Gotowe bibliografie tematyczne / And MediaPipe

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  1. Artykuły w czasopismach
  2. Rozprawy doktorskie
  3. Części książek
  4. Streszczenia konferencji

Gotowa bibliografia na temat „And MediaPipe”

Autor: Grafiati
Data publikacji: 2 czerwca 2025
Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "And MediaPipe"

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Ratusaria, Rahul, Tushar Baghel, Ayush Chander Vanshi, and Neeraj Garg. "GYM REP TRACKER USING MEDIAPIPE AND PYTHON." International journal of multidisciplinary advanced scientific research and innovation 1, no. 10 (2021): 240–45. http://dx.doi.org/10.53633/ijmasri.2021.1.10.001.

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Human Pose estimation has grabbed the eye of the computer vision community for the past few decades. It is a vital step closer to knowledge people in pics and motion pictures. Strong articulations, small and hardly visible joints, occlusions, apparel, and lighting changes make it very difficult to perform estimate pose. Human Pose estimation is an important problem that needed to be study. It is used to detect human anatomical key points (e.g., shoulder, elbows, legs, wrist, etc.) in real time using less computational resources. There are many Artificial Intelligence models i.e, Posenet, OpenP
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Nguyen, Phuoc Thanh, Thanh Hoang Nguyen, Ngoc Xuan Nguyen Hoang, Huynh Thanh Binh Phan, Hoang Son Hai Vu, and Hieu Nhan Huynh. "Exploring MediaPipe optimization strategies for real-time sign language recognition." CTU Journal of Innovation and Sustainable Development 15, ISDS (2023): 142–52. http://dx.doi.org/10.22144/ctujoisd.2023.045.

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The present study meticulously investigates optimization strategies for real-time sign language recognition (SLR) employing the MediaPipe framework. We introduce an innovative multi-modal methodology, amalgamating four distinct Long Short-Term Memory (LSTM) models dedicated to processing skeletal coordinates ascertained from the MediaPipe framework. Rigorous evaluations were executed on esteemed sign language datasets. Empirical findings underscore that the multi-modal approach significantly elevates the accuracy of the SLR model while preserving its real-time capabilities. In comparative anal
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G, Dr Thanigavel, Keerthika D, Harini P, Dilli Raj M, and Sonali D. "Smart Fit: Innovative Solutions to Enhance Fitness Activities and Promote a Healthy Lifestyle." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 3229–37. https://doi.org/10.22214/ijraset.2025.68961.

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Abstract: Fitness tracking and guidance have evolved significantly with advancements in artificial intelligence and computer vision. This project, titled "SMART FIT: Innovative Solutions To EnhanceFitness Activities And Promote a Healthy Lifestyle" harnesses the power of OpenCV and Mediapipe to provide real-time feedback on workout accuracy. By leveraging Mediapipe's pose estimation model, the system effectively tracks skeletal landmarks and analyzes body posture during exercises. This technology enables a seamless integration of AI- driven solutions into fitness training, catering to individu
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Suwabe, Ryota, Takeshi Saito, and Toyohiro Hamaguchi. "Verification of Criterion-Related Validity for Developing a Markerless Hand Tracking Device." Biomimetics 9, no. 7 (2024): 400. http://dx.doi.org/10.3390/biomimetics9070400.

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Physicians, physical therapists, and occupational therapists have traditionally assessed hand motor function in hemiplegic patients but often struggle to evaluate complex hand movements. To address this issue, in 2019, we developed Fahrenheit, a device and algorithm that uses infrared camera image processing to estimate hand paralysis. However, due to Fahrenheit’s dependency on specialized equipment, we conceived a simpler solution: developing a smartphone app that integrates MediaPipe. The objective of this study was to measure hand movements in stroke patients using both MediaPipe and Fahren
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Nand, Rugwed. "Personalized Gym Trainer Using Mediapipe." International Journal for Research in Applied Science and Engineering Technology 11, no. 12 (2023): 1250–54. http://dx.doi.org/10.22214/ijraset.2023.57569.

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Abstract: With the growing demand in personalised fitness experiences, this paper takes a fresh look at home workouts by creating a Personalised Gym Trainer with the Mediapipe library. This sophisticated device combines pose detection technology with voice assistance to provide users with real-time feedback and personalised instruction while exercising. The system identifies various exercises accurately and leverages torso point detection for greater precision by leveraging the capabilities of the Mediapipe library and OpenCV for camera tasks. Individual Python modules for certain workouts suc
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S M, Dikshith. "AirCanvas using OpenCV and MediaPipe." International Journal for Research in Applied Science and Engineering Technology 13, no. 1 (2025): 14671–1473. https://doi.org/10.22214/ijraset.2025.66601.

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Human-Computer Interaction (HCI) has undergone significant transformations with the advent of Artificial Intelligence (AI) and Machine Learning (ML), enhancing the ways in which users engage with computing systems. This paper introduces AirCanvas, a novel hands-free digital interaction tool that leverages air gestures for intuitive and seamless computer control. The system uses advanced image processing techniques, specifically OpenCV for visual data analysis and MediaPipe for accurate hand gesture recognition, enabling users to manipulate virtual environments without physical touch. By integr
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KAVIYA,, D. "Open CV Based Hand Gesture Recognition for Virtual Keyboard Control System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42134.

