Siberian Federal University

Issue

Journal of Siberian Federal University. Engineering & Technologies. 2018 11 (1)

Authors

Bogoslovsky, Andrey V.; Ponomarev, Andrey V.

Contact information

Богословский, А.В.: Военный учебно-научный центр Военно-воздушных сил «Военно-воздушная академия имени профессора Н.Е. Жуковского и Ю.А. Гагарина» Россия, 394064, Воронеж, ул. Старых Большевиков, 54а; Пономарев, А.В.: Военный учебно-научный центр Военно-воздушных сил «Военно-воздушная академия имени профессора Н.Е. Жуковского и Ю.А. Гагарина» Россия, 394064, Воронеж, ул. Старых Большевиков, 54а; Bogoslovsky, Andrey V.: Military Education and Research Centre of Military-Air Forces "Military-Air Academy Named After Professor N.E. Zhukovsky and Yu.A. Gagarin" 54а Starykh Bolshevikov Str., Voronezh, 394064, Russia; Ponomarev, Andrey V.: Military Education and Research Centre of Military-Air Forces "Military-Air Academy Named After Professor N.E. Zhukovsky and Yu.A. Gagarin" 54а Starykh Bolshevikov Str., Voronezh, 394064, Russia;

Keywords

machine vision; a still image; a video sequence

Abstract

A model of phase-energy characteristics, allowing to identify the objects of uneven brightness in the image. It has been shown that the expression obtained and measured the possible definition of the object in the image characteristics, such as size and localization of inhomogeneities, the location of the object in the image field. Select objects and information about their mutual arrangement may be helpful in solving the problem also enhance the image quality by removing the object of heterogeneity caused by dynamic noise. The use of energy characteristics for the analysis of multivariate data has advantages associated with versatility as static images and video sequences. The results can be used in machine vision systems

Pages

16-23

Paper at repository of SibFU

https://elib.sfu-kras.ru/handle/2311/70387

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).