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Proceedings Volume 11605, Thirteenth International Conference on Machine Vision
SPIE, 2021.
Line detection is an important computer vision task traditionally solved by Hough Transform. With the advance of deep learning, however, trainable approaches to line detection became popular. In this paper we propose a lightweight CNN for line detection with an embedded parameter-free Hough layer, which allows the network neurons to have global strip-like receptive fields. We argue that traditional convolutional networks have two inherent problems when applied to the task of line detection and show how insertion of a Hough layer into the network solves them. Additionally, we point out some major inconsistencies in the current datasets used for line detection.
Lebedev O. B., Черкасов Р. И., Известия ЮФУ. Технические науки 2025 № 5(247) С. 254–276
This paper considers the application of artificial intelligence technologies, in particular computer vision, in visual information processing systems. A comprehensive analysis of neural network approaches to solving computer vision problems is carried out, including systematization of key types of problems: image classification, object detection and semantic segmentation. The architectural principles of convolutional neural networks are ...
Added: September 10, 2026
Kazimirov D., Vitalii Gulevskii, Kroshnin A. et al., Mathematics 2026 Article 1136
The Hough transform (HT) is widely used in computer vision, tomography, and neural networks. Numerous algorithms for HT computation have been proposed, making their systematic comparison essential. However, existing comparative methodologies are either non-universal and limited to certain HT formulations, or task-oriented, relying on application-specific criteria that do not fully capture algorithmic properties. This paper ...
Added: May 28, 2026
Pikul A. S., Безопасность информационных технологий 2024 Т. 31 № 4 С. 116–127
This article explores the potential use of modern computer vision architectures for the task of deepfake detection. The following architectures are considered: EfficientNet, Vision Transformer (ViT), VisionLSTM (ViL), Vision KAN, and Mamba Vision. The novelty of the approach lies in the application and comparison of these architectures, as well as their combination into paired ensembles ...
Added: December 12, 2025
Seleznev L. E., Chupakhin A. A., Kostenko V. A. et al., Optical Memory and Neural Networks (Information Optics) 2023 Vol. 32 No. 2 P. 73–85
We analyze a classification problem of mentally pronounced Russian phonemes based on data obtained by means of an electroencephalography device. We describe the data collection method as well as the methods of the obtained data processing. To solve the small sample size problem we present the augmentation techniques that use the time stretching and the ...
Added: October 2, 2025
Zabolotniy A., Chan R. W., Moiseeva V. et al., Frontiers in Neuroscience 2025 Vol. 19 Article 1623380
We demonstrated the feasibility of finger movement decoding with a tailored Convolutional Neural Network. The performance of our approach was comparable to complex deep learning architectures, while providing faster and interpretable outcome. This algorithmic strategy holds high potential for the investigation of the mechanisms underlying non-invasive neurophysiological recordings in cognitive neuroscience. ...
Added: October 2, 2025
Morozov D., Garipov T., Lyashevskaya O. et al., Journal of Language and Education 2024 Vol. 10 No. 4 P. 71–84
Introduction: Numerous algorithms have been proposed for the task of automatic morpheme segmentation of Russian words. Due to the differences in task formulation and datasets utilized, comparing the quality of these algorithms is challenging. It is unclear whether the errors in the models are due to the ineffectiveness of algorithms themselves or to errors and inconsistencies ...
Added: January 7, 2025
Demidovskij A., Artyom Tugaryov, Aleksei Trutnev et al., Mathematics 2023 Vol. 14 No. 11 Article 3120
Due to industrial demands to handle increasing amounts of training data, lower the cost of computing one model at a time, and lessen the ecological effects of intensive computing resource consumption, the job of speeding the training of deep neural networks becomes exceedingly challenging. Adaptive Online Importance Sampling and IDS are two brand-new methods for ...
Added: September 12, 2023
Demochkina P., Savchenko A., , in: Pattern Recognition. ICPR International Workshops and Challenges. Virtual Event, January 10–15, 2021, Proceedings, Part V.: Springer, 2021. P. 266–274.
In this paper, we address the emotion classification problem in videos using a two-stage approach. At the first stage, deep features are extracted from facial regions detected in each video frame using a MobileNet-based image model. This network has been preliminarily trained to identify the age, gender, and identity of a person, and further fine-tuned ...
Added: April 10, 2022
Churaev E., Savchenko A., , in: 2021 International Russian Automation Conference (RusAutoCon).: IEEE, 2021. P. 633–638.
In this paper, we examine the issue of video-based facial emotion recognition algorithms which show excellent performance on some benchmarks, but have much worse accuracy in practical applications. For example, the typical error rate of contemporary deep neural networks on the RAVDESS dataset is less than 5%. We argue that such results are obtained only ...
Added: October 7, 2021
Sidorov Nikita, Slastnikov Sergey, Journal of Physics: Conference Series 2021 Vol. 1740 P. 1–6
Sentiment analysis of different language texts is one of the very popular machine learning tasks. The complexity of its solution depends both on the characteristics of a particular language, and on the length of the evaluated texts. In our work, we consider the task of creating a sentiment analysis software tool for Russian posts and ...
