https://oldena.lpnu.ua/handle/ntb/52548| Title: | Quantization of the Space of Structural Image Features as a Way to Increase Recognition Performance |
| Authors: | Gorokhovatskyi, Volodymyr Putyatin, Yevgenyi Gorokhovatskyi, Oleksii Peredrii, Olena |
| Affiliation: | Kharkiv National University of Radio Electronics Simon Kuznets Kharkiv National University of Economics |
| Bibliographic description (Ukraine): | Quantization of the Space of Structural Image Features as a Way to Increase Recognition Performance / Volodymyr Gorokhovatskyi, Yevgenyi Putyatin, Oleksii Gorokhovatskyi, Olena Peredrii // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Львів : Lviv Politechnic Publishing House, 2018. — P. 464–467. — (Machine Vision and Pattern Recognition). |
| Bibliographic description (International): | Quantization of the Space of Structural Image Features as a Way to Increase Recognition Performance / Volodymyr Gorokhovatskyi, Yevgenyi Putyatin, Oleksii Gorokhovatskyi, Olena Peredrii // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Lviv Politechnic Publishing House, 2018. — P. 464–467. — (Machine Vision and Pattern Recognition). |
| Is part of: | Data stream mining and processing : proceedings of the IEEE second international conference, 2018 |
| Conference/Event: | IEEE second international conference "Data stream mining and processing" |
| Issue Date: | 28-Feb-2018 |
| Publisher: | Lviv Politechnic Publishing House |
| Place of the edition/event: | Львів |
| Temporal Coverage: | 21-25 August 2018, Lviv |
| Keywords: | computer vision structural recognition methods set of characteristic features descriptor quantization competitive learning recognition performance noise immunity |
| Number of pages: | 4 |
| Page range: | 464-467 |
| Start page: | 464 |
| End page: | 467 |
| Abstract: | A modification of the structural image recognition method in computer vision systems is proposed. In order to improve the performance of recognition, quantization (clustering) is applied in the space of characteristic features that form the pattern of the object. Due to the transformation of structural objects descriptions from a set representation to a vector form, the amount of computation is reduced tens of times. The results of experiments that confirmed the effectiveness and increase of decision-making process are shown. |
| URI: | https://ena.lpnu.ua/handle/ntb/52548 |
| ISBN: | © Національний університет „Львівська політехніка“, 2018 © Національний університет „Львівська політехніка“, 2018 |
| Copyright owner: | © Національний університет “Львівська політехніка”, 2018 |
| URL for reference material: | https://www.researchgate.net/publication/292157133_ http://opencv.org |
| References (Ukraine): | [1] V. Gorokhovatsky, “Structural analysis and intellectual data processing in computer vision,” SMIT, Kharkov, 2014. (in Russian). [2] H. Bay, T. Tuytelaars and L.Van Gool, “Surf: Speeded up robust features,” in: 9th European Conference on Computer Vision, Graz, Austria, pp. 404– 417, May 7-13, 2006. [3] V. Gorokhovatsky, “Efficient Estimation of Visual Object Relevance during Recognition through their Vector Descriptions,” in: Telecommunications and Radio Engineering, Vol. 75, No 14, pp. 1271–1283, 2016. [4] V. Gorokhovatsky, A. Gorokhovatsky and A.Berestovsky, “Intellectual Data Processing and Self-Organization of Structural Features at Recognition of Visual Objects,” in: Telecommunications and Radio Engineering, Vol. 75, No 2, pp. 155–168, 2016. [5] L. Shapiro and G. Stockman, “Computer vision,” Prentice Hall., 2001. [6] T. Kohonen, “Self-Organizing Maps,” Springer Science & Business Media, 2001. [7] E. Karami, S. Prasad and M. Shehata, “Image Matching Using SIFT, SURF, BRIEF and ORB: Performance Comparison for Distorted Images,” [Online]. Available: https://www.researchgate.net/publication/292157133_[July 