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HSV Brightness Factor Matching for Gesture Recognition System
Mokhtar M. Hasan, Pramod K. Mishra
Pages - 456 - 467     |    Revised - 30-11-2010     |    Published - 20-12-2010
Volume - 4   Issue - 5    |    Publication Date - December 2010  Table of Contents
Brightness Calculation, HSV color model, Gesture Recognition, Template Matching, Image Segmentation., Laplacian Edge Detection
The main goal of gesture recognition research is to establish a system which can identify specific human gestures and use these identified gestures to be carried out by the machine, In this paper, we introduce a new method for gesture recognition that based on computing the local brightness for each block of the gesture image, the gesture image is divided into 25x25 blocks each of 5x5 block size, and we calculated the local brightness of each block, so, each gesture produces 25x25 features value, our experimental shows that more that %60 of these features are zero value which leads to minimum storage space, this brightness value is calculated from the HSV (Hue, Saturation and Value) color model that used for segmentation operation, the recognition rate achieved is %91 using 36 training gestures and 24 different testing gestures. This Paper focuses on the hand gesture instead of the whole body movement since hands are the most flexible part of the body and can transfer the most meaning, we build a gesture recognition system that can communicate with the machine in natural way without any mechanical devices and without using the normal input devices which are the keyboard and mouse and the mathematical equations will be the translator between the gestures and the telerobotic.
CITED BY (32)  
1 Mahajan, P. M., & Chaudhari, J. S. Review of Finger Spelling Sign Language Recognition.
2 Dongare, Y. B., & Patole, R. skin color detection and background subtraction fusion for hand gesture segmentation.
3 Bulgakov, A., Emelianov, S., Schach, R., Sayfeddine, D., & Erofeev, V. (2015). Maintaining vertical gardens using quadrotor aerial inspection.
4 Kaushik, D., & Bhardwaj, A. Hand Gesture Recognition with Shape and Color for HCI.
5 Nagarajan, S., & Subashini, T. S. (2015). Weighted Euclidean Distance Based Sign Language Recognition Using Shape Features. In Artificial Intelligence and Evolutionary Algorithms in Engineering Systems (pp. 149-156). Springer India.
6 Premaratne, P. (2014). Feature Extraction. In Human Computer Interaction Using Hand Gestures (pp. 75-103). Springer Singapore.
7 Rokade, R. S., & Doye, D. D. (2014). Spelled sign word recognition using key frame. IET Image Processing, 9(5), 381-388.
8 Rokade, R. S., & Doye, D. D. (2014, August). Spelled sentence recognition using radon transform. In Science and Information Conference (SAI), 2014 (pp. 351-354). IEEE.
9 Sunyoto, A., & Hardjoko, A. (2014). Review Teknik, Teknologi, Metodologi dan Implementasi Pengenalan Gesture Tangan Berbasis Visi. In Seminar Nasional Aplikasi Teknologi Informasi 2014 (SNATi 2014).
10 Thomas, M. C., & Pradeepa, A. P. M. S. (2014). a comprehensive review on vision based hand gesture recognition technology. International Journal, 2(1).
11 Karishma, S. N., & Lathasree, V. (2014). Fusion of Skin Color Detection and Background Subtraction for Hand Gesture Segmentation. International Journal of Advanced Trends in Computer Science and Engineering, 3(1), 13-18.
12 Thomas, C., & Lakshmi, D. Gesture Based Computing as an Alternative to Mouse by Calibrating Principal Contour Process Actions.
13 Hasan, M. M., & Misra, P. K. brightness factor matching for gesture recognition system using scaled.
14 Mule, K. C., & Holambe, A. N. Hand Gesture Recognition Using PCA and Histogram Projection. International Journal on Advanced Computer Theory and Engineering (IJACTE), Osmanabad, India.
15 Rokade, R. S. (2013). Palm Sign Recognition Using Key Frame. Available at SSRN 2298589.
16 Jalilian, B., & Chalechale, A. (2013). Face and Hand Shape Segmentation Using Statistical Skin Detection for Sign Language Recognition. Computer Science and Information Technology, 1(3), 196-201.
