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Comparison of Contour Detection Methods in Images on the Example of Photos with Road Surface Damage. / Zhuravlev, Alexander a.; Aksyonov, Konstantin a.
Proceedings - 2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2023: book. Institute of Electrical and Electronics Engineers Inc., 2023. p. 183-186.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Harvard

Zhuravlev, AA & Aksyonov, KA 2023, Comparison of Contour Detection Methods in Images on the Example of Photos with Road Surface Damage. in Proceedings - 2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2023: book. Institute of Electrical and Electronics Engineers Inc., pp. 183-186, 2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT), Екатеринбург, Russian Federation, 15/05/2023. https://doi.org/10.1109/USBEREIT58508.2023.10158863

APA

Zhuravlev, A. A., & Aksyonov, K. A. (2023). Comparison of Contour Detection Methods in Images on the Example of Photos with Road Surface Damage. In Proceedings - 2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2023: book (pp. 183-186). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/USBEREIT58508.2023.10158863

Vancouver

Zhuravlev AA, Aksyonov KA. Comparison of Contour Detection Methods in Images on the Example of Photos with Road Surface Damage. In Proceedings - 2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2023: book. Institute of Electrical and Electronics Engineers Inc. 2023. p. 183-186 doi: 10.1109/USBEREIT58508.2023.10158863

Author

Zhuravlev, Alexander a. ; Aksyonov, Konstantin a. / Comparison of Contour Detection Methods in Images on the Example of Photos with Road Surface Damage. Proceedings - 2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2023: book. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 183-186

BibTeX

@inproceedings{a4e1713a748f44d29cd8711f353f621c,
title = "Comparison of Contour Detection Methods in Images on the Example of Photos with Road Surface Damage",
abstract = "Due to the development of technology, there are currently many information systems designed to detect various damages on the road surface. However, these systems work based on certain image processing algorithms, which include methods for contour detection. Each algorithm has its own features that can affect the operation of the information device. For a more accurate assessment of each method, a comparative analysis of its work is necessary. This article discusses the following algorithm for contour detection in images: Line Detection (LD), Point Detection (PD), Kirsch Edge Detection (KED), Canny Edge Detection (CED), Marr-Hildreth Edge Detection (MHED). The average image processing time is chosen as an indicator of the evaluation of algorithms. To calculate it, a series of experiments is carried out for 5 images, each of which is processed 10 times (a total of 50 tests for one algorithm). The experiment is performed for image of 7 different sizes (in pixels): 60000, 80000, 120000, 240000, 480000, 720000, 960000. It is established that LD and PD have linear time complexity (O(n)), KED has O(nlog(n)), while CED and MHED have quadratic complexity (O(n2)). MHED, CED and KED highlight clearer contours compared to LD and PD.",
author = "Zhuravlev, {Alexander a.} and Aksyonov, {Konstantin a.}",
year = "2023",
month = may,
day = "15",
doi = "10.1109/USBEREIT58508.2023.10158863",
language = "English",
pages = "183--186",
booktitle = "Proceedings - 2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",
note = "2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT) ; Conference date: 15-05-2023 Through 17-05-2023",

}

RIS

TY - GEN

T1 - Comparison of Contour Detection Methods in Images on the Example of Photos with Road Surface Damage

AU - Zhuravlev, Alexander a.

AU - Aksyonov, Konstantin a.

PY - 2023/5/15

Y1 - 2023/5/15

N2 - Due to the development of technology, there are currently many information systems designed to detect various damages on the road surface. However, these systems work based on certain image processing algorithms, which include methods for contour detection. Each algorithm has its own features that can affect the operation of the information device. For a more accurate assessment of each method, a comparative analysis of its work is necessary. This article discusses the following algorithm for contour detection in images: Line Detection (LD), Point Detection (PD), Kirsch Edge Detection (KED), Canny Edge Detection (CED), Marr-Hildreth Edge Detection (MHED). The average image processing time is chosen as an indicator of the evaluation of algorithms. To calculate it, a series of experiments is carried out for 5 images, each of which is processed 10 times (a total of 50 tests for one algorithm). The experiment is performed for image of 7 different sizes (in pixels): 60000, 80000, 120000, 240000, 480000, 720000, 960000. It is established that LD and PD have linear time complexity (O(n)), KED has O(nlog(n)), while CED and MHED have quadratic complexity (O(n2)). MHED, CED and KED highlight clearer contours compared to LD and PD.

AB - Due to the development of technology, there are currently many information systems designed to detect various damages on the road surface. However, these systems work based on certain image processing algorithms, which include methods for contour detection. Each algorithm has its own features that can affect the operation of the information device. For a more accurate assessment of each method, a comparative analysis of its work is necessary. This article discusses the following algorithm for contour detection in images: Line Detection (LD), Point Detection (PD), Kirsch Edge Detection (KED), Canny Edge Detection (CED), Marr-Hildreth Edge Detection (MHED). The average image processing time is chosen as an indicator of the evaluation of algorithms. To calculate it, a series of experiments is carried out for 5 images, each of which is processed 10 times (a total of 50 tests for one algorithm). The experiment is performed for image of 7 different sizes (in pixels): 60000, 80000, 120000, 240000, 480000, 720000, 960000. It is established that LD and PD have linear time complexity (O(n)), KED has O(nlog(n)), while CED and MHED have quadratic complexity (O(n2)). MHED, CED and KED highlight clearer contours compared to LD and PD.

UR - http://www.scopus.com/inward/record.url?partnerID=8YFLogxK&scp=85164938299

U2 - 10.1109/USBEREIT58508.2023.10158863

DO - 10.1109/USBEREIT58508.2023.10158863

M3 - Conference contribution

SP - 183

EP - 186

BT - Proceedings - 2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT)

Y2 - 15 May 2023 through 17 May 2023

ER -

ID: 41991828