Cellular-local Algorithm for Localizing and Estimating Changes in Binary Images
https://doi.org/10.18255/1818-1015-2014-4-64-74
Abstract
In this paper we consider the problem of detection of changes and estimation of the degree of these changes in a dynamically changing binary image. The authors introduce the numerical characteristic degree of change areas in dynamically changing binary images, based on the Jaccard similarity coefficient. To calculate this characteristic the authors developed an original architecture of a two-dimensional cellular automaton with the diffusion dynamics. We establish that cellular automaton configurations converge to a stationary configuration. The stationary configuration of a cellular automaton defines the desired characteristics for each area in dynamically changing binary images. The result can be presented as a grayscale image, that greatly facilitates the visual analysis of the dynamics of changes in dynamically changing binary images. The suggested approach can be used to detect and numerically estimate changes in the case when a number of brightness gradation comprises more than two values.
About the Authors
A. A. Korotkin
P.G. Demidov Yaroslavl State University
Russian Federation
канд. техн. наук, доцент, Sovetskaya str., 14, Yaroslavl, 150000
Russian Federation
канд. техн. наук, доцент, Sovetskaya str., 14, Yaroslavl, 150000
A. A. Maksimov
Russia Sberbank of Russia
Russian Federation
ведущий инженер, Sovetskaya str., 34, Yaroslavl, 150003, Russia
Russian Federation
ведущий инженер, Sovetskaya str., 34, Yaroslavl, 150003, Russia
References
1. Кашкин В.Б., Сухинин А.И. Дистанционное зондирование Земли из космоса. Цифровая обработка изображений. М.: Логос, 2001. [Kashkin V.B, Sukhinin A.I. Distancionnoe zondirovanie Zemli iz kosmosa. Cifrovaya obrabotka izobrazhenij. M.: Logos, 2001 (in Russian)].
2. Preston K.J., Duff M.J.B., Levialdi S., Norgren P.E., Toriwaki J. Basics on cellular logic with some applications in medical image processing // Proceedings IEEE. May 1979. Vol. 67, No. 5. P. 826–857.
3. Preston K., Duff M. Modern Cellular Automata. Theory and Applications. Plenum Press, 1984.
4. Rosin Paul L. Training Cellular Automata for Image Processing // Image Analysis. Lecture Notes in Computer Science. 2005. Vol. 3540. P. 195–204.
5. Тоффоли Т., Марголус Н. Машины клеточных автоматов. М.: Мир, 1991. (In English: Toffoli Т., Margolus N. Cellular Automata Machines. Cambridge: MIT Press, 1987).
6. Weickert J. Theoretical foundations of anisotropic diffusion in image processing. ECMI Series, Teubner Verlag, Stuttgart, 1998.
7. Борисенко Г.В., Денисов A.M. Нелинейный источник в диффузионных методах фильтрации изображений // Журнал выч. мат. и мат. физ. 2007. Том 47. №10. C. 1701–1705. (English transl.: Borisenko G.V., Denisov A. M. Nonlinear source in diffusion filtering methods for image processing // Computational Mathematics and Mathematical Physics. October 2007. Vol. 47. Issue 10. P. 1631–1635).
8. Бандман О.Л. Клеточно-автоматные модели пространственной динамики // Системная информатика. Новосибирск: СО РАН. 2006. Вып. 10. С. 59–113. [Bandman O.L. Kletochno-avtomatnye modeli prostranstvennoj dinamiki // Sistemnaya informatika Novosibirsk: SO RAN. 2006. V. 10. P. 59–113 (in Russian)].
9. Короткин А.А., Майоров В.В. Нейронная сеть с диффузионным взаимодействием элементов для селекции изменений динамического изображения // Журнал выч. мат. и мат. физ. 2000. Т. 40, №2. C. 300–306. (English transl.: Korotkin A.A., Majorov V.V. A neural network with diffusive interaction between elements for selecting changes in a dynamic image // Computational Mathematics and Mathematical Physics. 2000. 40:2. P. 287–292.)
10. Марманис Х., Бабенко Д. Алгоритмы интеллектуального Интернета. Передовые методики сбора, анализа и обработки данных: Пер. с англ. СПб.: Символ-Плюс, 2011. (In English: Marmanis H., Babenko D. Algorithms of the Intelligent Web. Manning Publications Co. May, 2009).
11. Kaneko K. Theory and Application of Coupled Map Lattices. John Wiley&Sons Ltd, 1993.
12. Феллер В. Введение в теорию вероятностей и ее приложения. М.: Мир, 1964. Т. 1. (In English: Feller W. An introduction to probability theory and its applications. 2d ed. New York: Wiley, 1957. Vol. 1.)
Review
For citations:
Korotkin A.A., Maksimov A.A. Cellular-local Algorithm for Localizing and Estimating Changes in Binary Images. Modeling and Analysis of Information Systems. 2014;21(4):64-74. (In Russ.) https://doi.org/10.18255/1818-1015-2014-4-64-74
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