17 Hasan, M. M. (2013). New Rotation Invariance Features Based on Circle Partitioning. J Comput Eng Inf Technol 2: 2. doi: http://dx. doi. org/10.4172/2324, 9307, 2.
18 Lekova, A. K., & Dimitrova, M. (2013, December). Hand gestures recognition based on lightweight evolving fuzzy clustering method. In Image Information Processing (ICIIP), 2013 IEEE Second International Conference on (pp. 505-510). IEEE.
19 Majid, M. A., & Zain, J. M. (2013). A Review on the Development of Indonesian Sign Language Recognition System. Journal of Computer Science, 9(11), 1496.
20 Mekala, P., Fan, J., Lai, W. C., & Hsue, C. W. (2013). Gesture recognition using neural networks based on HW/SW cosimulation platform. Advances in Software Engineering, 2013, 2.
21 Ratnaparkhi, P. S., & Nawgaje, D. D. Comparative Study of AI Based Gesture Recognition.
22 Hasan, M. M., & Mishra, P. K. (2012, May). Direction analysis algorithm using statistical approaches. In Fourth International Conference on Digital Image Processing (ICDIP 2012) (pp. 83340L-83340L). International Society for Optics and Photonics.
23 Hasan, M. M., & Mishra, P. K. (2012). Novel algorithm for multi hand detection and geometric features extraction and recognition. International Journal of Scientific & Engineering Research, 3(5).
24 Khan, R. Z., & Ibraheem, N. A. (2012). Comparative study of hand gesture recognition system. In Proc. of International Conference of Advanced Computer Science & Information Technology in Computer Science & Information Technology (CS & IT) (Vol. 2, No. 3, pp. 203-213).
25 Ibraheem, N. A., & Khan, R. Z. (2012). Vision based gesture recognition using neural networks approaches: A review. International Journal of human Computer Interaction (IJHCI), 3(1), 1.
26 Khan, R. Z., & Ibraheem, N. A. (2012). Survey on gesture recognition for hand image postures. Computer and Information Science, 5(3), p110.
27 Khan, R. Z., & Ibraheem, N. A. (2012). hand gesture recognition: ALiterature.
28 Hasan, M. M., & Mishra, P. K. (2012). Hand gesture modeling and recognition using geometric features: a review. Canadian Journal on Image Processing and Computer Vision, 3(1), 12-26.
29 Vishwakarma, D. K., & Kapoor, R. (2012, December). Simple and intelligent system to recognize the expression of speech-disabled person. In Intelligent Human Computer Interaction (IHCI), 2012 4th International Conference on (pp. 1-6). IEEE.
30 Hasan, M. M., & Mishra, P. K. (2011). Comparative Study for Construction of Gesture Recognition System. International Journal of Computer Science and Software Technology, 4(1), 15-21.
31 Hasan, M. M., & Misra, P. K. (2011, June). Gesture recognition using modified HSV segmentation. In Communication Systems and Network Technologies (CSNT), 2011 International Conference on (pp. 328-332). IEEE.
