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Dissertations / Theses on the topic 'Vision based hand gesture recognition'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Crawford, Gordon Finlay. "Vision-based analysis, interpretation and segmentation of hand shape using six key marker points." Thesis, University of Ulster, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.243732.

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2

Bernard, Arnaud Jean Marc. "Human computer interface based on hand gesture recognition." Thesis, Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/42748.

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With the improvement of multimedia technologies such as broadband-enabled HDTV, video on demand and internet TV, the computer and the TV are merging to become a single device. Moreover the previously cited technologies as well as DVD or Blu-ray can provide menu navigation and interactive content. The growing interest in video conferencing led to the integration of the webcam in different devices such as laptop, cell phones and even the TV set. Our approach is to directly use an embedded webcam to remotely control a TV set using hand gestures. Using specific gestures, a user is able to control

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3

Hettiarachchi, Randima. "Multi-Manifold learning and Voronoi region-based segmentation with an application in hand gesture recognition." Elsevier, 2015. http://hdl.handle.net/1993/31969.

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A computer vision system consists of many stages, depending on its application. Feature extraction and segmentation are two key stages of a typical computer vision system and hence developments in feature extraction and segmentation are significant in improving the overall performance of a computer vision system. There are many inherent problems associated with feature extraction and segmentation processes of a computer vision system. In this thesis, I propose novel solutions to some of these problems in feature extraction and segmentation. First, I explore manifold learning, which is a no

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4

Lam, Benny, and Jakob Nilsson. "Creating Good User Experience in a Hand-Gesture-Based Augmented Reality Game." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-156878.

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The dissemination of new innovative technology requires feasibility and simplicity. The problem with marker-based augmented reality is similar to glove-based hand gesture recognition: they both require an additional component to function. This thesis investigates the possibility of combining markerless augmented reality together with appearance-based hand gesture recognition by implementing a game with good user experience. The methods employed in this research consist of a game implementation and a pre-study meant for measuring interactive accuracy and precision, and for deciding upon which g

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5

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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Zanghieri, Marcello. "sEMG-based hand gesture recognition with deep learning." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18112/.

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Hand gesture recognition based on surface electromyographic (sEMG) signals is a promising approach for the development of Human-Machine Interfaces (HMIs) with a natural control, such as intuitive robot interfaces or poly-articulated prostheses. However, real-world applications are limited by reliability problems due to motion artifacts, postural and temporal variability, and sensor re-positioning. This master thesis is the first application of deep learning on the Unibo-INAIL dataset, the first public sEMG dataset exploring the variability between subjects, sessions and arm postures, by colle

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7

Huang, Yu. "Hand gesture recognition methods based on concept learning." Thesis, University of Wales Trinity Saint David, 2015. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.667760.

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8

Akremi, Mohamed. "Manifold-Based Approaches for Action and Gesture Recognition." Electronic Thesis or Diss., université Paris-Saclay, 2025. http://www.theses.fr/2025UPAST045.

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La reconnaissance des actions humaines (HAR) est devenue un domaine de recherche essentiel en raison de ses nombreuses applications dans le monde réel, notamment l'interaction homme-machine, la santé intelligente, la réalité virtuelle, la surveillance, le contrôle des drones (UAV) et les systèmes autonomes. Au cours des dernières décennies, de nombreuses approches ont été développées pour reconnaître les actions humaines à partir de séquences vidéo RGB monoculaires. Plus récemment, l'émergence des capteurs de profondeur a favorisé le développement de l'analyse des activités en 3D et de la reco

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Abi-Rached, Habib. "Stereo-based hand gesture tracking and recognition in immersive stereoscopic displays /." Thesis, Connect to this title online; UW restricted, 2006. http://hdl.handle.net/1773/6012.

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10

Forsberg, Axel. "A Wavelet-Based Surface Electromyogram Feature Extraction for Hand Gesture Recognition." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-39766.

