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Information technology for processing digital textures in aerospace monitoring systems

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Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this article, we developed and described a method for segmentation of informative textured regions of an image that are close in color and structure, consisting of the stages of primary and secondary segmentation that provide the solution to the problem of localization of image areas. Step-by-step processing of the image by the proposed method ensures maximum elimination of localization errors in false regions. In addition, the transition from one processing step to the next decreases the analyzed amount of information, namely the area of segmented image areas. In order to reduce the time during the practical implementation of the method, it becomes possible to parallelize the processing and solving problems in a time scale close to the real one. The input parameters are an aerial photograph, a priori information about the areas to be segmented, and meteorological and navigation-technical conditions for aerial photography. Output parameters are images with localized informative areas.
Twórcy
autor
  • Institute for Information Recording NAS of Ukraine, 2, M. Shpaka, 03113 Kyiv, Ukraine
Bibliografia
  • 1. Kashkin V. B. 2001. Remote sensing of the Earth from space. Digital imaging: [tutorial] / V.B. Kashkin, A.I. Sukhinin. - Moscow: Logos, 264. (in Russian).
  • 2. Brytik V. I., Zhilina O. Yu., Kobziev V. G. 2014. Structural method of describing the texure images // Econtechmod. An international quarterly journal. Vol. 3, No. 3, 89-98.
  • 3. Chao K. 2002. Machine vision technology for agricultural applications / K. Chao, Y.R. Chen, M.S. Kim // Elsevier science transactions on computers and electronics in agriculture. – Vol. 36, 173–191.
  • 4. Chittooru J. 2005. Edge detection and segmentation for machine vision / Chittooru J., Munasinghe R., Davari A. // Proceedings of the Thirty-Seventh South-eastern Symposium on System Theory. 457–461.
  • 5. Ganchenko V. 2008. Joint segmentation of Aerial Photographs with the Various Resolution / V. Ganchenko, A. Petrovsky, B. Sobkoviak // Proc. of the 5th Int. Conf. on Neural Networks and Artificial Intelligence., – Minsk, 177-181. (in Russian).
  • 6. Haddon J. F. 1990. Image Segmentation by Unifying Region and Boundary Information / Haddon J. F., Boyce J. F. // IEEE Trans. on Pattern Analysis and Machilie Intelligence. – Vol. 12, No. 10, 20–27.
  • 7. Kanai Y. 1998. Image Segmentation Using Intensity and Color Information / Kanai Y. // SPIE –Visual Communications and Image Processing, 41–50.
  • 8. Lansing E. 2000. Object localization using color, texture and shape / E. Lansing // MI 48824, USA, Available online 28 January 2000. – 31–39.
  • 9. Mark S. 2002. Nixon Feature Extraction and Image Processing / Mark S. Nixon, Alberto S. Aguado //Linacre House, Jordan Hill, Oxford OX2 8DP, 350.
  • 10. Pavlidis T. and Liow Y. 1990. Integrating Region Growing and Edge Detection. – IEEE, vol.12, №3, – 208–214.
  • 11. Plastinin A. 2008. Color textural analysis of the blood preparation images [Text] / A. Plastinin, A. Kupriyanov, N. Ilyasova // Optical Memory & Neural Networks. – Vol.17, 201-207.
  • 12. Popescu D. 2008. Carriage Road Pursuit Based On Statistical And Fractal Analysis Of The Texture / D. Popescu, R. Dobrescu // NAUN International Journal of Education and Information Technologies. – Vol. 2, 62-70.
  • 13. Tomita F., Tsuji S. 1990. Computer Analysis of Visual Textures. – Boston: Kluver Academic Publishers, 381.
  • 14. Wang L., D.C. He. 1990. Texture classification using texture spectrum. Pattern Recog. Lett, v.13, 905–910.
  • 15. Zhao D. 1997. Range-Data-Based Object Surface Segmentation via Edges and Critical points / Zhao D., Zhang X. // IEEE Trans. on Image Processing,– Vol.6, – № 6, 826–832.
  • 16. Pratt W. 1982. Digital image processing: [in 2 volumes]. - Moscow: Mir, - T. 1,. - 312. - T. 2, - 480. (in Russian).
  • 17. Ablameiko S. V. 1999. Image processing: technology, methods, application / S.V. Ablameiko, D.M. Lagunovsky. - Minsk: Institute of Technical Cybernetics of the National Academy of Sciences of Belarus, 300. (in Russian).
  • 18. Wisilter Yu. V. 2010. Processing and analysis of images in computer vision problems: a course of lectures and practical exercises / Yu. V. Visilter, S. Yu. Zheltov, etc. - Moscow: Fizmatkniga, 672. (in Russian).
  • 19. Kovalenko Т. V. 2016. Models and methods of processing digital textural images in aerospace monitoring systems // Nauchnye trudy: Nauchno-metodicheskiy zhurnal. - Vip. 275. T.287. Computer technology. - Mikolaiv: CHDU P. Mogili, 132-137. (in Russian).
  • 20. Gonzalez R., Woods R. 2002. Digital Image Processing. Second Edition / R. Gonzalez. - Prentice Hall, 793.
  • 21. Howarth P., Ruger S. 2006. Robust texture features for still image retrieval. In Proc. IEE Vis. Image Signal Processing, vol. 152, No. 6.
  • 22. Fisenko V. T. 2008. Computer processing and image recognition: [manual] / V.T. Fisenko, T.Yu. Fisenko. - SPb: SPbSU ITMO, 192. (in Russian).
  • 23. Filatov V. 2014. Fuzzy models presentation and realization by means of relational systems // Econtechmod. An international quarterly journal. – Vol. 3, No. 3, 99-102.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-8a50e3ac-ba59-4a97-880a-7974e4c52a30
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