Relevant bibliographies by topics / Optical pattern recognition – Mathematical models

Contents

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

Academic literature on the topic 'Optical pattern recognition – Mathematical models'

Author: Grafiati
Published: 4 June 2021
Last updated: 19 February 2022

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Journal articles on the topic "Optical pattern recognition – Mathematical models"

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Kirtchik, Olessia. "From Pattern Recognition to Economic Disequilibrium." History of Political Economy 51, S1 (2019): 180–203. http://dx.doi.org/10.1215/00182702-7903288.

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This article is focused on the economic works of the Soviet machinelearning pioneer Emmanuil Braverman, who published, during the 1970s, a series of papers introducing disequilibrium fixed-price models of the Soviet economy. This highly original theory, developed independently from the Western analyses of disequilibria, proposed rationing mechanisms capable, under some conditions, of bringing a system to the state of equilibrium. However, in a fixed-price economy, equilibria are not necessarily optimal or effective; therefore specific observational and analytic procedures aiming at bringing a
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WANG, XIAOLONG, DANIEL S. YEUNG, JAMES N. K. LIU, ROBERT LUK, and XUAN WANG. "A HYBRID LANGUAGE MODEL BASED ON STATISTICS AND LINGUISTIC RULES." International Journal of Pattern Recognition and Artificial Intelligence 19, no. 01 (2005): 109–28. http://dx.doi.org/10.1142/s0218001405003934.

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Language modeling is a current research topic in many domains including speech recognition, optical character recognition, handwriting recognition, machine translation and spelling correction. There are two main types of language models, the mathematical and the linguistic. The most widely used mathematical language model is the n-gram model inferred from statistics. This model has three problems: long distance restriction, recursive nature and partial language understanding. Language models based on linguistics present many difficulties when applied to large scale real texts. We present here
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Kapkan, O. O., O. M. Khudolii, and P. Bartik. "Pattern Recognition: Motor Skills Development in Girls Aged 15." Teorìâ ta Metodika Fìzičnogo Vihovannâ 19, no. 1 (2019): 44–52. http://dx.doi.org/10.17309/tmfv.2019.1.06.

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The study objective is to determine physical exercises modes when developing motor skills in girls aged 15.Materials and methods. The participants in the study were 40 girls aged 15. To achieve the objectives set, the following research methods were used: study and analysis of scientific and methodological literature; pedagogical observation, timing of training tasks; pedagogical experiment, methods of mathematical statistics, methods of mathematical experiment planning, discriminant analysis. To achieve the objective set, the study examined the effect of different variants of performing exerc
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Fukushima, Takuya, Tomoharu Nakashima, Taku Hasegawa, and Vicenç Torra. "Optimal Value Estimation of Intentional-Value-Substitution for Learning Regression Models." Journal of Advanced Computational Intelligence and Intelligent Informatics 25, no. 2 (2021): 153–61. http://dx.doi.org/10.20965/jaciii.2021.p0153.

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This paper focuses on a method to train a regression model from incomplete input values. It is assumed in this paper that there are no missing values in a training dataset while missing values exist during a prediction phase using the trained model. Under this assumption, we propose Intentional-Value-Substitution (IVS) training to obtain a machine learning model that makes the prediction error as minimum as possible. In IVS training, a model is trained to approximate the target function using a modified training dataset in which some feature values are substituted with a certain value even tho
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Haussecker, H. W., and D. J. Fleet. "Computing optical flow with physical models of brightness variation." IEEE Transactions on Pattern Analysis and Machine Intelligence 23, no. 6 (2001): 661–73. http://dx.doi.org/10.1109/34.927465.

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Huang, Hongxia, Yuanyuan Lv, Xiaoyi Sun, Shuangshuang Fu, Xuefang Lou, and Zengmei Liu. "Rapid determination of tannin in Danshen and Guanxinning injections using UV spectrophotometry for quality control." Journal of Innovative Optical Health Sciences 11, no. 06 (2018): 1850034. http://dx.doi.org/10.1142/s1793545818500347.

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A technique for the determination of tannin content in traditional Chinese medicine injections (TCMI) was developed based on ultraviolet (UV) spectroscopy. Chemometrics were used to construct a mathematical model of absorption spectrum and tannin reference content of Danshen and Guanxinning injections, and the model was verified and applied. The results showed that the established UV-based spectral partial least squares regression (PLS) tannin content model performed well with a correlation coefficient ([Formula: see text]) of 0.952, root mean square error of calibration (RMSEC) of 0.476[Formu
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Sun, Deqing, Xiaodong Yang, Ming-Yu Liu, and Jan Kautz. "Models Matter, So Does Training: An Empirical Study of CNNs for Optical Flow Estimation." IEEE Transactions on Pattern Analysis and Machine Intelligence 42, no. 6 (2020): 1408–23. http://dx.doi.org/10.1109/tpami.2019.2894353.

