Relevant bibliographies by topics / Pattern recognition (computing)

Contents

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

Academic literature on the topic 'Pattern recognition (computing)'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Pattern recognition (computing)"

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Pal, Nikhil R. "Soft computing for pattern recognition." Fuzzy Sets and Systems 103, no. 2 (1999): 197–200. http://dx.doi.org/10.1016/s0165-0114(98)00221-8.

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Pal, Sankar K. "Soft Computing Tools and Pattern Recognition." IETE Journal of Research 44, no. 1-2 (1998): 61–87. http://dx.doi.org/10.1080/03772063.1998.11416030.

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Davies, E. Keith, Meir Glick, Karl N. Harrison, and W. Graham Richards. "Pattern recognition and massively distributed computing." Journal of Computational Chemistry 23, no. 16 (2002): 1544–50. http://dx.doi.org/10.1002/jcc.10107.

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POTAPOV, ALEXEI B., and M. K. ALI. "PATTERN RECOGNITION WITH HAMILTONIAN DYNAMICS." International Journal of Modern Physics C 12, no. 05 (2001): 751–58. http://dx.doi.org/10.1142/s0129183101001948.

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We consider pattern recognition schemes that are based upon Hamiltonian dynamical system. Different oscillatory modes are used for storing and encoding patterns, and the effect of resonance is used for determining the most excited mode. We also propose a new technique for pattern orthogonalization resorting to hidden dimensions. Numerical experiments confirm high storage capacity and absence of false memories for the proposed system. Hamiltonian systems may be important as classical analogs of quantum computing systems or quantum neural networks.
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Suzuki, Y., K. Itakura, S. Saga, and J. Maeda. "Signal processing and pattern recognition with soft computing." Proceedings of the IEEE 89, no. 9 (2001): 1297–317. http://dx.doi.org/10.1109/5.949486.

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CHAPLINE, GEORGE. "QUANTUM MECHANICS AND PATTERN RECOGNITION." International Journal of Quantum Information 02, no. 03 (2004): 295–303. http://dx.doi.org/10.1142/s0219749904000328.

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It has long been known that the Schrodinger equation provides a description of a certain kind of statistical Markov process. We show here that as a consequence, one can view quantum mechanics as an extension of the Helmholtz machine approach to pattern recognition. Not only does this observation throw new light on the relationship between quantum mechanics and computing theory, but it suggests that mathematical techniques developed for inverse scattering problems might provide an alternative to conventional pattern recognition algorithms.
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Ghalib, Muhammad Rukunuddin, and J. Gitanjali. "Sequential Pattern based Activity Recognition model for Ambient Computing." International Journal of Advanced Intelligence Paradigms 10, no. 1 (2018): 1. http://dx.doi.org/10.1504/ijaip.2018.10033336.

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Tsakalos, Karolos-Alexandros, Georgios Ch Sirakoulis, Andrew Adamatzky, and Jim Smith. "Protein Structured Reservoir Computing for Spike-Based Pattern Recognition." IEEE Transactions on Parallel and Distributed Systems 33, no. 2 (2022): 322–31. http://dx.doi.org/10.1109/tpds.2021.3068826.

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Ray, Kumar S. "Pattern Recognition Based on Fuzzy Set and Genetic Algorithm." International Journal of Image and Graphics 14, no. 03 (2014): 1450009. http://dx.doi.org/10.1142/s0219467814500090.

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In this paper, we consider a soft computing approach to pattern classification. Our basic tools for soft computing are fuzzy relational calculus (FRC) and genetic algorithm (GA). We introduce a new interpretation of multidimensional fuzzy implication (MFI) to represent our knowledge about the training data set. We also consider the notion of a fuzzy pattern vector to handle the fuzzy information granules of the quantized pattern space and to represent a population of training patterns in the quantized pattern space. The construction of the pattern classifier is essentially based on the estimat
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Nguyen, Sinh Hoa, Tuan Trung Nguyen, Marcin Szczuka, and Hung Son Nguyen. "An Approach to Pattern Recognition Based on Hierarchical Granular Computing." Fundamenta Informaticae 127, no. 1-4 (2013): 369–84. http://dx.doi.org/10.3233/fi-2013-915.

