Готові списки джерел за темами / REAL TIME RECOGNITION

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Добірка наукової літератури з теми "REAL TIME RECOGNITION"

Автор: Grafiati
Опубліковано: 11 вересня 2023

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Статті в журналах з теми "REAL TIME RECOGNITION"

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Anam, Sarawat, Shohidul Islam, M. A. Kashem, and 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.

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Billings, Rachel M., and Alan J. Michaels. "Real-Time Mask Recognition." IoT 2, no. 4 (November 8, 2021): 688–716. http://dx.doi.org/10.3390/iot2040035.

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While a variety of image processing studies have been performed to quantify the potential performance of neural network-based models using high-quality still images, relatively few studies seek to apply those models to a real-time operational context. This paper seeks to extend prior work in neural-network-based mask detection algorithms to a real-time, low-power deployable context that is conducive to immediate installation and use. Particularly relevant in the COVID-19 era with varying rules on mask mandates, this work applies two neural network models to inference of mask detection in both live (mobile) and recorded scenarios. Furthermore, an experimental dataset was collected where individuals were encouraged to use presentation attacks against the algorithm to quantify how perturbations negatively impact model performance. The results from evaluation on the experimental dataset are further investigated to identify the degradation caused by poor lighting and image quality, as well as to test for biases within certain demographics such as gender and ethnicity. In aggregate, this work validates the immediate feasibility of a low-power and low-cost real-time mask recognition system.
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CRL Smetic Technology. "Real-time image recognition." NDT & E International 27, no. 1 (February 1994): 59–60. http://dx.doi.org/10.1016/0963-8695(94)90119-8.

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Patil, Anuradha, Chandrashekhar M. Tavade, and . "Methods on Real Time Gesture Recognition System." International Journal of Engineering & Technology 7, no. 3.12 (July 20, 2018): 982. http://dx.doi.org/10.14419/ijet.v7i3.12.17617.

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Gesture recognition deals with discussion of various methods, techniques and concerned algorithms related to it. Gesture recognition uses a simple & basic sign languages like movement of hand, position of lips & eye ball as well as eye lids positions. The various methods for image capturing, gesture recognition, gesture tracking, gesture segmentation and smoothing methods compared, and by the overweighing advantage of different gesture recognitions and their applications. In recent days gesture recognition is widely utilized in gaming industries, biomedical applications, and medical diagnostics for dumb and deaf people. Due to their wide applications, high efficiency, high accuracy and low expenditure gestures are using in many applications including robotics. By using gestures to develop human computer interaction (HCI) method it is necessary to identify the proper and meaning full gesture from different gesture images. The Gesture recognition avoids use of costly hardware devices for understanding the activities and recognition example lots of I/O devices like keyboard mouse etc. Can be Limited.
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Y.Mohammed, Zaid, and Abdul Sattar M. Khidhir. "Real-Time Arabic Speech Recognition." International Journal of Computer Applications 81, no. 4 (November 15, 2013): 43–45. http://dx.doi.org/10.5120/14003-2048.

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Cetiner, Halit, Bayram Cetisli, and 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.

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Albukhary, N., and Y. M. Mustafah. "Real-time Human Activity Recognition." IOP Conference Series: Materials Science and Engineering 260 (November 2017): 012017. http://dx.doi.org/10.1088/1757-899x/260/1/012017.

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MARIN, Florin-Bogdan, Gheorghe GURĂU, and Mihaela MARIN. "Real-Time Assembly Operation Recognition." Annals of “Dunarea de Jos” University of Galati. Fascicle IX, Metallurgy and Materials Science 45, no. 4 (December 15, 2022): 92–95. http://dx.doi.org/10.35219/mms.2022.4.15.

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This research is concerned to propose a computer vision algorithm to track manual assembly task. Manual assembly in case of electronics parts are used largely in automotive industry. The phases tracking of assembly could also be used for learning purposes such in case showed in this research, checking the assembly of an electronic educational board. The algorithms used for detection of different components are CNN (Convolutional Neuronal Network) as well as blob detection.
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Wu, Yi-Chang, Yao-Cheng Liu, and Ru-Yi Huang. "Real-time microreaction recognition system." IAES International Journal of Robotics and Automation (IJRA) 12, no. 2 (June 1, 2023): 157. http://dx.doi.org/10.11591/ijra.v12i2.pp157-166.

