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Automated blurred image region classification

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper authors present a simple method for recognizing blurred regions in the image. Proposed algorithm is based on 81 simple features — moments of histogram of image subbands, that were obtained during image decomposition, and ratio derived from gray level co-occurrence matrix (GLCM) are used. The method is compared with a different method, that is based on approaches found in literature. To increase the efficiency of algorithms, authors combined three solutions (edge-detection, gray level co-occurrence matrix and fast image sharpness). The aim of the research was to verify whether it is possible to use simpler methods of feature extraction to achieve similar, or even better, results.
Słowa kluczowe
Rocznik
Strony
37--47
Opis fizyczny
Bibliogr. 12 poz., rys., tab.
Twórcy
autor
  • Faculty of Computer Science and Information Technology,West Pomeranian University of Technology, Szczecin, Poland
  • Faculty of Computer Science and Information Technology,West Pomeranian University of Technology, Szczecin, Poland
autor
  • Faculty of Computer Science and Information Technology,West Pomeranian University of Technology, Szczecin, Poland
Bibliografia
  • [1] Yun-fang, Z.: Blur detection for surveillance video based on heavy-tailed distribution. In: Microelectronics and Electronics (PrimeAsia), 2010 Asia Pacific Conference on Postgraduate Research in, pp. 101–105. 2010.
  • [2] Bradski, G., Kaehler, A.: Learning OpenCV: Computer Vision with the OpenCV Library. O’Reilly Media, 2008. ISBN 9780596554040.
  • [3] Fang, C., Zhang, J., Zhu, S., Li, G., Wang, R.: Analysis of Texture Images Generated by Olfactory System Bionic Model.
  • [4] Vu, P. V., Chandler, D. M.: A Fast Wavelet-Based Algorithm for Global and Local Image Sharpness Estimation. IEEE Signal Process. Lett., 19(7), pp. 423–426, 2012.
  • [5] Akansu, A. N., Serdijn, W. A., Selesnick, I. W.: Full Length Article: Emerging Applications of Wavelets: A Review. Phys. Commun., 3(1), pp. 1–18, 2010. ISSN 1874-4907.
  • [6] Madsen, H., Thyregod, P.: Introduction to General and Generalized Linear Models. Chapman & Hall/CRC Texts in Statistical Science Series. Chapman & Hall/CRC, 2011. ISBN 9781420091557.
  • [7] Fan, R.-E., Chang, K.-W., Hsieh, C.-J., Wang, X.-R., Lin, C.-J.: LIBLINEAR: A Library for Large Linear Classification. J. Mach. Learn. Res., 9, pp. 1871–1874, 2008. ISSN 1532-4435.
  • [8] Rosenblatt, F.: Principles of neurodynamics: perceptrons and the theory of brain mechanisms. Report (Cornell Aeronautical Laboratory). Spartan Books, 1962.
  • [9] Rumelhart, D. E., Hinton, G. E., Williams, R. J.: Neurocomputing: Foundations of Research. chapter Learning Representations by Back-propagating Errors, pp. 696–699. MIT Press, Cambridge, MA, USA, 1988. ISBN 0-262-01097-6.
  • [10] Han, J., Moraga, C.: The Influence of the Sigmoid Function Parameters on the Speed of Backpropagation Learning. In: Mira, J., Hernndez, F. S. (eds.), IWANN, volume 930 of Lecture Notes in Computer Science, pp. 195–201. Springer, 1995. ISBN 3-540-59497-3.
  • [11] Powers, D. M. W.: Evaluation: From Precision, Recall and F-Factor to ROC, Informedness, Markedness & Correlation. Technical Report SIE-07-001, School of Informatics and Engineering, Flinders University, Adelaide, Australia, 2007.
  • [12] Dua, S., Du, X.: Data Mining and Machine Learning in Cybersecurity. Taylor & Francis, 2011. ISBN 9781439839430.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-e59dcd50-6945-45fb-bf85-8ccc9e70cc1d
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