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In the evolving landscape of computer technology, the interaction between humans and machines has undergone significant transformation, particularly emphasizing inclusivity and accessibility for individuals with disabilities. This project introduces a hand gesture recognition system designed to enable disabled individuals to interact with computers effortlessly, replacing traditional hardware like keyboards . Leveraging advanced technologies such as OpenCV, MediaPipe, and Python, the system allows users to control a virtual keyboard through intuitive hand gestures. By using a camera to track a
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USHA, M. NITHIN GOWDA H. SANTHOSH M. NANDA KUMAR S. NUTHAN PAWAR E. "REAL-TIME HAND SIGN TRAINING AND DETECTION." International Journal For Technological Research In Engineering 11, no. 5 (2024): 149–51. https://doi.org/10.5281/zenodo.10554229.

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Real-time hand sign recognition and detection are major for applications in human computer interaction, sign language interpretation, and gesture-based control systems.This project focuses on creating realtime hand gesture and finger gesture annotations using the MediaPipe framework in Python.The  hand gestures and finger gestures using keypoints and finger coordinates found by the MediaPipe framework.The system offers two machine learning models: one for recognizing hand signs and another for detecting finger gestures. It provides resources, including sample programs, model files, and tr
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Vaishnavi D. A., Lavanya, Anil Kumar C., Harish S., and Divya M. L. "MediaPipe to Recognise the Hand Gestures." WSEAS TRANSACTIONS ON SIGNAL PROCESSING 18 (July 2, 2022): 134–39. http://dx.doi.org/10.37394/232014.2022.18.19.

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Human Computer Interaction (HCI) can be improved drastically using the hand gesture based recognition system. This system is designed to detect the gestures of the hands in the images captured in real time. There are certain areas of intersect in the hands that are there for the classification. The gaming devices like Xbox, PS4 and smart phones are also using this method to solve few problems. In this paper a smart method i9s developed to solve the problem. Using Python 3.9 and MediaPipe, the hand gestures are recognised in the real-time images. The background subtraction is the key method use
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Yasumuro, Masanao, and Kenya Jin'no. "Japanese fingerspelling identification by using MediaPipe." Nonlinear Theory and Its Applications, IEICE 13, no. 2 (2022): 288–93. http://dx.doi.org/10.1587/nolta.13.288.

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Rozprawy doktorskie na temat "And MediaPipe"

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Galvani, Simone. "B-R1ING MoCap: registrazione e riproduzione dei movimenti umani su avatar 3D in realtà aumentata." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25594/.

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Già da qualche anno si è stati introdotti alla possibilità di vivere in un mondo virtuale; basta indossare un paio di visori di realtà aumentata, virtuale e mista che riproducono nell’ambiente circostante oggetti che fisicamente non esistono. Negli ultimi mesi, inoltre, questa possibilità sta diventando sempre più concreta con l’introduzione, da parte dei colossi dell’informatica, del concetto di "Metaverso": un universo parallelo completamente digitale dove sarà possibile svolgere ogni attività sociale. L’obiettivo di questa tesi è quello di contribuire in piccola parte a questo enorme proge
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Teixeira, Francisco João Mendonça. "Interações gestuais para plataformas de streaming de vídeo." Master's thesis, 2021. http://hdl.handle.net/10400.13/3845.

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A popularidade do rato de computador como modo preferencial de interação com aplicações de computador é inigualável. Mesmo no tempo dos smartphones e tablets, que trouxeram ao público em geral inovações como o modo de controlo através do toque, não existe uma grande variedade nos modos de interação com os diversos dispositivos que usamos no quotidiano. Este trabalho propõe uma nova abordagem para controlo, à distância, de aplicações de streaming de vídeo. A aplicação desenvolvida nesta dissertação procura um meio mais natural de interação com este tipo de plataformas usando intera
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Części książek na temat "And MediaPipe"

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Pauzi, Ainun Syarafana Binti, Firdaus Bin Mohd Nazri, Salisu Sani, et al. "Movement Estimation Using Mediapipe BlazePose." In Advances in Visual Informatics. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-90235-3_49.

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Sanalohit, Jinnavat, and Tatpong Katanyukul. "TFS Recognition Using MediaPipe Hands." In Lecture Notes in Computer Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-0695-5_14.

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Ashrafi, Arifa, Victor Mokhnachev, Yuriy Philippovich, Alexey Harlamenkov, and Sergey Chernenko. "Russian Sign Language Recognition Using MediaPipe." In Artificial Intelligence in Models, Methods and Applications. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-22938-1_21.

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Veluri, Ravi Kishore, S. Rama Sree, A. Vanathi, G. Aparna, and S. Prasanth Vaidya. "Hand Gesture Mapping Using MediaPipe Algorithm." In Proceedings of Third International Conference on Communication, Computing and Electronics Systems. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8862-1_39.

