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Relevant bibliographies by topics / Recognition algorithms

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Academic literature on the topic 'Recognition algorithms'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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Journal articles on the topic "Recognition algorithms"

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Toleushova, A. T., D. M. Uypalakova, and A. B. Imansakipova. "SIGNATURE RECOGNITION ALGORITHMS. BEZIER ALGORITHM." Bulletin of Shakarim University. Technical Sciences, no. 3(7) (February 10, 2023): 47–53. http://dx.doi.org/10.53360/2788-7995-2022-1(5)-7.

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This article focuses on improving the human and machine interface, which should ensure efficient processing of data and knowledge in simple, fast and accessible ways. One of the ways to organize it is the introduction of the manuscript (entering text, drawings, drawings, etc.). Handwritten signatures can be considered as handwritten words, but they are more suitable for drawings, because the signer tries to make his signature unique, using not only his first and last names, but also additional graphic elements. Creating a signature is quite simple, although it is impossible to reproduce the re

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Malikova, F. U., N. ZH Zhanat, A. K. Saginayeva, and R. S. Ryskeldy. "FEATURES OF FACIAL RECOGNITION." BULLETIN Series of Physics & Mathematical Sciences 69, no. 1 (2020): 374–77. http://dx.doi.org/10.51889/2020-1.1728-7901.67.

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The facial recognition system is used to provide identification and authentication during functional testing. It can also be used to identify people in different situations. This article presents a comparative study of the algorithms used for facial isolation and recognition. Algorithms are general algorithms that match a recognizable face. The concept of each algorithm is explained and a corresponding description is given. In addition, the results of the algorithms are evaluated in a data set and are displayed as graphs for evaluating the effectiveness of each algorithm. Algorithms work with

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Dirin, Amir, Nicolas Delbiaggio, and Janne Kauttonen. "Comparisons of Facial Recognition Algorithms Through a Case Study Application." International Journal of Interactive Mobile Technologies (iJIM) 14, no. 14 (2020): 121. http://dx.doi.org/10.3991/ijim.v14i14.14997.

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<p class="affiliations"><strong>Abstract— </strong>Computer visions and their applications have become important in contemporary life. Hence, researches on facial and object recognition have become increasingly important both from academicians and practitioners. Smart gadgets such as smartphones are nowadays capable of high processing power, memory capacity, along with high resolutions camera. Furthermore, the connectivity bandwidth and the speed of the interaction have significantly impacted the popularity of mobile object recognition applications. These developments in addi

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Valke, A. A., and D. G. Lobov. "CHARACTER RECOGNITION ALGORITHMS." Dynamics of Systems, Mechanisms and Machines 6, no. 4 (2018): 164–68. http://dx.doi.org/10.25206/2310-9793-2018-6-4-164-168.

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Valke, A. A., and D. G. Lobov. "Character recognition algorithms." Journal of Physics: Conference Series 1210 (March 2019): 012154. http://dx.doi.org/10.1088/1742-6596/1210/1/012154.

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Havrysh, B. M., O. V. Tymchenko, and M. P. Klyp. "Object recognition algorithms." Printing and Publishing 1, no. 83 (2022): 47–58. http://dx.doi.org/10.32403/0554-4866-2022-1-83-47-58.

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Kurbanbayev, Allayar Jannazarovich. "TEXT RECOGNITION ALGORITHMS." International journal of advanced research in education, technology and management 2, no. 4 (2023): 811–22. https://doi.org/10.5281/zenodo.7976619.

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Computing systems have been around for decades. The purpose of their creation was to replace a person, the ability to perform laborious work requiring complex calculations for him. This article talks about text recognition algorithms.  

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Ergasheva, Durdona, and Madina Moʻydinova. "ALGORITHMS USED IN FINGERPRINT PATTERN RECOGNITION." MODERN SCIENCE AND RESEARCH 3, no. 1 (2024): 542–45. https://doi.org/10.5281/zenodo.10535702.

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<em>Fingerprint recognition algorithms are used to automatically identify and match the unique characteristics of fingerprints for personal identification purposes. These algorithms may include processing fingerprint images, extracting fingerprint features, generating fingerprint templates, and comparing the templates to a database. There are many fingerprint recognition algorithms that can be used to automatically identify individuals based on fingerprints. This article provides information about the most common algorithms, the minute detail matching algorithm, surface correlation algorithms.

