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Please use this identifier to cite or link to this item: https://oldena.lpnu.ua/handle/ntb/52122
Title: The Object Recognition System in the Video Stream
Authors: Rudchick, Illya
Basyuk, Taras
Affiliation: Lviv Polytechnic National University
Bibliographic description (Ukraine): Rudchick I. The Object Recognition System in the Video Stream / Illya Rudchick, Taras Basyuk // Computational linguistics and intelligent systems : proceedings of the 4nd International conference, 23-24 April 2020, Lviv, Ukraine. — Lviv : Lviv Politechnic Publishing House, 2020. — Vol 2 : Proceedings of the 4nd International conference, COLINS 2020. Workshop, Lviv, Ukraine April 23-24, 2020. — P. 141–144. — (Intelligent Systems).
Bibliographic description (International): Rudchick I. The Object Recognition System in the Video Stream / Illya Rudchick, Taras Basyuk // Computational linguistics and intelligent systems : proceedings of the 4nd International conference, 23-24 April 2020, Lviv, Ukraine. — Lviv : Lviv Politechnic Publishing House, 2020. — Vol 2 : Proceedings of the 4nd International conference, COLINS 2020. Workshop, Lviv, Ukraine April 23-24, 2020. — P. 141–144. — (Intelligent Systems).
Is part of: Computational linguistics and intelligent systems : proceedings of the 4nd International conference (2), 2020
Issue Date: 23-Apr-2020
Publisher: Видавництво Львівської політехніки
Lviv Politechnic Publishing House
Place of the edition/event: Львів
Lviv
Temporal Coverage: 23-24 April 2020, Lviv, Ukraine
Keywords: Computer vision
object recognition
motion detection
noise cancellation
Number of pages: 4
Page range: 141-144
Start page: 141
End page: 144
Abstract: The article describes the analysis of known approaches and systems of pattern recognition, shows their shortcomings and shows the relevance of this task. The system using a structural approach was designed and a software tool that implements the process of object recognition in the video stream was developed.
URI: https://ena.lpnu.ua/handle/ntb/52122
ISSN: 2523-4013
Copyright owner: © Національний університет “Львівська політехніка”, 2020
URL for reference material: https://www.learnopencv.com/opencv-c-vs-python-vs-matlab-for-computer-vision
References (Ukraine): 1. Tufte, E. (2001) The Visual Display of Quantitative Information / E. Tufte. Second edition. Connecticut: Graphics Press, 206p.
2. Dix, A.(2009) Human-Computer Interaction / A. Dix. New York, USA: Springer US, P. 1327–1331.
3. OpenCV (C++ vs Python) vs MATLAB for Computer Vision [Electronic source] / Access mode: https://www.learnopencv.com/opencv-c-vs-python-vs-matlab-for-computer-vision.
4. Demchuk, A., Lozynska, O.: The Typhlocomments Rules for Audiodescription System of the Video Content Formation for People with Visual Impairments. In: Computational Linguistics and Intelligent Systems, COLINS, 2, 53-59. (2018)
5. Lytvyn, V., Vysotska, V., Mykhailyshyn, V., Rzheuskyi, A., Semianchuk, S.: System Development for Video Stream Data Analyzing. In: Advances in Intelligent Systems and Computing, 1020, 315-331. (2020)
6. Veres, O., Rishnyak, I., Rishniak, H.: Application of Methods of Machine Learning for the Recognition of Mathematical Expressions. In: Computational linguistics and intelligent systems, COLINS, 378-389. (2019)
7. Bakumenko, N., Strilets, V., Ugryumov, M.: Application of the C-Means Fuzzy Clustering Method for the Patient's State Recognition Problems in the Medical Monitoring System. In: Computational linguistics and intelligent systems, COLINS, 218-227. (2019)
8. Dovbysh, A., Shelehov, I., Pylypenko, S., Berest, O.: Estimation of Informativeness of Recognition Signs at Extreme Information Machine Learning of Knowledge Control System. In: Computational linguistics and intelligent systems, COLINS, 143-152. (2019)
9. Dovbysh, A., Alieksieiev, V.: Embedding Speech Recognition Tools for Custom Software: Engines Overview. In: Computational Linguistics and Intelligent Systems, COLINS, 2, 114-121. (2018)
10. Lytvyn, V., Vysotska, V., Pukach, P., Bobyk, І., Uhryn, D.: Development of a method for the recognition of author’s style in the Ukrainian language texts based on linguometry, stylemetry and glottochronology. In: Eastern-European Journal of Enterprise Technologies, 4(2-88), 10-19. (2017)
11. Lytvyn, V., Peleshchak, I., Vysotska, V., Peleshchak, R.: Satellite spectral information recognition based on the synthesis of modified dynamic neural networks and holographic data processing techniques. In: Proceedings of the International Conference on Computer Sciences and Information Technologies, CSIT, 330-334. (2018)
12. Zdebskyi, P., Vysotska, V., Peleshchak, R., Peleshchak, I., Demchuk, A., Krylyshyn, M.: An Application Development for Recognizing of View in Order to Control the Mouse Pointer. In: CEUR Workshop Proceedings, Vol-2386, 55-74. (2019)
13. Shu, C., Dosyn, D., Lytvyn, V., Vysotska V., Sachenko, A., Jun, S.: Building of the Predicate Recognition System for the NLP Ontology Learning Module. In: International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS, 2, 802-808. (2019)
References (International): 1. Tufte, E. (2001) The Visual Display of Quantitative Information, E. Tufte. Second edition. Connecticut: Graphics Press, 206p.