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<span lang="EN-US">This study constructed a real-time microreaction recognition system that can give real-time assistance to investigators. Test results indicated that the number of frames per second (30 or 190); angle of the camera, namely the front view of the interviewee or left (+45°) or right (−45°) view; and image resolution (480 or 680 p) did not have major effects on the system’s recognition ability. However, when the camera was placed at a distance of 300 cm, recognition did not always succeed. Value changes were larger when the camera was placed at an elevation 45° than when it was placed directly in front of the person being interrogated. Within a specific distance, the recognition results of the proposed real-time microreaction recognition system concurred with the six reaction case videos. In practice, only the distance and height of the camera must be adjusted in the real-time microreaction recognition system.</span>
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Shah, Dr Dipti M., and Parul D. Sindha. "Color detection in real time traffic sign detection and recognition system." Indian Journal of Applied Research 3, no. 7 (October 1, 2011): 152–53. http://dx.doi.org/10.15373/2249555x/july2013/43.

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Дисертації з теми "REAL TIME RECOGNITION"

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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.

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The 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.

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Morrill, Jeffrey P., and Jonathan Delatizky. "REAL-TIME RECOGNITION OF TIME-SERIES PATTERNS." International Foundation for Telemetering, 1993. http://hdl.handle.net/10150/608854.

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International Telemetering Conference Proceedings / October 25-28, 1993 / Riviera Hotel and Convention Center, Las Vegas, Nevada
This 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.
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Zhu, Jian Ke. "Real-time face recognition system." Thesis, University of Macau, 2005. http://umaclib3.umac.mo/record=b1636556.

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Amplianitis, Konstantinos. "3D real time object recognition." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät, 2017. http://dx.doi.org/10.18452/17717.

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Die Objekterkennung ist ein natürlicher Prozess im Menschlichen Gehirn. Sie ndet im visuellen Kortex statt und nutzt die binokulare Eigenschaft der Augen, die eine drei- dimensionale Interpretation von Objekten in einer Szene erlaubt. Kameras ahmen das menschliche Auge nach. Bilder von zwei Kameras, in einem Stereokamerasystem, werden von Algorithmen für eine automatische, dreidimensionale Interpretation von Objekten in einer Szene benutzt. Die Entwicklung von Hard- und Software verbessern den maschinellen Prozess der Objek- terkennung und erreicht qualitativ immer mehr die Fähigkeiten des menschlichen Gehirns. Das Hauptziel dieses Forschungsfeldes ist die Entwicklung von robusten Algorithmen für die Szeneninterpretation. Sehr viel Aufwand wurde in den letzten Jahren in der zweidimen- sionale Objekterkennung betrieben, im Gegensatz zur Forschung zur dreidimensionalen Erkennung. Im Rahmen dieser Arbeit soll demnach die dreidimensionale Objekterkennung weiterent- wickelt werden: hin zu einer besseren Interpretation und einem besseren Verstehen von sichtbarer Realität wie auch der Beziehung zwischen Objekten in einer Szene. In den letzten Jahren aufkommende low-cost Verbrauchersensoren, wie die Microsoft Kinect, generieren Farb- und Tiefendaten einer Szene, um menschenähnliche visuelle Daten zu generieren. Das Ziel hier ist zu zeigen, wie diese Daten benutzt werden können, um eine neue Klasse von dreidimensionalen Objekterkennungsalgorithmen zu entwickeln - analog zur Verarbeitung im menschlichen Gehirn.
Object 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.
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David, Afshin. "Real-time methods for face recognition." Thesis, University of Ottawa (Canada), 1996. http://hdl.handle.net/10393/9664.

