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Добірка наукової літератури з теми "RECOGNITION APPLICATIONS"

Автор: Grafiati

Опубліковано: 11 вересня 2023

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Статті в журналах з теми "RECOGNITION APPLICATIONS"

Begalinova, A., and A. Shintemirov. "EMBEDDED GESTURE RECOGNITION SYSTEM FOR ROBOTIC APPLICATIONS." Eurasian Journal of Mathematical and Computer Applications 2, no. 1 (2014): 81–89. http://dx.doi.org/10.32523/2306-3172-2014-2-4-81-89.

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Hamzh, Al Rubaie Evan Madhi. "Text Recognition Applications." IJARCCE 5, no. 10 (October 30, 2016): 603–7. http://dx.doi.org/10.17148/ijarcce.2016.510122.

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Templeton, Douglas, and Michael Schwenk. "Immunochemical Recognition and Applications." Pure and Applied Chemistry 86, no. 10 (October 21, 2014): 1433–34. http://dx.doi.org/10.1515/pac-2014-5053.

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Singh, Nilu, R. A. Khan, and Raj Shree. "Applications of Speaker Recognition." Procedia Engineering 38 (2012): 3122–26. http://dx.doi.org/10.1016/j.proeng.2012.06.363.

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L. Almeida, Leandro, Maria S.V. Paiva, Francisco A. Silva, and Almir O. Artero. "Super-Resolution Images Enhanced for Applications to Character Recognition." SIJ Transactions on Computer Science Engineering & its Applications (CSEA) 01, no. 03 (August 16, 2013): 09–16. http://dx.doi.org/10.9756/sijcsea/v1i3/0103520101.

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Busschaert, Nathalie, Claudia Caltagirone, Wim Van Rossom, and Philip A. Gale. "Applications of Supramolecular Anion Recognition." Chemical Reviews 115, no. 15 (May 21, 2015): 8038–155. http://dx.doi.org/10.1021/acs.chemrev.5b00099.

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Schlenoff, Craig, Zeid Kootbally, Anthony Pietromartire, Marek Franaszek, and Sebti Foufou. "Intention recognition in manufacturing applications." Robotics and Computer-Integrated Manufacturing 33 (June 2015): 29–41. http://dx.doi.org/10.1016/j.rcim.2014.06.007.

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Rabiner, Lawrence R. "Speech recognition: Technology and applications." Journal of the Acoustical Society of America 88, S1 (November 1990): S197. http://dx.doi.org/10.1121/1.2028883.

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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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Анотація:

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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Arnav Madan. "Face Recognition using Haar Cascade Classifier." January 2021 7, no. 01 (January 29, 2021): 85–87. http://dx.doi.org/10.46501/ijmtst070119.

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With development of machine learning technology many applications have been revolutionized which earlier usedto utilize high amoun to fresources. Face recognition is a crucial security application. Though this paper we present this application using optimized amount of resources and high efficiency.

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Дисертації з теми "RECOGNITION APPLICATIONS"

Olausson, Erik. "Face Recognition for Mobile Phone Applications." Thesis, Linköping University, Department of Science and Technology, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-11850.

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Att applicera ansiktsigenkänning direkt på en mobiltelefon är en utmanande uppgift, inte minst med tanke på den begränsade minnes- och processorkapaciteten samt den stora variationen med avseende på ansiktsuttryck, hållning och ljusförhållande i inmatade bilder.

Det är fortfarande långt kvar till ett färdigutvecklat, robust och helautomatiskt ansiktsigenkänningssystem för den här miljön. Men resultaten i det här arbetet visar att genom att plocka ut feature-värden från lokala regioner samt applicera en välgjord warpstrategi för att minska problemen med variationer i position och rotation av huvudet, är det möjligt att uppnå rimliga och användbara igenkänningsnivåer. Speciellt för ett halvautomatiskt system där användaren har sista ordet om vem personen på bilden faktiskt är.

