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Visual data processing framework for a skin-based human detection
Ch. 12. P. 138–149.
For a large variety of tasks autonomous robots require a robust visual data processing system. This paper presents a new human detection framework that combines rotation-invariant histogram of oriented gradients (RIHOG) features and binarized normed gradients (BING) pre-processing and skin segmentation. For experimental evaluation a new Human body dataset of over 60000 images was constructed using the Human-Parts dataset, the Simulated disaster victim dataset, and the Servosila Engineer robot dataset. Random, Liner SVM, Quadratic SVM, AdaBoost, and Random Forest approaches were compared using the Human body dataset. Experimental evaluation demonstrated an average precision of 90.4% for the Quadratic SVM model and showed the efficiency of RIHOG features as a descriptor for human detection tasks.
Aleksei Samarin, Alexander Savelev, Aleksei Toropov et al., Optical Memory and Neural Networks (Information Optics) 2024 Vol. 33 P. 424–434
This study explores the development of classifiers for microbial images, specifically focusing on streptococci captured via microscopy of live samples. Our approach uses AutoML-based techniques and automates the creation and analysis of feature spaces to produce optimal descriptors for classifying these microscopic images. This technique leverages interpretable taxonomic features based on the external geometric attributes ...
Added: September 21, 2026
Aleksei Samarin, Alexander Savelev, Aleksei Toropov et al., Pattern Recognition and Image Analysis 2025 Vol. 35 No. 2 P. 148–158
This paper describes our research on creating classifiers for microbial images (micrococci microscopy images) obtained from pictures of unfixed microscopic scenes. In our work, we propose an AutoML approach based on the automatic generation and analysis of the feature space for constructing the most optimal descriptors of microorganism images for subsequent classification. This makes it ...
Added: September 19, 2026
Aleksei Samarin, Alexander Savelev, Aleksei Toropov et al., Pattern Recognition and Image Analysis 2026 Vol. 36 No. 2 P. 302–312
The lack of annotated microscopic datasets remains a major obstacle to training robust deep learning models for microbial classification. In this paper, a novel data augmentation pipeline that uses visual–linguistic large-scale models to generate synthetic microscopic images of six different bacterial and nonbacterial classes has been proposed. Synthetic samples have gradually been added to the ...
Added: September 19, 2026
Aleksei Samarin, Aleksei Toropov, Kotenko E. et al., , in: Proceedings of the 37th Conference of Open Innovations Association FRUCT, Helsinki, Finland, 14-16 May 2025, Issue 1 (Full Papers)Vol. 37.: Helsinki: FRUCT Oy, 2025. P. 278–284.
This study introduces an innovative method for recognizing automatically generated images by utilizing adapted descriptors specifically designed to analyze unique structural and morphological features characteristic of artificially created content. The methodology focuses on analyzing features inherent to image generation processes, ensuring the optimization of descriptors for identifying complex and subtle patterns associated with generative algorithms. ...
Added: September 19, 2026
Faizullin R., Imamov N., Tsoy T. et al., , in: 10th International Conference, ICR 2025, Hanoi, Vietnam, November 10–13, 2025, Proceedings, Part II. Interactive Collaborative Robotics. (LNCS, volume 16304).: Springer, 2026. P. 385–398.
Autonomous agricultural vehicles operating under the Controlled Traffic Farming (CTF) paradigm face complex routing challenges when minimizing soil compaction, total mission time, and station placement under battery constraints. This paper introduces Multi-Objective Coordinated Autonomous Routing and Placement with Fixed Lanes (MO-CARP-FL), a novel multi-objective evolutionary algorithm designed to optimize the coordinated routing of homogeneous autonomous ...
Added: March 17, 2026
Faizullin R., Imamov N., Tsoy T. et al., , in: Proceedings of the Fifth International Conference on Agriculture Digitalization and Organic Production (ADOP 2025), Volume 1. Robotics in Agriculture. (SIST, volume 453).: Springer, 2026. P. 25 – 34.