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The research field of robotic prosthetic hands have expanded immensely in the last couple of decades and prostheses are in more commercial use than ever. Classification of hand gestures using sensory data from electromyographic signals in the forearm are primary for any advanced prosthetic hand. Improving classification accuracy could lead to more user friendly and more naturally controlled prostheses. In this thesis, features were extracted from wavelet transform coefficients of four channel electromyographic data and used for classifying ten different hand gestures. Extensive search for suit

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11

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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12

Dominio, Fabio. "Real-time hand gesture recognition exploiting multiple 2D and 3D cues." Doctoral thesis, Università degli studi di Padova, 2015. http://hdl.handle.net/11577/3424298.

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The recent introduction of several 3D applications and stereoscopic display technologies has created the necessity of novel human-machine interfaces. The traditional input devices, such as keyboard and mouse, are not able to fully exploit the potential of these interfaces and do not offer a natural interaction. Hand gestures provide, instead, a more natural and sometimes safer way of interacting with computers and other machines without touching them. The use cases for gesture-based interfaces range from gaming to automatic sign language interpretation, health care, robotics, and vehicle autom

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13

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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14

De, Smedt Quentin. "Dynamic hand gesture recognition : from traditional handcrafted to recent deep learning approaches." Thesis, Lille 1, 2017. http://www.theses.fr/2017LIL10139/document.

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Les gestes de la main sont le moyen de communication non verbal le plus naturel et le plus intuitif lorsqu'il est question d'interaction avec un ordinateur. L'analyse des gestes de la main s'appuie sur l'estimation de la pose de la main et la reconnaissance de gestes. L'estimation de la pose de la main est considérée comme un défi difficile du fait de la petite taille d'une main, de sa plus grande complexité et de ses nombreuses occultations. Par ailleurs, le développement d'un système de reconnaissance des gestes est également difficile du fait des grandes dissimilarités entre les gestes déri

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15

Mancini, Mattia. "Development of an embedded EMG-based wristband for hand gesture recognition using machine learning algorithms." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/15259/.

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With the recent improvement of flexible electronics, wearable devices are becoming more and more non-invasive and comfortable, pervading fitness and health-care applications. Wearable devices allow unobtrusive monitoring of vital signs and physiological parameters, enabling advanced Human Machine Interaction (HMI) as well. On the other hand, battery lifetime remains a challenge especially when they are equipped with bio-medical sensors and not used as simple data logger. In this thesis, we present a flexible wristband, designed on a flexible PCB strip, for real-time EMG-based hand gesture reco

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Blonski, Brian M. "THE USE OF CONTEXTUAL CLUES IN REDUCING FALSE POSITIVES IN AN EFFICIENT VISION-BASED HEAD GESTURE RECOGNITION SYSTEM." DigitalCommons@CalPoly, 2010. https://digitalcommons.calpoly.edu/theses/295.

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This thesis explores the use of head gesture recognition as an intuitive interface for computer interaction. This research presents a novel vision-based head gesture recognition system which utilizes contextual clues to reduce false positives. The system is used as a computer interface for answering dialog boxes. This work seeks to validate similar research, but focuses on using more efficient techniques using everyday hardware. A survey of image processing techniques for recognizing and tracking facial features is presented along with a comparison of several methods for tracking and ident

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17

Wang, Chong, and 王翀. "Joint color-depth restoration with kinect depth camera and its applications to image-based rendering and hand gesture recognition." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/206343.

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18

Saleh, Alraimi Adel. "Development of New Models for Vision-Based Human Activity Recognition." Doctoral thesis, Universitat Rovira i Virgili, 2019. http://hdl.handle.net/10803/670893.

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Els mètodes de reconeixement d'accions permeten als sistemes intel·ligents reconèixer accions humanes en vídeos de la vida quotidiana. No obstant, molts mètodes de reconeixement d'accions donen taxes notables d’error de classificació degut a les grans variacions dins dels vídeos de la mateixa classe i als canvis en el punt de vista, l'escala i el fons. Per reduir la classificació incorrecta , proposem un nou mètode de representació de vídeo que captura l'evolució temporal de l'acció que succeeix en el vídeo, un nou mètode per a la segmentació de mans i un nou mètode per al reconeixement d'act

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19

Nguyen, Van Toi. "Visual interpretation of hand postures for human-machine interaction." Thesis, La Rochelle, 2015. http://www.theses.fr/2015LAROS035/document.

