![]() ![]() Kang SK, Nam MY, Rhee PK (2008) Color based hand and finger detection technology for user interaction. Kakoty NM, Sharma MD (2018) Recognition of sign language alphabets and numbers based on hand kinematics using a data glove. Joshi A, Sierra H, Arzuaga E (2017) American sign language translation using edge detection and cross correlation, 2017 IEEE Colomb Conf Commun Comput COLCOM 2017 - Proc., 2017. Johnston T, Schembri A (2007) Australian sign language (Auslan): an introduction to sign language linguistics ![]() Imagawa K, Lu S, Igi S (1998) Color-based hands tracking system for sign language recognition. Journal of King Saud University-Computer and Information Sciences 30(4):470–477 Ibrahim NB, Selim MM, Zayed HH (2018) An automatic arabic sign language recognition system (ArSLRS). Hoseinnezhad R, Bab-Hadiashar A, Suter D (2010) Finite sample bias of robust estimators in segmentation of closely spaced structures: a comparative study. Goh TY, Basah SN, Yazid H, Aziz Safar MJ, Ahmad Saad FS (2018) Performance analysis of image thresholding: Otsu technique. Int J Mach Learn Cybern 10(1):131–153ĭong C, Leu MC, Yin Z (2015) American sign language alphabet recognition using microsoft kinect, pp. J Ambient Intell Humaniz Comput.Ĭheok MJ, Omar Z, Jaward MH (2019) A review of hand gesture and sign language recognition techniques. Research of improving semantic image segmentation based on a feature fusion model. (Accessed: 0).Ĭhen Y, Tao J, Liu L, Xiong J, Xia R, Xie J. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics)īasah SN, Hoseinnezhad R, Bab-Hadiashar A (2014) Analysis of planar-motion segmentation using affine fundamental matrix, IET Comput VisĬambridge Dictionary, Definition of ‘sign language’ (2018). International Journal of Data Science and Analytics 1(2):77–87īasah SN, Hoseinnezhad R, Bab-Hadiashar A (2008) Limits of motion-background segmentation using fundamental matrix estimation, in Proceedings - Digital Image Computing: Techniques and Applications, DICTAīasah SN, Bab-Hadiashar A, Hoseinnezhad R (2009) Conditions for motion-background segmentation using fundamental matrix, IET Comput Visīasah SN, Bab-Hadiashar A, Hoseinnezhad R (2009) Conditions for segmentation of 2D translations of 3D objects. J Pediatr 175(4):246–247īadi H (2016) Recent methods in vision-based hand gesture recognition. Sensors 18(7):2208Īnjna E (2016) Review of image segmentation technique. In conclusion, the success of sign language segmentation could be predicted beforehand with obtainable scene parameters.Īhmed MA, Zaidan BB, Zaidan AA, Salih MM, Lakulu MMB (2018) A review on systems-based sensory gloves for sign language recognition state of the art between 20. Experiment using real images demonstrate the capability of the conditions to correctly predict the outcome of sign language segmentation using Otsu technique. The result showed that the sign alphabets with handheld shape like A, E, I, M, N, S, and T is easier to segment, while sign alphabets with finger-extend shape like C, D, F, G, H, K, L, P, R, U, V, W, and Y is harder to segment. The analysis of this work was developed based on Monte Carlo statistical method, which showed that the success of sign language segmentation depends on hand size, hand background intensity difference, and noise measurement. ![]() The focus is on image thresholding using Otsu technique, since it is the most commonly used in initial process of sign language segmentation. As such, the main motivation of this paper is to critically analyse the feasibility of successful sign language segmentation under variation of dynamic scene parameters such as noise, hand size, and intensity difference between hand and background. Despite the many sign language recognition system algorithms proposed in the literature and their well-understood usage, their performance analyses are relatively limited. Image segmentation plays a crucial role as the initial step in sign language recognition. Sign language recognition system generally consists of three main processes, which are segmentation, modelling, and classification. ![]()
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