Littérature scientifique sur le sujet « REAL TIME RECOGNITION »
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Articles de revues sur le sujet "REAL TIME RECOGNITION"
Anam, Sarawat, Shohidul Islam, M. A. Kashem et M. A. Rahman. « Real Time Face Recognition Using Step Error Tolerance BPN ». International Journal of Engineering and Technology 1, no 1 (2009) : 92–96. http://dx.doi.org/10.7763/ijet.2009.v1.17.
Texte intégralBillings, Rachel M., et Alan J. Michaels. « Real-Time Mask Recognition ». IoT 2, no 4 (8 novembre 2021) : 688–716. http://dx.doi.org/10.3390/iot2040035.
Texte intégralCRL Smetic Technology. « Real-time image recognition ». NDT & ; E International 27, no 1 (février 1994) : 59–60. http://dx.doi.org/10.1016/0963-8695(94)90119-8.
Texte intégralPatil, Anuradha, Chandrashekhar M. Tavade et . « Methods on Real Time Gesture Recognition System ». International Journal of Engineering & ; Technology 7, no 3.12 (20 juillet 2018) : 982. http://dx.doi.org/10.14419/ijet.v7i3.12.17617.
Texte intégralY.Mohammed, Zaid, et Abdul Sattar M. Khidhir. « Real-Time Arabic Speech Recognition ». International Journal of Computer Applications 81, no 4 (15 novembre 2013) : 43–45. http://dx.doi.org/10.5120/14003-2048.
Texte intégralCetiner, Halit, Bayram Cetisli et Ibrahim Cetiner. « Real Time Identification Number Recognition ». SAÜ Fen Bilimleri Enstitüsü Dergisi 16, no 2 (2012) : 123–29. http://dx.doi.org/10.5505/saufbe.2012.18894.
Texte intégralAlbukhary, N., et Y. M. Mustafah. « Real-time Human Activity Recognition ». IOP Conference Series : Materials Science and Engineering 260 (novembre 2017) : 012017. http://dx.doi.org/10.1088/1757-899x/260/1/012017.
Texte intégralMARIN, Florin-Bogdan, Gheorghe GURĂU et Mihaela MARIN. « Real-Time Assembly Operation Recognition ». Annals of “Dunarea de Jos” University of Galati. Fascicle IX, Metallurgy and Materials Science 45, no 4 (15 décembre 2022) : 92–95. http://dx.doi.org/10.35219/mms.2022.4.15.
Texte intégralWu, Yi-Chang, Yao-Cheng Liu et Ru-Yi Huang. « Real-time microreaction recognition system ». IAES International Journal of Robotics and Automation (IJRA) 12, no 2 (1 juin 2023) : 157. http://dx.doi.org/10.11591/ijra.v12i2.pp157-166.
Texte intégralShah, Dr Dipti M., et Parul D. Sindha. « Color detection in real time traffic sign detection and recognition system ». Indian Journal of Applied Research 3, no 7 (1 octobre 2011) : 152–53. http://dx.doi.org/10.15373/2249555x/july2013/43.
Texte intégralThèses sur le sujet "REAL TIME RECOGNITION"
Cao, Hua. « Real Time Traffic Recognition ». Thesis, Uppsala University, Department of Information Technology, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-89414.
Texte intégralThe rapid growth of Internet in size and complexity, and frequent emergence of new network applications have made it necessary to develop techniques that can monitor and control the traffic. Efficient and accurate recognition of traffic is the key to the management in real time. This thesis work accomplishes the performance evaluation and optimization of a traffic recognition tool called Traffic Analyzer Module (TAM) which implements a technique that is based on passively observing and identifying signature patterns of the packet payload at the application layer, says signature-based payload recognition. This technique has two highlighted features. Firstly, in contrast to most of previous works which perform classification with offline trace files; this technique applies in online mode which can identify the traffic in real time. Secondly, instead of packet inspection, this technique adopts flow inspection, i.e. identifying traffic in terms of flows each of which consists of the well-known 5-tuple, which canproduce more accurate and reliable results.
