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Dissertations / Theses on the topic 'Sign language recognition'

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Published: 4 June 2021

Last updated: 25 July 2025

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1

Nel, Warren. "An integrated sign language recognition system." Thesis, University of Western Cape, 2014. http://hdl.handle.net/11394/3584.

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Doctor Educationis<br>Research has shown that five parameters are required to recognize any sign language gesture: hand shape, location, orientation and motion, as well as facial expressions. The South African Sign Language (SASL) research group at the University of the Western Cape has created systems to recognize Sign Language gestures using single parameters. Using a single parameter can cause ambiguities in the recognition of signs that are similarly signed resulting in a restriction of the possible vocabulary size. This research pioneers work at the group towards combining multiple

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2

Zafrulla, Zahoor. "Automatic recognition of American sign language classifiers." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/53461.

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Automatically recognizing classifier-based grammatical structures of American Sign Language (ASL) is a challenging problem. Classifiers in ASL utilize surrogate hand shapes for people or "classes" of objects and provide information about their location, movement and appearance. In the past researchers have focused on recognition of finger spelling, isolated signs, facial expressions and interrogative words like WH-questions (e.g. Who, What, Where, and When). Challenging problems such as recognition of ASL sentences and classifier-based grammatical structures remain relatively unexplored in the

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3

Nayak, Sunita. "Representation and learning for sign language recognition." [Tampa, Fla] : University of South Florida, 2008. http://purl.fcla.edu/usf/dc/et/SFE0002362.

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4

Nurena-Jara, Roberto, Cristopher Ramos-Carrion, and Pedro Shiguihara-Juarez. "Data collection of 3D spatial features of gestures from static peruvian sign language alphabet for sign language recognition." Institute of Electrical and Electronics Engineers Inc, 2020. http://hdl.handle.net/10757/656634.

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El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.<br>Peruvian Sign Language Recognition (PSL) is approached as a classification problem. Previous work has employed 2D features from the position of hands to tackle this problem. In this paper, we propose a method to construct a dataset consisting of 3D spatial positions of static gestures from the PSL alphabet, using the HTC Vive device and a well-known technique to extract 21 keypoints from the hand to obtain a feature vector. A dataset of 35, 400

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5

Cooper, H. M. "Sign language recognition : generalising to more complex corpora." Thesis, University of Surrey, 2010. http://epubs.surrey.ac.uk/843617/.

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The aim of this thesis is to find new approaches to Sign Language Recognition (SLR) which are suited to working with the Limited corpora currently available. Data available for SLR is of limited quality; low resolution and frame rates make the task of recognition even more complex. The content is rarely natural, concentrating on isolated signs and filmed under laboratory conditions. In addition, the amount of accurately labelled data is minimal. To this end, several contributions are made: Tracking the hands is eschewed in favour of detection based techniques more robust to noise; for both sig

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Li, Pei. "Hand shape estimation for South African sign language." Thesis, University of the Western Cape, 2012. http://hdl.handle.net/11394/4374.

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>Magister Scientiae - MSc<br>Hand shape recognition is a pivotal part of any system that attempts to implement Sign Language recognition. This thesis presents a novel system which recognises hand shapes from a single camera view in 2D. By mapping the recognised hand shape from 2D to 3D,it is possible to obtain 3D co-ordinates for each of the joints within the hand using the kinematics embedded in a 3D hand avatar and smooth the transformation in 3D space between any given hand shapes. The novelty in this system is that it does not require a hand pose to be recognised at every frame, but rather

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7

Belissen, Valentin. "From Sign Recognition to Automatic Sign Language Understanding : Addressing the Non-Conventionalized Units." Electronic Thesis or Diss., université Paris-Saclay, 2020. http://www.theses.fr/2020UPASG064.