Med ett galleri bestående av 85 personer och endast en referensbild per person nådde systemet en igenkänningsgrad på 60% på en svårklassificerad serie testbilder. Totalt 73% av gångerna var den rätta individen inom de fyra främsta gissningarna.

Att lägga till extra referensbilder till galleriet höjer igenkänningsgraden rejält, till nästan 75% för helt korrekta gissningar och till 83,5% för topp fyra. Detta visar att en strategi där inmatade bilder läggs till som referensbilder i galleriet efterhand som de identifieras skulle löna sig ordentligt och göra systemet bättre efter hand likt en inlärningsprocess.

Detta exjobb belönades med pris för "Bästa industrirelevanta bidrag" vid Svenska sällskapet för automatiserad bildanalys årliga konferens i Lund, 13-14 mars 2008.

Applying face recognition directly on a mobile phone is a challenging proposal due to the unrestrained nature of input images and limitations in memory and processor capabilities.

A robust, fully automatic recognition system for this environment is still a far way off. However, results show that using local feature extraction and a warping scheme to reduce pose variation problems, it is possible to capitalize on high error tolerance and reach reasonable recognition rates, especially for a semi-automatic classification system where the user has the final say.

With a gallery of 85 individuals and only one gallery image per individual available the system is able to recognize close to 60 % of the faces in a very challenging test set, while the correct individual is in the top four guesses 73% of the time.

Adding extra reference images boosts performance to nearly 75% correct recognition and 83.5% in the top four guesses. This suggests a strategy where extra reference images are added one by one after correct classification, mimicking an online learning strategy.

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Thompson, J. R. "Applications of pattern recognition in medicine." Thesis, Open University, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.377939.

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Al-Rajab, Moaath. "Hand gesture recognition for multimedia applications." Thesis, University of Leeds, 2008. http://etheses.whiterose.ac.uk/607/.

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Hand gesture is potentially a very natural and useful modality for human-machine interaction. It is considered to be one of the most complicated and interesting challenges in computer vision due to its articulated structure and environmental variations. Solving such challenges requires robust hand detection, feature description, and viewpoint invariant classification. This thesis introduces several steps to tackle these challenges and applies them in a hand-gesture-based application (a game) to demonstrate the proposed approach. Techniques on new feature description, hand gesture detection and viewpoint invariant methods are explored and evaluated. A normal webcam is used in the research as input device. Hands are segmented using pre-trained skin colour models and tracked using the CAMShift tracker. Moment invariants are used as a shape descriptor. A new approach utilising the Zernike Velocity Moments (ZVMs, first introduced by Shutler and Nixon [1,2]), is examined on hand gestures. Results obtained using the ZVMs as spatial-temporal descriptor are compared to an HMM with Zemike moments (ZMs). Manually isolated hand gestures are used as input to the ZVM descriptor which generates vectors of features that are classified using a regression classifier. The performance of ZVM is evaluated using isolated, user-independent and user-dependent data. Isolating (segmenting) the gesture manually from a video stream for gesture recognition is a research proposition only and real life scenarios require an automatic hand gesture detection mechanism. Two methods for detecting gestures are examined. Firstly, hand gesture detection is performed using a sliding window which segments sequences of frames and then evaluates them against pre-trained HMMs. Secondly, the set of class-specific HMMs is combined into a single HMM and the Viterbi algorithm is then used to find the optimal sequence of gestures. Finally, the thesis proposes a flexible application that provides the user with options to perform the gesture from different viewpoints. A usable hand gesture recognition system should be able to cope with such viewpoint variations. To solve this problem, a new approach is introduced which makes use of 3D models of hand gestures (not postures) for generating projections. A virtual arm with 3D models of real hands is created. After that, virtual movements of the hand are simulated using animation software and projected from different viewpoints. Using a multi-Gaussian HMM, the system is trained on the projected sequences. Each set of hand gesture projections is marked with its specific class and used to train the single multi-class HMNI with gestures across different viewpoints.