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Aujourd'hui, les utilisateurs souhaitent interagir plus naturellement avec les systèmes numériques. L'une des modalités de communication la plus naturelle pour l'homme est le geste de la main. Parmi les différentes approches que nous pouvons trouver dans la littérature, celle basée sur la vision est étudiée par de nombreux chercheurs car elle ne demande pas de porter de dispositif complémentaire. Pour que la machine puisse comprendre les gestes à partir des images RGB, la reconnaissance automatique de ces gestes est l'un des problèmes clés. Cependant, cette approche présente encore de multiple

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20

Ribeiro, Hebert Luchetti. "Reconhecimento de gestos usando segmentação de imagens dinâmicas de mãos baseada no modelo de mistura de gaussianas e cor de pele." Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/18/18133/tde-27112006-132158/.

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O objetivo deste trabalho é criar uma metodologia capaz de reconhecer gestos de mãos, a partir de imagens dinâmicas, para interagir com sistemas. Após a captação da imagem, a segmentação ocorre nos pixels pertencentes às mãos que são separados do fundo pela segmentação pela subtração do fundo e filtragem de cor de pele. O algoritmo de reconhecimento é baseado somente em contornos, possibilitando velocidade para se trabalhar em tempo real. A maior área da imagem segmentada é considerada como região da mão. As regiões detectadas são analisadas para determinar a posição e a orientação da mão. A p

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21

Mráz, Stanislav. "Rozpoznání gest ruky v obrazu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2011. http://www.nusl.cz/ntk/nusl-219059.

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This master’s thesis is dealing with recognition of an easy static gestures in order to computer controlling. First part of this work is attended to the theoretical review of methods used to hand segmentation from the image. Next methods for hang gesture classification are described. The second part of this work is devoted to choice of suitable method for hand segmentation based on skin color and movement. Methods for hand gesture classification are described in next part. Last part of this work is devoted to description of proposed system.

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Bravenec, Tomáš. "Počítačové vidění a detekce gest rukou a prstů." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2019. http://www.nusl.cz/ntk/nusl-400533.

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Diplomová práce je zaměřena na detekci a rozpoznání gest rukou a prstů ve statických obrazech i video sekvencích. Práce obsahuje shrnutí několika různých přístupů k samotné detekci a také jejich výhody i nevýhody. V práci je též obsažena realizace multiplatformní aplikace napsané v Pythonu s použitím knihoven OpenCV a PyTorch, která dokáže zobrazit vybraný obraz nebo přehrát video se zvýrazněním rozpoznaných gest.

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23

Chang, Kuo-Jun, and 張國君. "Computer Vision Based Hand Gesture Recognition System." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/51191244799518697469.

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24

Wu, Chen-Hao, and 吳振豪. "Vision Based Hand Gesture Recognition in Cluttered Backgrounds." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/25796162095989573012.

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碩士<br>國立臺灣大學<br>電機工程學研究所<br>103<br>A robust algorithm capable of segmenting specified hand gestures in cluttered image sequences is proposed. Typically, vision-based gesture recognition systems suffer from the difficulty of hand region segmentation, which includes the change of lighting conditions, the presence of other skin color objects and the movement of background objects. The main concern of this paper is to design an algorithm for RGB camera that locates hand region correctly even under complex backgrounds. The proposed segmentation algorithm, thumb-cover detection algorithm, restricts

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25

Jyun-SiangFan and 范鈞翔. "Study on Vision-Based Static Hand Gesture Recognition." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/fazwg7.

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Ghosh, Dipak Kumar. "A Framework for Vision-based Static Hand Gesture Recognition." Thesis, 2016. http://ethesis.nitrkl.ac.in/8052/1/2016_510EC106_Dipak_k_Ghosh_Framework.pdf.