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Identification of individuals on the basis of facial features is the most natural method of distinguishing one individual from another. Automating such a process, based upon quantifiable measures, is of great interest in a variety of applications, such as passport identification and automatic teller machine verification. The most crucial aspect of such applications is their tolerance with respect to variations in facial expressions and the noise introduced by the operating environment. In this thesis, various face recognition methods are evaluated under conditions of real-time response, varying operating factors, and implementation feasibility. The approaches are based on histogram mapping, wavelet transform, Karhunen and Loeve transform, and optical correlation techniques. A brief review of the basic concepts in optics is first presented. This is followed by a detailed review of optical methods in pattern recognition. A comprehensive background of algorithmic approaches for face recognition is described. A detailed analysis of the photobook system, which is based on the Karhunen and Loeve transform (KLT), is presented. It is argued that, even though the KLT possesses many useful attributes in image processing applications, the performance of KLT face recognition systems is based entirely upon the initial training set. A method for choosing the proper training set is presented. Novel statistical methods that exploit the stationary behaviour of the operating environment are introduced. It is shown that under the condition that control may be exercised on the operating environment, these methods provide a satisfactory result in real-time. The application of histogram, moment, and 2-D discrete wavelet transforms in statistical methods is described. A novel optical correlation based system is presented. It is shown that such a system tolerates changes in facial expressions and can operate under real time constraints.
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Liaqat, 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.

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License plate recognition (LPR) system plays an important role in numerous applications, such as parking accounting systems, traffic law enforcement, road monitoring, expressway toll system, electronic-police system, and security systems. In recent years, there has been a lot of research in license plate recognition, and many recognition systems have been proposed and used. But these systems have been developed for computers. In this project, we developed a mobile LPR system for Android Operating System (OS). LPR involves three main components: license plate detection, character segmentation and Optical Character Recognition (OCR). For License Plate Detection and character segmentation, we used JavaCV and OpenCV libraries. And for OCR, we used tesseract-ocr. We obtained very good results by using these libraries. We also stored records of license numbers in database and for that purpose SQLite has been used.
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Papageorgiu, Dimitrios. "Cursive script recognition in real time." Thesis, University of Sussex, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.317243.

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Ord, 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.

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With the development of powerful, relatively low cost, digital image processing hardware capable of handling multiple image streams, it has become possible to implement affordable digital photogrammetry systems based on this technology. In addition, high speed versions of this hardware have the ability to manipulate these image streams in 'realtime', enabling the photogrammetry systems developed to expand their functionality from the off-line surveying of conventional photogrammetry to more time-critical domains such as object tracking and control systems. One major hurdle facing these 'real-time' photogrammetry systems is the need to extract the corresponding points from the multiple input images in order that they may be processed and measurements obtained. Even a highly skilled operator is not capable of manually processing the images in such a time that the speed of operation of the system would not be severely compromised. Thus an automatic system of matching these points is required. The use of automated point matching in the field of photogrammetry has been extensively investigated in the past. The objective has, however, been primarily to reduce the need for trained operators employed in the extraction of data from conventional photogrammetric studies and in the automation of data extraction from large data sets. The work presented here attempts to adapt these methods to the more time dominated problem of 'real-time' image matching.
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Pettersson, 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.

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Shape-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.

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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.

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The aim of this thesis project is to develop the Traffic Sign Recognition algorithm for real time. Inreal time environment, vehicles move at high speed on roads. For the vehicle intelligent system itbecomes essential to detect, process and recognize the traffic sign which is coming in front ofvehicle with high relative velocity, at the right time, so that the driver would be able to pro-actsimultaneously on instructions given in the Traffic Sign. The system assists drivers about trafficsigns they did not recognize before passing them. With the Traffic Sign Recognition system, thevehicle becomes aware of the traffic environment and reacts according to the situation.The objective of the project is to develop a system which can recognize the traffic signs in real time.The three target parameters are the system’s response time in real-time video streaming, the trafficsign recognition speed in still images and the recognition accuracy. The system consists of threeprocesses; the traffic sign detection, the traffic sign recognition and the traffic sign tracking. Thedetection process uses physical properties of traffic signs based on a priori knowledge to detect roadsigns. It generates the road sign image as the input to the recognition process. The recognitionprocess is implemented using the Pattern Matching algorithm. The system was first tested onstationary images where it showed on average 97% accuracy with the average processing time of0.15 seconds for traffic sign recognition. This procedure was then applied to the real time videostreaming. Finally the tracking of traffic signs was developed using Blob tracking which showed theaverage recognition accuracy to 95% in real time and improved the system’s average response timeto 0.04 seconds. This project has been implemented in C-language using the Open Computer VisionLibrary.
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Книги з теми "REAL TIME RECOGNITION"

