- Tytuł:
- Recognition of Sign Language from High Resolution Images Using Adaptive Feature Extraction and Classification
- Autorzy:
-
Csóka, Filip
Polec, Jaroslav
Csóka, Tibor
Kačur, Juraj - Powiązania:
- https://bibliotekanauki.pl/articles/226004.pdf
- Data publikacji:
- 2019
- Wydawca:
- Polska Akademia Nauk. Czytelnia Czasopism PAN
- Tematy:
-
sign language
gesture
sign
recognition
CNN
LoG
real-time
pattern recognition
machine learning - Opis:
- 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-the-art 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.
- Źródło:
-
International Journal of Electronics and Telecommunications; 2019, 65, 2; 303-308
2300-1933 - Pojawia się w:
- International Journal of Electronics and Telecommunications
- Dostawca treści:
- Biblioteka Nauki
Artykuł