To demonstrate this technique, its throughput is evaluated in online mode within a high bandwidth network. Besides throughput measurement, optimizing the recognition algorithm in order to improve its performance is also a task of this thesis work. The results of performance measurement demonstrate the feasibility and reliability of this technique, as well as indicate some clues for future work.
Morrill, Jeffrey P., et Jonathan Delatizky. « REAL-TIME RECOGNITION OF TIME-SERIES PATTERNS ». International Foundation for Telemetering, 1993. http://hdl.handle.net/10150/608854.
Texte intégralThis paper describes a real-time implementation of the pattern recognition technology originally developed by BBN [Delatizky et al] for post-processing of time-sampled telemetry data. This makes it possible to monitor a data stream for a characteristic shape, such as an arrhythmic heartbeat or a step-response whose overshoot is unacceptably large. Once programmed to recognize patterns of interest, it generates a symbolic description of a time-series signal in intuitive, object-oriented terms. The basic technique is to decompose the signal into a hierarchy of simpler components using rules of grammar, analogous to the process of decomposing a sentence into phrases and words. This paper describes the basic technique used for pattern recognition of time-series signals and the problems that must be solved to apply the techniques in real time. We present experimental results for an unoptimized prototype demonstrating that 4000 samples per second can be handled easily on conventional hardware.
Zhu, Jian Ke. « Real-time face recognition system ». Thesis, University of Macau, 2005. http://umaclib3.umac.mo/record=b1636556.
Texte intégralAmplianitis, Konstantinos. « 3D real time object recognition ». Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät, 2017. http://dx.doi.org/10.18452/17717.
Texte intégralObject recognition is a natural process of the human brain performed in the visual cor- tex and relies on a binocular depth perception system that renders a three-dimensional representation of the objects in a scene. Hitherto, computer and software systems are been used to simulate the perception of three-dimensional environments with the aid of sensors to capture real-time images. In the process, such images are used as input data for further analysis and development of algorithms, an essential ingredient for simulating the complexity of human vision, so as to achieve scene interpretation for object recognition, similar to the way the human brain perceives it. The rapid pace of technological advancements in hardware and software, are continuously bringing the machine-based process for object recognition nearer to the inhuman vision prototype. The key in this eld, is the development of algorithms in order to achieve robust scene interpretation. A lot of recognisable and signi cant e ort has been successfully carried out over the years in 2D object recognition, as opposed to 3D. It is therefore, within this context and scope of this dissertation, to contribute towards the enhancement of 3D object recognition; a better interpretation and understanding of reality and the relationship between objects in a scene. Through the use and application of low-cost commodity sensors, such as Microsoft Kinect, RGB and depth data of a scene have been retrieved and manipulated in order to generate human-like visual perception data. The goal herein is to show how RGB and depth information can be utilised in order to develop a new class of 3D object recognition algorithms, analogous to the perception processed by the human brain.
David, Afshin. « Real-time methods for face recognition ». Thesis, University of Ottawa (Canada), 1996. http://hdl.handle.net/10393/9664.
Texte intégralLiaqat, Ahmad Gull. « Mobile Real-Time License Plate Recognition ». Thesis, Linnéuniversitetet, Institutionen för datavetenskap, fysik och matematik, DFM, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-15944.
Texte intégralPapageorgiu, Dimitrios. « Cursive script recognition in real time ». Thesis, University of Sussex, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.317243.
Texte intégralOrd, Leslie B. « Real-time stereo image matching for a real time photogrammetry system ». Thesis, University of Aberdeen, 1997. http://digitool.abdn.ac.uk/R?func=search-advanced-go&find_code1=WSN&request1=AAIU603183.
Texte intégralPettersson, Johan. « Real-time Object Recognition on a GPU ». Thesis, Linköping University, Department of Electrical Engineering, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-10238.