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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.

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Herout, Adam. Real-Time Detection of Lines and Grids: By PClines and Other Approaches. London: Springer London, 2013.

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3

Jain, Anil K. Real-Time Object Measurement and Classification. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988.

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Bahram, Javidi, Society of Photo-optical Instrumentation Engineers., Engineering Society of Detroit, and New Mexico State University. Applied Optics Laboratory., eds. Real-time signal processing for industrial applications, 27-28 June, 1988, Dearborn, Michigan. Bellingham, Wash., USA: SPIE Optical Engineering Press, 1989.

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International 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.

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Colloquium 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.

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International, 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.

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Shweta, Dour. Real Time Recognition of Indian Sign Language. Blurb, Incorporated, 2022.

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Herout, Adam, Markéta Dubská, and Jirí Havel. Real-Time Detection of Lines and Grids. Springer, 2012.

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Advanced Real-Time Manipulation of Video Streams. Springer Vieweg, 2014.

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Частини книг з теми "REAL TIME RECOGNITION"

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Camastra, Francesco, and Alessandro Vinciarelli. "Real-Time Hand Pose Recognition." In Advanced Information and Knowledge Processing, 467–84. London: Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-6735-8_15.

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Srivastava, Saumil. "Real Time Facial Expression Recognition." In 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.

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Bombonato, Leonardo, Guillermo Camara-Chavez, and Pedro Silva. "Real-Time Brand Logo Recognition." In 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.

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Ashwini, Vijay Balaji, Srivarshini Srinivasan, and Kavya Monisha. "Real Time Facial Recognition System." In 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.

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Zhou, Xian, You-Ji Feng, and Xi Zhou. "Real-Time Object Detection Using Efficient Convolutional Networks." In Biometric Recognition, 633–41. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69923-3_68.

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Carlsohn, Matthias F. "Near Real-Time Pattern Recognition in a Special Purpose Computer with Parallel Architecture." In Real Time Computing, 676–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-88049-0_100.

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Fanello, Sean Ryan, Ilaria Gori, Giorgio Metta, and Francesca Odone. "Keep It Simple and Sparse: Real-Time Action Recognition." In Gesture Recognition, 303–28. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57021-1_10.

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Tang, Yunqi, Zhenan Sun, and Tieniu Tan. "Real-Time Head Pose Estimation Using Random Regression Forests." In Biometric Recognition, 66–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25449-9_9.

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Chakraborty, Bhaskar, Andrew D. Bagdanov, and Jordi Gonzàlez. "Towards Real-Time Human Action Recognition." In 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.

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Zhang, Yongliang, Shanshan Fang, Yingjie Bian, and Yuanhong Li. "Real-Time Rolled Fingerprint Construction Based on Key-Column Extraction." In Biometric Recognition, 201–7. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02961-0_25.

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Тези доповідей конференцій з теми "REAL TIME RECOGNITION"

1

Houghton, A., N. L. Seed, and R. W. M. Smith. "Real Time Vehicle Recognition." In 1988 Los Angeles Symposium--O-E/LASE '88, edited by Gary W. Hughes, Patrick E. Mantey, and Bernice E. Rogowitz. SPIE, 1988. http://dx.doi.org/10.1117/12.944706.

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2

Anwar, Suzan, Mariofanna Milanova, Andrea Bigazzi, Leonardo Bocchi, and Andrea Guazzini. "Real time intention recognition." In IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2016. http://dx.doi.org/10.1109/iecon.2016.7794016.