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Les langues des signes (LS) se sont développées naturellement au sein des communautés de Sourds. Ne disposant pas de forme écrite, ce sont des langues orales, utilisant les canaux gestuel pour l’expression et visuel pour la réception. Ces langues peu dotées ne font pas l'objet d'un large consensus au niveau de leur description linguistique. Elles intègrent des signes lexicaux, c’est-à-dire des unités conventionnalisées du langage dont la forme est supposée arbitraire, mais aussi – et à la différence des langues vocales, si on ne considère pas la gestualité co-verbale – des structures iconiques

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8

Rupe, Jonathan C. "Vision-based hand shape identification for sign language recognition /." Link to online version, 2005. https://ritdml.rit.edu/dspace/handle/1850/940.

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9

Mudduluru, Sravani. "Indian Sign Language Numbers Recognition using Intel RealSense Camera." DigitalCommons@CalPoly, 2017. https://digitalcommons.calpoly.edu/theses/1815.

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The use of gesture based interaction with devices has been a significant area of research in the field of computer science since many years. The main idea of these kind of interactions is to ease the user experience by providing high degree of freedom and provide more interactive way of communication with the technology in a natural way. The significant areas of applications of gesture recognition are in video gaming, human computer interaction, virtual reality, smart home appliances, medical systems, robotics and several others. With the availability of the devices such as Kinect, Leap Motion

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10

Brashear, Helene Margaret. "Improving the efficacy of automated sign language practice tools." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/34703.

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The CopyCat project is an interdisciplinary effort to create a set of computer-aided language learning tools for deaf children. The CopyCat games allow children to interact with characters using American Sign Language (ASL). Through Wizard of Oz pilot studies we have developed a set of games, shown their efficacy in improving young deaf children's language and memory skills, and collected a large corpus of signing examples. Our previous implementation of the automatic CopyCat games uses automatic sign language recognition and verification in the infrastructure of a memory repetition and phr

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11

Yin, Pei. "Segmental discriminative analysis for American Sign Language recognition and verification." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/33939.

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This dissertation presents segmental discriminative analysis techniques for American Sign Language (ASL) recognition and verification. ASL recognition is a sequence classification problem. One of the most successful techniques for recognizing ASL is the hidden Markov model (HMM) and its variants. This dissertation addresses two problems in sign recognition by HMMs. The first is discriminative feature selection for temporally-correlated data. Temporal correlation in sequences often causes difficulties in feature selection. To mitigate this problem, this dissertation proposes segmentally-boosted

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12

Starner, Thad. "Visual recognition of American sign language using hidden Markov models." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/29089.

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13

Adam, Jameel. "Video annotation wiki for South African sign language." Thesis, University of the Western Cape, 2011. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_1540_1304499135.

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<p>The SASL project at the University of the Western Cape aims at developing a fully automated translation system between English and South African Sign Language (SASL). Three important aspects of this system require SASL documentation and knowledge. These are: recognition of SASL from a video sequence, linguistic translation between SASL and English and the rendering of SASL. Unfortunately, SASL documentation is a scarce resource and no official or complete documentation exists. This research focuses on creating an online collaborative video annotation knowledge management system for SASL whe

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14

Feng, Qianli. "Automatic American Sign Language Imitation Evaluator." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1461233570.

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15

Zhou, Mingjie. "Deep networks for sign language video caption." HKBU Institutional Repository, 2020. https://repository.hkbu.edu.hk/etd_oa/848.

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In the hearing-loss community, sign language is a primary tool to communicate with people while there is a communication gap between hearing-loss people with normal hearing people. Sign language is different from spoken language. It has its own vocabulary and grammar. Recent works concentrate on the sign language video caption which consists of sign language recognition and sign language translation. Continuous sign language recognition, which can bridge the communication gap, is a challenging task because of the weakly supervised ordered annotations where no frame-level label is provided. To

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Holden, Eun-Jung. "Visual recognition of hand motion." University of Western Australia. Dept. of Computer Science, 1997. http://theses.library.uwa.edu.au/adt-WU2003.0007.