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Muller, Neil Leonard. "Image recognition using the Eigenpicture Technique (with specific applications in face recognition and optical character recognition)." Master's thesis, University of Cape Town, 1998. http://hdl.handle.net/11427/14381.

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Includes bibliographical references.
In the first part of this dissertation, we present a detailed description of the eigenface technique first proposed by Sirovich and Kirby and subsequently developed by several groups, most notably the Media Lab at MIT. Other significant contributions have been made by Rockefeller University, whose ideas have culminated in a commercial system known as Faceit. For a different techniques (i.e. not eigenfaces) and a detailed comparison of some other techniques, the reader is referred to [5]. Although we followed ideas in the open literature (we believe there that there is a large body of advanced proprietary knowledge, which remains inaccessible), the implementation is our own. In addition, we believe that the method for updating the eigenfaces to deal with badly represented images presented in section 2. 7 is our own. The next stage in this section would be to develop an experimental system that can be extensively tested. At this point however, another, nonscientific difficulty arises, that of developing an adequately large data base. The basic problem is that one needs a training set representative of all faces to be encountered in future. Note that this does not mean that one can only deal with faces in the database, the whole idea is to be able to work with any facial image. However, a data base is only representative if it contains images similar to anything that can be encountered in future. For this reason a representative database may be very large and is not easy to build. In addition for testing purposes one needs multiple images of a large number of people, acquired over a period of time under different physical conditions representing the typical variations encountered in practice. Obviously this is a very slow process. Potentially the variation between the faces in the database can be large suggesting that the representation of all these different images in terms of eigenfaces may not be particularly efficient. One idea is to separate all the facial images into different, more or less homogeneous classes. Again this can only be done with access to a sufficiently large database, probably consisting of several thousand faces.

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A, Anila H. "Synthesis of fluorescent probes for specific recognition and imaging applications." Thesis(Ph.D.), CSIR-National Chemical Laboratory, Pune, 2018. http://dspace.ncl.res.in:8080/xmlui/handle/20.500.12252/4271.

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Brown, Georgina. "Considering accent recognition technology for forensic applications." Thesis, University of York, 2017. http://etheses.whiterose.ac.uk/20393/.

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Speaker recognition technology is becoming more available to forensic speech analysts to help to arrive at conclusions around how likely the speech in multiple recordings was produced by the same speaker. However, there is not currently a suitable technological tool that could assist with speaker profiling tasks (i.e. tasks where we wish to deduce information about an unknown speaker). Accent recognition technology could play a role in speaker profiling tasks. This thesis therefore presents numerous automatic accent recognition experiments that have been motivated by forensic applications. This thesis conducts a detailed examination of one automatic accent recognition system in particular, the York ACCDIST-based automatic accent recognition system (the Y-ACCDIST system). It is trained to assign an accent label to a speaker's speech sample. Unlike other accent recognition system architectures, Y-ACCDIST takes a segmental approach by forming models of speakers' accents using representations of individual phonemes. Implementing a segmentation phase comes at a practical cost, but it is expected that Y-ACCDIST's segmental approach captures a more detailed reflection of a speaker's accent than other accent recognition systems. When classifying speech samples into one of four categories, Y-ACCDIST achieved a recognition rate of 86.7% correct, while the best-performing text-independent system obtained 47.5%. This thesis also shows Y-ACCDIST's performance on spontaneous speech data. On a three-way classification task on Northern English accents, we witness a recognition rate of 86.7% correct. Additionally, we achieved 63.1% correct when classifying recordings into one of seven non-native English categories. The latter task is also a demonstration of Y-ACCDIST's capabilities on telephone data.

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Aleixo, Patrícia Nunes. "Object detection and recognition for robotic applications." Master's thesis, Universidade de Aveiro, 2014. http://hdl.handle.net/10773/13811.

