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Computer Optics, 2023, Volume 47, Issue 2, Pages 287–305
DOI: https://doi.org/10.18287/2412-6179-CO-1154
(Mi co1128)
 

IMAGE PROCESSING, PATTERN RECOGNITION

Modern automatic recognition technologies for visual communication tools

V. O. Yachnayaab, V. R. Lutsiva, R. O. Malashinab

a Saint-Petersburg State University of Aerospace Instrumentation
b Pavlov Institute of Physiology Russian Academy of Sciences
References:
Abstract: Communication refers to a wide range of different behaviors and activities aimed at handing over information. The communication process includes verbal, paraverbal and non-verbal components, conveying the informational part of a message and its emotional part respectively. A complex analysis of all communication components makes it possible to evaluate not only the content, but also the situational context of what is being said, as well as to identify additional factors inherent in the mental and somatic state of the speaker. There are several methods of conveying a verbal message, among which are oral and gestural speech (such as the sign language and fingerspelling). Various forms of communication can be contained in multiple data transmission channels, such as audio or video channels. The review is concerned with video data analysis systems, as the audio channel is incapable of non-verbal components transmission that contribute supplemental details. The article analyzes databases of static and dynamic images and systems, developed to recognize the verbal component conveyed by oral and gestural speech, as well as systems that evaluate paraverbal and non-verbal components of communication. Challenges of designing such databases and systems are specified. Prospective directions in complex analysis of all communication components and its combinations for the most complete evaluation of messages are defined.
Keywords: visual speech recognition, sign language recognition, affective computing, computer vision, neural networks.
Funding agency Grant number
Ministry of Science and Higher Education of the Russian Federation 0134-2019-0006
The work was carried out with the support of State Program 47 State Enterprise “Scientific and Technological Development of the Russian Federation” (2019-2030), topic 0134-2019-0006.
Received: 27.04.2022
Accepted: 29.09.2022
Document Type: Article
Language: Russian
Citation: V. O. Yachnaya, V. R. Lutsiv, R. O. Malashin, “Modern automatic recognition technologies for visual communication tools”, Computer Optics, 47:2 (2023), 287–305
Citation in format AMSBIB
\Bibitem{YacLutMal23}
\by V.~O.~Yachnaya, V.~R.~Lutsiv, R.~O.~Malashin
\paper Modern automatic recognition technologies for visual communication tools
\jour Computer Optics
\yr 2023
\vol 47
\issue 2
\pages 287--305
\mathnet{http://mi.mathnet.ru/co1128}
\crossref{https://doi.org/10.18287/2412-6179-CO-1154}
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  • https://www.mathnet.ru/eng/co1128
  • https://www.mathnet.ru/eng/co/v47/i2/p287
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    Computer Optics
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