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Hand gesture recognition is an active area of research in recent years, being used in various applications from deaf sign recognition systems to human-machine interaction applications. The gesture recognition process, in general, may be divided into two stages: the motion sensing, which extracts useful data from hand motion; and the classification process, which classifies the motion sensing data as gestures. The existing vision-based gesture recognition systems extract 2-D shape and trajectory descriptors from the visual input, and classify them using various classification techniques from ma

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Buehler, Patrick. "Automatic learning of British Sign Language from signed TV broadcasts." Thesis, University of Oxford, 2010. http://ora.ox.ac.uk/objects/uuid:2930e980-4307-41bf-b4ff-87e8c4d0d722.

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In this work, we will present several contributions towards automatic recognition of BSL signs from continuous signing video sequences. Specifically, we will address three main points: (i) automatic detection and tracking of the hands using a generative model of the image; (ii) automatic learning of signs from TV broadcasts using the supervisory information available from subtitles; and (iii) generalisation given sign examples from one signer to recognition of signs from different signers. Our source material consists of many hours of video with continuous signing and corresponding subtitles r

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Achmed, Imran. "Independent hand-tracking from a single two-dimensional view and its application to South African sign language recognition." Thesis, University of Western Cape, 2014. http://hdl.handle.net/11394/3330.

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Philosophiae Doctor - PhD<br>Hand motion provides a natural way of interaction that allows humans to interact not only with the environment, but also with each other. The effectiveness and accuracy of hand-tracking is fundamental to the recognition of sign language. Any inconsistencies in hand-tracking result in a breakdown in sign language communication. Hands are articulated objects, which complicates the tracking thereof. In sign language communication the tracking of hands is often challenged by the occlusion of the other hand, other body parts and the environment in which they are bei

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Naidoo, Nathan Lyle. "South African sign language recognition using feature vectors and Hidden Markov Models." Thesis, University of the Western Cape, 2010. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_8533_1297923615.

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<p>This thesis presents a system for performing whole gesture recognition for South African Sign Language. The system uses feature vectors combined with Hidden Markov models. In order to constuct a feature vector, dynamic segmentation must occur to extract the signer&rsquo<br>s hand movements. Techniques and methods for normalising variations that occur when recording a signer performing a gesture, are investigated. The system has a classification rate of 69%</p>

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Ding, Liya. "Modelling and Recognition of Manuals and Non-manuals in American Sign Language." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1237564092.

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Blair, James M. "Architectures for Real-Time Automatic Sign Language Recognition on Resource-Constrained Device." UNF Digital Commons, 2018. https://digitalcommons.unf.edu/etd/851.

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Powerful, handheld computing devices have proliferated among consumers in recent years. Combined with new cameras and sensors capable of detecting objects in three-dimensional space, new gesture-based paradigms of human computer interaction are becoming available. One possible application of these developments is an automated sign language recognition system. This thesis reviews the existing body of work regarding computer recognition of sign language gestures as well as the design of systems for speech recognition, a similar problem. Little work has been done to apply the well-known architect

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Neyra-Gutierrez, Andre, and Pedro Shiguihara-Juarez. "Feature Extraction with Video Summarization of Dynamic Gestures for Peruvian Sign Language Recognition." Institute of Electrical and Electronics Engineers Inc, 2020. http://hdl.handle.net/10757/656630.

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El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.<br>In peruvian sign language (PSL), recognition of static gestures has been proposed earlier. However, to state a conversation using sign language, it is also necessary to employ dynamic gestures. We propose a method to extract a feature vector for dynamic gestures of PSL. We collect a dataset with 288 video sequences of words related to dynamic gestures and we state a workflow to process the keypoints of the hands, obtaining a feature vector for

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Viswavarapu, Lokesh Kumar. "Real-Time Finger Spelling American Sign Language Recognition Using Deep Convolutional Neural Networks." Thesis, University of North Texas, 2018. https://digital.library.unt.edu/ark:/67531/metadc1404616/.

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This thesis presents design and development of a gesture recognition system to recognize finger spelling American Sign Language hand gestures. We developed this solution using the latest deep learning technique called convolutional neural networks. This system uses blink detection to initiate the recognition process, Convex Hull-based hand segmentation with adaptive skin color filtering to segment hand region, and a convolutional neural network to perform gesture recognition. An ensemble of four convolutional neural networks are trained with a dataset of 25254 images for gesture recognition an

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De, Villiers Hendrik Adrianus Cornelis. "A vision-based South African sign language tutor." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/86333.