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Mestrado em Engenharia Eletrónica e Telecomunicações
The computer vision assumes an important relevance in the development of robotic applications. In several applications, robots need to use vision to detect objects, a challenging and sometimes difficult task. This thesis is focused on the study and development of algorithms to be used in detection and identification of objects on digital images to be applied on robots that will be used in practice cases. Three problems are addressed: Detection and identification of decorative stones for textile industry; Detection of the ball in robotic soccer; Detection of objects in a service robot, that operates in a domestic environment. In each case, different methods are studied and applied, such as, Template Matching, Hough transform and visual descriptors (like SIFT and SURF). It was chosen the OpenCv library in order to use the data structures to image manipulation, as well as other structures for all information generated by the developed vision systems. Whenever possible, it was used the implementation of the described methods and have been developed new approaches, both in terms of pre-processing algorithms and in terms of modification of the source code in some used functions. Regarding the pre-processing algorithms, were used the Canny edge detector, contours detection, extraction of color information, among others. For the three problems, there are presented and discussed experimental results in order to evaluate the best method to apply in each case. The best method for each application is already integrated or in the process of integration in the described robots.
A visão por computador assume uma importante relevância no desenvolvimento de aplicações robóticas, na medida em que há robôs que precisam de usar a visão para detetar objetos, uma tarefa desafiadora e por vezes difícil. Esta dissertação foca-se no estudo e desenvolvimento de algoritmos para a deteção e identificação de objetos em imagem digital para aplicar em robôs que serão usados em casos práticos. São abordados três problemas: Deteção e identificação de pedras decorativas para a indústria têxtil; Deteção da bola em futebol robótico; Deteção de objetos num robô de serviço, que opera em ambiente doméstico. Para cada caso, diferentes métodos são estudados e aplicados, tais como, Template Matching, transformada de Hough e descritores visuais (como SIFT e SURF). Optou-se pela biblioteca OpenCv com vista a utilizar as suas estruturas de dados para manipulação de imagem, bem como as demais estruturas para toda a informação gerada pelos sistemas de visão desenvolvidos. Sempre que possivel utilizaram-se as implementações dos métodos descritos tendo sido desenvolvidas novas abordagens, quer em termos de algoritmos de preprocessamento quer em termos de alteração do código fonte das funções utilizadas. Como algoritmos de pre-processamento foram utilizados o detetor de arestas Canny, deteção de contornos, extração de informação de cor, entre outros. Para os três problemas, são apresentados e discutidos resultados experimentais, de forma a avaliar o melhor método a aplicar em cada caso. O melhor método em cada aplicação encontra-se já integrado ou em fase de integração dos robôs descritos.

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PAOLANTI, MARINA. "Pattern Recognition for challenging Computer Vision Applications." Doctoral thesis, Università Politecnica delle Marche, 2018. http://hdl.handle.net/11566/252904.

