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American and Russian Sign Language Dactyl Recognition
P. 204–210.
Sign languages are the main way for people from deaf community to communicate with other people. In this paper, we have compared several real-time sign language dactyl recognition systems using deep convolutional neural networks. Our system is able to recognize words from natural language gestured using signs for each letter. We evaluate our approach on American (ASL) and Russian (RSL) sign languages. For ASL, we trained on dataset prepared by Massey University, Institute of Information and Mathematical Sciences, for RSL we collect our own dataset, which we aim to enlarge together with RSL community in Russia. The results showed 100% accuracy for ASL Massey dataset, while RSL recognition quality is behind sufficient quality due to much more complex nature of real-world RSL dataset.
Burkova S., Kimmelman V., Filimonova E., , in: Number in the World's Languages: A Comparative Handbook.: Berlin: De Gruyter Mouton, 2022. P. 805–828.
Added: June 3, 2026
Burkova S., Khristoforova E., Kimmelman V., , in: Advances in Sign Language Corpus Linguistics.: John Benjamins Publishing Company, 2023. P. 90–129.
This chapter presents the Russian Sign Language (RSL) Corpus and demonstrates
its capabilities as a research tool by summarizing three corpus-based
studies primarily focused on syntactic functions of nonmanual markers.
The first study considers question marking in regular wh-questions and in
question-answer pairs. It shows that the two constructions have very different
nonmanual markers. The second study analyzes marking of ...
Added: June 3, 2026
Shadrina E. V., Мохова В. О., Загоскин В. А. et al., Нижегородский психологический альманах 2024 № 2
The article considers the problem of learning of recognizing emotions from pictures. A review and analysis of domestic and foreign works of scientists dealing with the problem of emotional intelligence was carried out. Its formation, influence on human activity and existing variants of its structure were considered, and common features in the understanding of emotional ...
Added: April 9, 2026
Bolshakov N., Колесников В. В., В кн.: Альманах «Исследуя сообщество глухих: 1». [Материалы междисциплинарной конференции «Что значит быть глухим? Новые вызовы для сообщества», Дом культуры «ГЭС-2», Международная лаборатория исследований социальной интеграции Национального исследовательского университета «Высшая школа экономики», май 2023 года].: М.: V–A–С Press, 2024. С. 14–26.
Современные исследования, посвященные глухим и слабослыша- щим людям, а также глухоте в целом, в западных странах принято рассматривать в рамках такого междисциплинарного направле- ния, как Deaf Studies, появившегося во второй половине XX века. На русский язык перевести название этого направления наиболее корректно можно как «исследования сообщества глухих» в широком смысле: охватываются не только глухие и слабослышащие ...
Added: June 6, 2024
Bolshakov N., Макаркин М., Меренкова В. et al., В кн.: Альманах «Исследуя сообщество глухих: 1». [Материалы междисциплинарной конференции «Что значит быть глухим? Новые вызовы для сообщества», Дом культуры «ГЭС-2», Международная лаборатория исследований социальной интеграции Национального исследовательского университета «Высшая школа экономики», май 2023 года].: М.: V–A–С Press, 2024. С. 235–254.
Studies in various countries have addressed the social phenomena of d/Deaf users socialising online: in online spaces, d/Deaf users are able to hide their deafness, establish “weak ties” online (with people they don’t know personally), and acquire access to more information. In Russia, such studies have revealed social networks and messengers to be a very ...
Added: June 6, 2024
Kimmelman V., Komarova A., Luchkova L. et al., Frontiers in Psychology 2022 Vol. 12 Article 740734
When describing variation at the lexical level in sign languages, researchers often distinguish between phonological and lexical variants, using the following principle: if two signs differ in only one of the major phonological components (handshape, orientation, movement, location), then they are considered phonological variants, otherwise they are considered separate lexemes. We demonstrate that this principle ...
Added: September 7, 2023
Makarov I., Bakhanova M., Nikolenko S. et al., PeerJ Computer Science 2022 Vol. 8 Article e865
Depth estimation has been an essential task for many computer vision applications, especially in autonomous driving, where safety is paramount. Depth can be estimated not only with traditional supervised learning but also via a self-supervised approach that relies on camera motion and does not require ground truth depth maps. Recently, major improvements have been introduced ...