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Robinett, Warren, and Richard Holloway. "The Visual Display Transformation for Virtual Reality." Presence: Teleoperators and Virtual Environments 4, no. 1 (1995): 1–23. http://dx.doi.org/10.1162/pres.1995.4.1.1.

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The visual display transformation for virtual reality (VR) systems is typically much more complex than the standard viewing transformation discussed in the literature for conventional computer graphics. The process can be represented as a series of transformations, some of which contain parameters that must match the physical configuration of the system hardware and the user's body. Because of the number and complexity of the transformations, a systematic approach and a thorough understanding of the mathematical models involved are essential. This paper presents a complete model for the visual
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Schweiger, M., S. R. Arridge, and D. T. Delpy. "Application of the finite-element method for the forward and inverse models in optical tomography." Journal of Mathematical Imaging and Vision 3, no. 3 (1993): 263–83. http://dx.doi.org/10.1007/bf01248356.

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Kališnik, Miroslav, Andrej Blejec, Zdenka Pajer, and Janja Majhenc. "METRIC CHARACTERISTICS OF VARIOUS METHODS FOR NUMERICAL DENSITY ESTIMATION IN TRANSMISSION LIGHT MICROSCOPY – A COMPUTER SIMULATION." Image Analysis & Stereology 20, no. 1 (2011): 15. http://dx.doi.org/10.5566/ias.v20.p15-25.

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In the introduction the evolution of methods for numerical density estimation of particles is presented shortly. Three pairs of methods have been analysed and compared: (1) classical methods for particles counting in thin and thick sections, (2) original and modified differential counting methods and (3) physical and optical disector methods. Metric characteristics such as accuracy, efficiency, robustness, and feasibility of methods have been estimated and compared. Logical, geometrical and mathematical analysis as well as computer simulations have been applied. In computer simulations a model
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Dissertations / Theses on the topic "Optical pattern recognition – Mathematical models"

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Rajah, Christopher. "Chereme-based recognition of isolated, dynamic gestures from South African sign language with Hidden Markov Models." Thesis, University of the Western Cape, 2006. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_4979_1183461652.

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<p>Much work has been done in building systems that can recognize gestures, e.g. as a component of sign language recognition systems. These systems typically use whole gestures as the smallest unit for recognition. Although high recognition rates have been reported, these systems do not scale well and are computationally intensive. The reason why these systems generally scale poorly is that they recognize gestures by building individual models for each separate gesture<br>as the number of gestures grows, so does the required number of models. Beyond a certain threshold number of gestures to be
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Naidoo, Nathan Lyle. "South African sign language recognition using feature vectors and Hidden Markov Models." Thesis, University of the Western Cape, 2010. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_8533_1297923615.

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<p>This thesis presents a system for performing whole gesture recognition for South African Sign Language. The system uses feature vectors combined with Hidden Markov models. In order to constuct a feature vector, dynamic segmentation must occur to extract the signer&rsquo<br>s hand movements. Techniques and methods for normalising variations that occur when recording a signer performing a gesture, are investigated. The system has a classification rate of 69%</p>
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Hill, Evelyn June. "Applying statistical and syntactic pattern recognition techniques to the detection of fish in digital images." University of Western Australia. School of Mathematics and Statistics, 2004. http://theses.library.uwa.edu.au/adt-WU2004.0070.

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This study is an attempt to simulate aspects of human visual perception by automating the detection of specific types of objects in digital images. The success of the methods attempted here was measured by how well results of experiments corresponded to what a typical human’s assessment of the data might be. The subject of the study was images of live fish taken underwater by digital video or digital still cameras. It is desirable to be able to automate the processing of such data for efficient stock assessment for fisheries management. In this study some well known statistical pattern classif
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Davis, Richard I. A. "Training Hidden Markov Models for spatio-temporal pattern recognition /." [St. Lucia, Qld.], 2004. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe18500.pdf.

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Odeh, Inakwu Ominyi Akots. "Soil pattern recognition in a South Australian subcatchment /." Title page, contents and abstract only, 1990. http://web4.library.adelaide.edu.au/theses/09PH/09pho23.pdf.

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Liu, Nianjun. "Hand gesture recognition by Hidden Markov Models /." [St. Lucia, Qld.], 2004. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe18158.pdf.