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La Pattern Recognition è lo studio di come le macchine osservano l'ambiente, imparano a distinguere i pattern di interesse dal loro background e prendono decisioni valide e ragionevoli sulle categorie di modelli. Oggi l'applicazione degli algoritmi e delle tecniche di Pattern Recognition è trasversale. Con i recenti progressi nella computer vision, abbiamo la capacità di estrarre dati multimediali per ottenere informazioni preziose su ciò che sta accadendo nel mondo. Partendo da questa premessa, questa tesi affronta il tema dello sviluppo di sistemi di Pattern Recognition per applicazioni reali come la biologia, il retail, la sorveglianza, social media intelligence e i beni culturali. L'obiettivo principale è sviluppare applicazioni di computer vision in cui la Pattern Recognition è il nucleo centrale della loro progettazione, a partire dai metodi generali, che possono essere sfruttati in più campi di ricerca, per poi passare a metodi e tecniche che affrontano problemi specifici. Di fronte a molti tipi di dati, come immagini, dati biologici e traiettorie, una difficoltà fondamentale è trovare rappresentazioni vettoriali rilevanti. Per la progettazione del sistema di riconoscimento dei modelli vengono eseguiti i seguenti passaggi: raccolta dati, estrazione delle caratteristiche, approccio di apprendimento personalizzato e analisi e valutazione comparativa. Per una valutazione completa delle prestazioni, è di grande importanza collezionare un dataset specifico perché i metodi di progettazione che sono adattati a un problema non funzionano correttamente su altri tipi di problemi. I metodi su misura, adottati per lo sviluppo delle applicazioni proposte, hanno dimostrato di essere in grado di estrarre caratteristiche statistiche complesse e di imparare in modo efficiente le loro rappresentazioni, permettendogli di generalizzare bene attraverso una vasta gamma di compiti di visione computerizzata.
Pattern Recognition is the study of how machines can observe the environment, learn to distinguish patterns of interest from their background, and make sound and reasonable decisions about the patterns categories. Nowadays, the application of Pattern Recognition algorithms and techniques is ubiquitous and transversal. With the recent advances in computer vision, we now have the ability to mine such massive visual data to obtain valuable insight about what is happening in the world. The availability of affordable and high resolution sensors (e.g., RGB-D cameras, microphones and scanners) and data sharing have resulted in huge repositories of digitized documents (text, speech, image and video). Starting from such a premise, this thesis addresses the topic of developing next generation Pattern Recognition systems for real applications such as Biology, Retail, Surveillance, Social Media Intelligence and Digital Cultural Heritage. The main goal is to develop computer vision applications in which Pattern Recognition is the key core in their design, starting from general methods, that can be exploited in more fields, and then passing to methods and techniques addressing specific problems. The privileged focus is on up-to-date applications of Pattern Recognition techniques to real-world problems, and on interdisciplinary research, experimental and/or theoretical studies yielding new insights that advance Pattern Recognition methods. The final ambition is to spur new research lines, especially within interdisciplinary research scenarios. Faced with many types of data, such as images, biological data and trajectories, a key difficulty was to nd relevant vectorial representations. While this problem had been often handled in an ad-hoc way by domain experts, it has proved useful to learn these representations directly from data, and Machine Learning algorithms, statistical methods and Deep Learning techniques have been particularly successful. The representations are then based on compositions of simple parameterized processing units, the depth coming from the large number of such compositions. It was desirable to develop new, efficient data representation or feature learning/indexing techniques, which can achieve promising performance in the related tasks. The overarching goal of this work consists of presenting a pipeline to select the model that best explains the given observations; nevertheless, it does not prioritize in memory and time complexity when matching models to observations. For the Pattern Recognition system design, the following steps are performed: data collection, features extraction, tailored learning approach and comparative analysis and assessment. The proposed applications open up a wealth of novel and important opportunities for the machine vision community. The newly dataset collected as well as the complex areas taken into exam, make the research challenging. In fact, it is crucial to evaluate the performance of state of the art methods to demonstrate their strength and weakness and help identify future research for designing more robust algorithms. For comprehensive performance evaluation, it is of great importance developing a library and benchmark to gauge the state of the art because the methods design that are tuned to a specic problem do not work properly on other problems. Furthermore, the dataset selection is needed from different application domains in order to offer the user the opportunity to prove the broad validity of methods. Intensive attention has been drawn to the exploration of tailored learning models and algorithms, and their extension to more application areas. The tailored methods, adopted for the development of the proposed applications, have shown to be capable of extracting complex statistical features and efficiently learning their representations, allowing it to generalize well across a wide variety of computer vision tasks, including image classication, text recognition and so on.

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Hayes, William S. "Pattern recognition and signal detection in gene finding." Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/25420.

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Abbott, Kevin Toney. "Applications of algebraic geometry to object/image recognition." [College Station, Tex. : Texas A&M University, 2007. http://hdl.handle.net/1969.1/ETD-TAMU-1935.

