Development of Methods for Determining the Contours of Objects for a Complex Structured Color Image Based on the Ant Colony Optimization Algorithm

Physics and Engineering, (1), 34-47, 2020. doi. 10.21303/2461-4262.2020.001108

14 Pages Posted: 26 Feb 2021

See all articles by Hennadii Khudov

Hennadii Khudov

Ivan Kozhedub Kharkiv National Air Force University

Igor Ruban

Kharkiv National University of Radio Electronics

Oleksandr Makoveichuk

Kharkiv National University of Radio Electronics

Hennady Pevtsov

Ivan Kozhedub Kharkiv National Air Force University

Vladyslav Khudov

Kharkiv National University of Radio Electronics

Irina Khizhnyak

Ivan Kozhedub Kharkiv National Air Force University

Sergii Fryz

affiliation not provided to SSRN

Viacheslav Podlipaiev

Institute of Telecommunications and Global Information Space of the National Academy of Sciences of Ukraine

Yurii Polonskyi

Ivan Kozhedub Kharkiv National Air Force University

Rostyslav Khudov

Kharkiv National University named after V. N. Karazin

Date Written: December 31, 2019

Abstract

A method for determining the contours of objects on complexly structured color images based on the ant colony optimization algorithm is proposed. The method for determining the contours of objects of interest in complexly structured color images based on the ant colony optimization algorithm, unlike the known ones, provides for the following. Color channels are highlighted. In each color channel, a brightness channel is allocated. The contours of objects of interest are determined by the method based on the ant colony optimization algorithm. At the end, the transition back to the original color model (the combination of color channels) is carried out.

A typical complex structured color image is processed to determine the contours of objects using the ant colony optimization algorithm. The image is presented in the RGB color space. It is established that objects of interest can be determined on the resulting image. At the same time, the presence of a large number of "garbage" objects on the resulting image is noted. This is a disadvantage of the developed method.

A visual comparison of the application of the developed method and the known methods for determining the contours of objects is carried out. It is established that the developed method improves the accuracy of determining the contours of objects. Errors of the first and second kind are chosen as quantitative indicators of the accuracy of determining the contours of objects in a typical complex structured color image. Errors of the first and second kind are determined by the criterion of maximum likelihood, which follows from the generalized criterion of minimum average risk. The errors of the first and second kind are estimated when determining the contours of objects in a typical complex structured color image using known methods and the developed method. The well-known methods are the Canny, k-means (k=2), k-means (k=3), Random forest methods. It is established that when using the developed method based on the ant colony optimization algorithm, the errors in determining the contours of objects are reduced on average by 5–13 %.

Keywords: contour; object; color image; ant colony optimization algorithm; color space

Suggested Citation

Khudov, Hennadii and Ruban, Igor and Makoveichuk, Oleksandr and Pevtsov, Hennady and Khudov, Vladyslav and Khizhnyak, Irina and Fryz, Sergii and Podlipaiev, Viacheslav and Polonskyi, Yurii and Khudov, Rostyslav, Development of Methods for Determining the Contours of Objects for a Complex Structured Color Image Based on the Ant Colony Optimization Algorithm (December 31, 2019). Physics and Engineering, (1), 34-47, 2020. doi. 10.21303/2461-4262.2020.001108, Available at SSRN: https://ssrn.com/abstract=3752924

Hennadii Khudov (Contact Author)

Ivan Kozhedub Kharkiv National Air Force University ( email )

Sumska str., 77/79
Kharkiv, 61023
Ukraine

Igor Ruban

Kharkiv National University of Radio Electronics ( email )

14 Nauka Av.
Kharkov, 61166
Ukraine

Oleksandr Makoveichuk

Kharkiv National University of Radio Electronics ( email )

14 Nauka Av.
Kharkov, 61166
Ukraine

Hennady Pevtsov

Ivan Kozhedub Kharkiv National Air Force University ( email )

Sumska str., 77/79
Kharkiv, 61023
Ukraine

Vladyslav Khudov

Kharkiv National University of Radio Electronics ( email )

14 Nauka Av.
Kharkov, 61166
Ukraine

Irina Khizhnyak

Ivan Kozhedub Kharkiv National Air Force University ( email )

Sumska str., 77/79
Kharkiv, 61023
Ukraine

Sergii Fryz

affiliation not provided to SSRN

Viacheslav Podlipaiev

Institute of Telecommunications and Global Information Space of the National Academy of Sciences of Ukraine ( email )

Chokolivskiy blvd., 13
Kyiv, 03186
Ukraine

Yurii Polonskyi

Ivan Kozhedub Kharkiv National Air Force University ( email )

Sumska str., 77/79
Kharkiv, 61023
Ukraine

Rostyslav Khudov

Kharkiv National University named after V. N. Karazin ( email )

KHARKIV, KHARKIV 61022
Ukraine

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