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Thesis (PhD)--Stellenbosch University, 2014.<br>ENGLISH ABSTRACT: A sign language tutoring system capable of generating detailed context-sensitive feedback to the user is presented in this dissertation. This stands in contrast with existing sign language tutor systems, which lack the capability of providing such feedback. A domain specific language is used to describe the constraints placed on the user’s movements during the course of a sign, allowing complex constraints to be built through the combination of simpler constraints. This same linguistic description is then used to evaluate t

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JAFARI, MUHAMMAD REZA. "PERSIAN SIGN GESTURE TRANSLATION TO ENGLISH SPOKEN LANGUAGE ON SMARTPHONE." Thesis, DELHI TECHNOLOGICAL UNIVERSITY, 2020. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18787.

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Hearing impaired and others with verbal challenges face difficulty to communicate with society; Sign Language represents their communication such as numbers or phrases. The communication becomes a challenge with people from other countries using different languages. Additionally, the sign language is different from one country to another. That is, learning one sign language doesn’t mean learning all sign languages. To translate a word from sign language to a spoken language is a challenge and to change a particular word from that language to another language is even a bigger challenge. I

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Potrus, Dani. "Swedish Sign Language Skills Training and Assessment." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209129.

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Sign language is used widely around the world as a first language for those that are unable to use spoken language and by groups of people that have a disability which precludes them from using spoken language (such as a hearing impairment). The importance of effective learning of sign language and its applications in modern computer science has grown widely in the modern aged society and research around sign language recognition has sprouted in many different directions, some examples using hidden markov models (HMMs) to train models to recognize different sign language patterns (Swedish sign

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Sarella, Kanthi. "An image processing technique for the improvement of Sign2 using a dual camera approach /." Online version of thesis, 2008. http://hdl.handle.net/1850/5721.

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Segers, Vaughn Mackman. "The efficacy of the Eigenvector approach to South African sign language identification." Thesis, University of the Western Cape, 2010. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_2697_1298280657.

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<p>The communication barriers between deaf and hearing society mean that interaction between these communities is kept to a minimum. The South African Sign Language research group, Integration of Signed and Verbal Communication: South African Sign Language Recognition and Animation (SASL), at the University of the Western Cape aims to create technologies to bridge the communication gap. In this thesis we address the subject of whole hand gesture recognition. We demonstrate a method to identify South African Sign Language classifiers using an eigenvector ap- proach. The classifiers researched w

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Achmed, Imran. "Upper body pose recognition and estimation towards the translation of South African sign language." Thesis, University of the Western Cape, 2011. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_2493_1304504127.

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<p>Recognising and estimating gestures is a fundamental aspect towards translating from a sign language to a spoken language. It is a challenging problem and at the same time, a growing phenomenon in Computer Vision. This thesis presents two approaches, an example-based and a learning-based approach, for performing integrated detection, segmentation and 3D estimation of the human upper body from a single camera view. It investigates whether an upper body pose can be estimated from a database of exemplars with labelled poses. It also investigates whether an upper body pose can be estimated usin

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Mohamed, Asif, Paul Sujeet, and Vishnu Ullas. "Gauntlet-X1: Smart Glove System for American Sign Language translation using Hand Activity Recognition." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-428743.

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The most common forms of Human Computer Interaction (HCI) devices these dayslike the keyboard, mouse and touch interfaces, are limited to working on atwo-dimensional (2-D) surface, and thus do not provide complete freedom ofaccessibility using our hands. With the vast number of gestures a hand can perform,including the different combinations of motion of fingers, wrist and elbow, we canmake accessibility and interaction with the digital environment much more simplified,without restrictions to the physical surface. Fortunately, this is possible due to theadvancements of Microelectromechanical s

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Gonzalez, Preciado Matilde. "Computer vision methods for unconstrained gesture recognition in the context of sign language annotation." Toulouse 3, 2012. http://thesesups.ups-tlse.fr/1798/.

