?
Об одном подходе к последовательному иерархическому распознаванию изображений
С. 50–58.
Savchenko A., Милов В. Р.
In book
Ч. 3. , М.: НИЯУ МИФИ, 2015.
Savchenko A., / Series Computer Science "arxiv.org". 2022.
In this paper, we present the results of the HSE-NN team in the 4th competition on Affective Behavior Analysis in-the-wild (ABAW). The novel multi-task EfficientNet model is trained for simultaneous recognition of facial expressions and prediction of valence and arousal on static photos. The resulting MT-EmotiEffNet extracts visual features that are fed into simple feed-forward ...
Added: October 21, 2022
Ali N., Alshahrani A., Alghamdi A. M. et al., Applied Sciences (Switzerland) 2022 Vol. 12 No. 13 Article 6378
Organizations analyze customers’ personal data to understand and model their behavior. Identifying customers’ gender is a significant factor in analyzing markets that help plan the promotional campaigns, determine target customers and provide relevant offers. Several techniques were developed to analyze different types of data, including text, image, speech, and biometrics, to identify the gender of ...
Added: October 4, 2022
Savchenko A., Belova N. S., Expert Systems with Applications 2022 Vol. 207 Article 117885
In this paper, the computational complexity of the probabilistic neural network for the classification of high-dimensional data is improved. At first, the class probability densities are estimated by using only a few principal components of an observed point. The Gaussian–Parzen kernel is replaced by the orthogonal series estimates of class-conditional densities for each principal component using the Fourier series to speed ...
Added: June 29, 2022
Cham: Springer, 2020.
ICIAR 2020 was the 17th edition of the series of annual conferences on Image Analysis and Recognition, organized, this year, as a virtual conference due to the pandemic outbreak of Covid-19 affecting all the world, with an intensity never felt by the humanity in the last hundred years. These are difficult and challenging times, nevertheless ...
Added: October 1, 2020
Samara: CEUR Workshop Proceedings, 2020.
This volume contains the papers presented at the session "Image Processing and Earth Remote Sensing" within the VI International Conference on Information Technology and Nanotechnology (ITNT-2020). The conference was held in Samara, Russia, during May 26-29, 2020 (itnt-conf.org). The conference is a forum for leading researchers from all over the world aimed to discuss the ...
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
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
Savchenko A., Miasnikov E., , in: Advances in Intelligent Data Analysis XVIII (IDA 2020)Vol. 12080.: Cham: Springer, 2020. Ch. 33 P. 418–430.
In this paper, we consider the problem of event recognition on single images. In contrast to conventional fine-tuning of convolutional neural networks (CNN), we proposed to use image captioning, i.e., a generative model that converts images to textual descriptions. The motivation here is the possibility to combine conventional CNNs with a completely different approach in ...
Added: May 17, 2020
Savchenko A., IEEE Transactions on Neural Networks and Learning Systems 2020 Vol. 31 No. 2 P. 651–660
If the training data set in image recognition task is not very large, the feature extraction with a convolutional neural network is usually applied. Here, we focus on the nonparametric classification of extracted feature vectors using the probabilistic neural network (PNN). The latter is characterized by the high runtime and memory space complexity. We propose ...
Added: November 1, 2019
Sen’ko O. V., Kiselyova N. N., Dudarev V. et al., , in: Selected Papers of the XX International Conference on Data Analytics and Management in Data Intensive Domains (DAMDID/RCDL 2018). Moscow, Russia, October 9-12, 2018.: CEUR-WS, 2018. P. 152–156.
Various machine learning methods («Recognition» package and «Scikit-learn» package for Python) accuracy comparison was made on example of inorganic chemistry tasks solution. The crossvalidation and the ROC-analysis were applied to accuracy estimation ...
Added: October 31, 2019
Springer, 2019.
This 2-volume set constitutes the refereed proceedings of the 9th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2019, held in Madrid, Spain, in July 2019.
The 99 papers in these volumes were carefully reviewed and selected from 137 submissions. They are organized in topical sections named:
Part I: best ranked papers; machine learning; pattern recognition; ...
Added: September 23, 2019
Ростов н/Д: Ростовский государственный экономический университет "РИНХ", 2018.
The conference proceedings present the results of research of young scientists of the leading scientific organizations of Russia on a wide range of topical problems of Informatics, management and system analysis. Interdisciplinary research and intensive use of data are key features of modern science. The IMSA-2018 conference (Informatics, Management and Systems Analysis) aims to establish ...
Added: September 3, 2019
Dubatovka A., Mikhailova E., Zotov M. et al., , in: Communications in Computer and Information ScienceVol. 615: Databases and Information Systems - 12th International Baltic Conference.: Springer, 2016. P. 113–125.
The paper presents algorithms for automatic detection of non-stationary periods of cardiac rhythm during professional activity. While working and subsequent rest operator passes through the phases of mobilization, stabilization, work, recovery and the rest. The amplitude and frequency of non-stationary periods of cardiac rhythm indicates the human resistance to stressful conditions. We introduce and analyze ...
Added: February 13, 2019
Gostev I. M., В кн.: Оптико-электронные приборы и устройства в системах распознавания образов, обработки изображений и символьной информации. Распознавание - 2018. Сборник материалов XI Международной научно-технической конференции.: Юго-западный государственный университет, 2018. Гл. 30 С. 89–91.
Рассмотрены методы идентификации графических объектов на основе метрики DTW. ...
Added: January 9, 2019
Юго-западный государственный университет, 2018.
Сборник содержит материалы XIV международной конференции «Оптико-электронные приборы и устройства в системах распознавания образов, обработки изображений и символьной информации» (Курск, 25-28 сентября 2018 г.), целью которой является ознакомление с имеющимися достижениями по созданию оптико-электронных приборов, систем и внедрение информационных технологий в научные исследования, учебный процесс и промышленность, а также координация по эффективному их применению в ...
Added: January 9, 2019
Gostev I. M., Sevastianov L., RUDN Journal of Mathematics, Information Sciences and Physics 2018 Vol. 26 No. 4 P. 331–342
The paper sets out one of the methodologies on image processing and recognition of the form of graphic objects. In it, at the first stage preliminary processing of the image with the purpose of extracting of characteristic attributes of the form of objects is made. Contours of objects are used as such attributes. For transformation ...
Added: December 19, 2018
Minsk: [б.и.], 2007.
Added: December 7, 2018
Savchenko A., , in: Proceedings of the 24th International Conference on Pattern Recognition (ICPR).: IEEE, 2018. P. 3262–3267.
In this paper we deal with unconstrained face recognition with few training samples. The facial images are described with the off-the shelf high-dimensional features extracted with a deep convolutional neural network (CNN), which was preliminarily trained with an external very-large dataset. We focus on drawbacks of conventional probabilistic neural network (PNN), namely, low recognition performance ...
Added: December 2, 2018
IEEE, 2018.
Added: December 2, 2018