Segmentation Algorithm to Determine Group for Hand Gesture Recognition
Keywords:Hand Gesture, Segmentation, Adaptive Neuro-Fuzzy Inference System (ANFIS),
AbstractThe main principle of hand gesture is recognizing any forms of gesture in the form of alphabet letters. The goal is to help the disabled to communicate with each other. Our system runs in real time without the help of sensors, gloves, etc. With such lighting conditions, different conditions of human hand and background of shooting become a problem in the completion of the process. This research proposed a segmentation method to resolve these problems. The method begins with capturing a picture using a webcam, which is followed by the segmentation process. We also proposed several conditions of skin detection. In this research, the segmented image undergoes the extraction process, which adopts three forms of feature extraction, namely slimness, roundness, and rectangularity. The final step of the method is measuring the resemblance of the images data features using adaptive neuro fuzzy inference system.
M. Panwar, “Hand gesture recognition based on shape parameters,” Int. Conf. Comput. Commun. Appl., pp. 1–6, 2012.
A. Jalal, M. Uddin, and T. S. Kim, “Depth video-based human activity recognition system using translation and scaling invariant features for life logging at smart home,” IEEE Trans. Consum. Electron., vol. 58, no. 3, pp. 863–871, 2012.
A. A. Randive and S. D. Lokhande, “Hand Gesture Segmentation,” Int. J. Comput. Technol. Electron. Eng., vol. 2, no. 3, pp. 125–129, 2012.
A. Y. Dawod, M. J. Nordin, and J. Abdullah, “Gesture Segmentation : Automatic Continuous Sign Language Technique Based on Adaptive Contrast Stretching Approach Pattern Recognition Research Group , Centre for Artificial Intelligence Technology ( CAIT ), Assistive Technology SIG , Faculty of Computing,” Middle-East J. Sci. Res., vol. 24, no. 2, pp. 347–352, 2016.
H. S. Abdulbaqi, M. Z. M. Jafri, A. F. Omar, K. N. Mutter, L. K. Abood, and I. S. Bin Mustafa, “Segmentation and estimation of brain tumor volume in computed tomography scan images using hidden Markov random field Expectation Maximization algorithm,” 2015 IEEE Student Conf. Res. Dev., vol. 8, no. 3, pp. 55–60, 2015.
Z. Zainal Abidin et al., “Brain Lesion Segmentation from Diffusionweighted MRI based on Adaptive Thresholding and Gray Level Cooccurrence Matrix Faculty of Electrical Engineering , Universiti Teknikal Malaysia Melaka ,” 2015 IEEE Student Conf. Res. Dev., vol. 8, no. 2, pp. 41–48, 2011.
M. Sharma and S. Mukharjee, “Artificial Neural Network Fuzzy Inference System ( ANFIS ) For Brain Tumor Detection,” Int. J. Comput. Appl. Technol. Res., vol. 3, no. 3, pp. 150–154, 2014.
S. Manjare and S. . Chougule, “Skin Detection for Face Recognition Based on HSV Color Space,” Int. J. Eng. Sci. Res. Technol., vol. 2, no. 7, pp. 3–7, 2013.
A. N. Ghomseh, “Pixel-based Skin Detection Based on Statistical Models,” J. Telecommun. Electron. Comput. Eng., vol. 8, no. 5, pp. 7– 14, 2016.
F. Utaminingrum, K. Uchimura, and G. Koutaki, “Mixed gaussian and impulse noise removal based on kernel observation and edge direction,” Int. J. Innov. Comput. Inf. Control, vol. 11, no. 5, pp. 1509– 1523, 2015.
Q. Wu, C. Zhou, and C. Wang, “Feature Extraction and Automatic Recognition of Plant Leaf Using Artificial Neural Network,” Av. en Ciencias la Comput., pp. 5–12, 2006.
K. Singh, I. Gupta, and S. Gupta, “SVM-BDT PNN and Fourier Moment Technique for Classification of Leaf Shape,” Int. J. Signal Process. Image Process. Pattern Recognit., vol. 3, no. 4, pp. 67–78, 2010.
G. D. Santika, W. F. Mahmudy, and A. Naba, “Electrical Load Forecasting using Adaptive Neuro-Fuzzy Inference System,” Accept. IJASCA, pp. 1–20, 2016.
J. S. R. Jang, “ANFIS: Adaptive-Network-Based Fuzzy Inference System,” IEEE Trans. Syst. Man Cybern., vol. 23, no. 3, pp. 665–685, 1993.
J. E. Nash and J. V. Sutcliffe, “River flow forecasting through conceptual models part I - A discussion of principles,” J. Hydrol., vol. 10, no. 3, pp. 282–290, 1970.
A. A. M. Ahmed and S. M. A. Shah, “Application of adaptive neurofuzzy inference system (ANFIS) to estimate the biochemical oxygen demand (BOD) of Surma River,” J. King Saud Univ. - Eng. Sci., p. , 2015.
How to Cite
TRANSFER OF COPYRIGHT AGREEMENT
The manuscript is herewith submitted for publication in the Journal of Telecommunication, Electronic and Computer Engineering (JTEC). It has not been published before, and it is not under consideration for publication in any other journals. It contains no material that is scandalous, obscene, libelous or otherwise contrary to law. When the manuscript is accepted for publication, I, as the author, hereby agree to transfer to JTEC, all rights including those pertaining to electronic forms and transmissions, under existing copyright laws, except for the following, which the author(s) specifically retain(s):
- All proprietary right other than copyright, such as patent rights
- The right to make further copies of all or part of the published article for my use in classroom teaching
- The right to reuse all or part of this manuscript in a compilation of my own works or in a textbook of which I am the author; and
- The right to make copies of the published work for internal distribution within the institution that employs me
I agree that copies made under these circumstances will continue to carry the copyright notice that appears in the original published work. I agree to inform my co-authors, if any, of the above terms. I certify that I have obtained written permission for the use of text, tables, and/or illustrations from any copyrighted source(s), and I agree to supply such written permission(s) to JTEC upon request.