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In today’s technical world, the intellectual computing of a efficient human-computer interaction (HCI) or human alternative and augmentative communication (HAAC) is essential in our lives. Hand gesture recognition is one of the most important techniques that can be used to build up a gesture based interface system for HCI or HAAC application. Therefore, suitable development of gesture recognition method is necessary to design advance hand gesture recognition system for successful applications like robotics, assistive systems, sign language communication, virtual reality etc. However, the varia

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Reddy, Dandu Amarnatha. "Vision Based Hand Gesture Recognition for Human Computer Interaction." Thesis, 2018. http://ethesis.nitrkl.ac.in/9978/1/2018_MT_216EC6254_DAReddy_Vision.pdf.

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The hand gesture recognition system is widely used in the development of human-machine interaction. The vision-based hand gesture recognition is achieved by the following steps: preprocessing, feature extraction and classification. The aim of preprocessing stage is to localize the hand region from the image frame. The Laplacian of Gaussian filtering technique along with zero crossing detector is applied on hand gesture images to detect the edges of hand region. This work proposes a novel feature extraction technique, which is based on local histogram feature descriptor (LHFD). The proposed fea

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Wu, Wen-Chin, and 吳玟槿. "Computer Vision-Based Hand Gesture Recognition for Human-Robot Interactions." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/qnqd5m.

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碩士<br>國立臺北科技大學<br>電機工程系所<br>99<br>This thesis focuses on the research of human hand gesture detection and recognition for the indoor surveillance system, which is capable of interacting with a mobile robot. Through recognizing human hand gesture, the developed system can direct the robot to carry out various actions. In the strategy of hand gesture detection, the original image is transformed to YCbCr color space and the subspace of human skin color in the face and hands can then be segmented. By means of the posture feature of human body, the fuzzy segment model is designed to identify the ha

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Liao, Chung-ju, and 廖崇儒. "Vision-based hand gesture recognition system for users on wheelchairs." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/50702407836241470709.

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碩士<br>國立臺灣科技大學<br>電機工程系<br>98<br>In this study, a hand gesture recognition system is proposed for users on wheelchairs. Unlike other studies that focus on the stage of hand gesture recognition, many problems are considered, such as detection of when a hand reaches in the field of the camera view or detection of a full palm. There are 4 stages in the proposed system, detection of the appearance of hands, segmentation of hand regions, detection of full palm and hand gesture recognition. Detection of the appearance of hands is to find out when a hand appears in the front of the camera. The hand r

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Liu, Ming-Shan, and 劉明山. "A Study on Vision-Based Hand Tracking and Gesture Recognition." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/14638549661509171398.

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碩士<br>國立成功大學<br>電機工程學系碩博士班<br>97<br>Nowadays, most of human computer interface (HCI) use the keyboard or mouse as the command input device. However, gesture is considered one of the most natural ways for human to communicate with other people or to issue commands. Generally, the gesture recognition system is designed based on computer vision to automatically detect/track human hands and also recognize the gesture. In object detection and tracking, this thesis develops a multi-cue similarity measurement algorithm for hand detection, and use an object based method to continuously track the hand.

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Chung-Yang, Hsieh, and 謝中揚. "A Study on Vision-based Human Action and Hand Gesture Recognition." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/ws4zza.

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博士<br>國立中正大學<br>資訊工程研究所<br>104<br>In this dissertation, we develop three systems for gesture and action recognition. First system uses the trajectory of hand motion as the datum source for sign language classification and retrieval. In this system, a trajectory is ﬁrstly projected by the Kernel Principal Component Analysis (KPCA) which can be considered as an implicit mapping to a much higher-dimensional feature space. The high dimensionality can effectively improve the accuracy in recognizing motion trajectories. Then, Nonparametric Discriminant Analysis (NDA) is used to extract the most disc

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Yao, Hong-Lin, and 姚宏霖. "NI-Vision-Based Hand Gesture Recognition Applying to an AX-12 Robot Arm." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/49036731411766519887.