2. Dix, A.(2009) Human-Computer Interaction, A. Dix. New York, USA: Springer US, P. 1327–1331.
3. OpenCV (C++ vs Python) vs MATLAB for Computer Vision [Electronic source], Access mode: https://www.learnopencv.com/opencv-c-vs-python-vs-matlab-for-computer-vision.
4. Demchuk, A., Lozynska, O., The Typhlocomments Rules for Audiodescription System of the Video Content Formation for People with Visual Impairments. In: Computational Linguistics and Intelligent Systems, COLINS, 2, 53-59. (2018)
5. Lytvyn, V., Vysotska, V., Mykhailyshyn, V., Rzheuskyi, A., Semianchuk, S., System Development for Video Stream Data Analyzing. In: Advances in Intelligent Systems and Computing, 1020, 315-331. (2020)
6. Veres, O., Rishnyak, I., Rishniak, H., Application of Methods of Machine Learning for the Recognition of Mathematical Expressions. In: Computational linguistics and intelligent systems, COLINS, 378-389. (2019)
7. Bakumenko, N., Strilets, V., Ugryumov, M., Application of the C-Means Fuzzy Clustering Method for the Patient's State Recognition Problems in the Medical Monitoring System. In: Computational linguistics and intelligent systems, COLINS, 218-227. (2019)
8. Dovbysh, A., Shelehov, I., Pylypenko, S., Berest, O., Estimation of Informativeness of Recognition Signs at Extreme Information Machine Learning of Knowledge Control System. In: Computational linguistics and intelligent systems, COLINS, 143-152. (2019)
9. Dovbysh, A., Alieksieiev, V., Embedding Speech Recognition Tools for Custom Software: Engines Overview. In: Computational Linguistics and Intelligent Systems, COLINS, 2, 114-121. (2018)
10. Lytvyn, V., Vysotska, V., Pukach, P., Bobyk, I., Uhryn, D., Development of a method for the recognition of authors style in the Ukrainian language texts based on linguometry, stylemetry and glottochronology. In: Eastern-European Journal of Enterprise Technologies, 4(2-88), 10-19. (2017)
11. Lytvyn, V., Peleshchak, I., Vysotska, V., Peleshchak, R., Satellite spectral information recognition based on the synthesis of modified dynamic neural networks and holographic data processing techniques. In: Proceedings of the International Conference on Computer Sciences and Information Technologies, CSIT, 330-334. (2018)
12. Zdebskyi, P., Vysotska, V., Peleshchak, R., Peleshchak, I., Demchuk, A., Krylyshyn, M., An Application Development for Recognizing of View in Order to Control the Mouse Pointer. In: CEUR Workshop Proceedings, Vol-2386, 55-74. (2019)
13. Shu, C., Dosyn, D., Lytvyn, V., Vysotska V., Sachenko, A., Jun, S., Building of the Predicate Recognition System for the NLP Ontology Learning Module. In: International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS, 2, 802-808. (2019)
Content type: Article
Appears in Collections:Computational linguistics and intelligent systems. – 2020 р.

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