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Dissertations / Theses on the topic "Pattern recognition (computing)"

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Petrov, Nedyalko. "Intelligent methods for pattern recognition and optimisation." Thesis, University of Portsmouth, 2015. https://researchportal.port.ac.uk/portal/en/theses/intelligent-methods-for-pattern-recognition-and-optimisation(13d07545-80a6-4804-b095-a48704dda2c0).html.

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This dissertation presents and discusses the processes of investigation, implementation, testing, validation and evaluation of several computational intelligence-based systems for solving four large-scale real-world problems. In particular, two industrial problems from the pattern recognition and two from the process optimisation areas are studied and intelligent methods to address them are proposed, developed and tested using real-world data. The first problem investigated is the application of an intelligent visual inspection system for classification of texture images. Two major approaches,
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Osanlou, Ardeshir. "Soft computing and fractal geometry in signal processing and pattern recognition." Thesis, De Montfort University, 2000. http://hdl.handle.net/2086/4242.

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Shen, Judong. "Fusing support vector machines and soft computing for pattern recognition and regression /." Search for this dissertation online, 2005. http://wwwlib.umi.com/cr/ksu/main.

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Byrne, John. "An investigation of parallel computing techniques in clinical image processing, using transputers." Thesis, University of Aberdeen, 1992. http://digitool.abdn.ac.uk/R?func=search-advanced-go&find_code1=WSN&request1=AAIU045188.

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The objective of this work is to investigate the prospects for parallel computing in image processing applications in medicine. The tasks involved in filtered back projection reconstruction and retinal image registration are implemented on a hardware system based on the Inmos Transputer, using the OCCAM language. A number of task decomposition methods are used and their advantages and disadvantages discussed. An example which uses a pipeline illustrates the sensitivity of such an approach to uneven computational load over the network of processors. Farm networks and methods which use simple bl
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Ross, Philip. "Network accessible parallel computing systems, based upon transputers, for image processing strategies." Thesis, University of Aberdeen, 1993. http://digitool.abdn.ac.uk/R?func=search-advanced-go&find_code1=WSN&request1=AAIU059635.

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Over the last decade there has been a steady increase in the size of primary data sets collected from medical imaging devices, and a correspondingly increased requirement for the computational power needed for associated image processing techniques. Although conventional processors have shown considerable advances throughout this period, they have failed to keep pace with the demands placed upon them by clinicians keen to utilise techniques such as pseudo three dimensional volume image presentation and high speed dynamic display of multiple frames of data. One solution that has the capability
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Pirttikangas, S. (Susanna). "Routine Learning: from Reactive to Proactive Environments." Doctoral thesis, University of Oulu, 2004. http://urn.fi/urn:isbn:9514275659.

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Abstract Technological development and various information services becoming common has had the effect that data from everyday situations is available. Utilizing this technology and the data it produces in an efficient manner is called context-aware or ubiquitous computing. The research includes the specifications of each application, the requirements of the communication systems, issues of privacy, and human - computer interaction, for example. The environment should learn from the user's behaviour and communicate with the user. The communication should not be only reactive, but proactive as
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Khalid, Fakhar. "Use of soft computing pattern recognition techniques to analyse tropical cyclones from meteorological satellite data." Thesis, University of Greenwich, 2013. http://gala.gre.ac.uk/11887/.

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Tropical cyclones are potentially the most destructive of all natural meteorological hazards. When these cloud systems make landfall, they cause significant amounts of human death and injury as well as extensive damage to property, therefore reliable cyclone detection and classification form important weather forecasting activities. The work carried out in this research has focused on developing a unique fuzzy logic based patter recognition algorithm for identifying key recognisable structural elements of North Atlantic basin hurricanes. Due to vaguely defined boundaries and cloud patterns ass
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Nowshin, Fabiha. "Spiking Neural Network with Memristive Based Computing-In-Memory Circuits and Architecture." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/103854.

