Relevant bibliographies by topics / Gesture Recognition

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Gesture Recognition'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Gesture Recognition"

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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 (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 di
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Meng, Yuting, Haibo Jiang, Nengquan Duan, and Haijun Wen. "Real-Time Hand Gesture Monitoring Model Based on MediaPipe’s Registerable System." Sensors 24, no. 19 (2024): 6262. http://dx.doi.org/10.3390/s24196262.

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Hand gesture recognition plays a significant role in human-to-human and human-to-machine interactions. Currently, most hand gesture detection methods rely on fixed hand gesture recognition. However, with the diversity and variability of hand gestures in daily life, this paper proposes a registerable hand gesture recognition approach based on Triple Loss. By learning the differences between different hand gestures, it can cluster them and identify newly added gestures. This paper constructs a registerable gesture dataset (RGDS) for training registerable hand gesture recognition models. Addition
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Ma, Xianmin, and Xiaofeng Li. "Dynamic Gesture Contour Feature Extraction Method Using Residual Network Transfer Learning." Wireless Communications and Mobile Computing 2021 (October 13, 2021): 1–11. http://dx.doi.org/10.1155/2021/1503325.

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The current dynamic gesture contour feature extraction method has the problems that the recognition rate of dynamic gesture contour feature and the recognition accuracy of dynamic gesture type are low, the recognition time is long, and comprehensive is poor. Therefore, we propose a dynamic gesture contour feature extraction method using residual network transfer learning. Sensors are used to integrate dynamic gesture information. The distance between the dynamic gesture and the acquisition device is detected by transfer learning, the dynamic gesture image is segmented, and the characteristic c
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Jasim, Mahmood, Tao Zhang, and Md Hasanuzzaman. "A Real-Time Computer Vision-Based Static and Dynamic Hand Gesture Recognition System." International Journal of Image and Graphics 14, no. 01n02 (2014): 1450006. http://dx.doi.org/10.1142/s0219467814500065.

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This paper presents a novel method for computer vision-based static and dynamic hand gesture recognition. Haar-like feature-based cascaded classifier is used for hand area segmentation. Static hand gestures are recognized using linear discriminant analysis (LDA) and local binary pattern (LBP)-based feature extraction methods. Static hand gestures are classified using nearest neighbor (NN) algorithm. Dynamic hand gestures are recognized using the novel text-based principal directional features (PDFs), which are generated from the segmented image sequences. Longest common subsequence (LCS) algor
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Badagan, Sana, Deeksha R, K. Tarun Sai Teja, and Chetan J. "HAND GESTURE RECOGNITION." International Journal of Engineering Applied Sciences and Technology 8, no. 6 (2023): 56–59. http://dx.doi.org/10.33564/ijeast.2023.v08i06.007.

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Continuous and dynamic gesture recognition is a vital research area that aims to develop systems capable of interpreting and understanding hand gestures involving continuous motion and temporal dynamics. This project focuses on addressing the challenges associated with recognizing and analyzing gestures that go beyond static poses. By leveraging techniques such as temporal modeling, motion analysis, and deep learning, the goal is to develop algorithms and models that can robustly track and interpret the fluidity and expressiveness of human hand movements. The project aims to enhance the unders
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K, Srinivas, and Manoj Kumar Rajagopal. "STUDY OF HAND GESTURE RECOGNITION AND CLASSIFICATION." Asian Journal of Pharmaceutical and Clinical Research 10, no. 13 (2017): 25. http://dx.doi.org/10.22159/ajpcr.2017.v10s1.19540.

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To recognize different hand gestures and achieve efficient classification to understand static and dynamic hand movements used for communications.Static and dynamic hand movements are first captured using gesture recognition devices including Kinect device, hand movement sensors, connecting electrodes, and accelerometers. These gestures are processed using hand gesture recognition algorithms such as multivariate fuzzy decision tree, hidden Markov models (HMM), dynamic time warping framework, latent regression forest, support vector machine, and surface electromyogram. Hand movements made by bo
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Park, Jisun, Yong Jin, Seoungjae Cho, Yunsick Sung, and Kyungeun Cho. "Advanced Machine Learning for Gesture Learning and Recognition Based on Intelligent Big Data of Heterogeneous Sensors." Symmetry 11, no. 7 (2019): 929. http://dx.doi.org/10.3390/sym11070929.