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碩士<br>國立勤益科技大學<br>機械工程系<br>102<br>This research applied a 4-DOF robot arm, which is assembled with the AX-12 intelligent servo motors manufactured by ROBOTICS Inc. Based on the Inverse Kinematics, the rotation angles of the motors were calculated to move the arm to the target precisely. This research introduces the Android system combined with the NI (National Instrument) vision system for image recognition. A self-defined hand gesture was used to control the closure of the gripper which is located at the end of the robot arm. To response with the varieties of different environments during the

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Trigueiros, Paulo José de Albuquerque Cardoso. "Hand gesture recognition system based in computer vision and machine learning: Applications on human-machine interaction." Doctoral thesis, 2014. http://hdl.handle.net/1822/40407.

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Tese de Doutoramento em Engenharia de Eletrónica e de Computadores<br>Sendo uma forma natural de interação homem-máquina, o reconhecimento de gestos implica uma forte componente de investigação em áreas como a visão por computador e a aprendizagem computacional. O reconhecimento gestual é uma área com aplicações muito diversas, fornecendo aos utilizadores uma forma mais natural e mais simples de comunicar com sistemas baseados em computador, sem a necessidade de utilização de dispositivos extras. Assim, o objectivo principal da investigação na área de reconhecimento de gestos aplicada à

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Krüger, Maximilian [Verfasser]. "Vision based tracking and recognition of dynamic hand gestures / vorgelegt von Maximilian Krüger." 2007. http://d-nb.info/98738449X/34.

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Mandal, Itishree, and Samiksha Ray. "Hand gesture based digit recognition." Thesis, 2014. http://ethesis.nitrkl.ac.in/6488/1/E-30.pdf.

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Recognition of static hand gestures in our daily plays an important role in human-computer interaction. Hand gesture recognition has been a challenging task now a days so a lot of research topic has been going on due to its increased demands in human computer interaction. Since Hand gestures have been the most natural communication medium among human being, so this facilitate efficient human computer interaction in many electronics gazettes . This has led us to take up this task of hand gesture recognition. In this project different hand gestures are recognized and no of fingers are counted. R

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Chao-Hui, Huang, and 黃朝暉. "Silhouette-Based Hand Gesture Recognition System." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/93267423724812911907.

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碩士<br>中華大學<br>資訊工程學系碩士班<br>89<br>Let computer science taking over some jobs of human is a dream. However, Duplicating of human intuition is very difficult. In this paper, we try to duplicate the vision of human intuition, and present that by human hand gesture recognition with low computation requirement. Usually, the human hand gesture recognition system requires either high cost of computation, or special auxiliary devices. Due to this, a faster and convenient method becomes necessary. For the sake of real-time implementation, we developed two main algorithms: Curve Detection Algorithm (CDA)

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Farng, Hsiang-Chien, and 房祥騫. "Hand Gesture Recognition Based Messenger System." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/41613833826678599352.

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碩士<br>淡江大學<br>電機工程學系碩士班<br>101<br>This thesis develops a messenger system based on hand gesture recognition technique. Flex sensors and an accelerometer combined with a wearable gesture sensing device are used to measure the bending angle of each finger and the wrist. The measurements are transmitted to PC by RS232 transmission protocol. After filtering to smooth the data, these data are processed through the gesture recognition algorithm to generate the corresponding text. Finally, the users can interact with the others by the homemade messenger system interface through TCP / IP protocol. The

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Han, Cheng-han, and 韓承翰. "Reduced Dimensional SURF Based Hand Gesture Recognition." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/25614920324976868386.

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碩士<br>國立臺灣科技大學<br>電機工程系<br>98<br>Gesture recognition is a popular research topic. The application includes human-computer interaction interface for video games and other household appliances etc. In this research, we use SURF(speeded-up robust features) based with PCA(principal component analysis) for static hand gesture recognition. First, we find the stable points in scale space. Moreover, we use a Hessian Matrix-based measure for the detector, because of its good performance in computation time and accuracy. Next, in order to be invariant to rotation, we detect the orientation of the points

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Yu, Yu-Tien, and 余玉田. "Intelligent Hand Gesture Recognition System Design Based On Hand Contour." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/87286017743409235017.