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Qiu, Jia-Gang, Yi Li, Hao-Qi Liu, et al. "Research on motion recognition based on multi-dimensional sensing data and deep learning algorithms." Mathematical Biosciences and Engineering 20, no. 8 (2023): 14578–95. http://dx.doi.org/10.3934/mbe.2023652.

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<abstract> <p>Motion recognition provides movement information for people with physical dysfunction, the elderly and motion-sensing games production, and is important for accurate recognition of human motion. We employed three classical machine learning algorithms and three deep learning algorithm models for motion recognition, namely Random Forests (RF), K-Nearest Neighbors (KNN) and Decision Tree (DT) and Dynamic Neural Network (DNN), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Compared with the Inertial Measurement Unit (IMU) worn on seven parts of bod

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Viriri, Serestina, and Brett Lagerwall. "Increasing Face Recognition Rates Using Novel Classification Algorithms." International Journal of Computers Communications & Control 11, no. 3 (2016): 381. http://dx.doi.org/10.15837/ijccc.2016.3.571.

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This paper describes and discusses a set of algorithms which can improve ace recognition rates. These algorithms include adaptive K-Nearest Neighbour, daptive weighted average, reverse weighted average and exponential weighted average. ssentially, the algorithms are extensions to the basic classification algorithm sed in most face recognition research. Whereas the basic classification algorithm elects the subject with the shortest associated distance, the algorithms presented in his paper manipulate and extract information from the set of distances between a est image and the training image se

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Dissertations / Theses on the topic "Recognition algorithms"

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Pham, Quoc Henry. "Hierarchical processing algorithms for object recognition." Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/13562.

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Ragothaman, Pradeep. "EFFICIENT ALGORITHMS FOR CORRELATION PATTERN RECOGNITION." Doctoral diss., University of Central Florida, 2007. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2132.

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The mathematical operation of correlation is a very simple concept, yet has a very rich history of application in a variety of engineering fields. It is essentially nothing but a technique to measure if and to what degree two signals match each other. Since this is a very basic and universal task in a wide variety of fields such as signal processing, communications, computer vision etc., it has been an important tool. The field of pattern recognition often deals with the task of analyzing signals or useful information from signals and classifying them into classes. Very often, these classes ar

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Huang, Jian. "Discriminant analysis algorithms for face recognition." HKBU Institutional Repository, 2006. http://repository.hkbu.edu.hk/etd_ra/655.

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Suliman, Ayman, and Joakim Bäverlind. "Experiments With Four Pattern Recognition Algorithms." Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-214730.

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A big part of computer vision concerns the issue ofhow well images can be classified into their corresponding classes.Image classification is a big part of reducing the gap betweenhuman and AI performance. Images are classified by first usinga dataset of images together with their given classifications totrain the system with machine learning. This can then be usedon images without any classification to test and see how well thealgorithm can classify an unknown image. However, this can bedone in many different ways and methods. The aim of the projectis to compare the performance of four algori

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Tyler, J. E. M. "Speech recognition by computer : algorithms and architectures." Thesis, University of Greenwich, 1988. http://gala.gre.ac.uk/8707/.

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This work is concerned with the investigation of algorithms and architectures for computer recognition of human speech. Three speech recognition algorithms have been implemented, using (a) Walsh Analysis, (b) Fourier Analysis and (c) Linear Predictive Coding. The Fourier Analysis algorithm made use of the Prime-number Fourier Transform technique. The Linear Predictive Coding algorithm made use of LeRoux and Gueguen's method for calculating the coefficients. The system was organised so that the speech samples could be input to a PC/XT microcomputer in a typical office environment. The PC/XT was

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Bellino, Kathleen Ann. "Computational Algorithms for Face Alignment and Recognition." Thesis, Virginia Tech, 2002. http://hdl.handle.net/10919/32847.

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Real-time face recognition has recently become available for the government and industry due to developments in face recognition algorithms, human head detection algorithms, and faster/low cost computers. Despite these advances, however, there are still some critical issues that affect the performance of real-time face recognition software. This paper addresses the problem of off-centered and out-of-pose faces in pictures, particularly in regard to the eigenface method for face recognition. We first demonstrate how the representation of faces by the eigenface method, and ultimately the p

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Feng, Yicheng. "Template protecting algorithms for face recognition system." HKBU Institutional Repository, 2007. http://repository.hkbu.edu.hk/etd_ra/832.