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Cette thèse porte sur l'étude des méthodes de vision par ordinateur pour la reconnaissance de gestes naturels dans le contexte de l'annotation de la Langue des Signes. La langue des signes (LS) est une langue gestuelle développée par les sourds pour communiquer. Un énoncé en LS consiste en une séquence de signes réalisés par les mains, accompagnés d'expressions du visage et de mouvements du haut du corps, permettant de transmettre des informations en parallèles dans le discours. Même si les signes sont définis dans des dictionnaires, on trouve une très grande variabilité liée au contexte lors

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Jacobs, Kurt. "South African Sign Language Hand Shape and Orientation Recognition on Mobile Devices Using Deep Learning." University of the Western Cape, 2017. http://hdl.handle.net/11394/5647.

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>Magister Scientiae - MSc<br>In order to classify South African Sign Language as a signed gesture, five fundamental parameters need to be considered. These five parameters to be considered are: hand shape, hand orientation, hand motion, hand location and facial expressions. The research in this thesis will utilise Deep Learning techniques, specifically Convolutional Neural Networks, to recognise hand shapes in various hand orientations. The research will focus on two of the five fundamental parameters, i.e., recognising six South African Sign Language hand shapes for each of five different han

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Yang, Ruiduo. "Dynamic programming with multiple candidates and its applications to sign language and hand gesture recognition." [Tampa, Fla.] : University of South Florida, 2008. http://purl.fcla.edu/usf/dc/et/SFE0002310.

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Parashar, Ayush S. "Representation and Interpretation of Manual and Non-Manual Information for Automated American Sign Language Recognition." [Tampa, Fla.] : University of South Florida, 2003. http://purl.fcla.edu/fcla/etd/SFE0000055.

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

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<p>Much work has been done in building systems that can recognize gestures, e.g. as a component of sign language recognition systems. These systems typically use whole gestures as the smallest unit for recognition. Although high recognition rates have been reported, these systems do not scale well and are computationally intensive. The reason why these systems generally scale poorly is that they recognize gestures by building individual models for each separate gesture<br>as the number of gestures grows, so does the required number of models. Beyond a certain threshold number of gestures to be

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Halvardsson, Gustaf, and Johanna Peterson. "Interpretation of Swedish Sign Language using Convolutional Neural Networks and Transfer Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-277859.

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The automatic interpretation of signs of a sign language involves image recognition. An appropriate approach for this task is to use Deep Learning, and in particular, Convolutional Neural Networks. This method typically needs large amounts of data to be able to perform well. Transfer learning could be a feasible approach to achieve high accuracy despite using a small data set. The hypothesis of this thesis is to test if transfer learning works well to interpret the hand alphabet of the Swedish Sign Language. The goal of the project is to implement a model that can interpret signs, as well as t

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Ghaziasgar, Mehrdad. "The use of mobile phones as service-delivery devices in sign language machine translation system." Thesis, University of the Western Cape, 2010. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_7216_1299134611.

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<p>This thesis investigates the use of mobile phones as service-delivery devices in a sign language machine translation system. Four sign language visualization methods were evaluated on mobile phones. Three of the methods were synthetic sign language visualization methods. Three factors were considered: the intelligibility of sign language, as rendered by the method<br>the power consumption<br>and the bandwidth usage associated with each method. The average intelligibility rate was 65%, with some methods achieving intelligibility rates of up to 92%. The average le size was 162 KB and, on aver

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Glatt, Ruben [UNESP]. "Deep learning architecture for gesture recognition." Universidade Estadual Paulista (UNESP), 2014. http://hdl.handle.net/11449/115718.