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Книги з теми "RECOGNITION APPLICATIONS"

Fred, Ana, Maria De Marsico, and Mário Figueiredo, eds. Pattern Recognition: Applications and Methods. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27677-9.

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Fred, Ana, Maria De Marsico, and Gabriella Sanniti di Baja, eds. Pattern Recognition Applications and Methods. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53375-9.

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

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De Marsico, Maria, Gabriella Sanniti di Baja, and Ana Fred, eds. Pattern Recognition Applications and Methods. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05499-1.

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De Marsico, Maria, Gabriella Sanniti di Baja, and Ana Fred, eds. Pattern Recognition Applications and Methods. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66125-0.

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Fred, Ana, and Maria De Marsico, eds. Pattern Recognition Applications and Methods. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-12610-4.

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De Marsico, Maria, Gabriella Sanniti di Baja, and Ana Fred, eds. Pattern Recognition Applications and Methods. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93647-5.

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Devijver, Pierre A., and Josef Kittler, eds. Pattern Recognition Theory and Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-83069-3.

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Latorre Carmona, Pedro, J. Salvador Sánchez, and Ana L. N. Fred, eds. Pattern Recognition - Applications and Methods. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36530-0.

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Частини книг з теми "RECOGNITION APPLICATIONS"

Huang, Thomas, Ziyou Xiong, and Zhenqiu Zhang. "Face Recognition Applications." In Handbook of Face Recognition, 617–38. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-932-1_24.

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Berretti, Stefano, Boulbaba Ben Amor, Hassen Drira, Mohamed Daoudi, Anuj Srivastava, Alberto del Bimbo, and Pietro Pala. "Applications." In 3D Face Modeling, Analysis and Recognition, 149–202. Solaris South Tower, Singapore: John Wiley & Sons SingaporePte Ltd, 2013. http://dx.doi.org/10.1002/9781118592656.ch5.

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Croall, Ian F., and John P. Mason. "Pattern Recognition." In Industrial Applications of Neural Networks, 55–117. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-84837-7_4.

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Cristescu, Gabriela, and Liana Lupşa. "Applications in pattern recognition." In Non-Connected Convexities and Applications, 227–46. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0003-2_9.

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Stork, David G., and Marcus E. Hennecke. "Machine Recognition and Applications." In Speechreading by Humans and Machines, 549–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-662-13015-5_44.

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Leca-Bouvier, Béatrice D., and Loïc J. Blum. "Enzyme for Biosensing Applications." In Recognition Receptors in Biosensors, 177–220. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-1-4419-0919-0_4.

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Zimmermann, H. J. "Pattern Recognition." In Fuzzy Set Theory — and Its Applications, 217–40. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-015-7949-0_11.

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Zimmermann, H. J. "Pattern Recognition." In Fuzzy Set Theory — and Its Applications, 187–212. Dordrecht: Springer Netherlands, 1985. http://dx.doi.org/10.1007/978-94-015-7153-1_11.

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Konar, Amit, and Sriparna Saha. "EEG-Gesture Based Artificial Limb Movement for Rehabilitative Applications." In Gesture Recognition, 243–68. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62212-5_8.

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Tistarelli, Massimo, and Enrico Grosso. "Active Vision-based Face Recognition: Issues, Applications and Techniques." In Face Recognition, 262–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72201-1_14.

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Тези доповідей конференцій з теми "RECOGNITION APPLICATIONS"

Miral Kazmi, Syeda. "Hand Gesture Recognition for Sign language." In Human Interaction and Emerging Technologies (IHIET-AI 2022) Artificial Intelligence and Future Applications. AHFE International, 2022. http://dx.doi.org/10.54941/ahfe100925.