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3

Imaoka, Hitoshi, Yusuke Morishita, and Akihiro Hayasaka. "Real-time face recognition demonstration." In Gesture Recognition (FG 2011). IEEE, 2011. http://dx.doi.org/10.1109/fg.2011.5771422.

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4

Chen, Chih-Wei, Chen Wu, and Hamid Aghajan. "Real-time social interaction analysis." In Gesture Recognition (FG 2011). IEEE, 2011. http://dx.doi.org/10.1109/fg.2011.5771326.

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5

Lagerwall, B., and S. Viriri. "Robust real-time face recognition." In 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.

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6

Murveit, Hy. "Real-time speech recognition systems." In the workshop. Morristown, NJ, USA: Association for Computational Linguistics, 1989. http://dx.doi.org/10.3115/1075434.1075521.

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Weintraub, Mitchel. "Real-time speech recognition system." In the workshop. Morristown, NJ, USA: Association for Computational Linguistics, 1992. http://dx.doi.org/10.3115/1075527.1075665.

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8

"Robust real-time face recognition." In AFRICON 2013. IEEE, 2013. http://dx.doi.org/10.1109/afrcon.2013.6757719.

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9

Mittal, Shobhit, Shubham Agarwal, and Madhav J. Nigam. "Real Time Multiple Face Recognition." In the 2018 International Conference. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3299852.3299853.

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Murveit, Hy, and Mitchel Weintraub. "Real-time speech recognition system." In the workshop. Morristown, NJ, USA: Association for Computational Linguistics, 1991. http://dx.doi.org/10.3115/112405.1138661.

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Звіти організацій з теми "REAL TIME RECOGNITION"

1

Schwartz, Richard, and Owen Kimball. Toward Real-Time Continuous Speech Recognition. Fort Belvoir, VA: Defense Technical Information Center, March 1989. http://dx.doi.org/10.21236/ada208196.

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2

Sklansky, Jack. Real-Time Recognition of Multiple Targets. Fort Belvoir, VA: Defense Technical Information Center, April 1991. http://dx.doi.org/10.21236/ada238364.

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3

Meteer, Marie, Christopher Barclay, and Sean Colbath. Real Time Continuous Speech Recognition for C3I Applications. Fort Belvoir, VA: Defense Technical Information Center, January 2001. http://dx.doi.org/10.21236/ada387178.

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4

Nguyen, Hoa G., Paul J. Heckman, Pai Jr., and A. L. Real-Time Pattern Recognition for Guidance of an Autonomous Undersea Submersible. Fort Belvoir, VA: Defense Technical Information Center, January 1988. http://dx.doi.org/10.21236/ada422544.

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5

Nguyen, Long, Richard Schwartz, Francis Kubala, and Paul Placeway. Search Algorithms for Software-Only Real-Time Recognition with Very Large Vocabularies. Fort Belvoir, VA: Defense Technical Information Center, January 1993. http://dx.doi.org/10.21236/ada457473.

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David, Philip, Philip Emmerman, and Sean Ho. Design and Analysis of a Parallel, Real-Time, Automatic Target Recognition Algorithm. Fort Belvoir, VA: Defense Technical Information Center, September 1996. http://dx.doi.org/10.21236/ada315570.

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7

Murveit, Hy, Peter Monaco, Vassilios Digalakis, and John Butzberger. Techniques to Achieve an Accurate Real-Time Large-Vocabulary Speech Recognition System. Fort Belvoir, VA: Defense Technical Information Center, January 1994. http://dx.doi.org/10.21236/ada460505.

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Dugan, Peter J., Christopher W. Clark, Yann A. LeCun, and 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, September 2012. http://dx.doi.org/10.21236/ada572279.

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Dugan, Peter J., Christopher W. Clark, Yann A. LeCun, and 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, September 2012. http://dx.doi.org/10.21236/ada573473.

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Dugan, Peter J., Christopher W. Clark, Yann A. LeCun, and 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, September 2014. http://dx.doi.org/10.21236/ada617980.

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