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Woo, Myung Chul. "Biologically-inspired translation, scale, and rotation invariant object recognition models /." Online version of thesis, 2007. http://hdl.handle.net/1850/3933.

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Elliott, Steven C. "Context sensitive optical character recognition using neural networks and hidden Markov models /." Online version of thesis, 1992. http://hdl.handle.net/1850/11054.

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Zapata, Iván R. "Detecting humans in video sequences using statistical color and shape models." [Gainesville, Fla.] : University of Florida, 2001. http://etd.fcla.edu/etd/uf/2001/anp1058/ivan%5Fthesis2.pdf.

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Thesis (M.S.)--University of Florida, 2001.<br>Title from first page of PDF file. Document formatted into pages; contains viii, 49 p.; also contains graphics. Vita. Includes bibliographical references (p. 47-48).
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Chen, Dan, and 陳丹. "An adaptive weighting algorithm for limited dataset verification problems." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B3204897X.

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Books on the topic "Optical pattern recognition – Mathematical models"

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Ponting, Keith. Computational Models of Speech Pattern Processing. Springer Berlin Heidelberg, 1999.

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Li, Yanjun. Jing xiang pi pei yu mu biao shi bie ji shu. Xi bei gong ye ta xue chu ban she, 2009.

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Haken, Hermann. Principles of Brain Functioning: A Synergetic Approach to Brain Activity, Behavior and Cognition. Springer Berlin Heidelberg, 1996.

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service), SpringerLink (Online, ed. Introduction to Computational Cardiology: Mathematical Modeling and Computer Simulation. Springer Science+Business Media, LLC, 2010.

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A, Fink Gernot, and SpringerLink (Online service), eds. Markov Models for Handwriting Recognition. Thomas Plötz, 2011.

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Javier, Ruiz-del-Solar, Meriçli Çetin, Oudeyer Pierre-Yves, and SpringerLink (Online service), eds. Human Behavior Understanding: Third International Workshop, HBU 2012, Vilamoura, Portugal, October 7, 2012. Proceedings. Springer Berlin Heidelberg, 2012.

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Sinvhal, Amita. Seismic modelling and pattern recognition in oil exploration. Kluwer Academic Publishers, 1992.

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Fukunaga, Keinosuke. Introduction to statistical pattern recognition. 2nd ed. Academic Press, 1990.

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Pattern recognition problems in geology and paleontology. Springer-Verlag, 1985.

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Johnson, Mark. Mathematical Foundations of Speech and Language Processing. Springer New York, 2004.

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Book chapters on the topic "Optical pattern recognition – Mathematical models"

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Li, Stan Z. "Mathematical MRF Models." In Advances in Pattern Recognition. Springer London, 2009. http://dx.doi.org/10.1007/978-1-84800-279-1_2.

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Fink, Gernot A. "Foundations of Mathematical Statistics." In Markov Models for Pattern Recognition. Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-6308-4_3.

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Lucena, Manuel J., José M. Fuertes, Nicolas Perez de la Blanca, Antonio Garrido, and Nicolás Ruiz. "Probabilistic Observation Models for Tracking Based on Optical Flow." In Pattern Recognition and Image Analysis. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-44871-6_54.

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Kaur, Harleen, Ritu Chauhan, and M. Alam. "An Optimal Categorization of Feature Selection Methods for Knowledge Discovery." In Data Mining. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2455-9.ch005.

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With the continuous availability of massive experimental medical data has given impetus to a large effort in developing mathematical, statistical and computational intelligent techniques to infer models from medical databases. Feature selection has been an active research area in pattern recognition, statistics, and data mining communities. However, there have been relatively few studies on preprocessing data used as input for data mining systems in medical data. In this chapter, the authors focus on several feature selection methods as to their effectiveness in preprocessing input medical data. They evaluate several feature selection algorithms such as Mutual Information Feature Selection (MIFS), Fast Correlation-Based Filter (FCBF) and Stepwise Discriminant Analysis (STEPDISC) with machine learning algorithm naive Bayesian and Linear Discriminant analysis techniques. The experimental analysis of feature selection technique in medical databases has enable the authors to find small number of informative features leading to potential improvement in medical diagnosis by reducing the size of data set, eliminating irrelevant features, and decreasing the processing time.
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Estrela, Vania Vieira, Hermes Aguiar Magalhães, and Osamu Saotome. "Total Variation Applications in Computer Vision." In Handbook of Research on Emerging Perspectives in Intelligent Pattern Recognition, Analysis, and Image Processing. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-8654-0.ch002.