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Made available in DSpace on 2015-03-03T11:52:29Z (GMT). No. of bitstreams: 0 Previous issue date: 2014-07-25Bitstream added on 2015-03-03T12:06:38Z : No. of bitstreams: 1 000807195.pdf: 2462524 bytes, checksum: 91686fbe11c74337c40fe57671eb8d82 (MD5)<br>O reconhecimento de atividade de visão de computador desempenha um papel importante na investigação para aplicações como interfaces humanas de computador, ambientes inteligentes, vigilância ou sistemas médicos. Neste trabalho, é proposto um sistema de reconhecimento de gestos com base em uma arquitetura de aprendizagem profunda. Ele é usado pa

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Glatt, Ruben. "Deep learning architecture for gesture recognition /." Guaratinguetá, 2014. http://hdl.handle.net/11449/115718.

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Orientador: José Celso Freire Junior<br>Coorientador: Daniel Julien Barros da Silva Sampaio<br>Banca: Galeno José de Sena<br>Banca: Luiz de Siqueira Martins Filho<br>Resumo: O reconhecimento de atividade de visão de computador desempenha um papel importante na investigação para aplicações como interfaces humanas de computador, ambientes inteligentes, vigilância ou sistemas médicos. Neste trabalho, é proposto um sistema de reconhecimento de gestos com base em uma arquitetura de aprendizagem profunda. Ele é usado para analisar o desempenho quando treinado com os dados de entrada multi-modais em

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de, la Cruz Nathan. "Autonomous facial expression recognition using the facial action coding system." University of the Western Cape, 2016. http://hdl.handle.net/11394/5121.

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>Magister Scientiae - MSc<br>The South African Sign Language research group at the University of the Western Cape is in the process of creating a fully-edged machine translation system to automatically translate between South African Sign Language and English. A major component of the system is the ability to accurately recognise facial expressions, which are used to convey emphasis, tone and mood within South African Sign Language sentences. Traditionally, facial expression recognition research has taken one of two paths: either recognising whole facial expressions of which there are six i.e.

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Koller, Oscar Anatol Tobias [Verfasser], Hermann [Akademischer Betreuer] Ney, and Richard [Akademischer Betreuer] Bowden. "Towards large vocabulary continuous sign language recognition: from artificial to real-life tasks / Oscar Tobias Anatol Koller ; Hermann Ney, Richard Bowden." Aachen : Universitätsbibliothek der RWTH Aachen, 2020. http://d-nb.info/1233315951/34.

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Clark, Evan M. "A multicamera system for gesture tracking with three dimensional hand pose estimation /." Link to online version, 2006. https://ritdml.rit.edu/dspace/handle/1850/1909.

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Freitas, Fernando de Almeida. "Reconhecimento automático de expressões faciais gramaticais na língua brasileira de sinais." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/100/100131/tde-10072015-100311/.

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O Reconhecimento das Expressões Faciais tem atraído bastante a atenção dos pesquisadores nas últimas décadas, principalmente devido às suas ponteciais aplicações. Nas Línguas de Sinais, por serem línguas de modalidade visual-espacial e não contarem com o suporte sonoro da entonação, as Expressões Faciais ganham uma importância ainda maior, pois colaboram também para formar a estrutura gramatical da língua. Tais expressões são chamadas Expressões Faciais Gramaticais e estão presentes nos níveis morfológico e sintático das Línguas de Sinais, elas ganham destaque no processo de reconhecimento aut

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Mekala, Priyanka. "Field Programmable Gate Array Based Target Detection and Gesture Recognition." FIU Digital Commons, 2012. http://digitalcommons.fiu.edu/etd/723.

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The move from Standard Definition (SD) to High Definition (HD) represents a six times increases in data, which needs to be processed. With expanding resolutions and evolving compression, there is a need for high performance with flexible architectures to allow for quick upgrade ability. The technology advances in image display resolutions, advanced compression techniques, and video intelligence. Software implementation of these systems can attain accuracy with tradeoffs among processing performance (to achieve specified frame rates, working on large image data sets), power and cost constraints

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Borgia, Fabrizio. "Informatisation d'une forme graphique des Langues des Signes : application au système d'écriture SignWriting." Thesis, Toulouse 3, 2015. http://www.theses.fr/2015TOU30030/document.

