99
DAFTAR PUSTAKA
Alex, D.S. & Wahi, A. 2014. BSFD : Background subtraction frame difference algorithm for moving object detection and extraction. Journal of Theoretical & Applied Information Technology 60(3) : 623-628.
Annadurai, S. & Shanmugalakshmi, R. 2007. Fundamental of Digital Image Processing. New Delhi : Pearson Education.
Bhatt, R., Fernandes, N. & Dhage, A. 2013. Vision based hand gesture recognition for human computer interaction. International Journal of Engineering Science and Innovative Technology (IJESIT) 2(3) : 110-115.
Bradski, G. & Kaehler, A. 2008. Learning OpenCV. O’Relly Media, Inc : Sebastopol. Chitra, S. & Balakrishnan, G. 2012. Comparative study for two color spaces HSCbCr
and YCbCr in skin color detection. Applied Mathematical Sciences 6(85) : 4229-4238.
Dhawan, A. & Honrao, V. 2013. Implementation of hand detection based techniques for human computer interaction. International Journal of Computer Applications 72(17) : 6-13.
Du, E.Y. & Chang, C.I. 2002. Thresholding video images for text detection.
Proceedings of 16th International Conference on Pattern Recognition, pp 919-922.
Ennehar, B.C., Brahim, O. & Hicham, T. 2010. An appropriate color space to improve human skin detection.INFOCOMP Journal of Computer Science, 9(4), 1-10. Febriani, A. 2014. Identifikasi Diabetic Retinopathy melalui citra retina menggunakan
100
Goswami, S., Goswami, J. & Kumar, N. 2015. Unusual event detection in low resolution video for enhancing ATM security. IEEE 2nd International
Conference on Signal Processing and Integrated Networks (SPIN), pp 848 – 853.
Intachak, T. & Kaewapichai, W. 2011. Real-time illumination feedback system for adaptive background subtraction working in traffic video monitoring. IEEE International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), pp 1-5.
Jalab, H. A. 2012. Static hand gesture recognition for human computer interaction.
Information Technology Journal 11(9) : 1265-1271.
Kang, J. & Hayes, M. H. 2015. Face recognition for vehicle personalization with near-IR frame differencing and pose clustering. IEEE International Conference on Consumer Electronics (ICCE), pp 455 – 456.
Kawulok, M., Kawulok, J., & Nalepa, J. 2014. Spatial-based skin detection using discriminative skin-presence features. Pattern Recognition Letters 41 (2014): 3-13.
Kawulok, M., Kawulok, J., & Nalepa, J., Knyc, M.: Database for hand gesture recognition. http://sun.aei.polsl.pl/~mkawulok/gestures/ (diakses 23 Agustus 2015).
Li, Q., Chen, X., Zhang, H., Yin, L., Chen, S., Wang, T., Lin, S., Liu, X., Zhang, X., & Zhang, R. 2012. Automatic human spermatozoa detection in microscopic video streams based on OpenCV. IEEE 5th International Conference on Biomedical Engineering and Informatics (BMEI), pp 224-227.
Liao, P.S., Chen, T.S. & Chung, P.C. 2001. A fast algorithm for multilevel thresholding. Journal of Information Science and Engineering 17(5): 713-727. Ling, Y., Xue, Y., Xing, J., Jiang, T. & Guo, C. 2013. Experimental studies on static
101
Moeslund, T.B. 2012. Introduction to Video and Image Processing. New York : Springer.
Mritunjayrai, VijendraBhootna & Yadav, R.K. 2015. Performance based algorithm for the detection and extraction of human skin. First International Conference on Futuristic Trend in Computational Analysis and Knowledge Management (ABLAZE), pp 127-131.
Nagarajan, S. & Subashini, T.S. 2013. Static hand gesture recognition for sign language alphabets using Edge Oriented Histogram and Multi Class SVM. International Journal of Computer Applications 82(4) : 28-35.
Nayakwadi, V. & Pokale, N. B. 2014. Dynamic hand gesture recognition system with natural hand. International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE) 4(4) : 1239-1243.
Nayana, P.B. & Kubakaddi, S. 2014. Implementation of hand gesture recognition technique for HCI using OpenCV. International Journal of Recent Development in Engineering and Technology 2(5) : 17-21.
OpenCV Documentation : Introduction to Support Vector Machines. http://docs.opencv.org/doc/tutorials/ml/introduction_to_svm/introduction_to_s vm.html/. (diakses 18 Maret 2015).
Phung, S. L., Bouzerdoum, A., & Chai, D. 2005. Skin segmentation using color pixel classification : analysis and comparison. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(1) : 148-154.
Pratt, W.K. 2007. Digital Image Processing. New York : Wiley.
Premal, C.E. & Vinsley, S.S. 2014. Image processing based forest fire detection using YCbCr colour model. IEEE International Conference on Circuit, Power and Computing Technologies (ICCPCT), pp 1229-1237.
102
Shapiro, L.G. & Stockman G.C. 2001. Computer Vision. Prentice-Hall : Upper Saddle River.
Tan, W. R., Chan, C. S., Yogarajah, P., & Condell, J. 2012. A fusion approach for efficient human skin detection. IEEE Transactions on Industrial Informatics 8(1) : 138-147.
Williamson, A. 2014. Vision based cursor control using hand gesture. Skripsi. University of The West Indies.
Wu, Y. 2009. Research on bank intelligent video image processing and monitoring control system based on OpenCV. IEEE 3rd International Conference on Anti-counterfeiting, Security, and Identification in Communication, pp 211-214. Yesugade, K.D., Salunke, S., Shinde, K., Gaikwad, S. & Shingare, M. 2014. Hand
motion recognition. International Journal of Technology and Exploring Engineering (IJITEE) 3(11) : 55-61.
Yi, Z. & Liangzhong, F. 2010. Moving object detection based on running average background and temporal difference. International Conference on Intelligent Systems and Knowledge Engineering (ISKE), pp 270-272.
Youssef, M.M., Asari, K.V., Tompkins, R.C. & Foytik, J. 2010. Hull convexity defects features for human activity recognition. IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pp.1-7.
Zarit, B. D., Super, B. J. & Quek, F. K. H. 1999. Comparison of five color models in skin pixel classification. Proceedings of the International Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems,pp. 58-63.
Zhaoxue, C., Shengdong, N., Lijun, Q., Zeng’ai, C. & Jianrong, X. 2008. Automatic liver segmentation method based on a gaussian blurring technique for CT images. IEEE The 2nd International Conference on Bioinformatics and