This article considers a dual problem of optimizing field coverage while minimizing a soil compaction and managing energy constraints of agricultural robots. The soil compaction in precision agriculture is a major challenge, as mobile robots are becoming increasingly common in field operations. A proposed optimization combines a soil compaction risk assessment with energy-efficient trajectory planning ...
Added: March 17, 2026
Gataullin R., Abbyasov B., Zhukova V. et al., , in: 6th International Conference, CIT&DS 2025, Volgograd, Russia, September 22–25, 2025, Proceedings. Creativity in Intelligent Technologies and Data Science. (CCIS, volume 2803).: Cham: Springer, 2025. P. 477 – 491.
Mobile robots are increasingly used to automate repetitive and labor-intensive tasks in environments such as hospitals, warehouses, offices, and manufacturing facilities. When working together, multiple robots can achieve greater efficiency by sharing workloads and coordinating complex tasks, which enables faster completion of large-scale operations. Managing such teams of robots, however, requires an intuitive system for ...
Added: March 17, 2026
Roslavtsev M., Eryomin A., Safin R. et al., , in: 2024 8th International Conference on Information, Control, and Communication Technologies (ICCT).: IEEE, 2024. P. 1–5.
Modern map-dependent algorithms for mobile robot navigation typically overload a CPU and memory with a gradually increasing amount of environmental data. In contrast, Bug family local path planning algorithms operate without mapping and have significantly lower hardware requirements. Bug algorithms use real-time measurements from visual and touch sensors to make immediate decisions on direction of ...
Added: November 25, 2025
Абрамов А. С., Chernyshev V. L., Mikhaylets E. et al., / Series Social Science Research Network "Social Science Research Network". 2025.
Computer vision is one of the most relevant modern research areas with broad practical applications. However, traditional solutions based on deep learning have signicant limitations and can be misleading. Topological data analysis, on the other hand, is a modern approach to solving similar problems using mathematically deterministic methods of algebraic topology that reduce the risk ...
Added: September 23, 2025
Moiseev N., Абрамов И. А., Камакин А. Ю., В кн.: Параллельные вычислительные технологии – XIX всероссийская конференция с международным участием, ПаВТ'2025, г. Москва, 8–10 апреля 2025 г. Короткие статьи и описания плакатов.: Челябинск: Издательский центр ЮУрГУ, 2025. С. 301–301.
In recent years, with the advancement of deep learning and neural network methods, their application in geospatial analysis tasks has become particularly relevant. A key challenge in this field is assessing the state of urban infrastructure, including the classification of buildings by their functional purpose (residential, commercial, governmental, industrial). The use of neural networks significantly ...
Added: September 17, 2025
Roman M., Eryomin A., Tsoy T. et al., , in: 2024 8th International Conference on Information, Control, and Communication Technologies (ICCT).: IEEE, 2024. Ch. 51 P. 1–4.
In this paper, we present an implementation of the CautiousBug algorithm within the Noetic distribution of the
Robot Operating System (ROS). Bug algorithms address a challenge of robot navigation in unknown environments without relying on pre-existing maps or constructing new ones. These algorithms utilize odometry data, operate without a map, require minimal computational resources, and can ...
Added: May 28, 2025
Gorshkov S., Ignatov D. I., Chernysheva A., IEEE Access 2025 Vol. 13 P. 46732–46746
This paper presents a novel approach to predicting student dropout by analyzing publicly available data from VKontakte social network profiles. Unlike traditional methods that primarily rely on academic and institutional data, our method leverages publicly available content, including photos, videos, music preferences, and textual posts. Image and video content were analyzed using scene recognition models ...
Added: March 20, 2025
Кочкаров А. А., Yatskin D., Программные продукты и системы 2016 № 3 С. 60–66
This paper is devoted to the task of space monitoring, algorithm and software package for solving it. From this problem the authors go on to the problem of detection, and then – to the problem of geometrical sensors location. It is proposed to use a decentralized robotic network to solve this problem. Limitations and assumptions ...