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碩士<br>國立交通大學<br>電機學院碩士在職專班電機與控制組<br>98<br>This thesis proposes an intelligent recognition system based on neural network for hand gesture recognition. The design process is partitioned into three parts. First, the hand gesture is abstracted from an image by image procession and the characteristics of hand gesture are retrieved by the hand contour scanning method. Then, based on the back-propagation algorithm, the neural network is trained to learn the charateristics of the hand gesture. Finally, experiments are included to demonstrate the feasibility of the developed intelligent hand gesture r

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Chen, Wei-Lun, and 陳帷綸. "Depth-based Hand Gesture Recognition Using Hand Movements And Defects." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/39155254521751013827.

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碩士<br>國立臺灣科技大學<br>電子工程系<br>103<br>The hand gesture recognition has a long history within the computer vision community and is one natural and intuitional way to communicate with human and machine. Since low-cost depth cameras have been launched, depth cameras become more and more affordable in consumer electronics. In this thesis, we proposed a dynamic hand gesture recognition system by using only the depth information. The proposed system can recognize twelve different dynamic hand gestures, including swipes, scales, push, wave, rotates, circle, and drag. First, the background subtraction is

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Lai, Yun-Chien, and 賴韻芊. "A Computer-Vision Based Gesture Recognition System." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/20550549557880099745.

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碩士<br>國立臺灣大學<br>資訊網路與多媒體研究所<br>97<br>This paper presents a new approach to 3D model-based gesture recognition for controlling multimedia player. The motivation of this paper is to make home appliance aware of user’s intention. This 3D model-based gesture recognition system adopts a Bayesian framework to track the user’s hand posture and to recognize meaning of these postures for controlling 3D player interactively. To avoid the high dimensionality of the whole 3D upper body model, which may complicate the gesture tracking problem, our system applies a new hierarchical tracking algorithm to imp

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Hu, Jhen-Da, and 胡振達. "Hybrid Hand Gesture Recognition Based on Depth Camera." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/7febjn.

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碩士<br>國立交通大學<br>多媒體工程研究所<br>103<br>Hand gesture recognition (HRG) becomes one of most popular topics in recent years because that hand gesture is one of the most natural and intuitive way of communication between Human and machines. It is widely used in HCI (Human-Computer-interaction). In this paper, we proposed a method for hand gesture recognition based on depth camera. Firstly, the hand information within depth image is separated from background based on a specific range of depth. And the contour of hand is detected after segmentation. After that, we estimate centroid of hand, and palm siz

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Kuo, Yi-Hua, and 郭鎰華. "Hand Gesture Recognition Method Based On Smart Watch." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/qvar4a.

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碩士<br>元智大學<br>通訊工程學系<br>105<br>Gesture recognition is a popular research. Image-based gesture recognition requires a large amount of image processing technology and is limited to use in a particular location, making it difficult to implement on a mobile device. Because of the prevalence of micro-electromechanical systems, the acceleration sensor has many features, such as small size, low power consumption, low cost, high accuracy and so on. With smart devices, users will not be limited by places. These features make the acceleration sensors have been widely applied. The paper presents a method

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Ou, Ju-Hsuan, and 歐雨軒. "Mouse Manipulation Simulation based on Hand Gesture Recognition." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/50626150787111151949.

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碩士<br>元智大學<br>通訊工程學系<br>98<br>This thesis presents a novel vision-based human-computer interaction system. We use webcam to capture hand gesture pictures and simulate mouse manipulation. The system employs moving object detection, shadow detection, and skin color detection to obtain the hand region from captured images. Kernel density estimation (KDE) and many features on hand contour are used to recognize the mouse manipulation based on hand gestures. Additionally, the system also includes the mouse gesture recognition. It is able to recognize four kinds of mouse gestures by a mouse gesture r

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Kai-JungChung and 鍾凱融. "Hand Gesture Recognition Based on Dynamic Bayesian Network." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/55769250383578545818.