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With intelligent big data, a variety of gesture-based recognition systems have been developed to enable intuitive interaction by utilizing machine learning algorithms. Realizing a high gesture recognition accuracy is crucial, and current systems learn extensive gestures in advance to augment their recognition accuracies. However, the process of accurately recognizing gestures relies on identifying and editing numerous gestures collected from the actual end users of the system. This final end-user learning component remains troublesome for most existing gesture recognition systems. This paper p
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Fan, Jinlong, Yang Yue, Yu Wang, Bei Wan, Xudong Li, and Gengpai Hua. "A Continuous Gesture Segmentation and Recognition Method for Human-Robot Interaction." Journal of Physics: Conference Series 2213, no. 1 (2022): 012039. http://dx.doi.org/10.1088/1742-6596/2213/1/012039.

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Abstract The process of human-computer cooperation using gesture recognition can make people get rid of the limitations of traditional input devices such as mouse and keyboard, and control artificial intelligence devices more efficiently and naturally. As a new way of human-robot interaction (HRI), gesture recognition has made some progress. There are many ways to realize gesture recognition combined with visual recognition, motion information acquisition and EMG signal. The research on isolated language gesture recognition has been quite mature, but the expression semantics of isolated gestur
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Izuta, Ryo, Kazuya Murao, Tsutomu Terada, and Masahiko Tsukamoto. "Early gesture recognition method with an accelerometer." International Journal of Pervasive Computing and Communications 11, no. 3 (2015): 270–87. http://dx.doi.org/10.1108/ijpcc-03-2015-0016.

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Purpose – This paper aims to propose a gesture recognition method at an early stage. An accelerometer is installed in most current mobile phones, such as iPhones, Android-powered devices and video game controllers for the Wii or PS3, which enables easy and intuitive operations. Therefore, many gesture-based user interfaces that use accelerometers are expected to appear in the future. Gesture recognition systems with an accelerometer generally have to construct models with user’s gesture data before use and recognize unknown gestures by comparing them with the models. Because the recognition pr
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Nyirarugira, Clementine, Hyo-rim Choi, and TaeYong Kim. "Hand Gesture Recognition Using Particle Swarm Movement." Mathematical Problems in Engineering 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/1919824.

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We present a gesture recognition method derived from particle swarm movement for free-air hand gesture recognition. Online gesture recognition remains a difficult problem due to uncertainty in vision-based gesture boundary detection methods. We suggest an automated process of segmenting meaningful gesture trajectories based on particle swarm movement. A subgesture detection and reasoning method is incorporated in the proposed recognizer to avoid premature gesture spotting. Evaluation of the proposed method shows promising recognition results: 97.6% on preisolated gestures, 94.9% on stream gest
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Dissertations / Theses on the topic "Gesture Recognition"

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Davis, James W. "Gesture recognition." Honors in the Major Thesis, University of Central Florida, 1994. http://digital.library.ucf.edu/cdm/ref/collection/ETH/id/126.

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This item is only available in print in the UCF Libraries. If this is your Honors Thesis, you can help us make it available online for use by researchers around the world by following the instructions on the distribution consent form at http://library.ucf.edu/Systems/DigitalInitiatives/DigitalCollections/InternetDistributionConsentAgreementForm.pdf You may also contact the project coordinator, Kerri Bottorff, at kerri.bottorff@ucf.edu for more information.<br>Bachelors<br>Arts and Sciences<br>Computer Science
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Cheng, You-Chi. "Robust gesture recognition." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/53492.

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It is a challenging problem to make a general hand gesture recognition system work in a practical operation environment. In this study, it is mainly focused on recognizing English letters and digits performed near the steering wheel of a car and captured by a video camera. Like most human computer interaction (HCI) scenarios, the in-car gesture recognition suffers from various robustness issues, including multiple human factors and highly varying lighting conditions. It therefore brings up quite a few research issues to be addressed. First, multiple gesturing alternatives may share the same me
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Kaâniche, Mohamed Bécha. "Human gesture recognition." Nice, 2009. http://www.theses.fr/2009NICE4032.