Added: March 7, 2025
Кочкаров А. А., Yatskin D., Рахманов О. А., Известия ЮФУ. Технические науки 2016 № 2 С. 158–168
The problem of limited space monitoring is formulated. The connection between the monitoring space and the detection of objects in this space sets up. After introducing some assumptions we conclude the necessity of solving the covering set (connected space) problem. The presence of obstacles in the monitoring area is the characteristic feature of the problem. ...
Added: March 7, 2025
Kirill Muravyev, Melekhin A., Yudin D. et al., IEEE Robotics and Automation Letters 2025 Vol. 10 No. 4 P. 3126–3133
Mapping is one of the crucial tasks enabling autonomous navigation of a mobile robot. Conventional mapping methods output a dense geometric map representation, e.g. an occupancy grid, which is not trivial to keep consistent for prolonged runs covering large environments. Meanwhile, capturing the topological structure of the workspace enables fast path planning, is typically less ...
Added: March 3, 2025
Sulaiman S., Sudheer A. P., Magid E., , in: 2024 4th International Conference on Computer, Control and Robotics (ICCCR).: IEEE, 2024. P. 269–273.
Mobile robots with wheeled, legged and hybrid bases are currently used in industrial and service sectors for diverse applications. In this paper, a comparative study of various hybrid path planning algorithms for avoiding static obstacles were carried out for determining a most efficient path planning algorithm for a given task. A modified Probabilistic Road Map ...
Added: February 19, 2025
Sobyanin K., Kulikova S., Информатика и автоматизация (Труды СПИИРАН) 2024 Т. 23 № 4 С. 1022–1046
The problem of training deep neural networks on small samples is especially relevant for medical problems. The paper examines the impact of pixel-wise marking of significant objects in the image, over the true class label, on the quality of the classification. To achieve better classification results on small samples, we propose a multitasking architecture -- ...
Added: June 29, 2024
Lychko S., Tsoy T., Li H. et al., , in: 2022 International Siberian Conference on Control and Communications (SIBCON).: IEEE, 2022. P. 1–6.
Internet of Medical Things (IoMT) is a rapidly growing branch of IoT (Internet of Things), which requires special treatment to cyber security due to confdentiality of healthcare data and patient health threat. Healthcare data and
automated medical devices might become vulnerable targets of malicious cyber-attacks. While a large number of robotic applications, including medical and healthcare, employ robot operating system (ROS) as their ...
Added: January 16, 2024
Torres-Martínez A. A., Martínez-García E., Lavrenov R. et al., , in: Artificial Intelligence for Robotics and Autonomous Systems Applications.: Springer, 2023. Ch. 15 P. 461–486.
Added: November 25, 2023
Apurin A., Abbyasov B., Martínez-García E. et al., , in: 8th International Conference, ICR 2023, Baku, Azerbaijan, October 25–29, 2023, Proceedings. Interactive Collaborative Robotics. LNCS, volume 14214.: Springer, 2023. Ch. 11 P. 116–126.
Added: November 25, 2023
Алхаддад М., Миронов К. В., Dergachev S. et al., Робототехника и техническая кибернетика 2023 Т. 11 № 3 С. 205–214
The task of local trajectory planning for an autonomous wheeled robotic platform in cluttered indoor environment is considered. Such environment might include narrow passages, which width is less than the length of the platform. Therefore, it is not possible to apply standard approach, when the obstacles are inflated with the maximum radius of the platform. ...
Added: September 8, 2023
White M., Tarakanov A., Withers P. et al., Computational Materials Science 2023 Vol. 218 Article 111985
Finding efficient means of quantitatively describing material microstructure is a critical step towards harnessing data-centric machine learning approaches to understanding and predicting processing–microstructure–property relationships. Current quantitative descriptors of microstructure tend to consider only specific, narrow features such as grain size or phase fractions, but these metrics discard vast amounts of information. Since the gain in ...
Added: April 10, 2023