Software Implementation of Gesture Recognition Algorithm Using Computer Vision

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dc.citation.epage26
dc.citation.issue1
dc.citation.spage21
dc.contributor.affiliationLviv Polytechnic National University
dc.contributor.authorKotyk, Vladyslav
dc.contributor.authorLashko, Oksana
dc.coverage.placenameЛьвів
dc.coverage.placenameLviv
dc.date.accessioned2022-05-24T08:53:07Z
dc.date.available2022-05-24T08:53:07Z
dc.date.created2021-03-01
dc.date.issued2021-03-01
dc.description.abstractThis paper examines the main methods and principles of image formation, display of the sign language recognition algorithm using computer vision to improve communication between people with hearing and speech impairments. This algorithm allows to effectively recognize gestures and display information in the form of labels. A system that includes the main modules for implementing this algorithm has been designed. The modules include the implementation of perception, transformation and image processing, the creation of a neural network using artificial intelligence tools to train a model for predicting input gesture labels. The aim of this work is to create a full-fledged program for implementing a real-time gesture recognition algorithm using computer vision and machine learning.
dc.format.extent21-26
dc.format.pages6
dc.identifier.citationKotyk V. Software Implementation of Gesture Recognition Algorithm Using Computer Vision / Vladyslav Kotyk, Oksana Lashko // Advances in Cyber-Physical Systems. — Lviv : Lviv Politechnic Publishing House, 2021. — Vol 6. — No 1. — P. 21–26.
dc.identifier.citationenKotyk V. Software Implementation of Gesture Recognition Algorithm Using Computer Vision / Vladyslav Kotyk, Oksana Lashko // Advances in Cyber-Physical Systems. — Lviv : Lviv Politechnic Publishing House, 2021. — Vol 6. — No 1. — P. 21–26.
dc.identifier.doihttps://doi.org/10.23939/acps2021.01.021
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/56847
dc.language.isoen
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofAdvances in Cyber-Physical Systems, 1 (6), 2021
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dc.relation.references[2] Wang, H., Chai, X. and Chen, X. (2016). Sparse Observation (SO) Alignment for Sign Language Recognition. Neurocomputing, 175, pp. 674–685.
dc.relation.references[3] Wang, Q., Chen, X., Zhang, L., Wang, C. and Gao, W. (2007). Viewpoint invariant sign language recognition. Computer Vision and Image Understanding, 108(1–2).
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dc.relation.references[18] Hamill, P. (2009). Unit Test Frameworks for High-Quality Software Development. Sebastopol: O'Reilly Media, Inc.
dc.relation.references[19] Lingojam.com. 2021. American Sign Language Translator (ASL) ―LingoJam. [online] Available at: https://lingojam.com/AmericanSignLanguageTranslator/
dc.relation.references[20] Techcrunch.com. (2021). TechCrunch is now a part of Verizon Media. [online] Available at: https://techcrunch.com/2014/06/06/motionsavvy-is-a-tablet-app-that-understands-si
dc.relation.referencesen[1] Stenger, B., Thayananthan, A., Torr, P. and Cipolla, R., (2006). Model-based hand tracking using a hierarchical Bayesian filter. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(9), pp. 1372–1384.
dc.relation.referencesen[2] Wang, H., Chai, X. and Chen, X. (2016). Sparse Observation (SO) Alignment for Sign Language Recognition. Neurocomputing, 175, pp. 674–685.
dc.relation.referencesen[3] Wang, Q., Chen, X., Zhang, L., Wang, C. and Gao, W. (2007). Viewpoint invariant sign language recognition. Computer Vision and Image Understanding, 108(1–2).
dc.relation.referencesen[4] Nixon, M. and Aguado, A. (2019). Feature extraction and image processing for computer vision. 4th ed. New York: Academic Press, p. 650.
dc.relation.referencesen[5] Barghout, L. (2016). Image Segmentation Using Fuzzy SpatialTaxon Cut: Comparison of Two Different Stage One Perception Based Input Models of Color (Bayesian Classifier and Fuzzy Constraint). Electronic Imaging, 2016(16), p. 1-6.
dc.relation.referencesen[6] Zhang, Y. and Wu, L. (2011). Optimal Multi-Level Thresholding Based on Maximum Tsallis Entropy via an Artificial Bee Colony Approach. Entropy, 13(4), pp. 841-859.
dc.relation.referencesen[7] Lai, Y. and Rosin, P. (2014). Efficient Circular Thresholding. IEEE Transactions on Image Processing, 23(3), pp. 992–1001.
dc.relation.referencesen[8] Brinkmann, R. (1999). The Art and science of digital compositing. San Diego, Calif., Morgan Kaufmann, p. 184.
dc.relation.referencesen[9] Shapiro, L. and Stockman, G., (2001). Computer vision. Upper Saddle River, NJ: Prentice Hall, p. 137, 150.
dc.relation.referencesen[10] Morris, T. (2004). Computer vision and image processing. Basingstoke: Palgrave Macmillan.
dc.relation.referencesen[11] Vandoni, C. and Huang, T. (1996). Proceedings, 1996 CERN School of Computing. Geneva: CERN.
dc.relation.referencesen[12] Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, pp. 85–117.
dc.relation.referencesen[13] Bengio, Y. (2009). Learning Deep Architectures for AI. Foundations and Trends® in Machine Learning, 2(1), pp. 1–127.
dc.relation.referencesen[14] Cireşan, D., Meier, U., Masci, J. and Schmidhuber, J. (2012). Multi-column deep neural network for traffic sign classification. Neural Networks, 32, pp. 333–338.
dc.relation.referencesen[15] Capellman, J. (2020). Hands-On Machine Learning with ML.NET.
dc.relation.referencesen[16] Esposito, D. and Esposito, F. (2020). Introducing Machine Learning. 1st ed. Microsoft Press, p. 256.
dc.relation.referencesen[17] Asthana, A. (2021). Introducing ML.NET: Cross-platform, Proven and Open Source Machine Learning Framework.NET Blog. [online] Available at: https://devblogs.microsoft.com/dotnet/introducing-ml-net-cross-platform-provenand-open-source-machine-learning-framework/.
dc.relation.referencesen[18] Hamill, P. (2009). Unit Test Frameworks for High-Quality Software Development. Sebastopol: O'Reilly Media, Inc.
dc.relation.referencesen[19] Lingojam.com. 2021. American Sign Language Translator (ASL) ―LingoJam. [online] Available at: https://lingojam.com/AmericanSignLanguageTranslator/
dc.relation.referencesen[20] Techcrunch.com. (2021). TechCrunch is now a part of Verizon Media. [online] Available at: https://techcrunch.com/2014/06/06/motionsavvy-is-a-tablet-app-that-understands-si
dc.relation.urihttps://devblogs.microsoft.com/dotnet/introducing-ml-net-cross-platform-provenand-open-source-machine-learning-framework/
dc.relation.urihttps://lingojam.com/AmericanSignLanguageTranslator/
dc.relation.urihttps://techcrunch.com/2014/06/06/motionsavvy-is-a-tablet-app-that-understands-si
dc.rights.holder© Національний університет “Львівська політехніка”, 2021
dc.rights.holder© Kotyk V., Lashko O., 2021
dc.subjectComputer Vision
dc.subjectDeep Learnig
dc.subjectGaussian Blur
dc.subjectGrayScale
dc.subjectImage Classification
dc.subjectImage Segmenation
dc.subjectML.NET
dc.subjectThresholding
dc.titleSoftware Implementation of Gesture Recognition Algorithm Using Computer Vision
dc.typeArticle

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