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Dans cette thèse, nous voulons reconnaître les gestes (par ex. Lever la main) et plus généralement les actions brèves (par ex. Tomber, se baisser) effectués par un individu. De nombreux travaux ont été proposés afin de reconnaître des gestes dans un contexte précis (par ex. En laboratoire) à l’aide d’une multiplicité de capteurs (par ex. Réseaux de cameras ou individu observé muni de marqueurs). Malgré ces hypothèses simplificatrices, la reconnaissance de gestes reste souvent ambiguë en fonction de la position de l’individu par rapport aux caméras. Nous proposons de réduire ces hypothèses afin
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Semprini, Mattia. "Gesture Recognition: una panoramica." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/15672/.

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Per decenni, l’uomo ha interagito con i calcolatori e altri dispositivi quasi esclusivamente premendo i tasti e facendo "click" sul mouse. Al giorno d’oggi, vi è un grande cambiamento in atto a seguito di una ondata di nuove tecnologie che rispondono alle azioni più naturali, come il movimento delle mani o dell’intero corpo. Il mercato tecnologico è stato scosso in un primo momento dalla sostituzione delle tecniche di interazione standard con approcci di tipo "touch and motion sensing"; il passo successivo è l’introduzione di tecniche e tecnologie che permettano all’utente di accedere e manipo
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Gingir, Emrah. "Hand Gesture Recognition System." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612532/index.pdf.

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This thesis study presents a hand gesture recognition system, which replaces input devices like keyboard and mouse with static and dynamic hand gestures, for interactive computer applications. Despite the increase in the attention of such systems there are still certain limitations in literature. Most applications require different constraints like having distinct lightning conditions, usage of a specific camera, making the user wear a multi-colored glove or need lots of training data. The system mentioned in this study disables all these restrictions and provides an adaptive, effort free envi
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Dang, Darren Phi Bang. "Template based gesture recognition." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/41404.

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Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.<br>Includes bibliographical references (p. 65-66).<br>by Darren PHi Bang Dang.<br>M.S.
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Wang, Lei. "Personalized Dynamic Hand Gesture Recognition." Thesis, KTH, Medieteknik och interaktionsdesign, MID, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231345.

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Human gestures, with the spatial-temporal variability, are difficult to be recognized by a generic model or classifier that are applicable for everyone. To address the problem, in this thesis, personalized dynamic gesture recognition approaches are proposed. Specifically, based on Dynamic Time Warping(DTW), a novel concept of Subject Relation Network is introduced to describe the similarity of subjects in performing dynamic gestures, which offers a brand new view for gesture recognition. By clustering or arranging training subjects based on the network, two personalization algorithms are propo
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Espinoza, Victor. "Gesture Recognition in Tennis Biomechanics." Master's thesis, Temple University Libraries, 2018. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/530096.

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Electrical and Computer Engineering<br>M.S.E.E.<br>The purpose of this study is to create a gesture recognition system that interprets motion capture data of a tennis player to determine which biomechanical aspects of a tennis swing best correlate to a swing efficacy. For our learning set this work aimed to record 50 tennis athletes of similar competency with the Microsoft Kinect performing standard tennis swings in the presence of different targets. With the acquired data we extracted biomechanical features that hypothetically correlated to ball trajectory using proper technique and tested th
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Nygård, Espen Solberg. "Multi-touch Interaction with Gesture Recognition." Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9126.

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<p>This master's thesis explores the world of multi-touch interaction with gesture recognition. The focus is on camera based multi-touch techniques, as these provide a new dimension to multi-touch with its ability to recognize objects. During the project, a multi-touch table based on the technology Diffused Surface Illumination has been built. In addition to building a table, a complete gesture recognition system has been implemented, and different gesture recognition algorithms have been successfully tested in a multi-touch environment. The goal with this table, and the accompanying gesture r
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Khan, Muhammad. "Hand Gesture Detection & Recognition System." Thesis, Högskolan Dalarna, Datateknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:du-6496.