32 Hasan, M. M., & Mishra, P. K. (2011). Performance Evaluation of Modified Segmentation on Multi Block For Gesture Recognition System. International Journal of Signal Processing, Image Processing and Pattern Recognition, 4(4), 17-28.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Socol@r  
5 Scribd 
6 SlideShare 
8 PdfSR 
H. B. Amor, S. Ikemoto, T. Minato, H. Ishiguro. “Learning Android Control using Growing Neural Networks”. Department Of Adaptive Machine Systems Osaka University, Osaka, Japan, 2003
M. Swain and D. Ballard. “Indexing via Color Histograms”. In Proceedings of Third International Conference on Computer Vision, 390-393, 1990
. The AF Research Laboratory. “Language and Cognition”. Elsevier, Neural Networks, 22: 247-257, 2009
Abin – Roozgard. “Convolutional Neural Networks”. lectures in Neural Networks
B. Heisele, P. Ho, T. Poggio. “Face Recognition with Support Vector Machines: Global versus Component-based Approach”. Massachusetts Institute of Technology Center for Biological and Computational Learning Cambridge, 2001
C. Karlof, D. Wagner. “Hidden Markov Model Cryptanalysis”. Computer Science Division (EECS) Univertsity of California Berkeley, California 94720, 2004
C.C. Lo, S. J. Wang. “Video Segmentation using a Histogram-Based Fuzzy C-Means Clustering Algorithm”. Institute of Information Management, National Chiao-Tung University, Computer Standards & Interfaces, 23:429–438, 2001
H. Gunes, M. Piccardi, T. Jan.”Face and Body Gesture Recognition for a Vision-Based Multimodal Analyzer”. Computer Vision Research Group, University of Technology, Sydney (UTS), 2007
Internet Web Site. Available at:http://commons.wikimedia.org
J. J. Phu, Y. H. Tay. “Computer Vision Based Hand Gesture Recognition using Artificial Neural Network”. Faculty of Information and Communication Technology, University Tunku Abdul Rahman (Utar), Malaysia, 2006
J. Triesch, C. Malsburg. “Robust Classification of Hand Postures Against Complex Backgrounds”. IEEE Computer Society, In Proceedings of Second International Conference On Automatic Face And Gesture Recognition, 1996
J. Wachs, U. Kartoun, H. Stern, Y. Edan. “Real-Time Hand Gesture Telerobotic System using Fuzzy C-Means Clustering”. Department of Industrial Engineering and Management, Ben-Gurion University of the Negov, 1999
K. Jain, R. P.W. Duin J. Mao. “Statistical Pattern Recognition: A Review”. IEEE Transactions on Patterns Analysis and Machine Intelligence, 22(1):4-35, 2000
K. Symeonidis. “Hand Gesture Recognition using Neural Networks”, Master Thesis, School Of Electronic And Electrical Engineering, 2000
R. Brunelli, T. Poggio. “Face Recognition: Features versus Templates”. IEEE Transactions on Pattern Analysis And Machine Intelligence, 15(10):1042-1052, 1993
S. Marcel, O. Bernier, J. Viallet, D. Collobert. “Hand Gesture Recognition using Input– Output Hidden Markov Models”. France Telecom Cnet 2 Avenue Pierre Marzin 22307 Lannion, France, 1999
S. Naidoo, C.W. Omlin, M. Glaser, “Vision-Based Static Hand Gesture Recognition using Support Vector Machines”. Department of Computer Science, University of the Western Cape, South Africa, 1999
S. S. Keerthi, O. Chapelle, D. DeCoste. “Building Support Vector Machines with Reduced Classifier”. Complexity, Journal of Machine Learning Research, 8:1-22, 2006
S. Venkataraman, V. Gunaseelan. ”Hidden Markov Models in Computational Biology”.lectures in HMM
T. Yang, Y. Xu. “Hidden Markov Model for Gesture Recognition”. The Robotics Institute Carnegie Mellon University Pittsburgh, Pennsylvania 15213, 1994
W. T. Freeman, M. Roth. “Orientation Histograms for Hand Gesture Recognition”. Mitsubishi Electric Research Laboratories, Cambridge, Ma 02139 USA, 1994
Wikipedia Internet Web Site
X. Li. “Gesture Recognition based on Fuzzy C-Means Clustering Algorithm”. Department Of Computer Science The University Of Tennessee Knoxville, 2003
X. Li. “Vision Based Gesture Recognition System with High Accuracy”. Department of Computer Science, The University of Tennessee, Knoxville, TN 37996-3450, 2005
Y. Lu, S. Lu, F. Fotouhi, Y. Deng, Susan J. Brown. “A Fast Genetic K-Means Clustering Algorithm”. Wayne State University, Kansas State University Manhattan, USA, 2000
Y. P. Lew, A. R. Ramli, S. Y. Koay, A. ali, V. Prakash. “A Hand Segmentation Scheme using Clustering Technique in Homogeneous Background”. Student Conference on Research and Development Proceedings, Shad Alam, Malaysia, 2002
Mr. Mokhtar M. Hasan
BHU University - India
Mr. Pramod K. Mishra
- India

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