@ARTICLE{Csóka_Filip_Recognition_2019, author={Csóka, Filip and Polec, Jaroslav and Csóka, Tibor and Kačur, Juraj}, volume={vol. 65}, number={No 2}, journal={International Journal of Electronics and Telecommunications}, pages={303-308}, howpublished={online}, year={2019}, publisher={Polish Academy of Sciences Committee of Electronics and Telecommunications}, abstract={A variety of algorithms allows gesture recognition in video sequences. Alleviating the need for interpreters is of interest to hearing impaired people, since it allows a great degree of self-sufficiency in communicating their intent to the non-sign language speakers without the need for interpreters. State-of-theart in currently used algorithms in this domain is capable of either real-time recognition of sign language in low resolution videos or non-real-time recognition in high-resolution videos. This paper proposes a novel approach to real-time recognition of fingerspelling alphabet letters of American Sign Language (ASL) in ultra-high-resolution (UHD) video sequences. The proposed approach is based on adaptive Laplacian of Gaussian (LoG) filtering with local extrema detection using Features from Accelerated Segment Test (FAST) algorithm classified by a Convolutional Neural Network (CNN). The recognition rate of our algorithm was verified on real-life data.}, type={Artykuły / Articles}, title={Recognition of Sign Language from High Resolution Images Using Adaptive Feature Extraction and Classification}, URL={http://www.czasopisma.pan.pl/Content/110227/PDF/40.pdf}, doi={10.24425/ijet.2019.126314}, keywords={sign language, gesture, sign, recognition, CNN, LoG, real-time, pattern recognition, machine learning}, }