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The project introduces an application using computer vision for Hand gesture recognition. A camera records a live video stream, from which a snapshot is taken with the help of interface. The system is trained for each type of count hand gestures (one, two, three, four, and five) at least once. After that a test gesture is given to it and the system tries to recognize it.A research was carried out on a number of algorithms that could best differentiate a hand gesture. It was found that the diagonal sum algorithm gave the highest accuracy rate. In the preprocessing phase, a self-developed algori
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Books on the topic "Gesture Recognition"

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Escalera, Sergio, Isabelle Guyon, and Vassilis Athitsos, eds. Gesture Recognition. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57021-1.

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Konar, Amit, and Sriparna Saha. Gesture Recognition. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-62212-5.

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Dempsey, R. Dataglove gesture recognition using a neural network. UMIST, 1993.

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Chaudhary, Ankit. Robust Hand Gesture Recognition for Robotic Hand Control. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-4798-5.

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Yang, Ming-Hsuan, and Narendra Ahuja. Face Detection and Gesture Recognition for Human-Computer Interaction. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1423-7.

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Yang, Ming-Hsuan. Face Detection and Gesture Recognition for Human-Computer Interaction. Springer US, 2001.

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1950-, Ahuja Narendra, ed. Face detection and gesture recognition for human-computer interaction. Kluwer Academic, 2001.

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Sowa, Timo. Understanding coverbal iconic gestures in shape descriptions. Akademische Verlagsgesellschaft Aka, 2006.

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Mäntylä, Vesa-Matti. Discrete hidden Markov models with application to isolated user-dependent hand gesture recognition. Technical Research Centre of Finland, 2001.

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Eleni, Efthimiou, Kouroupetroglou Georgios, and Fotinia Stavroula-Evita, eds. Gesture and sign language in human-computer interaction and embodied communication: 9th International Gesture Workshop, GW 2011, Athens, Greece, May 25-27, 2011 : revised selected papers. Springer, 2012.

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Book chapters on the topic "Gesture Recognition"

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Escalera, Sergio, Vassilis Athitsos, and Isabelle Guyon. "Challenges in Multi-modal Gesture Recognition." In Gesture Recognition. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57021-1_1.

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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. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57021-1_10.

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Wan, Jun, Qiuqi Ruan, Wei Li, and Shuang Deng. "One-Shot Learning Gesture Recognition from RGB-D Data Using Bag of Features." In Gesture Recognition. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57021-1_11.

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Konečný, Jakub, and Michal Hagara. "One-Shot-Learning Gesture Recognition Using HOG-HOF Features." In Gesture Recognition. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57021-1_12.

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Jiang, Feng, Shengping Zhang, Shen Wu, Yang Gao, and Debin Zhao. "Multi-layered Gesture Recognition with Kinect." In Gesture Recognition. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57021-1_13.

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Wu, Jiaxiang, and Jian Cheng. "Bayesian Co-Boosting for Multi-modal Gesture Recognition." In Gesture Recognition. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57021-1_14.

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Goussies, Norberto A., Sebastián Ubalde, and Marta Mejail. "Transfer Learning Decision Forests for Gesture Recognition." In Gesture Recognition. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57021-1_15.

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Pitsikalis, Vassilis, Athanasios Katsamanis, Stavros Theodorakis, and Petros Maragos. "Multimodal Gesture Recognition via Multiple Hypotheses Rescoring." In Gesture Recognition. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57021-1_16.

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Gillian, Nicholas, and Joseph A. Paradiso. "The Gesture Recognition Toolkit." In Gesture Recognition. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57021-1_17.

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Nguyen-Dinh, Long-Van, Alberto Calatroni, and Gerhard Tröster. "Robust Online Gesture Recognition with Crowdsourced Annotations." In Gesture Recognition. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57021-1_18.