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Les recherches et les logiciels présentés dans cette étude s'adressent à une importante minorité au sein de notre société, à savoir la communauté des sourdes. De nombreuses recherches démontrent que les sourdes se heurtent à de grosses difficultés avec la langue vocale, ce qui explique pourquoi la plu- part d'entre eux préfère communiquer dans la langue des signes. Du point de vue des sciences de l'information, les LS constituent un groupe de minorités linguistiques peu représentées dans l'univers du numérique. Et, de fait, les sourds sont les sujets les plus touchés par la fracture numérique.

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Teodoro, Beatriz Tomazela. "Sistema de reconhecimento automático de Língua Brasileira de Sinais." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/100/100131/tde-20122015-212746/.

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O reconhecimento de língua de sinais é uma importante área de pesquisa que tem como objetivo atenuar os obstáculos impostos no dia a dia das pessoas surdas e/ou com deficiência auditiva e aumentar a integração destas pessoas na sociedade majoritariamente ouvinte em que vivemos. Baseado nisso, esta dissertação de mestrado propõe o desenvolvimento de um sistema de informação para o reconhecimento automático de Língua Brasileira de Sinais (LIBRAS), que tem como objetivo simplificar a comunicação entre surdos conversando em LIBRAS e ouvintes que não conheçam esta língua de sinais. O reconhecimento

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Cardoso, Maria Eduarda de Araújo. "Segmentação automática de Expressões Faciais Gramaticais com Multilayer Perceptrons e Misturas de Especialistas." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/100/100131/tde-25112018-203224/.

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O reconhecimento de expressões faciais é uma área de interesse da ciência da computação e tem sido um atrativo para pesquisadores de diferentes áreas, pois tem potencial para promover o desenvolvimento de diferentes tipos de aplicações. Reconhecer automaticamente essas expressões tem se tornado um objetivo, principalmente na área de análise do comportamento humano. Especialmente para estudo das línguas de sinais, a análise das expressões faciais é importante para a interpretação do discurso, pois é o elemento que permite expressar informação prosódica, suporta o desenvolvimento da estrutura gr

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Silva, Renato Kimura da. "Interfaces naturais e o reconhecimento das línguas de sinais." Pontifícia Universidade Católica de São Paulo, 2013. https://tede2.pucsp.br/handle/handle/18125.

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Made available in DSpace on 2016-04-29T14:23:20Z (GMT). No. of bitstreams: 1 Renato Kimura da Silva.pdf: 3403382 bytes, checksum: 99bab2a00a7da4496b0eea8ad640d9bf (MD5) Previous issue date: 2013-06-07<br>Interface is an intermediate layer between two faces. In the computational context, we could say that the interface exists on the interactive intermediation between two subjects, or between subject and program. Over the years, the interfaces have evolved constantly: from the monochromatic text lines to the mouse with the exploratory concept of graphic interfaces to the more recent natura

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Anjo, Mauro dos Santos. "Avaliação das técnicas de segmentação, modelagem e classificação para o reconhecimento automático de gestos e proposta de uma solução para classificar gestos da libras em tempo real." Universidade Federal de São Carlos, 2013. https://repositorio.ufscar.br/handle/ufscar/523.

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Made available in DSpace on 2016-06-02T19:06:03Z (GMT). No. of bitstreams: 1 4988.pdf: 3663610 bytes, checksum: 1eb03927c23747c4a6420de5624f8571 (MD5) Previous issue date: 2013-10-22<br>Universidade Federal de Sao Carlos<br>Multimodal interfaces are becoming popular and trying to enhance user experience through the use of natural forms of interaction. Among these forms we have speech and gestures inputs. Speech recognition is already a common feature in our daily basis but gesture recognition has just now being widely used as a new form of interaction. The Brazilian Sign Language (Libras) wa

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Silva, Brunna Carolinne Rocha. "Desenvolvimento de tecnologia baseada em redes neurais artificiais para reconhecimento de gestos da língua de sinais." Universidade Federal de Goiás, 2018. http://repositorio.bc.ufg.br/tede/handle/tede/8725.

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