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Анотація:

We have come to know a very genuine issue of sign language recognition, that problem being the issue of two-way communication i.e. between normal person and deaf/dumb. Current sign language recognition applications lack basic characteristics which are very necessary for the interaction with environment. Our project is focused on providing a portable and customizable solution for understanding sign language through an android app. The report summarizes the basic concepts and methods in creating this android application that uses gestures recognition to understand American sign language words. The project uses different image processing tools to separate the hand from the rest and then uses pattern recognition techniques for gesture recognition. A complete summary of the results obtained from the various tests performed is also provided to demonstrate the validity of the application.

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K.V., Kiran Kumar, Chetan ., Kiran ., and Syed . "Face Recognition using RFID." In International Conference on Computer Applications — Computer Applications - II. Singapore: Research Publishing Services, 2010. http://dx.doi.org/10.3850/978-981-08-7304-2_1389.

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Lockhart, Jeffrey W., Tony Pulickal, and Gary M. Weiss. "Applications of mobile activity recognition." In the 2012 ACM Conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2370216.2370441.

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Wang, Patrick. "Intelligent pattern recognition and applications." In the First ACM International Conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1854776.1854782.

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Rotar, Danut, and Horia Popa Andreescu. "Face Recognition in Automotive Applications." In 2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC). IEEE, 2018. http://dx.doi.org/10.1109/synasc.2018.00061.

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Madeo, Renata C. B., Sarajane M. Peres, Daniel B. Dias, and Clodis Boscarioli. "Gesture recognition for fingerspelling applications." In the 12th international ACM SIGACCESS conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1878803.1878861.

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Lee, Jimmy Addison, and Kin Choong Yow. "Image Recognition for Mobile Applications." In 2007 IEEE International Conference on Image Processing. IEEE, 2007. http://dx.doi.org/10.1109/icip.2007.4379550.

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Nitta, Tsuneo. "Speech recognition applications in Japan." In 3rd International Conference on Spoken Language Processing (ICSLP 1994). ISCA: ISCA, 1994. http://dx.doi.org/10.21437/icslp.1994-170.

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Venkataraman, Sashikumar, Milind Sohoni, and Gershon Elber. "Blend recognition algorithm and applications." In the sixth ACM symposium. New York, New York, USA: ACM Press, 2001. http://dx.doi.org/10.1145/376957.376970.

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Kabasakal, Burak, and Emre Sumer. "Gender recognition using innovative pattern recognition techniques." In 2018 26th Signal Processing and Communications Applications Conference (SIU). IEEE, 2018. http://dx.doi.org/10.1109/siu.2018.8404306.

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Звіти організацій з теми "RECOGNITION APPLICATIONS"

Glenn, S. Automated Threat Recognition For Aviation Security Applications. Office of Scientific and Technical Information (OSTI), March 2020. http://dx.doi.org/10.2172/1643774.

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Chellappa, R., and S. A. Sirohey. Face recognition technology for law enforcement applications. Gaithersburg, MD: National Institute of Standards and Technology, 1994. http://dx.doi.org/10.6028/nist.ir.5465.

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Howell, J. A., G. W. Eccleston, R. Whiteson, H. O. Menlove, C. C. Fuyat, J. K. Halbig, S. F. Klosterbuer, and M. F. Mullen. Safeguards applications of pattern recognition and neural networks. Office of Scientific and Technical Information (OSTI), September 1993. http://dx.doi.org/10.2172/10102590.

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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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Glass, James R. Finding Acoustic Regularities in Speech: Applications to Phonetic Recognition. Fort Belvoir, VA: Defense Technical Information Center, December 1988. http://dx.doi.org/10.21236/ada207072.

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Bass, James D. Advancing Noise Robust Automatic Speech Recognition for Command and Control Applications. Fort Belvoir, VA: Defense Technical Information Center, March 2006. http://dx.doi.org/10.21236/ada461436.

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Тарасова, Олена Юріївна, and Ірина Сергіївна Мінтій. Web application for facial wrinkle recognition. Кривий Ріг, КДПУ, 2022. http://dx.doi.org/10.31812/123456789/7012.