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Conference papers on the topic "Gesture Recognition"

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Ge, Yuncheng, Yewei Huang, Ye Julei, Huazixi Zeng, Hechong Su, and Zengyao Yang. "DMGR: Divisible Multi-complex Gesture Recognition Based on Word Segmentation Processing." In 2024 AHFE International Conference on Human Factors in Design, Engineering, and Computing (AHFE 2024 Hawaii Edition). AHFE International, 2024. http://dx.doi.org/10.54941/ahfe1005654.

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In the realm of gesture recognition and computer algorithm optimization, traditional approaches have predominantly focused on recognizing isolated gestures. However, this paradigm proves inadequate when confronted with complex gestural sequences, resulting in cumbersome recognition processes and diminished accuracy. Contemporary human-computer interaction (HCI) applications often necessitate users to perform intricate series of gestures, rather than isolated movements. Consequently, there is a pressing need for systems capable of not only recognizing individual gestures but also accurately seg
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Nyaga, Casam, and Ruth Wario. "Towards Kenyan Sign Language Hand Gesture Recognition Dataset." In 14th International Conference on Applied Human Factors and Ergonomics (AHFE 2023). AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1003281.

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Datasets for hand gesture recognition are now an important aspect of machine learning. Many datasets have been created for machine learning purposes. Some of the notable datasets include Modified National Institute of Standards and Technology (MNIST) dataset, Common Objects in Context (COCO) dataset, Canadian Institute For Advanced Research (CIFAR-10) dataset, LeNet-5, AlexNet, GoogLeNet, The American Sign Language Lexicon Video Dataset and 2D Static Hand Gesture Colour Image Dataset for ASL Gestures. However, there is no dataset for Kenya Sign language (KSL). This paper proposes the creation
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Patel, Shubh, and R. Deepa. "Hand Gesture Recognition Used for Functioning System Using OpenCV." In International Research Conference on IOT, Cloud and Data Science. Trans Tech Publications Ltd, 2023. http://dx.doi.org/10.4028/p-4589o3.

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Recently much attention has been paid to the design of intelligent and natural user-computer interfaces. Hand Gesture Recognition systems has been developed continuously as its ability to interact with the machines. Now-a-days the news of metaverse ecosystem has increased the number of system in gesture recognition. Gestures are used to communicate with the PCs in a virtual environment. In this project Hand gestures are used to communicate information non-verbally which are free of expression to do a particular task. Here the hand gestures are recognized by using hand skeleton recognition usin
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Zhang, Hong, and Jeong-Hoi Koo. "Development of a Wearable Gesture Recognition System." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-80061.

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This paper presents an effort of developing a wearable gesture recognition system. The objective of this work is to design and build a mechatronic device that can recognize human gestures. This device can be used to help the communication between humans or humans and machines (such as unmanned vehicles). The device is composed of two main components, a data acquisition system and a gesture recognition system. The data acquisition system obtains sensory information from human motions and encodes the information for transmission to the gesture recognition system. Upon receiving the signals, the
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Chen, Haodong, Wenjin Tao, Ming C. Leu, and Zhaozheng Yin. "Dynamic Gesture Design and Recognition for Human-Robot Collaboration With Convolutional Neural Networks." In 2020 International Symposium on Flexible Automation. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/isfa2020-9609.

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Abstract Human-robot collaboration (HRC) is a challenging task in modern industry and gesture communication in HRC has attracted much interest. This paper proposes and demonstrates a dynamic gesture recognition system based on Motion History Image (MHI) and Convolutional Neural Networks (CNN). Firstly, ten dynamic gestures are designed for a human worker to communicate with an industrial robot. Secondly, the MHI method is adopted to extract the gesture features from video clips and generate static images of dynamic gestures as inputs to CNN. Finally, a CNN model is constructed for gesture reco
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Yi, Zhigang, Mingyu Zhou, Dan Xue, and Shusheng Peng. "Static Gesture Recognition in the cabin Based on 3D-TOF and Low Computing Power." In SAE 2023 Intelligent and Connected Vehicles Symposium. SAE International, 2023. http://dx.doi.org/10.4271/2023-01-7068.