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In recent years neuromorphic computing systems have achieved a lot of success due to its ability to process data much faster and using much less power compared to traditional Von Neumann computing architectures. There are two main types of Artificial Neural Networks (ANNs), Feedforward Neural Network (FNN) and Recurrent Neural Network (RNN). In this thesis we first study the types of RNNs and then move on to Spiking Neural Networks (SNNs). SNNs are an improved version of ANNs that mimic biological neurons closely through the emission of spikes. This shows significant advantages in terms of pow
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Komodakis, Nikos. "Graphical Model Inference and Learning for Visual Computing." Habilitation à diriger des recherches, Université Paris-Est, 2013. http://tel.archives-ouvertes.fr/tel-00866078.

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Computational vision and image analysis is a multidisciplinary scientific field that aims to make computers "see" in a way that is comparable to human perception. It is currently one of the most challenging research areas in artificial intelligence. In this regard, the extraction of information from the vast amount of visual data that are available today as well as the exploitation of the resulting information space becomes one of the greatest challenges in our days. To address such a challenge, this thesis describes a very general computational framework that can be used for performing efficient
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Karg, Michelle E. [Verfasser], Kolja [Akademischer Betreuer] Kühnlenz, and Gerhard [Akademischer Betreuer] Rigoll. "Pattern Recognition Algorithms for Gait Analysis with Application to Affective Computing / Michelle Karg. Gutachter: Gerhard Rigoll. Betreuer: Kolja Kühnlenz." München : Universitätsbibliothek der TU München, 2012. http://d-nb.info/1019589450/34.

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Books on the topic "Pattern recognition (computing)"

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Huang, De-Shuang, Kang Li, and George William Irwin, eds. Intelligent Computing in Signal Processing and Pattern Recognition. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-37258-5.

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Sarkar, Sudeep. Computing perceptual organization in computer vision. World Scientific, 1994.

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Melin, Patricia. Soft Computing Applications in Optimization, Control, and Recognition. Springer Berlin Heidelberg, 2013.

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Melin, Patricia, and Oscar Castillo. Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/b97585.

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Ray, Kumar S. Soft Computing Approach to Pattern Classification and Object Recognition. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-5348-2.

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Zhou, Changjiu. Autonomous Robotic Systems: Soft Computing and Hard Computing Methodologies and Applications. Physica-Verlag HD, 2003.

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Mäntyjärvi, Jani. Sensor-based context recognition for mobile applications. VTT Technical Research Centre of Finland, 2003.

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Osanlou, Ardeshir. Soft computing and fractal geometry in signal processing and pattern recognition. De Montfort University, 2000.

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service), SpringerLink (Online, ed. Soft Computing Approach to Pattern Classification and Object Recognition: A Unified Concept. Springer New York, 2012.

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Computing with spatial trajectories. Springer, c2011., 2011.

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Book chapters on the topic "Pattern recognition (computing)"

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De Bie, Tijl, Nello Cristianini, and Roman Rosipal. "Eigenproblems in Pattern Recognition." In Handbook of Geometric Computing. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/3-540-28247-5_5.

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Skowron, Andrzej, and Roman W. Swiniarski. "Information Granulation and Pattern Recognition." In Rough-Neural Computing. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-18859-6_25.

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Venables, W. N., and B. D. Ripley. "Multivariate Analysis and Pattern Recognition." In Statistics and Computing. Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4757-3121-7_11.

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Volna, Eva, Michal Janosek, Martin Kotyrba, and Vaclav Kocian. "Pattern Recognition Algorithm Optimization." In Advances in Intelligent Systems and Computing. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-33227-2_26.

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Das, Soumendu, and Sreeparna Banerjee. "Pattern Recognition Approaches to Japanese Character Recognition." In Advances in Intelligent and Soft Computing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30157-5_9.

