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Zhou Ren, Junsong Yuan, Jingjing Meng, Zhengyou Zhang (2013)
Robust Part-Based Hand Gesture Recognition Using Kinect SensorIEEE Transactions on Multimedia, 15
James Davis, M. Shah (1994)
Visual gesture recognition, 141
Chao Sun, Tianzhu Zhang, Bingkun Bao, Changsheng Xu, Tao Mei (2013)
Discriminative Exemplar Coding for Sign Language Recognition With KinectIEEE Transactions on Cybernetics, 43
S. Mitra, T. Acharya (2007)
Gesture Recognition: A SurveyIEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 37
Wen Gao, Gaolin Fang, Debin Zhao, Yiqiang Chen (2004)
A Chinese sign language recognition system based on SOFM/SRN/HMMPattern Recognit., 37
H. Cooper, Eng-Jon Ong, N. Pugeault, R. Bowden (2012)
Sign language recognition using sub-units
Greg Lee, F. Yeh, Yi-Han Hsiao (2014)
Kinect-based Taiwanese sign-language recognition systemMultimedia Tools and Applications, 75
S. Bhattacharya, Bogdan Czejdo, Nicolas Perez (2012)
Gesture classification with machine learning using Kinect sensor data2012 Third International Conference on Emerging Applications of Information Technology
K. Khoshelham, S. Elberink (2012)
Accuracy and Resolution of Kinect Depth Data for Indoor Mapping ApplicationsSensors (Basel, Switzerland), 12
Daniel Weinland, Rémi Ronfard, Edmond Boyer (2011)
A survey of vision-based methods for action representation, segmentation and recognitionComput. Vis. Image Underst., 115
L. Phadtare, R. Kushalnagar, N. Cahill (2012)
Detecting hand-palm orientation and hand shapes for sign language gesture recognition using 3D images2012 Western New York Image Processing Workshop
P. Hong, Thomas Huang, M. Turk (2000)
Constructing finite state machines for fast gesture recognitionProceedings 15th International Conference on Pattern Recognition. ICPR-2000, 3
Jie Huang, Wen-gang Zhou, Houqiang Li, Weiping Li (2015)
Sign Language Recognition using 3D convolutional neural networks2015 IEEE International Conference on Multimedia and Expo (ICME)
Le Nguyen, Cong Thanh, T. Ba, Cuong Viet, Ha Thanh (2013)
Contour Based Hand Gesture Recognition Using Depth Data
M. Mohandes, M. Deriche, Junzhao Liu (2014)
Image-Based and Sensor-Based Approaches to Arabic Sign Language RecognitionIEEE Transactions on Human-Machine Systems, 44
James Davis, M. Shah (1999)
Toward 3-D Gesture RecognitionInt. J. Pattern Recognit. Artif. Intell., 13
Saad Akram, J. Beskow, H. Kjellström (2012)
Visual Recognition of Isolated Swedish Sign Language SignsArXiv, abs/1211.3901
Zahoor Zafrulla, Helene Brashear, Thad Starner, H. Hamilton, P. Presti (2011)
American sign language recognition with the kinect
Cemil Öz, M. Leu (2011)
American Sign Language word recognition with a sensory glove using artificial neural networksEng. Appl. Artif. Intell., 24
Fabio Dominio, Mauro Donadeo, Giulio Marin, P. Zanuttigh, G. Cortelazzo (2013)
Hand gesture recognition with depth data
Anant Agarwal, M. Thakur (2013)
Sign language recognition using Microsoft Kinect2013 Sixth International Conference on Contemporary Computing (IC3)
A. Bobick, Andrew Wilson (1997)
A State-Based Approach to the Representation and Recognition of GestureIEEE Trans. Pattern Anal. Mach. Intell., 19
E. Arik (2012)
Space, Time, and Iconicity in Turkish Sign Language (TID)Trames-journal of The Humanities and Social Sciences, 16
Jaemin Lee, H. Takimoto, H. Yamauchi, Akihiro Kanazawa, Y. Mitsukura (2013)
A robust gesture recognition based on depth dataThe 19th Korea-Japan Joint Workshop on Frontiers of Computer Vision
Cem Keskin, Mustafa Kıraç, Yunus Kara, L. Akarun (2011)
Real time hand pose estimation using depth sensors2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)
P. Hong, Thomas Huang, M. Turk (2000)
Gesture modeling and recognition using finite state machinesProceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580)
Abbas Memiş, S. Albayrak (2013)
Turkish Sign Language recognition using spatio-temporal features on Kinect RGB video sequences and depth maps2013 21st Signal Processing and Communications Applications Conference (SIU)
Zhou Ren, Junsong Yuan, Zhengyou Zhang (2011)
Robust hand gesture recognition based on finger-earth mover's distance with a commodity depth cameraProceedings of the 19th ACM international conference on Multimedia
M. Yeasin, S. Chaudhuri (2000)
Visual understanding of dynamic hand gesturesPattern Recognit., 33
J. Shotton, T. Sharp, A. Kipman, A. Fitzgibbon, M. Finocchio, A. Blake, Mat Cook, R. Moore (2011)
Real-time human pose recognition in parts from single depth imagesCVPR 2011
Lionel Pigou, S. Dieleman, Pieter-Jan Kindermans, B. Schrauwen (2014)
Sign Language Recognition Using Convolutional Neural Networks
T. Shanableh, K. Assaleh, M. Al-Rousan (2007)
Spatio-Temporal Feature-Extraction Techniques for Isolated Gesture Recognition in Arabic Sign LanguageIEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 37
E. Saykol, M. Bastan, U. Güdükbay, Ö. Ulusoy (2010)
Keyframe labeling technique for surveillance event classificationOptical Engineering, 49
H. Takimoto, Jaemin Lee, A. Kanagawa (2013)
A Robust Gesture Recognition Using Depth DataInternational Journal of Machine Learning and Computing
Purpose– The purpose of this paper to classify a set of Turkish sign language (TSL) gestures by posture labeling based finite-state automata (FSA) that utilize depth values in location-based features. Gesture classification/recognition is crucial not only in communicating visually impaired people but also for educational purposes. The paper also demonstrates the practical use of the techniques for TSL. Design/methodology/approach– Gesture classification is based on the sequence of posture labels that are assigned by location-based features, which are invariant under rotation and scale. Grid-based signing space clustering scheme is proposed to guide the feature extraction step. Gestures are then recognized by FSA that process temporally ordered posture labels. Findings– Gesture classification accuracies and posture labeling performance are compared to k-nearest neighbor to show that the technique provides a reasonable framework for recognition of TSL gestures. A challenging set of gestures is tested, however the technique is extendible, and extending the training set will increase the performance. Practical implications– The outcomes can be utilized as a system for educational purposes especially for visually impaired children. Besides, a communication system would be designed based on this framework. Originality/value– The posture labeling scheme, which is inspired from keyframe labeling concept of video processing, is the original part of the proposed gesture classification framework. The search space is reduced to single dimension instead of 3D signing space, which also facilitates design of recognition schemes. Grid-based clustering scheme and location-based features are also new and depth values are received from Kinect. The paper is of interest for researchers in pattern recognition and computer vision.
Kybernetes – Emerald Publishing
Published: Apr 4, 2016
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