Hand Gestures Detecting Using Radon And Fan Beam Projection Features
Abstract
Recognizing hand gestures is one of the most efficient ways of human interaction with a computer and an important field in computer vision and machine learning. This subject matter enables many applications to allow users to communicate the interfaces of different systems, without the need for additional hardware. Therefore, the primary goal of gesture recognition is to create systems in order to identify specific human gestures and use them to transmit information and signals or control various devices. There are more research in the field of pattern recognition especially in gesture recognition .Some of these research focus on dimension reduction , other on recognition model, and in features selection type..etc. In this research, the static hand gesture is detected depending on applying radon features and fan beam projection on hand images to compute the projection by given angles. The reason for adopting these techniques is to take its advantages in giving features not related to the shape and size of the object . The decision tree model is used in recognition stage to detect five different hand gestures, and all the results are reported.
Full Text:
PDFReferences
SANJAY M., ,2011,A Study on Hand Gesture Recognition Technique , Master of Technology in Telematics and Signal Processing , Department of Electronics and Communication Engineering National Institute Of Technology, Rourkela Orissa 769 008, INDIA.
Stefano C., 2010 ,Gesture Recognition, (–( stefano.carrino@hefr.ch, http://aramis.project.eia-fr.ch
Lalit G. and Suwei M., 2001, Gesture-Based Interaction and Communication: Automated Classification of Hand Gesture Contours , IEEE transactions on systems, man, and cybernetics—part c: applications and reviews, vol. 31, no. 1, February.
Ankit C. , Karen D. and Sonia R., 2011, Intelligent Approaches to interact with Machines using Hand Gesture Recognition in Natural Way: A Survey, International Journal of Computer Science & Engineering Survey (IJCSES) Vol.2, No.1, Feb.
Nasser D., 2012 ,Real-time Hand Gesture Detection and Recognition for Human Computer Interaction , PH. thesis, Ottawa-Carleton Institute for Electrical and Computer Engineering , School of Electrical Engineering and Computer Science ,Faculty of Engineering ,University of Ottawa, Ottawa, Canada.
Helen C., Brian H., and Richard B., 2011, Sign language recognition , In Visual Analysis of Humans,pp. 539–562.
Oudah, M.; Al-Naji, A.; Chahl, J. Hand Gesture Recognition Based on Computer Vision: A Review of Techniques. J. Imaging 2020, 6, 73
Phyu Myo Thwea and May The` Yu ,'Hand Gesture Detection and Recognition System: A Critical Review ', International Journal of Computer (IJC) ,2019
Monu Verma , Ayushi Gupta and Santosh K. Vipparthi,' One for All: An End-to-End Compact Solution for Hand Gesture Recognition' , arXiv:2105.07143v1 [cs.CV] 15 May 2021.
Ahmed, S.; Cho, S.H. Hand Gesture Recognition Using an IR-UWB Radar with an Inception Module-Based Classifier. Sensors 2020, 20, 564
Mohamed Mansoor Roomi S., Jyothi Priya R. and Jayalakshmi H. , 2010,Hand Gesture Recognition for Human Computer Interaction ,India, Journal of Computer Science 6 (9): 1002-1007.
Sauvik D. G., Souvik K., Rahul G. and Rajesh B., 2012, Hand Gesture recognition and classification by Discriminant and Principal Component Analysis using Machine Learning techniques, (IJARAI) International Journal of Advanced Research in Artificial Intelligence,Vol. 1, No. 9.
Pavlo M., Shalini G., Kihwan K., and Jan K.,2015, Hand Gesture Recognition with 3D Convolutional Neural Networks, NVIDIA, Santa Clara, California, USA.
Feng J. , Shengping Z. , Shen W. ,and Shen W., 2015 , Multi-layered Gesture Recognition with Kinect , Journal of Machine Learning Research, 227-254.
Ying Y. and Randall D., 2014, Real-Time Continuous Gesture Recognition for Natural Human-Computer Interaction, IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC).
Abdullah Mujahid , Mazhar Javed Awan , Awais Yasin and Mazin Abed Mohammed ,' Real-Time Hand Gesture Recognition Based on Deep Learning YOLOv3 Model' , Appl. Sci. 2021, 11, 4164. https://doi.org/10.3390/app1109416
Hanwen Huang , Yanwen Chong, Congchong Nie and Shaoming Pan,' Hand Gesture Recognition with Skin Detection and Deep Learning Method', IOP Conf. Series: Journal of Physics: Conf. Series 1213 (2019) 022001.
R.Pradipa,and S.Kavitha, 2014, Hand Gesture Recognition – Analysis of Various Techniques, Methods and Their Algorithms, International Journal of Innovative Research in Science, Engineering and Technology ,Volume 3, Special Issue 3.
van Ginkel M., Luengo Hendriks C.L. and van Vliet L.J. ,2004,A short introduction to the Radon and Hough transforms and how they relate to each other , in the Quantitative Imaging Group Technical Report Series, Number QI-2004-01 .
Anil K. J., 2006, Fundamentals of Digital Image Processing, PHI Publication, Indian Reprint edition.
Deans S. R.,1983, "The Radon Transform and Some of Its Applications".New York: Wiley.
Naser M. A. , Adnan Mahmud, T. M. Arefin, Golam Sarowar and M. M. Naushad Ali, 2009,Comparative analysis of Radon and Fan-beam based feature extraction techniques for Bangla character recognition, IJCSNS International Journal of Computer Science and Network Security, VOL.9 No.9.
Aeritalia E. S. ,1992 ,BINARY DECISION TREE APPROACH TO CLASSIFICATION: A REVIW OF CART AND OTHER METHODS WITH SOM APPLICATION TO REAL DATA ,Statistical Applicate ,vol. 4 ,no. 3.
Michael S. L. , 2007, Introduction to Learning & Decision Trees , Artificial Intelligence: Representation and Problem Solving , ,15-381 ,April 10.
Thierry M., 2014 ,Static hand gesture recognition, Department of Informatics ,University of Fribourg ,1700 Fribourg , Switzerland.
DOI: https://doi.org/10.31449/inf.v46i5.3744
This work is licensed under a Creative Commons Attribution 3.0 License.