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The objectives of this chapter are: (i) to introduce a concise overview of regularization; (ii) to define and to explain the role of a particular type of regularization called total variation norm (TV-norm) in computer vision tasks; (iii) to set up a brief discussion on the mathematical background of TV methods; and (iv) to establish a relationship between models and a few existing methods to solve problems cast as TV-norm. For the most part, image-processing algorithms blur the edges of the estimated images, however TV regularization preserves the edges with no prior information on the observed and the original images. The regularization scalar parameter ? controls the amount of regularization allowed and it is essential to obtain a high-quality regularized output. A wide-ranging review of several ways to put into practice TV regularization as well as its advantages and limitations are discussed.
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"iOS App and Architecture of Convolutional Neural Networks." In Advances in Computer and Electrical Engineering. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1554-9.ch001.

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Deep convolutional neural networks (CNN) have attracted many attentions of researchers in the field of artificial intelligence. Based on several well-known architectures, more researchers and designers have joined the field of applying deep learning and devising a large number of CNNs for processing datasets of interesting. Equipped with modern audio, video, screen-touching components, and other sensors for online pattern recognition, the iOS mobile devices provide developers and users friendly testing and powerful computing environments. This chapter introduces the trend of developing pattern recognition CNN Apps on iOS devices and the neural organization of convolutional neural networks. Deep learning in Matlab and executing CNN models on iOS devices are introduced following the motivation of combining mathematical modelling and computation with neural architectures for developing pattern recognition iOS apps. This chapter also gives contexts of discussing typical hidden layers in the CNN architecture.
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Sytnik, Oleg, and Vladimir Kartashov. "Technical Vision Model of the Visual Systems for Industry Application." In Examining Optoelectronics in Machine Vision and Applications in Industry 4.0. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-6522-3.ch010.

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The problems of highlighting the main informational aspects of images and creating their adequate models are discussed in the chapter. Vision systems can receive information about an object in different frequency ranges and in a form that is not accessible to the human visual system. Vision systems distort the information contained in the image. Therefore, to create effective image processing and transmission systems, it is necessary to formulate mathematical models of signals and interference. The chapter discusses the features of perception by the human visual system and the issues of harmonizing the technical characteristics of industrial systems for receiving and transmitting images. Methods and algorithms of pattern recognition are discussed. The problem of conjugation of the characteristics of the technical vision system with the consumer of information is considered.
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Verbeek, Fons J., and Lu Cao. "L-systems from 3D-imaging of Phenotypes of Arborized Structures." In A Mosaic of Computational Topics: from Classical to Novel. IOS Press, 2020. http://dx.doi.org/10.3233/stal200017.

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Biology is 3D. Therefore, it is important to be able to analyze phenomena in a spatio-temporal manner. Different fields in computational sciences are useful for analysis in biology; i.e. image analysis, pattern recognition and machine learning. To fit an empirical model to a higher abstraction, however, theoretical computer science methods are probed. We explore the construction of empirical 3D graphical models and develop abstractions from these models in L-systems. These systems are provided with a profound formalization in a grammar allowing generalization and exploration of mathematical structures in topologies. The connections between these computational approaches are illustrated by a case study of the development of the lactiferous duct in mice and the phenotypical effects from different environmental conditions we can observe on it. We have constructed a workflow to get 3D models from different experimental conditions and use these models to extract features. Our aim is to construct an abstraction of these 3D models to an L-system from features that we have measured. From our measurements we can make the productions for an L-system. In this manner we can formalize the arborization of the lactiferous duct under different environmental conditions and capture different observations. All considered, this paper illustrates the joint of empirical with theoretical computational sciences and the augmentation of the interpretation of the results. At the same time, it shows a method to analyze complex 3D topologies and produces archetypes for developmental configurations.
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Nurunnabi, A. A. M., A. B. M. S. Ali, A. H. M. Rahmatullah Imon, and Mohammed Nasser. "Outlier Detection in Logistic Regression." In Multidisciplinary Computational Intelligence Techniques. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-1830-5.ch016.

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The use of logistic regression, its modelling and decision making from the estimated model and subsequent analysis has been drawn a great deal of attention since its inception. The current use of logistic regression methods includes epidemiology, biomedical research, criminology, ecology, engineering, pattern recognition, machine learning, wildlife biology, linguistics, business and finance, et cetera. Logistic regression diagnostics have attracted both theoreticians and practitioners in recent years. Detection and handling of outliers is considered as an important task in the data modelling domain, because the presence of outliers often misleads the modelling performances. Traditionally logistic regression models were used to fit data obtained under experimental conditions. But in recent years, it is an important issue to measure the outliers scale before putting the data as a logistic model input. It requires a higher mathematical level than most of the other material that steps backward to its study and application in spite of its inevitability. This chapter presents several diagnostic aspects and methods in logistic regression. Like linear regression, estimates of the logistic regression are sensitive to the unusual observations: outliers, high leverage, and influential observations. Numerical examples and analysis are presented to demonstrate the most recent outlier diagnostic methods using data sets from medical domain.
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Conference papers on the topic "Optical pattern recognition – Mathematical models"

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Bouchoffra, D., and F. Ykhlef. "Mathematical models for machine learning and pattern recognition." In 2013 8th InternationalWorkshop on Systems, Signal Processing and their Applications (WoSSPA). IEEE, 2013. http://dx.doi.org/10.1109/wosspa.2013.6602331.