30, 2017]. [8] S. Osovski, “Sieci neuronovane do przetwarzania informacji,” in: Oficyna Wydawnicza Politechniki Warszawskiej, 2000. [9] V.Gorokhovatsky, Y. Putyatin and V. Stolyarov, “Research of Effectivenss of Structural Image Classification Methods using Cluster Data Model,” in: Radio Electronics, Computer Science, Control, vol. 3 (42), pp. 78–85, 2017. [10] O. Gorokhovatskyi, “Neocognitron As a Tool for Optical Marks Recognition,” in: First IEEE International Conference on DataStream Mining & Processing (DSMP), Lviv, Ukraine, pp. 169 – 172, 23-27August 2016. [11] E. Rublee, V. Rabaud, K. Konolige and G. Bradski, “ORB: an efficient alternative to SIFT or SURF,” in: IEEE International Conference on Computer Vision (ICCV), pp. 2564 – 2571, 2011. [12] OpenCV Library [Online]. Available: http://opencv.org [November 15, 2017] [13] M. Sonka, V. Hlavac and R. Boyle, “Image Processing, Analysis and Machine Vision,” Thomson, 2008. [14] R. Szeliski, “Computer Vision: Algorithms and Applications,” Springer, 2010. [15] Y. Amit, “2D Object Detection and Recognition: models, algorithms and networks,” The MIT Press, 2002. |
| References (International): | [1] V. Gorokhovatsky, "Structural analysis and intellectual data processing in computer vision," SMIT, Kharkov, 2014. (in Russian). [2] H. Bay, T. Tuytelaars and L.Van Gool, "Surf: Speeded up robust features," in: 9th European Conference on Computer Vision, Graz, Austria, pp. 404– 417, May 7-13, 2006. [3] V. Gorokhovatsky, "Efficient Estimation of Visual Object Relevance during Recognition through their Vector Descriptions," in: Telecommunications and Radio Engineering, Vol. 75, No 14, pp. 1271–1283, 2016. [4] V. Gorokhovatsky, A. Gorokhovatsky and A.Berestovsky, "Intellectual Data Processing and Self-Organization of Structural Features at Recognition of Visual Objects," in: Telecommunications and Radio Engineering, Vol. 75, No 2, pp. 155–168, 2016. [5] L. Shapiro and G. Stockman, "Computer vision," Prentice Hall., 2001. [6] T. Kohonen, "Self-Organizing Maps," Springer Science & Business Media, 2001. [7] E. Karami, S. Prasad and M. Shehata, "Image Matching Using SIFT, SURF, BRIEF and ORB: Performance Comparison for Distorted Images," [Online]. Available: https://www.researchgate.net/publication/292157133_[July 30, 2017]. [8] S. Osovski, "Sieci neuronovane do przetwarzania informacji," in: Oficyna Wydawnicza Politechniki Warszawskiej, 2000. [9] V.Gorokhovatsky, Y. Putyatin and V. Stolyarov, "Research of Effectivenss of Structural Image Classification Methods using Cluster Data Model," in: Radio Electronics, Computer Science, Control, vol. 3 (42), pp. 78–85, 2017. [10] O. Gorokhovatskyi, "Neocognitron As a Tool for Optical Marks Recognition," in: First IEEE International Conference on DataStream Mining & Processing (DSMP), Lviv, Ukraine, pp. 169 – 172, 23-27August 2016. [11] E. Rublee, V. Rabaud, K. Konolige and G. Bradski, "ORB: an efficient alternative to SIFT or SURF," in: IEEE International Conference on Computer Vision (ICCV), pp. 2564 – 2571, 2011. [12] OpenCV Library [Online]. Available: http://opencv.org [November 15, 2017] [13] M. Sonka, V. Hlavac and R. Boyle, "Image Processing, Analysis and Machine Vision," Thomson, 2008. [14] R. Szeliski, "Computer Vision: Algorithms and Applications," Springer, 2010. [15] Y. Amit, "2D Object Detection and Recognition: models, algorithms and networks," The MIT Press, 2002. |
| Content type: | Conference Abstract |
| Appears in Collections: | Data stream mining and processing : proceedings of the IEEE second international conference |
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2018_Gorokhovatskyi_V-Quantization_of_464-467.pdf | 166.35 kB | Adobe PDF | View/Open | |
| 2018_Gorokhovatskyi_V-Quantization_of_464-467__COVER.png | 533.59 kB | image/png | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.