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Facial recognition technology is named one of the main trends of recent years. It’s wide range of applications, such as access control, biometrics, video surveillance and many other interactive humanmachine systems. Facial landmarks can be described as key characteristics of the human face. Commonly found landmarks are, for example, eyes, nose or mouth corners. Analyzing these key points is useful for a variety of computer vision use cases, including biometrics, face tracking, or emotion detection. Different methods produce different facial landmarks. Some methods use only basic facial landmarks, while others bring out more detail. We use 68 facial markup, which is a common format for many datasets. Cloud computing creates all the necessary conditions for the successful implementation of even the most complex tasks. We created a web application using the Django framework, Python language, OpenCv and Dlib libraries to recognize faces in the image. The purpose of our work is to create a software system for face recognition in the photo and identify wrinkles on the face. The algorithm for determining the presence and location of various types of wrinkles and determining their geometric determination on the face is programmed.

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Bilyk, Zhanna I., Yevhenii B. Shapovalov, Viktor B. Shapovalov, Anna P. Megalinska, Fabian Andruszkiewicz, and Agnieszka Dołhańczuk-Śródka. Assessment of mobile phone applications feasibility on plant recognition: comparison with Google Lens AR-app. [б. в.], November 2020. http://dx.doi.org/10.31812/123456789/4403.

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The paper is devoted to systemizing all mobile applications used during the STEM-classes and can be used to identify plants. There are 10 mobile applications that are plant identifiers worldwide. These applications can be divided into three groups, such as plant identifiers that can analyze photos, plant classification provides the possibility to identify plants manually, plants-care apps that remind water of the plant, or change the soil. In this work, mobile apps such as Flora Incognita, PlantNet, PlantSnap, PictureThis, LeafSnap, Seek, PlantNet were analyzed for usability parameters and accuracy of identification. To provide usability analysis, a survey of experts of digital education on installation simplicity, level of friendliness of the interface, and correctness of picture processing. It is proved that Flora Incognita and PlantNet are the most usable and the most informative interface from plant identification apps. However, they were characterized by significantly lower accuracy compared to Google Lens results. Further comparison of the usability of applications that have been tested in the article with Google Lens, proves that Google Lens characterize by better usability and therefore, Google Lens is the most recommended app to use to provide plant identification during biology classes.

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Rhody, H. E., J. Hillenbrand, and J. A. Biles. Northeast Artificial Intelligence Consortium Annual Report 1986: Artificial Intelligence Applications to Speech Recognition. Volume 7. Fort Belvoir, VA: Defense Technical Information Center, June 1988. http://dx.doi.org/10.21236/ada198058.

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Fatehifar, Mohsen, Josef Schlittenlacher, David Wong, and Kevin Munro. Applications Of Automatic Speech Recognition And Text-To-Speech Models To Detect Hearing Loss: A Scoping Review Protocol. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, January 2023. http://dx.doi.org/10.37766/inplasy2023.1.0029.

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Анотація:

Review question / Objective: This scoping review aims to identify published methods that have used automatic speech recognition or text-to-speech recognition technologies to detect hearing loss and report on their accuracy and limitations. Condition being studied: Hearing enables us to communicate with the surrounding world. According to reports by the World Health Organization, 1.5 billion suffer from some degree of hearing loss of which 430 million require medical attention. It is estimated that by 2050, 1 in every 4 people will experience some sort of hearing disability. Hearing loss can significantly impact people’s ability to communicate and makes social interactions a challenge. In addition, it can result in anxiety, isolation, depression, hindrance of learning, and a decrease in general quality of life. A hearing assessment is usually done in hospitals and clinics with special equipment and trained staff. However, these services are not always available in less developed countries. Even in developed countries, like the UK, access to these facilities can be a challenge in rural areas. Moreover, during a crisis like the Covid-19 pandemic, accessing the required healthcare can become dangerous and challenging even in large cities.

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