|
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
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.
Received: 27.04.2022 Accepted: 29.09.2022
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
Linking options:
https://www.mathnet.ru/eng/co1128 https://www.mathnet.ru/eng/co/v47/i2/p287
|
Statistics & downloads: |
Abstract page: | 22 | Full-text PDF : | 15 | References: | 14 |
|