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Luccio, Flaminia L. Carleton University Dissertation Computer Science. "Distributed algorithms for routing and string recognition." Ottawa, 1995.

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Domingues, Riaal. "A polynomial time algorithm for prime recognition." Diss., Pretoria : [s.n.], 2006. http://upetd.up.ac.za/thesis/available/etd-08212007-100529.

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Demirci, Muhammed Fatih Shokoufandeh Ali. "Many-to-many feature matching for structural pattern recognition /." Philadelphia, Pa. : Drexel University, 2005. http://dspace.library.drexel.edu/handle/1860/656.

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Books on the topic "Recognition algorithms"

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Maji, Pradipta, and Sushmita Paul. Scalable Pattern Recognition Algorithms. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05630-2.

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K, Pal Sankar, and Wang Paul P, eds. Genetic algorithms for pattern recognition. CRC Press, 1996.

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Blostein, Dorothea, and Young-Bin Kwon, eds. Graphics Recognition Algorithms and Applications. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45868-9.

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Bhanu, Bir, and Xuejun Tan. Computational Algorithms for Fingerprint Recognition. Springer US, 2004. http://dx.doi.org/10.1007/978-1-4615-0491-7.

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Tombre, Karl, and Atul K. Chhabra, eds. Graphics Recognition Algorithms and Systems. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/3-540-64381-8.

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Xuejun, Tan, ed. Computational algorithms for fingerprint recognition. Kluwer Academic Publishers, 2004.

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Réjean, Plamondon, Cheng Heng-Da, and Vision Interface '90 Conference (1990 : Halifax, N.S.), eds. Pattern recognition: Architectures, algorithms & applications. World Scientific, 1991.

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Bhanu, Bir. Computational algorithms for fingerprint recognition. Kluwer Academic Publishers, 2003.

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Li, Jun-Bao, Shu-Chuan Chu, and Jeng-Shyang Pan. Kernel Learning Algorithms for Face Recognition. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-0161-2.

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M, Newton Elaine, and Information Technology Laboratory (National Institute of Standards and Technology). Mathematical and Computational Sciences Division, eds. Meta-analysis of face recognition algorithms. U.S. Dept. of Commerce, Technology Administration, Mathematics and Computational Sciences Division, National Institute of Standards and Technology, 2001.

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Book chapters on the topic "Recognition algorithms"

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Fissore, Luciano, Alfred Kaltenmeier, Pietro Laface, Giorgio Micca, and Roberto Pieraccini. "The Recognition Algorithms." In Advanced Algorithms and Architectures for Speech Understanding. Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-84341-9_2.

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Zhao, Xin, Shiyu Hu, and Xu-Cheng Yin. "Algorithms." In Advances in Computer Vision and Pattern Recognition. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4558-9_3.

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Bhadane, Chetashri, M. Umair Siddiqui, Siddhant Soni, and Vijay Pratap Singh. "Human Activity Recognition." In Algorithms for Intelligent Systems. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3242-9_45.

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Monte, E., D. Hidalgo, J. Mariño, and I. Harnaez. "A VQ Algorithm Based on Genetic Algorithms and LVQ." In Speech Recognition and Coding. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-57745-1_71.

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Sohrabi, Shahin, and Ali Asghar Beheshti Shirazi. "A New Star Identification Algorithm Based on Fuzzy Algorithms." In Computer Recognition Systems 4. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20320-6_67.

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Deza, Michel Marie, and Monique Laurent. "Recognition of Hypercube Embeddable Metrics." In Algorithms and Combinatorics. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-04295-9_24.

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Pirlo, Giuseppe. "Algorithms for Signature Verification." In Fundamentals in Handwriting Recognition. Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-78646-4_26.

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Walrand, Jean. "Speech Recognition: B." In Probability in Electrical Engineering and Computer Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-49995-2_12.

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AbstractOnline learning algorithms update their estimates as additional observations are made. Section 12.1 explains a simple example: online linear regression. The stochastic gradient projection algorithm is a general technique to update estimates based on additional observations; it is widely used in machine learning. Section 12.2 presents the theory behind that algorithm. When analyzing large amounts of data, one faces the problems of identifying the most relevant data and of how to use efficiently the available data. Section 12.3 explains three examples of how these questions are addressed

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Maji, Pradipta, and Sushmita Paul. "Introduction to Pattern Recognition and Bioinformatics." In Scalable Pattern Recognition Algorithms. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05630-2_1.