Added: February 2, 2021
Криницкий М. А., Verezemskaya P., Гращенков К. В. et al., Atmosphere 2018 Vol. 9 No. 426 P. 1–23
Polar mesocyclones (MCs) are small marine atmospheric vortices. The class of intense MCs, called polar lows, are accompanied by extremely strong surface winds and heat fluxes and thus largely influencing deep ocean water formation in the polar regions. Accurate detection of polar mesocyclones in high-resolution satellite data, while challenging, is a time-consuming task, when performed ...
Added: November 26, 2020
Smetanin S., Komarov M. M., , in: 2019 IEEE 21st Conference on Business Informatics (CBI)Vol. 2.: The Institute of Electrical and Electronics Engineers, Inc. , 2019. P. 482–486.
Nowadays, product reviews on e-commerce sites tend to be a valuable resource in terms of evaluation of customers’ behavior, their preferences, and needs. This paper provides an approach for sentiment analysis of product reviews in Russian using convolutional neural networks. We use Word2Vec pre-trained vectors as inputs for neural networks. This approach utilizes no hand-crafted ...
Added: November 1, 2020
Solovyev R. A., Vakhrushev M., Radionov A. et al., , in: 2020 IEEE 40th International Conference on Electronics and Nanotechnology (ELNANO).: IEEE, 2020. Ch. 9088863 P. 688–693.
Automatic classification of sound commands is becoming increasingly important, especially for embedded and mobile devices. Many of these devices contain both microphones and cameras. The manufacturers that develop and produce them would like to use the same methodology for sound and image classification tasks. It’s possible to achieve by representing sound commands as images, and ...
Added: September 19, 2020
Savchenko A., Savchenko L., Savchenko V., , in: Mathematical Optimization Theory and Operations Research, 19th International Conference, MOTOR 2020, Novosibirsk, Russia, July 6–10, 2020, (Т. 12095).: Cham: Springer, 2020. Ch. 30 P. 440–454.
This paper considers an assessment and evaluation of the pronunciation quality in computer-aided language learning systems. We propose the novel distortion measure for speech processing by using the gain optimization of the symmetrized Itakura-Saito divergence. This dissimilarity is implemented in a complete algorithm for pronunciation
learning and improvement. At its first stage, a user has to achieve a stable pronunciation of ...
Added: September 2, 2020
Savchenko L., Информационные технологии 2020 Т. 26 № 5 С. 290–296
article deals with the problem of isolated words recognition based on deep convolutional neural networks. The use of
existing recognition systems in practice is limited by an insufficiently high degree of their reliability functioning in conditions of intense acoustic noise, such as street noise, sounds from passing vehicles, etc. Nowadays, the most accurate recognition methods are characterized by ...
Added: September 2, 2020
Tsvetkovskaya I. I., Tekutieva N. V., Prokofyeva E. N. et al., , in: 2020 Moscow Workshop on Electronic and Networking Technologies (MWENT).: IEEE, 2020. P. 1–5.
The availability of high-resolution satellite images obtained through space radio communications offers the opportunity to use the most advanced technologies and techniques for analyzing remote sensing data. The paper discusses the data obtained with the use of ground-based, airborne or space-based filming equipment, which makes it possible to obtain images in one or several sections ...
Added: June 23, 2020
Maltina L., Malafeev A., , in: Analysis of Images, Social Networks and Texts. 8th International Conference, AIST 2019, Kazan, Russia, July 17–19, 2019, Revised Selected Papers. Communications in Computer and Information ScienceVol. 1086.: Springer, 2020. P. 160–166.
This paper is aimed at evaluating the performance of existing models of morphemic analysis for Russian based on convolutional neural networks. The models were trained on a relatively small amount of annotated training data (38,368 words). We tuned the hyperparameters to accommodate the harder task setting, which helped improve the accuracy of the model. In ...
Added: November 16, 2019
Malafeev A., Nikolaev K., , in: Analysis of Images, Social Networks and Texts. 8th International Conference, AIST 2019, Kazan, Russia, July 17–19, 2019, Revised Selected Papers. Communications in Computer and Information ScienceVol. 1086.: Springer, 2020. P. 154–159.
In this paper, a deep learning method study is conducted to solve a new multiclass text classification problem, identifying user interests by text messages. We used an original dataset of almost 90 thousand forum text messages, labeled for ten interests. We experimented with different modern neural network architectures: recurrent and convolutional, as well as simpler ...
Added: November 7, 2019
Grechikhin I., Andrey V. Savchenko, , in: Pattern Recognition and Image Analysis* 2.: Springer, 2019. P. 429–440.
The article describes an approach for extraction of user preferences based on the analysis of a gallery of photos and videos on mobile device. It is proposed to firstly use fast SSD-based methods in order to detect objects of interests in offline mode directly on mobile device. Next we perform facial analysis of all visual ...
Added: September 23, 2019
Makarov I., Dmitrii Maslov, Gerasimova O. et al., , in: Proceedings of 27th ACM International Conference on Multimedia.: NY: ACM, 2019. P. 1080–1084.
In our recent papers, we proposed a new family of residual convolutional neural networks trained for semi-dense and sparse depth reconstruction without use of RGB channel. The proposed models can be used in low-resolution depth sensors or SLAM methods estimating partial depth with certain distributions. We proposed using perceptual loss for training depth reconstruction in ...
Added: September 16, 2019