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Mitra, Sunanda, and Yong Soo Kim. "Neuro-fuzzy models in pattern recognition." In Optical Tools for Manufacturing and Advanced Automation, edited by Bruno Bosacchi and James C. Bezdek. SPIE, 1993. http://dx.doi.org/10.1117/12.165040.

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Andrade, E. L., S. Blunsden, and R. B. Fisher. "Hidden Markov Models for Optical Flow Analysis in Crowds." In 18th International Conference on Pattern Recognition (ICPR'06). IEEE, 2006. http://dx.doi.org/10.1109/icpr.2006.621.

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Lucena, M., J. M. Fuertes, and N. P. de la Blanca. "Evaluation of three optical flow-based observation models for tracking." In Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. IEEE, 2004. http://dx.doi.org/10.1109/icpr.2004.1333747.

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DeCarlo, D., and D. Metaxas. "The integration of optical flow and deformable models with applications to human face shape and motion estimation." In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 1996. http://dx.doi.org/10.1109/cvpr.1996.517079.

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Krasilenko, Vladimir G., Aleksandr I. Nikolskyy, and Alexander A. Lazarev. "Modeling optical pattern recognition algorithms for object tracking based on nonlinear equivalent models and subtraction of frames." In Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), edited by Tianxu Zhang and Jianguo Liu. SPIE, 2015. http://dx.doi.org/10.1117/12.2205779.

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Krasilenko, Vladimir G., Alexander I. Nikolsky, Alexandr V. Zaitsev, and Victor M. Voloshin. "Optical pattern recognition algorithms on neural-logic equivalent models and demonstration of their prospects and possible implementations." In Aerospace/Defense Sensing, Simulation, and Controls, edited by David P. Casasent and Tien-Hsin Chao. SPIE, 2001. http://dx.doi.org/10.1117/12.421146.

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Mazda, Taiji, Hisanori Otsuka, Wataru Yabuki, and Kensuke Iwamoto. "Construction of Generalized Neural Network System for Recognizing Several Non-Linear Behaviors." In ASME 2003 Pressure Vessels and Piping Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/pvp2003-2114.

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Generally, in formulating a spring-mass model for hysteric behavior of materials and members with inelastic characteristic, a mathematical model based on load-deformation experimental results is considered. The model must approximate the inelastic hysteresis of the material. However, assumption of material’s behavior using mathematical models is crucial, since it may cause serious errors if inappropriate model is applied for a particular situation. This paper describes multiple layered neural network to simulate the non-linear hysteretic behavior like Ramberg-Osgood model, modified bilinear mo
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Gonchikar, Ugrasen, Holalu Venkatadasu Ravindra, Prathik Jain Sudhir, Umeshgowda Bettahally Mahadevegowda, and Shankarnarayan Maskibail Suresh. "Estimation and Comparison of Welding Responses Using MRA, GMDH and ANN Technique of Al6061 and Al7075 Material in FSW." In ASME 2019 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/imece2019-11168.

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Abstract Friction Stir Welding (FSW) is a solid state welding which uses non-consumable steel rod to weld two materials. Friction stir welding is an emerging process which is based on frictional heat generated through contact between a non-consumable rotating tool and work piece. Friction stir welding technique possesses several advantages over other conventional types of welding due to the fact that process is carried out in solid state. Removal of melting helps in minimizing porosity and eliminates oxide inclusion. In this study, we focus on the optimization of the process parameters in fric
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Singh, Prabhjot, and Debasish Dutta. "Detection for Parametric Form Features in Surface Meshes." In ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-61287.

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Reverse Engineering refers to the construction of solid models from existing parts. The process is initiated by taking dense surface measurements using an optical range sensor. These are then approximated to form a surface mesh representation. Polygonal surfaces meshes form the input for solid model construction. However, the C0 continuous meshes are devoid of high level form feature information which conveys design intent or engineering significance. Currently, only partial solutions to detect such features are known in literature. In this research we present algorithms to segment polygonal s
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