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Maji, Pradipta, and Sushmita Paul. "Possibilistic Biclustering for Discovering Value-Coherent Overlapping $$\delta $$ δ -Biclusters." In Scalable Pattern Recognition Algorithms. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05630-2_10.

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Conference papers on the topic "Recognition algorithms"

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Ross, Jacob, Shaun Stephens, and Ryan Shaver. "Template formation strategies for synthetic aperture radar automatic target recognition." In Algorithms for Synthetic Aperture Radar Imagery XXXII, edited by Edmund Zelnio and Frederick D. Garber. SPIE, 2025. https://doi.org/10.1117/12.3053348.

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Batts, Alex W., Brian Rigling, and Uttam Kumar Majumder. "SAR image augmentation for automatic target recognition via spectral estimation." In Algorithms for Synthetic Aperture Radar Imagery XXXII, edited by Edmund Zelnio and Frederick D. Garber. SPIE, 2025. https://doi.org/10.1117/12.3055965.

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Khan, Ayesha, Raja Hashim Ali, Urooj Akmal, and Alishba Ramazan. "ASL Recognition using Deep Learning Algorithms." In 2024 International Conference on IT and Industrial Technologies (ICIT). IEEE, 2024. https://doi.org/10.1109/icit63607.2024.10859803.

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Perju, Veaceslav, and Dorian Saranciuc. "Evaluation of the Multi-Algorithms Targets Recognition Systems." In 12th International Conference on Electronics, Communications and Computing. Technical University of Moldova, 2022. http://dx.doi.org/10.52326/ic-ecco.2022/cs.05.

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This paper presents the evaluation’s results of the new classes of the target recognition systems – multi- algorithms unimodal systems and multi-algorithms multimodal systems. The structures and the graphs of the systems are described. The mathematical descriptions and the formulas for evaluation of the system’s costs depending on the algorithm’s recognition probability and the relation between the costs of the algorithm’s software and the system’s hardware are presented. The approach to determine the cost of a system for an established threshold level of the system's recognition probability i

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Rogers, Jonathan, and Dakota Musso. "Interacting Multiple Model Algorithms for Rotorcraft Regime Recognition." In Vertical Flight Society 75th Annual Forum & Technology Display. The Vertical Flight Society, 2019. http://dx.doi.org/10.4050/f-0075-2019-14603.

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Regime recognition is a critical tool used for condition-based maintenance, fatigue life prediction, and creation of usage spectra for military and commercial rotorcraft. While a variety of regime recognition algorithms are currently in use, many current algorithms suffer from an over-reliance on training data or poor classification accuracy with respect to the stringent guidelines outlined in ADS-79E. This paper introduces a new type of regime recognition algorithm based on a multiple model adaptive estimation scheme, known as an interacting multiple model (IMM) estimator. IMM estimators use

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Bidkar, Rahul A., and Daniel A. McAdams. "Feature Recognition for Injection-Molded and Die-Cast Parts." In ASME 2004 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/detc2004-57754.

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In this article, a framework to perform the computer-aided Design for Manufacturability analysis of injection-molded and die-cast parts is presented. The framework includes an implemented algorithm to solve key outstanding challenges in feature recognition for manufacturability analysis. The proposed feature recognition algorithms operate on a 3D binary array obtained from the solid model of the part. Solutions to the feature recognition problems of finding the best direction of mold closure, finding the mold parting line of the part and determining planarity of the parting surface is presente

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Wu, Duan, Jingwen Tian, Renzhou Gui, et al. "Traffic Sign Visual Recognition Study Based on full-reference Image Quality Assessment Algorithms." In 16th International Conference on Applied Human Factors and Ergonomics (AHFE 2025). AHFE International, 2025. https://doi.org/10.54941/ahfe1006252.