Added: February 1, 2022
Krinitskiy M., Alexandrova M., Verezemskaya P. et al., Remote Sensing 2021 Vol. 13 No. 2 Article 326
Total Cloud Cover (TCC) retrieval from ground-based optical imagery is a problem that has been tackled by several generations of researchers. The number of human-designed algorithms for the estimation of TCC grows every year. However, there has been no considerable progress in terms of quality, mostly due to the lack of systematic approach to the ...
Added: September 24, 2021
Dmitrii Maslov, Makarov I., , in: Advances in Computational Intelligence: 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Virtual Event, June 16–18, 2021, Proceedings, Part I* 1. Vol. 12861.: Springer, 2021. Ch. 38 P. 456–467.
In this paper, we study depth reconstruction via RGB-based, Sparse-Depth, and RGBd approaches. We showed that combination of RGB and Sparse Depth approach in RGBd scenario provides the best results. We also proved that the models performance can be further tuned via proper selection of architecture blocks and number of depth points guiding RGB-to-depth reconstruction. ...
Added: September 1, 2021
Kudriavtseva P., Kashkinov M., Kertész-Farkas A., Journal of Proteome Research 2021 Vol. 20 No. 10 P. 4708–4717
Spectrum annotation is a challenging task due to the presence of unexpected peptide fragmentation ions as well as the inaccuracy of the detectors of the spectrometers. We present a deep convolutional neural network, called Slider, which learns an optimal feature extraction in its kernels for scoring mass spectrometry (MS)/MS spectra to increase the number of ...
Added: August 30, 2021
Savchenko A., , in: Proceedings of International Joint Conference on Neural Networks 2020 (IJCNN 2020).: Piscataway: IEEE, 2020. P. 1–8.
In this paper a new formulation of event recognition task is examined: it is required to predict event categories given a gallery of images, for which albums (groups of photos corresponding to a single event) are unknown. The novel two-stage approach is proposed. At first, features are extracted in each photo using the pre-trained convolutional ...
Added: October 15, 2020
Savchenko A., , in: Proceedings of International Joint Conference on Neural Networks 2020 (IJCNN 2020).: Piscataway: IEEE, 2020. P. 1–8.
In this paper the problem of high computational complexity of deep convolutional nets in image recognition is considered. An existing framework of adaptive neural networks is extended by appending the separate classifier to intermediate layers. The hierarchical representations of the input image are sequentially analyzed. If the first classifier returns rather high confidence score, the ...
Added: October 15, 2020
Kuznetsov A., Savchenko A., , in: Proceedings of the International Conference on Computer Vision and Graphics (ICCVG 2020)Vol. 12334.: Cham: Springer, 2020. Ch. 8 P. 87–97.
In this research we introduce a new labelled SportLogo dataset, that contains images of two kinds of sports: hockey (NHL) and basketball (NBA). This dataset presents several challenges typical for logo detection tasks. A huge number of occlusions and logo view changes during playing games lead to an ambiguity of a straightforward detection approach use. ...
Added: October 1, 2020
Miasnikov E., Savchenko A., , in: Proceedings of International Conference on Image Analysis and Recognition (ICIAR 2020)Vol. 12131.: Cham: Springer, 2020. Ch. 9 P. 83–94.
Food analysis is one of the most important parts of user preference prediction engines for recommendation systems in the travel domain. In this paper, we describe and study the neural network method that allows you to recognize food in a gallery of photos taken with mobile devices. The described method consists of three main stages, ...
Added: October 1, 2020
Kharchevnikova A., Savchenko A., Компьютерная оптика 2020 Т. 44 № 4 С. 618–626
В работе рассматривается задача извлечения предпочтений пользователя по его фотоальбому. Предложен новый подход на основе автоматического порождения текстовых описаний фотографий и последующей классификации таких описаний. Проведен анализ известных методов создания аннотаций по изображению на основе свёрточных и рекуррентных (Long short-term memory) нейронных сетей. С использованием набора данных Google’s Conceptual Captions обучены новые модели, в которых ...
Added: September 16, 2020