information into some sort of feature-space representation, e.g. through the use of edge-enhanced images, and makes the recognition decision based on this reduced data set.
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Magnus, A. L. y S. C. Gustafson. "Inquisitive pattern recognition". En Proceedings of the Third International Conference on Information Fusion. IEEE, 2000. http://dx.doi.org/10.1109/ific.2000.862459.

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Pinkse, P. W. H., S. A. Goorden, M. Horstmann, B. Skoric y A. P. Mosk. "Quantum pattern recognition". En 2013 Conference on Lasers & Electro-Optics Europe & International Quantum Electronics Conference CLEO EUROPE/IQEC. IEEE, 2013. http://dx.doi.org/10.1109/cleoe-iqec.2013.6801621.

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Krajewski, Jarek, Thomas Schnupp, Sebastian Schnieder, David Sommer, Christian Heinze y Martin Golz. "Pattern recognition methods". En the 7th International Conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1931344.1931369.

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Przybylek, Michal R. "Algebraic pattern recognition". En Fifth International Conference on Graphic and Image Processing, editado por Yulin Wang, Xudong Jiang, Ming Yang, David Zhang y Xie Yi. SPIE, 2014. http://dx.doi.org/10.1117/12.2050883.

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Casasent, David. "Optical pattern recognition". En Critical Review Collection. SPIE, 1992. http://dx.doi.org/10.1117/12.161585.

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Alfalou, A., C. Brosseau y M. S. Alam. "Smart pattern recognition". En SPIE Defense, Security, and Sensing, editado por David Casasent y Tien-Hsin Chao. SPIE, 2013. http://dx.doi.org/10.1117/12.2018249.

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Informes sobre el tema "Pattern recognition"

1

Montana, Shaun P. Statistical Pattern Recognition Tool Upgrades. Fort Belvoir, VA: Defense Technical Information Center, julio de 1999. http://dx.doi.org/10.21236/ada367916.

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Montana, Shaun P. A Statistical Pattern Recognition Tool. Fort Belvoir, VA: Defense Technical Information Center, junio de 1997. http://dx.doi.org/10.21236/ada327388.

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Paek, Eung Gi. Optical pattern recognition with microlasers. Gaithersburg, MD: National Institute of Standards and Technology, 1998. http://dx.doi.org/10.6028/nist.ir.6017.

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McKenney, B., M. McGrain, A. Klinger, J. Aggarwal, N. George y R. Haralick. Soviet image pattern recognition research. Office of Scientific and Technical Information (OSTI), diciembre de 1989. http://dx.doi.org/10.2172/6764408.

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Pelletier, Justin M. Pattern Recognition Software: Functional Methodology Document. Fort Belvoir, VA: Defense Technical Information Center, enero de 2009. http://dx.doi.org/10.21236/ada494881.

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George, Nicholas. Optoelectronic Workshops II. Automatic Pattern Recognition. Fort Belvoir, VA: Defense Technical Information Center, abril de 1988. http://dx.doi.org/10.21236/ada203779.

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MacDonald, Jacqueline A. y Mitchell J. Small. Statistical Methods for UXO Pattern Recognition. Fort Belvoir, VA: Defense Technical Information Center, diciembre de 2007. http://dx.doi.org/10.21236/ada603920.

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Olsen, Jamieson, Tiehui Ted Liu, Jim Hoff, Zhen Hu, Jim Yuan Wu y Zijun Xu. An FPGA-based Pattern Recognition Associative Memory. Office of Scientific and Technical Information (OSTI), julio de 2018. http://dx.doi.org/10.2172/1480099.

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Rizki, Mateen M. y Louis A. Tamburino. HELPR: Hybrid Evolutionary Learning for Pattern Recognition. Fort Belvoir, VA: Defense Technical Information Center, diciembre de 2005. http://dx.doi.org/10.21236/ada446893.

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León, Carlos, Paolo Barucca, Oscar Acero, Gerardo Gage y Fabio Ortega. Pattern recognition of financial institutions’ payment behavior. Banco de la República de Colombia, septiembre de 2020. http://dx.doi.org/10.32468/be.1130.

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