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The factors influencing the visual recognizability of traffic signage are diverse. To study the synergistic effects and critical value models of these visual recognition elements, it is necessary to conduct experiments and collect data on the recognition influence factors. However, data collection based on human testers is limited by experimental conditions, making it difficult to establish large-scale datasets and avoid individual errors.The full-reference algorithm for image quality evaluation is a technique used in the field of computer image recognition to identify image distortions and as

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Klobas, Nina, and Matjaž Krnc. "Fast Recognition of Some Parametric Graph Families." In 7th Student Computer Science Research Conference. University of Maribor Press, 2021. http://dx.doi.org/10.18690/978-961-286-516-0.7.

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Recognizing graphs with high level of symmetries is hard in general, and usually requires additional structural understanding. In this paper we study a particular graph parameter and motivate its usage by devising eÿcient recognition algorithm for the family of I-graphs. For integers m a simple graph is cycle regular if every path of length ` belongs to exactly cycles of length m. We identify all cycle regular I-graphs and, as a conse-quence, describe linear recognition algorithm for the observed family. Similar procedure can be used to devise the recog-nition algorithms for Double generalized

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Wang, Hanmo, Xiaojun Chang, Lei Shi, Yi Yang, and Yi-Dong Shen. "Uncertainty Sampling for Action Recognition via Maximizing Expected Average Precision." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/134.

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Recognizing human actions in video clips has been an important topic in computer vision. Sufficient labeled data is one of the prerequisites for the good performance of action recognition algorithms. However, while abundant videos can be collected from the Internet, categorizing each video clip is tedious and even time-consuming. Active learning is one way to alleviate the labeling labor by allowing the classifier to choose the most informative unlabeled instances for manual annotation. Among various active learning algorithms, uncertainty sampling is arguably the most widely-used strategy. Co

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Mahalanobis, Abhijit, and Bruce McIntosh. "A comparison of target detection algorithms using DSIAC ATR algorithm development data set." In Automatic Target Recognition XXIX, edited by Timothy L. Overman and Riad I. Hammoud. SPIE, 2019. http://dx.doi.org/10.1117/12.2517423.

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Reports on the topic "Recognition algorithms"

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Phillips, P. Jonathon, and Elaine M. Newton. Meta-analysis of face recognition algorithms. National Institute of Standards and Technology, 2001. http://dx.doi.org/10.6028/nist.ir.6719.

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Draelos, Timothy J., Stephen Heck, Jennifer Galasso, and Ronald Brogan. Seismic Phase Identification with Speech Recognition Algorithms. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1474260.

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Phillips, P. Jonathon, Fang Jiang, Abhijit Narvekar, Julianne Ayyad, and Alice J. O'Toole. An other race effect for face recognition algorithms. National Institute of Standards and Technology, 2010. http://dx.doi.org/10.6028/nist.ir.7666.

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Quinn, George W., and Patrick J. Grother. Performance of face recognition algorithms on compressed images. National Institute of Standards and Technology, 2011. http://dx.doi.org/10.6028/nist.ir.7830.

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Phillips, P. Jonathon, Hyeonjoon Moon, Syed A. Rizvi, and Patrick J. Rauss. The FERET evaluation methodology for face-recognition algorithms. National Institute of Standards and Technology, 1998. http://dx.doi.org/10.6028/nist.ir.6264.

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Quinn, George W., Patrick Grother, and James Matey. IREX IX part one, performance of iris recognition algorithms. National Institute of Standards and Technology, 2018. http://dx.doi.org/10.6028/nist.ir.8207.

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Rizvi, Syed A., P. Jonathon Phillips, and Hyeonjoon Moon. The FERET verification testing protocol for face recognition algorithms. National Institute of Standards and Technology, 1998. http://dx.doi.org/10.6028/nist.ir.6281.

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Phillips, P. Jonathon, Alice J. O'Toole, Yi Cheng, Brendan Ross, and Heather A. Wild. Assessing algorithms as computational models for human face recognition. National Institute of Standards and Technology, 1999. http://dx.doi.org/10.6028/nist.ir.6348.

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Grother, Patrick J., George W. Quinn, and P. Jonathon Phillips. Report on the evaluation of 2D still-image face recognition algorithms. National Institute of Standards and Technology, 2011. http://dx.doi.org/10.6028/nist.ir.7709.

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Ngan, Mei, and Patrick Grother. rFace Recognition Vendor Test (FRVT) - Performance of Automated Gender Classification Algorithms. National Institute of Standards and Technology, 2015. http://dx.doi.org/10.6028/nist.ir.8052.

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