Gradient-based technique for image structural analysis and applications
Asatryan D.G.

Russian-Armenian University, Armenia, Yerevan,

Institute for Informatics and Automation Problems of National Academy of Sciences of Armenia, Armenia, Yerevan

Аннотация:
This paper is devoted to application of gradients field characteristics in selected problems of image intellectual analysis and processing. To analyse the properties and structure of an image several approaches and models based on the use of the gradients field characteristics, are proposed. In this paper, models based on Weibull distribution are considered, an image dominant direction estimation algorithm using the parameters of scattering ellipse of gradients field components is proposed, and a similarity measure of two images with arbitrary dimensions and orientation is proposed. Some examples of applications of these models for estimation of blur and structuredness of an image, for the quality assessment of resizing and rotating algorithms, as well as for detection of a specified object on the image delivered by an unmanned aerial vehicle, are given.

Ключевые слова:
Image gradient field, Weibull distribution, similarity measure, dominant orientation, blur estimation, video stream analyse.

Цитирование:
Asatryan DG. Gradient-based technique for image structural analysis and applications. Computer Optics 2019; 43(2): 245-250. DOI: 10.18287/2412-6179-2019-43-2-245-250.

Литература:

  1. Wang, Z. A universal image quality index / Z. Wang, A.C. Bovik // IEEE Signal Processing Letters. – 2002. – Vol. 9, Issue 3. – P. 81-84.
  2. Wang, Z. Why is image quality assessment so difficult? / Z. Wang, A.C. Bovik, L. Lu // Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). – 2002. – Vol. 4. – P. 3313-3316.
  3. Wang, Z. Image quality assessment: from error visibility to structural similarity / Z. Wang, A.C. Bovik // IEEE Transactions on Image Processing. – 2004. – Vol. 13, Issue 4. – P. 600-612.
  4. Ponomarenko, N. Color image database for evaluation of image quality metrics / N. Ponomarenko, C. Carli, V. Lukin, K. Egiazarian, J. Astola, F. Battisti // Proceedings of the International Workshop on Multimedia Signal Processing. – 2008. – P. 403-408.
  5. Xue, W. Gradient magnitude similarity deviation: A highly efficient perceptual image quality index / W. Xue, L. Zhang, X. Mou, A.C. Bovik // IEEE Transactions on Image Processing. – 2014. – Vol. 23, Issue 2. – P. 684-695. – DOI: 10.1109/TIP.2013.2293423.
  6. Gonzales, R.C. Digital image processing / R.C. Gonzales, R.E. Woods. – 2nd ed. – Prentice Hall, 2002.
  7. Cramér, H. Mathematical methods of statistics / H. Cramér. – Princeton: Princeton University Press, 1991.
  8. Asatryan, D.G. Orientation estimation with applications to image analysis and registration / D.G. Asatryan, K.O. Egiazarian, V.V. Kurkchiyan // Information Theories and Applications. – 2010. – Vol. 17, Issue 4. – P. 303-311.
  9. Asatryan, D.G. Quality assessment measure based on image structural properties / D.G. Asatryan, K.O. Egiazarian // Proceedings of the International Workshop on Local and Non-Local Approximation in Image Processing. – 2009. – P. 70-73.
  10. Asatryan, D.G. Method for texture analysis and classification [In Russian] / D.G. Asatryan, V.V. Kurkchiyan, L.R. Kharatyan // Computer Optics. – 2014. – Vol. 38(3). – P. 574-579.
  11. Asatryan, D.G. Road tracking from UAV imagery using gradient information / D.G. Asatryan, S.M. Hovsepyan, V.V. Kurkchiyan // Information Technologies & Knowledge. – 2016. – Vol. 10, Issue 2. – P. 191-199.
  12. Koik, B.T. A literature survey on blur detection algorithms for digital imaging / B.T. Koik, I. Haidi // AIMS '13 Proc 2013 1st International Conference on Artificial Intelligence, Modelling and Simulation. – 2013. – P. 272-277.
  13. Garg, V. A survey on image blurring / V. Garg, M. Manchanda // International Journal of Engineering Applied and Management Sciences Paradigms. – 2014. – Vol. 15, Issue 1. – P. 2320-6608.
  14. Singh, D. A survey on various image deblurring techniques / D. Singh, R.K. Sahu // International Journal of Advanced Research in Computer and Communication Engineering. – 2013. – Vol. 2, Issue 12. – P. 4736-4739.
  15. Asatryan, D.G. Image blur estimation using gradient field analysis [In Russian] / D.G. Asatryan // Computer Optics. – 2017. – Vol. 41(6). – P. 957-962. – DOI: 10.18287/2412-6179-2017-41-6-957-962.
  16. Pankaj, S.P. Virparia image quality comparison using PSNR and UIQI for image interpolation algorithms / S.P. Pankaj, V. Paresh // International Journal of Innovative Research in Computer and Communication Engineering. – 2016. – Vol. 4, Issue 12. – P. 21679-21687. – DOI: 10.15680/IJIRCCE.2016. 0412056.
  17. Asatryan, D.G. Novel approach to content-based video indexing and retrieval by using a measure of structural similarity of frames / D.G. Asatryan, M.K. Zakaryan // Information Content and Processing. – 2015. – Vol. 2, Issue 1. – P. 71-81.
  18. Athanesious, J.J. Systematic survey on object tracking methods in video / J.J. Athanesious, P. Suresh // International Journal of Advanced Research in Computer Engineering and Technology. – 2012. – Vol. 1, Issue 8. – P. 242-247.
  19. Łoza, A. Structural similarity-based object tracking in multimodality surveillance videos / A. Łoza, L. Mihaylova, D. Bull, N. Canagarajah // Machine Vision and Applications. – 2009. – Vol. 20, Issue 2. – P. 71-83.

© 2009, IPSI RAS
Россия, 443001, Самара, ул. Молодогвардейская, 151; электронная почта: ko@smr.ru ; тел: +7 (846) 242-41-24 (ответственный секретарь), +7 (846) 332-56-22 (технический редактор), факс: +7 (846) 332-56-20