| DC Field | Value | Language |
|---|
| dc.contributor.author | Gorokhovatskyi, Volodymyr | |
| dc.contributor.author | Putyatin, Yevgenyi | |
| dc.contributor.author | Gorokhovatskyi, Oleksii | |
| dc.contributor.author | Peredrii, Olena | |
| dc.coverage.temporal | 21-25 August 2018, Lviv | |
| dc.date.accessioned | 2020-06-19T12:06:09Z | - |
| dc.date.available | 2020-06-19T12:06:09Z | - |
| dc.date.created | 2018-02-28 | |
| dc.date.issued | 2018-02-28 | |
| dc.identifier.citation | 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). | |
| dc.identifier.isbn | © Національний університет „Львівська політехніка“, 2018 | |
| dc.identifier.isbn | © Національний університет „Львівська політехніка“, 2018 | |
| dc.identifier.uri | https://ena.lpnu.ua/handle/ntb/52548 | - |
| dc.description.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. | |
| dc.format.extent | 464-467 | |
| dc.language.iso | en | |
| dc.publisher | Lviv Politechnic Publishing House | |
| dc.relation.ispartof | Data stream mining and processing : proceedings of the IEEE second international conference, 2018 | |
| dc.relation.uri | https://www.researchgate.net/publication/292157133_ | |
| dc.relation.uri | http://opencv.org | |
| dc.subject | computer vision | |
| dc.subject | structural recognition methods | |
| dc.subject | set of characteristic features | |
| dc.subject | descriptor | |
| dc.subject | quantization | |
| dc.subject | competitive learning | |
| dc.subject | recognition performance | |
| dc.subject | noise immunity | |
| dc.title | Quantization of the Space of Structural Image Features as a Way to Increase Recognition Performance | |
| dc.type | Conference Abstract | |
| dc.rights.holder | © Національний університет “Львівська політехніка”, 2018 | |
| dc.contributor.affiliation | Kharkiv National University of Radio Electronics | |
| dc.contributor.affiliation | Simon Kuznets Kharkiv National University of Economics | |
| dc.format.pages | 4 | |
| dc.identifier.citationen | 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). | |
| dc.relation.references | [1] V. Gorokhovatsky, “Structural analysis and intellectual data processing in computer vision,” SMIT, Kharkov, 2014. (in Russian). | |
| dc.relation.references | [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. | |
| dc.relation.references | [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. | |
| dc.relation.references | [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. | |
| dc.relation.references | [5] L. Shapiro and G. Stockman, “Computer vision,” Prentice Hall., 2001. | |
| dc.relation.references | [6] T. Kohonen, “Self-Organizing Maps,” Springer Science & Business Media, 2001. | |
| dc.relation.references | [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]. | |
| dc.relation.references | [8] S. Osovski, “Sieci neuronovane do przetwarzania informacji,” in: Oficyna Wydawnicza Politechniki Warszawskiej, 2000. | |
| dc.relation.references | [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. | |
| dc.relation.references | [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. | |
| dc.relation.references | [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. | |
| dc.relation.references | [12] OpenCV Library [Online]. Available: http://opencv.org [November 15, 2017] | |
| dc.relation.references | [13] M. Sonka, V. Hlavac and R. Boyle, “Image Processing, Analysis and Machine Vision,” Thomson, 2008. | |
| dc.relation.references | [14] R. Szeliski, “Computer Vision: Algorithms and Applications,” Springer, 2010. | |
| dc.relation.references | [15] Y. Amit, “2D Object Detection and Recognition: models, algorithms and networks,” The MIT Press, 2002. | |
| dc.relation.referencesen | [1] V. Gorokhovatsky, "Structural analysis and intellectual data processing in computer vision," SMIT, Kharkov, 2014. (in Russian). | |
| dc.relation.referencesen | [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. | |
| dc.relation.referencesen | [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. | |
| dc.relation.referencesen | [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. | |
| dc.relation.referencesen | [5] L. Shapiro and G. Stockman, "Computer vision," Prentice Hall., 2001. | |
| dc.relation.referencesen | [6] T. Kohonen, "Self-Organizing Maps," Springer Science & Business Media, 2001. | |
| dc.relation.referencesen | [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]. | |
| dc.relation.referencesen | [8] S. Osovski, "Sieci neuronovane do przetwarzania informacji," in: Oficyna Wydawnicza Politechniki Warszawskiej, 2000. | |
| dc.relation.referencesen | [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. | |
| dc.relation.referencesen | [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. | |
| dc.relation.referencesen | [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. | |
| dc.relation.referencesen | [12] OpenCV Library [Online]. Available: http://opencv.org [November 15, 2017] | |
| dc.relation.referencesen | [13] M. Sonka, V. Hlavac and R. Boyle, "Image Processing, Analysis and Machine Vision," Thomson, 2008. | |
| dc.relation.referencesen | [14] R. Szeliski, "Computer Vision: Algorithms and Applications," Springer, 2010. | |
| dc.relation.referencesen | [15] Y. Amit, "2D Object Detection and Recognition: models, algorithms and networks," The MIT Press, 2002. | |
| dc.citation.conference | IEEE second international conference "Data stream mining and processing" | |
| dc.citation.spage | 464 | |
| dc.citation.epage | 467 | |
| dc.coverage.placename | Львів | |
| Appears in Collections: | Data stream mining and processing : proceedings of the IEEE second international conference
|