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碩士<br>國立成功大學<br>資訊工程學系碩博士班<br>101<br>In this thesis, we construct a hand gesture recognition system based on dynamic Bayesian network model through using the human skeleton information captured by Kinect sensor. We estimate the model parameters of the dynamic Bayesian network with Expectation-maximization algorithm and the features including motion direction of both hands and the relative position between both hands and the face, and trained a gesture recognition system which is suitable for both one-hand and two-hand gestures. In the experiments, we focus on 10 common hand gestures of volleyb

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Tsai, Wei-Feng, and 蔡緯豐. "Hand Gesture Recognition Based on Deep Neural Network." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/b99k33.

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碩士<br>國立中央大學<br>電機工程學系<br>106<br>The purposes of this paper are to achieve hand gesture recognition and tracking hand position in real time via web camera. First, using skin-color detect and morphological operations to remove unnecessary noise. Then use the background subtraction method to determine the ROI(Region Of Intereest) region of hand. After obtaining the hand region, Kernel Correlation Filters (KCF) algorithm is used to track the hand. Finally, the hand area is scaled to the size of 100 * 120, then the fixed size of the image input to our CNN (Convolutional Neural Networks) network fo

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Chatterjee, Subhamoy. "Hand gesture recognition based on fusion of moments." Thesis, 2014. http://ethesis.nitrkl.ac.in/6485/1/E-31.pdf.

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This work is focussed on three main issues in developing a gesture recognition system. These are (i) Threshold independent skin colour segmentation using Modified K-means clustering and Mahalanobish distance (ii) illumination normalization (iii) user independent gesture recognition based on fusion of Moments. Since skin pixels can vary with different illumination condition, to find the range of skin pixels, becomes a hard task in case of colour space based skin colour segmentation. This work proposes a semi-supervised learning algorithm based on modified K-means clustering and Mahalanobis dist

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YEN, LI, and 嚴勵. "Machine Learning-Based Hand Gesture Recognition Based on mmWave Radar." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/9t38gb.

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碩士<br>國立臺北科技大學<br>電子工程系<br>107<br>Radar recognition plays an important role in the field of human-computer interaction (HCI). Based on radio wave, it is insensitive to sound, light, and atmosphere condition, leading to an advantageous sensing ability. As the usage of portable electronic products becomes a trend, the technology of micro-radar sensing on fine human-motion/hand-gesture recognition provides further HCI experience. Most of the studies about hand gesture sensing that uses mmWave FMCW Radar, build training dataset with spectrogram or range-Doppler image (RDI) produced from raw data,

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Hsu, Ying-shan, and 徐瑩珊. "Continuous 3D Gesture Recognition Based on Stereo Vision." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/60022886516784584160.

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碩士<br>國立中央大學<br>資訊工程研究所<br>98<br>In vision-based human-computer interaction system, hand region is usually the nearest object to cameras, therefore, it is effective to detect hand region by 3D information. We use Particle Swarm Optimization (PSO) algorithm to enhance the stability and robustness of continuous image hand region detection. This paper proposes a dual modal dynamic gesture recognition method. The first one (MIP) consists of motion history image (MHI), image moment feature extraction and probability neural network (PNN), the second one (FIS) is the fuzzy inference system based on t

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Hsieh, Min-Ting, and 謝旻廷. "Handwriting Pen Gesture Recognition Based on Computer Vision." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/71842083726352985367.

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碩士<br>元智大學<br>通訊工程學系<br>98<br>This thesis presents a handwriting pen gesture recognition system. The system recognizes handwriting pen gestures based on computer vision techniques. The fingertip writing trajectory of user is extracted from the video by image processing. After detecting the trajectory, the bending points are detected by angle calculating and kernel density estimation. The pen gesture trajectory is separated into sub-strokes according to the bending points. Then, a feature sequence is extracted from each sub-stroke. This system adopts the best clustering index and clustering alg

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