Texte intégralShape-Based matching (SBM) is a known method for 2D object recognition that is rather robust against illumination variations, noise, clutter and partial occlusion.
The objects to be recognized can be translated, rotated and scaled.
The translation of an object is determined by evaluating a similarity measure for all possible positions (similar to cross correlation).
The similarity measure is based on dot products between normalized gradient directions in edges.
Rotation and scale is determined by evaluating all possible combinations, spanning a huge search space.
A resolution pyramid is used to form a heuristic for the search that then gains real-time performance.
For SBM, a model consisting of normalized edge gradient directions, are constructed for all possible combinations of rotation and scale.
We have avoided this by using (bilinear) interpolation in the search gradient map, which greatly reduces the amount of storage required.
SBM is highly parallelizable by nature and with our suggested improvements it becomes much suited for running on a GPU.
This have been implemented and tested, and the results clearly outperform those of our reference CPU implementation (with magnitudes of hundreds).
It is also very scalable and easily benefits from future devices without effort.
An extensive evaluation material and tools for evaluating object recognition algorithms have been developed and the implementation is evaluated and compared to two commercial 2D object recognition solutions.
The results show that the method is very powerful when dealing with the distortions listed above and competes well with its opponents.
Khan, Taha. « Real-Time Recognition System for Traffic Signs ». Thesis, Högskolan Dalarna, Datateknik, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:du-3486.
Texte intégralLivres sur le sujet "REAL TIME RECOGNITION"
Ulrich, Markus. Hierarachical real-time recognition of compound objects in images. Munchen : Verlag der Bayerischen Akademie der Wissenschaften in Kommission beim Verlags C.H. Beck, 2003.
Trouver le texte intégralHerout, Adam. Real-Time Detection of Lines and Grids : By PClines and Other Approaches. London : Springer London, 2013.
Trouver le texte intégralJain, Anil K. Real-Time Object Measurement and Classification. Berlin, Heidelberg : Springer Berlin Heidelberg, 1988.
Trouver le texte intégralBahram, Javidi, Society of Photo-optical Instrumentation Engineers., Engineering Society of Detroit et New Mexico State University. Applied Optics Laboratory., dir. Real-time signal processing for industrial applications, 27-28 June, 1988, Dearborn, Michigan. Bellingham, Wash., USA : SPIE Optical Engineering Press, 1989.
Trouver le texte intégralInternational Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems (2001 Vancouver, B.C.). Recognition, analysis, and tracking of faces and gestures in real-time systems : Proceedings : IEEE ICCV Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems : 13 July, 2001, Vancouver, B.C., Canada. Los Alamitos, California : IEEE Computer Society, 2001.
Trouver le texte intégralColloquium on High Performance Architectures for Real-Time Image Processing (1998 London, England). Colloquium on High Performance Architectures for Real-Time Image Processing : Savoy Place, London, Thursday, 12 February 1998. [London] : IEE, 1998.
Trouver le texte intégralInternational, Workshop on Recognition Analysis and Tracking of Faces and Gestures in Real-Time Systems (1999 Kerkyra Greece). International Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems : September 26-27, 1999, Corfu, Greece. Los Alamitos, Calif : IEEE Computer Society, 1999.
Trouver le texte intégralShweta, Dour. Real Time Recognition of Indian Sign Language. Blurb, Incorporated, 2022.
Trouver le texte intégralHerout, Adam, Markéta Dubská et Jirí Havel. Real-Time Detection of Lines and Grids. Springer, 2012.
Trouver le texte intégralAdvanced Real-Time Manipulation of Video Streams. Springer Vieweg, 2014.
Trouver le texte intégralChapitres de livres sur le sujet "REAL TIME RECOGNITION"
Camastra, Francesco, et Alessandro Vinciarelli. « Real-Time Hand Pose Recognition ». Dans Advanced Information and Knowledge Processing, 467–84. London : Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-6735-8_15.
