Relevant bibliographies by topics / Pattern recognition

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Pattern recognition'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Pattern recognition"

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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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Klein, Freada Kapor, and Ana Díaz-Hernández. "Pattern recognition." XRDS: Crossroads, The ACM Magazine for Students 20, no. 4 (2014): 20–23. http://dx.doi.org/10.1145/2604991.

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Rutkove, S. B. "Pattern recognition." Neurology 61, no. 4 (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, no. 4 (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, no. 3-4 (2006): 153–61. http://dx.doi.org/10.1080/10408340600969411.

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Dixon, Jennifer. "Pattern recognition." Lancet 360, no. 9343 (2002): 1418. http://dx.doi.org/10.1016/s0140-6736(02)11396-1.

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Holmström, Lasse, and Petri Koistinen. "Pattern recognition." Wiley Interdisciplinary Reviews: Computational Statistics 2, no. 4 (2010): 404–13. http://dx.doi.org/10.1002/wics.99.

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

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Huang, K. Y., K. S. Fu, S. W. Cheng, and Z. S. Lin. "Syntactic pattern recognition and Hough transformation for reconstruction of seismic patterns." GEOPHYSICS 52, no. 12 (1987): 1612–20. http://dx.doi.org/10.1190/1.1442278.

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Hierarchical syntactic pattern recognition and the Hough transformation are proposed for automatic recognition and reconstruction of seismic patterns in seismograms. In the first step, the patterns are hierarchically decomposed or recognized into single patterns, straight‐line patterns, or hyperbolic patterns, using syntactic pattern recognition. In the second step, the Hough transformation technique is used for reconstruction, pattern by pattern. The system of syntactic seismic pattern recognition includes envelope generation, a linking process in the seismogram, segmentation, primitive recog
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Cheeney, R. F. "Pattern recognition and pattern analysis." Geological Magazine 122, no. 2 (1985): 203–4. http://dx.doi.org/10.1017/s0016756800031150.

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Dissertations / Theses on the topic "Pattern recognition"

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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
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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
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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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Books on the topic "Pattern recognition"

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Akata, Zeynep, Andreas Geiger, and Torsten Sattler, eds. Pattern Recognition. 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, and José Arturo Olvera-López, eds. Pattern Recognition. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77004-4.

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

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

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Wallraven, Christian, Qingshan Liu, and Hajime Nagahara, eds. Pattern Recognition. 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, and José Arturo Olvera-López, eds. Pattern Recognition. 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, and Ivo Ihrke, eds. Pattern Recognition. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16788-1.

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Theodoridis, S. Pattern recognition. 2nd ed. Academic Press, 2003.

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Carrasco-Ochoa, Jesús Ariel, José Francisco Martínez-Trinidad, Joaquín Salas Rodríguez, and Gabriella Sanniti di Baja, eds. Pattern Recognition. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38989-4.

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Hamprecht, Fred A., Christoph Schnörr, and Bernd Jähne, eds. Pattern Recognition. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74936-3.

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Book chapters on the topic "Pattern recognition"

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

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"PATTERN RECOGNITION." In Pattern Recognition. John Wiley & Sons, Inc., 2018. http://dx.doi.org/10.1002/9781119302872.ch1.

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"PATTERN RECOGNITION." In Pattern Recognition. 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." In Pattern Recognition. John Wiley & Sons, Inc., 2018. http://dx.doi.org/10.1002/9781119302872.ch3.

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"EVALUATING PATTERN RECOGNITION PROBLEM." In Pattern Recognition. John Wiley & Sons, Inc., 2018. http://dx.doi.org/10.1002/9781119302872.ch4.

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Conference papers on the topic "Pattern recognition"

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Lu, Jianyi, and T. William Lin. "Adaptive pattern recognition for binary images." In OSA Annual Meeting. 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
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Mikhailov, Alexei M., and Mikhail F. Karavay. "Pattern recognition by pattern inversion." In The Second International Conference on Image, Video Processing and Artificial Intelligence, edited by Ruidan Su. SPIE, 2019. http://dx.doi.org/10.1117/12.2539069.

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Kim, B. F., and J. Bohandy. "Partial Matching in a Hierarchical Pattern Recognition System." In Machine Vision. 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
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Magnus, A. L., and S. C. Gustafson. "Inquisitive pattern recognition." In 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, and A. P. Mosk. "Quantum pattern recognition." In 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, and Martin Golz. "Pattern recognition methods." In the 7th International Conference. ACM Press, 2010. http://dx.doi.org/10.1145/1931344.1931369.

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

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

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

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Clement, Brian E. P. "Automatic pattern recognition." In Aerospace Sensing, edited by Steven K. Rogers. SPIE, 1992. http://dx.doi.org/10.1117/12.140044.

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Reports on the topic "Pattern recognition"

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

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

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Paek, Eung Gi. Optical pattern recognition with microlasers. 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, and R. Haralick. Soviet image pattern recognition research. Office of Scientific and Technical Information (OSTI), 1989. http://dx.doi.org/10.2172/6764408.

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

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

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MacDonald, Jacqueline A., and Mitchell J. Small. Statistical Methods for UXO Pattern Recognition. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada603920.

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

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

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

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