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Zheng, Zejun, Thuy-Diem Nguyen, and Bertil Schmidt. "CRiSPy-CUDA: Computing Species Richness in 16S rRNA Pyrosequencing Datasets with CUDA." In Pattern Recognition in Bioinformatics. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24855-9_4.

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Verma, Animesh, Amrendra Tripathi, and Tanupriya Choudhury. "Implementing Brain–Machine Interface (BMI) with Smart Computing." In Computational Intelligence in Pattern Recognition. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2449-3_5.

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Bielecka, Marzena, and Marek Skomorowski. "Fuzzy-aided Parsing for Pattern Recognition." In Advances in Soft Computing. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-75175-5_39.

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You, J., K. H. Cheung, Q. Li, and P. Bhattacharya. "An Integration of Biometrics and Mobile Computing for Personal Identification." In Pattern Recognition and Image Analysis. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11552499_26.

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Szczepaniak, Piotr S., and Arkadiusz Tomczyk. "From Pattern Recognition to Image Understanding." In Artificial Intelligence and Soft Computing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59063-9_53.

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Conference papers on the topic "Pattern recognition (computing)"

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Mendlovic, D., E. Marom, and N. Konforti. "Scale Invariant Pattern Recognition." In Optical Computing '88, edited by Pierre H. Chavel, Joseph W. Goodman, and Gerard Roblin. SPIE, 1989. http://dx.doi.org/10.1117/12.947906.

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Coggins, James M. "Statistical image pattern recognition." In Visualization in Biomedical Computing, edited by Richard A. Robb. SPIE, 1992. http://dx.doi.org/10.1117/12.131123.

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Arsenault, Henri H. "Invariants In Optical Pattern Recognition." In 1986 Int'l Computing Conference, edited by Asher A. Friesem, Emanuel Marom, and Joseph Shamir. SPIE, 1987. http://dx.doi.org/10.1117/12.936932.

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Kandel, Abraham, and Gideon Langholz. "Fourier Optics And Pattern Recognition." In 1986 Int'l Computing Conference, edited by Asher A. Friesem, Emanuel Marom, and Joseph Shamir. SPIE, 1987. http://dx.doi.org/10.1117/12.936948.

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Arsenault, Henri H. "Concepts of pattern recognition." In 2000 International Topical Meeting on Optics in Computing (OC2000), edited by Roger A. Lessard and Tigran V. Galstian. SPIE, 2000. http://dx.doi.org/10.1117/12.386812.

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Haim, Joseph B., and Joseph Shamir. "Scale- And Rotation-Invariant Pattern Recognition." In 1986 Int'l Computing Conference, edited by Asher A. Friesem, Emanuel Marom, and Joseph Shamir. SPIE, 1987. http://dx.doi.org/10.1117/12.936934.

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Campos, Juan C., Esmail Ahouzi, and Maria J. Yzuel. "Edge-extraction preprocessing in pattern recognition." In Topical Meeting on Optical Computing, edited by Andrey M. Goncharenko, Fedor V. Karpushko, George V. Sinitsyn, and Sergey P. Apanasevich. SPIE, 1993. http://dx.doi.org/10.1117/12.147871.

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Yao, Min, Jianhua Luo, and Ming Wu. "Applications of generalized computing in image compression coding." In Multispectral Image Processing and Pattern Recognition, edited by Jun Tian, Tieniu Tan, and Liangpei Zhang. SPIE, 2001. http://dx.doi.org/10.1117/12.442927.

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Zhou, Ke, and Jiangling Zhang. "Module of network RAID implemention on cluster computing." In Multispectral Image Processing and Pattern Recognition, edited by Xubang Shen and Jianguo Liu. SPIE, 2001. http://dx.doi.org/10.1117/12.441688.

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Ritter, Gerhard X., Gonzalo Urcid, and Juan-Carlos Valdiviezo-N. "Two lattice metrics dendritic computing for pattern recognition." In 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2014. http://dx.doi.org/10.1109/fuzz-ieee.2014.6891551.

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