Texte intégralSrivastava, Saumil. « Real Time Facial Expression Recognition ». Dans Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 124–33. Berlin, Heidelberg : Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27317-9_13.
Texte intégralBombonato, Leonardo, Guillermo Camara-Chavez et Pedro Silva. « Real-Time Brand Logo Recognition ». Dans Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 111–18. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75193-1_14.
Texte intégralAshwini, Vijay Balaji, Srivarshini Srinivasan et Kavya Monisha. « Real Time Facial Recognition System ». Dans New Trends in Computational Vision and Bio-inspired Computing, 1721–26. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41862-5_175.
Texte intégralZhou, Xian, You-Ji Feng et Xi Zhou. « Real-Time Object Detection Using Efficient Convolutional Networks ». Dans Biometric Recognition, 633–41. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69923-3_68.
Texte intégralCarlsohn, Matthias F. « Near Real-Time Pattern Recognition in a Special Purpose Computer with Parallel Architecture ». Dans Real Time Computing, 676–77. Berlin, Heidelberg : Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-88049-0_100.
Texte intégralFanello, Sean Ryan, Ilaria Gori, Giorgio Metta et Francesca Odone. « Keep It Simple and Sparse : Real-Time Action Recognition ». Dans Gesture Recognition, 303–28. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57021-1_10.
Texte intégralTang, Yunqi, Zhenan Sun et Tieniu Tan. « Real-Time Head Pose Estimation Using Random Regression Forests ». Dans Biometric Recognition, 66–73. Berlin, Heidelberg : Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25449-9_9.
Texte intégralChakraborty, Bhaskar, Andrew D. Bagdanov et Jordi Gonzàlez. « Towards Real-Time Human Action Recognition ». Dans Pattern Recognition and Image Analysis, 425–32. Berlin, Heidelberg : Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02172-5_55.
Texte intégralZhang, Yongliang, Shanshan Fang, Yingjie Bian et Yuanhong Li. « Real-Time Rolled Fingerprint Construction Based on Key-Column Extraction ». Dans Biometric Recognition, 201–7. Cham : Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02961-0_25.
Texte intégralActes de conférences sur le sujet "REAL TIME RECOGNITION"
Houghton, A., N. L. Seed et R. W. M. Smith. « Real Time Vehicle Recognition ». Dans 1988 Los Angeles Symposium--O-E/LASE '88, sous la direction de Gary W. Hughes, Patrick E. Mantey et Bernice E. Rogowitz. SPIE, 1988. http://dx.doi.org/10.1117/12.944706.
Texte intégralAnwar, Suzan, Mariofanna Milanova, Andrea Bigazzi, Leonardo Bocchi et Andrea Guazzini. « Real time intention recognition ». Dans IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2016. http://dx.doi.org/10.1109/iecon.2016.7794016.
Texte intégralImaoka, Hitoshi, Yusuke Morishita et Akihiro Hayasaka. « Real-time face recognition demonstration ». Dans Gesture Recognition (FG 2011). IEEE, 2011. http://dx.doi.org/10.1109/fg.2011.5771422.
Texte intégralChen, Chih-Wei, Chen Wu et Hamid Aghajan. « Real-time social interaction analysis ». Dans Gesture Recognition (FG 2011). IEEE, 2011. http://dx.doi.org/10.1109/fg.2011.5771326.
Texte intégralLagerwall, B., et S. Viriri. « Robust real-time face recognition ». Dans the South African Institute for Computer Scientists and Information Technologists Conference. New York, New York, USA : ACM Press, 2013. http://dx.doi.org/10.1145/2513456.2513494.
Texte intégralMurveit, Hy. « Real-time speech recognition systems ». Dans the workshop. Morristown, NJ, USA : Association for Computational Linguistics, 1989. http://dx.doi.org/10.3115/1075434.1075521.
Texte intégralWeintraub, Mitchel. « Real-time speech recognition system ». Dans the workshop. Morristown, NJ, USA : Association for Computational Linguistics, 1992. http://dx.doi.org/10.3115/1075527.1075665.