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&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Traditional static gesture recognition algorithms are easily affected by the complex environment inside the cabin, resulting in low recognition rates. Compared with RGB photos captured by traditional cameras, the depth images captured by 3D-TOF cameras can not only reduce the influence of complex environments inside the cabin, but also protect crew privacy. Therefore, this paper proposes a low-computing static gesture recognition method based on 3D-TOF in the cabin. A low-parameter lightweight convolutional neural networ
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Radkowski, Rafael, and Christian Stritzke. "Comparison Between 2D and 3D Hand Gesture Interaction for Augmented Reality Applications." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-48155.

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This paper presents a comparison between 2D and 3D interaction techniques for Augmented Reality (AR) applications. The interaction techniques are based on hand gestures and a computer vision-based hand gesture recognition system. We have compared 2D gestures and 3D gestures for interaction in AR application. The 3D recognition system is based on a video camera, which provides an additional depth image to each 2D color image. Thus, spatial interactions become possible. Our major question during this work was: Do depth images and 3D interaction techniques improve the interaction with AR applicat
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Wang, Nana, Jianwei Niu, Xuefeng Liu, et al. "BeyondVision: An EMG-driven Micro Hand Gesture Recognition Based on Dynamic Segmentation." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/668.

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Hand gesture recognition (HGR) plays a pivotal role in natural and intuitive human-computer interactions. Recent HGR methods focus on recognizing gestures from vision-based images or videos. However, vision-based methods are limited in recognizing micro hand gestures (MHGs) (e.g., pinch within 1cm) and gestures with occluded fingers. To address these issues, combined with the electromyography (EMG) technique, we propose BeyondVision, an EMG-driven MHG recognition system based on deep learning. BeyondVision consists of a wristband-style EMG sampling device and a tailored lightweight neural netw
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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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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. T
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Teng, Zhiqiang, Haodong Chen, Qitao Hou, Wanbing Song, Chenchen Gu, and Ping Zhao. "Design of a Cognitive Rehabilitation System Based on Gesture Recognition." In ASME 2020 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/imece2020-23579.

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Abstract Computer-assisted cognitive training is an effective intervention for patients with mild cognitive impairment (MCI), which can avoid the disadvantages of traditional cognitive training that consumes a lot of medical resources and is difficult to be standardized. However, many computer-assisted cognitive training systems have unfriendly human-computer interaction, for not considering that most MCI patients have certain difficulties in using computers. In this paper, we design a cognitive training system which allows patients to implement human-computer interaction through gestures. Fir
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Reports on the topic "Gesture Recognition"

1

Yang, Jie, and Yangsheng Xu. Hidden Markov Model for Gesture Recognition. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada282845.

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Morton, Paul R., Edward L. Fix, and Gloria L. Calhoun. Hand Gesture Recognition Using Neural Networks. Defense Technical Information Center, 1996. http://dx.doi.org/10.21236/ada314933.

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Vira, Naren. Gesture Recognition Development for the Interactive Datawall. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada476755.

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Zhao, Ruyin. CSI-based Gesture Recognition and Object Detection. Iowa State University, 2021. http://dx.doi.org/10.31274/cc-20240624-456.

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Lampton, Donald R., Bruce W. Knerr, Bryan R. Clark, Glenn A. Martin, and Donald A. Washburn. Gesture Recognition System for Hand and Arm Signals. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada408459.

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Venetsky, Larry, Mark Husni, and Mark Yager. Gesture Recognition for UCAV-N Flight Deck Operations. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada422629.

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Pasupuleti, Murali Krishna. Next-Generation Extended Reality (XR): A Unified Framework for Integrating AR, VR, and AI-driven Immersive Technologies. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv325.

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Abstract: Extended Reality (XR), encompassing Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR), is evolving into a transformative technology with applications in healthcare, education, industrial training, smart cities, and entertainment. This research presents a unified framework integrating AI-driven XR technologies with computer vision, deep learning, cloud computing, and 5G connectivity to enhance immersion, interactivity, and scalability. AI-powered neural rendering, real-time physics simulation, spatial computing, and gesture recognition enable more realistic and adap
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