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Hii, Chang Soon Tony, Kok Beng Gan, Huay Woon You, Nasharuddin Zainal, Norlinah Mohamed Ibrahim, and Shahrul Azmin. "Frontal Plane Gait Assessment Using MediaPipe Pose." In Springer Proceedings in Physics. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0142-1_34.

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Nitin Kumar, R., Makkena Vaishnavi, K. R. Gayatri, Venigalla Prashanthi, and M. Supriya. "Air Writing Recognition Using Mediapipe and Opencv." In Smart Innovation, Systems and Technologies. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2541-2_35.

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Shetty, Sudheer, R. Rakshitha, S. Arundhathi Bhat, et al. "Automatic Music Control Using Image Processing and MediaPipe." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-75608-5_19.

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Makhijani, Richa, Shubham Sagar, Koppula Bhanu Prakash Reddy, Sonu Kumar Mourya, Jadhav Sai Krishna, and Manthan Milind Kulkarni. "Yoga Pose Rectification Using Mediapipe and Catboost Classifier." In Computer Vision and Machine Intelligence. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7867-8_30.

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Kanyal, Hoshiyar Singh, Shubham Gupta, Smriti Singh, and Sarita Kumari. "Indian sign language recognition using TensorFlow and MediaPipe." In Applications of Artificial Intelligence in 5G and Internet of Things. CRC Press, 2025. https://doi.org/10.1201/9781003532521-92.

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Saikia, Nimakhi, Golam Imran Hussain, and Chandana Dev. "Sign Language Recognition System Using MediaPipe and CNN." In Innovations in Sustainable Technologies and Computing. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-6794-6_44.

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Streszczenia konferencji na temat "And MediaPipe"

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Yerimbetova, Aigerim, Diana Kaidina, Bakzhan Sakenov, Elmira Daiyrbayeva, Mussa Turdalyuly, and Ulmeken Berzhanova. "Recognising Kazakh Sign Language with Mediapipe." In 2024 9th International Conference on Computer Science and Engineering (UBMK). IEEE, 2024. https://doi.org/10.1109/ubmk63289.2024.10773406.

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Rasulmuhamedov, Abduqayum, Kayathri Devi Devprasad, Gaurav Aggarwal, Naina Chaudhary, Shivani Gupta, and Sujit Prasad. "Active Face Liveness Detection Using Mediapipe." In 2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS). IEEE, 2024. https://doi.org/10.1109/ictacs62700.2024.10841222.

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Shu, Wenguang. "Improvement of Mediapipe Hands Network Structure." In 2025 IEEE 6th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT). IEEE, 2025. https://doi.org/10.1109/ainit65432.2025.11035067.

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Tran, Khang B., Uyen D. Nguyen, and Quoc T. Huynh. "Sentence-Level Sign Language Recognition Using MediaPipe." In 2024 RIVF International Conference on Computing and Communication Technologies (RIVF). IEEE, 2024. https://doi.org/10.1109/rivf64335.2024.11009051.

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Wu, Jim-Wei, You-En Wang, Ping-Hsuan Hsueh, and Chieh Yen. "SIFT Based on MediaPipe to Identify Athletic Posture." In 2025 Australian & New Zealand Control Conference (ANZCC). IEEE, 2025. https://doi.org/10.1109/anzcc65042.2025.10873344.

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Singh, Sukhdeep, Trilok Singh, Tarun Singh, and Diana Nagpal. "Real Time Posture Detector using MediaPipe and OpenCV." In 2024 4th International Conference on Soft Computing for Security Applications (ICSCSA). IEEE, 2024. https://doi.org/10.1109/icscsa64454.2024.00019.

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Snigdha, D., S. Tejasree, N. Rithvik, Hariharan Shanmuagasundaram, Siddharth Motikar, and S. Srinivas Reddy. "Yoga Posture Analysis and Feedback System Using Mediapipe." In 2025 International Conference on Multi-Agent Systems for Collaborative Intelligence (ICMSCI). IEEE, 2025. https://doi.org/10.1109/icmsci62561.2025.10894510.

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Pavan Kumar Gatikoppu, Sai Phanindra, Vimal Subramanian G, Thamizh devi C, and Maruthupandi V. "Yoga Pose Detection Using Mediapipe and Cue Method." In 2024 International Conference on Smart Electronics and Communication Systems (ISENSE). IEEE, 2024. https://doi.org/10.1109/isense63713.2024.10871988.

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Zhou, Qiang, and Byung Gook Lee. "Optical motion capture application based on Unity3D and MediaPipe." In 2024 3rd International Conference on Cloud Computing, Big Data Application and Software Engineering (CBASE). IEEE, 2024. https://doi.org/10.1109/cbase64041.2024.10824452.

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Santos, Bruno, Gil Gonçalves, and Vítor H. Pinto. "Shadow Dexterous Hand Control via MediaPipe and BioIK Integration." In 2025 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC). IEEE, 2025. https://doi.org/10.1109/icarsc65809.2025.10970167.

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