Texte intégral« Robust real-time face recognition ». Dans AFRICON 2013. IEEE, 2013. http://dx.doi.org/10.1109/afrcon.2013.6757719.
Texte intégralMittal, Shobhit, Shubham Agarwal et Madhav J. Nigam. « Real Time Multiple Face Recognition ». Dans the 2018 International Conference. New York, New York, USA : ACM Press, 2018. http://dx.doi.org/10.1145/3299852.3299853.
Texte intégralMurveit, Hy, et Mitchel Weintraub. « Real-time speech recognition system ». Dans the workshop. Morristown, NJ, USA : Association for Computational Linguistics, 1991. http://dx.doi.org/10.3115/112405.1138661.
Texte intégralRapports d'organisations sur le sujet "REAL TIME RECOGNITION"
Schwartz, Richard, et Owen Kimball. Toward Real-Time Continuous Speech Recognition. Fort Belvoir, VA : Defense Technical Information Center, mars 1989. http://dx.doi.org/10.21236/ada208196.
Texte intégralSklansky, Jack. Real-Time Recognition of Multiple Targets. Fort Belvoir, VA : Defense Technical Information Center, avril 1991. http://dx.doi.org/10.21236/ada238364.
Texte intégralMeteer, Marie, Christopher Barclay et Sean Colbath. Real Time Continuous Speech Recognition for C3I Applications. Fort Belvoir, VA : Defense Technical Information Center, janvier 2001. http://dx.doi.org/10.21236/ada387178.
Texte intégralNguyen, Hoa G., Paul J. Heckman, Pai Jr. et A. L. Real-Time Pattern Recognition for Guidance of an Autonomous Undersea Submersible. Fort Belvoir, VA : Defense Technical Information Center, janvier 1988. http://dx.doi.org/10.21236/ada422544.
Texte intégralNguyen, Long, Richard Schwartz, Francis Kubala et Paul Placeway. Search Algorithms for Software-Only Real-Time Recognition with Very Large Vocabularies. Fort Belvoir, VA : Defense Technical Information Center, janvier 1993. http://dx.doi.org/10.21236/ada457473.
Texte intégralDavid, Philip, Philip Emmerman et Sean Ho. Design and Analysis of a Parallel, Real-Time, Automatic Target Recognition Algorithm. Fort Belvoir, VA : Defense Technical Information Center, septembre 1996. http://dx.doi.org/10.21236/ada315570.
Texte intégralMurveit, Hy, Peter Monaco, Vassilios Digalakis et John Butzberger. Techniques to Achieve an Accurate Real-Time Large-Vocabulary Speech Recognition System. Fort Belvoir, VA : Defense Technical Information Center, janvier 1994. http://dx.doi.org/10.21236/ada460505.
Texte intégralDugan, Peter J., Christopher W. Clark, Yann A. LeCun et Sofie M. Van Parijs. DCL System Research Using Advanced Approaches for Land-based or Ship-based Real-Time Recognition and Localization of Marine Mammals. Fort Belvoir, VA : Defense Technical Information Center, septembre 2012. http://dx.doi.org/10.21236/ada572279.
Texte intégralDugan, Peter J., Christopher W. Clark, Yann A. LeCun et Sofie M. Van Parijs. DCL System Using Deep Learning Approaches for Land-based or Ship-based Real-Time Recognition and Localization of Marine Mammals. Fort Belvoir, VA : Defense Technical Information Center, septembre 2012. http://dx.doi.org/10.21236/ada573473.
Texte intégralDugan, Peter J., Christopher W. Clark, Yann A. LeCun et Sofie M. Van Parijs. DCL System Using Deep Learning Approaches for Land-based or Ship-based Real-Time Recognition and Localization of Marine Mammals. Fort Belvoir, VA : Defense Technical Information Center, septembre 2014. http://dx.doi.org/10.21236/ada617980.
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