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Localization of image fragments with high frequency intensity oscillation

Andrey Trubitsyn, Maksim Shadrin, Andrey Serezhin

Abstract


The problem of detecting image fragments characterized by high-frequency fluctuations in spatial intensity in the general case has not been previously considered in the literature.The article researches a sequence of known and new algorithms that allows detection and localization of such fragments.The geometric localization of the fragments is basedon the Hough transform of the pixel array of the external contours of the connected components.The components connecting becomes possible due to the use of the oscillation function proposed by the authors. The oscillation function turns out to be an effective tool for highlighting intensity fluctuations zones in an image and is superior in reliability to alternative methods for detecting such zones, based, for example, on gradient methods. The article demonstrates examples of localization of the image fragments with different levels of background complexity

Keywords


pattern recognition; salient object; integral sum image; binary image; connected component method; Hough transform

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References


1. El Hachem C, Santiago R, Painvin L, et al. Brick orientation adjustment in the automotive industry using image processing techniques. In: Proceedings of 2022 8th International Conference on Control, Decision and Information Technologies (CoDIT); 17–20 May 2022; Istanbul, Turkey. pp. 729–733.

2. Dougherty G. Digital Image Processing for Medical Applications. Cambridge University Press; 2009. pp. 1–459.

3. Shi W. The application of image processing in the criminal investigation. In: Proceedings of the 2016 4th International Conference on Machinery, Materials and Information Technology Applications; 10–11 December 2016; Xi’an, China. pp. 139–142.

4. Schwering PBW, Kemp RAW, Schutte K. Image enhancement technology research for army applications. Proceedings of SPIE 2013; 8706: 2–11. doi: 10.1117/12.2017855

5. Fan L. Image processing algorithm of Hartmann method aberration automatic measurement system with tensor product model. Journal on Image and Video Processing 2019; 2019: 43. doi: 10.1186/S13640-019-0440-9

6. Asht S, Dass R. Pattern recognition techniques: A review. International Journal of Computer Science and Telecommunications 2012; 3(8): 25–29.

7. Singh C. Machine learning in pattern recognition. European Journal of Engineering and Technology Research 2023; 8(2): 63–68. doi: 10.24018/ejeng.2023.8.2.3025

8. Karrach L, Pivarciova E. Recognition of data matrix codes in images and their applications in production processes. Management Systems in Production Engineering 2020; 28(3): 154–161. doi: 10.2478/mspe-2020-0023

9. Liu T, Yuan Z, Sun J, et al. Learning to detect a salient object. IEEE Transactions on Pattern Analysis and Machine Intelligence 2011; 33(2): 353–367. doi: 10.1109/TPAMI.2010.70

10. Chen C, Wei J, Peng C, et al. Improved saliency detection in RGB-D images using two-phase depth estimation and selective deep fusion. IEEE Transactions on Image Processing 2020; 29: 4296–4307. doi: 10.1109/TIP.2020.2968250

11. Cheng MM, Mitra NJ, Huang X, et al. Global contrast based salient region detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 2015; 37(3): 569–582. doi: 10.1109/TPAMI.2014.2345401

12. Viola P, Jones MJ. Robust real-time face detection. International Journal of Computer Vision 2004; 57(2): 137–154. doi: 10.1023/B:VISI.0000013087.49260.fb

13. Otsu N. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics 1979; 9(1): 62–66.

14. Bradley D, Roth G. Adaptive thresholding using the integral image. Journal of Graphics Tools 2007; 12(2): 13–21. doi: 10.1080/2151237X.2007.10129236

15. Niblack W. An Introduction to Digital Image Processing. Prentice Hall; 1986. pp. 1–215.

16. Sauvola J, Pietikainen M. Adaptive document image binarization. Pattern Recognition 2000; 33(2): 225–236. doi: 10.1016/S0031-3203(99)00055-2

17. Koleda P, Hrčková M. Global and local thresholding techniques for sawdust analysis. Acta Facultatis Technicae 2018; XXIII(1): 33–42.

18. Burger W, Burge MJ. Digital Image Processing: An Algorithmic Introduction Using Java. Springer-Verlag London Ltd.; 2016. pp. 1–811.

19. Fang L, Xie C. 1-D barcode localization in complex background. In: Proceedings of 2010 International Conference on Computational Intelligence and Software Engineering; 10–12 December 2010; Wuhan, China. pp. 1–3.

20. Santosa F, Goh M. Bar code decoding in a camera-based scanner: Analysis and algorithm. SIAM Journal on Imaging Sciences 2022; 15(3): 1017–1040. doi: 10.1137/21M1449658

21. Shapiro LG, Stockman GC. Computer Vision, 1st ed. Pearson; 2001. pp. 1–580.

22. Gonzalez R, Rafael R. Digital Image Processing. Pearson; 2018. pp. 1–1168.

23. Canny J. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 1986; PAMI-8(6): 679–698. doi: 10.1109/TPAMI.1986.4767851

24. Bradski G, Kaehler A. Learning Opencv: Computer Vision with the Opencv Library. O’ Reilly Media; 2008. pp. 1–555.

25. Jähne B, Scharr H, Körkel S. Principles of filter design. In: Jähne B, Haussecker H, Geissler P (editors). Handbook of Computer Vision and Applications. Cambridge: Academic Press; 1999. pp. 125–151.

26. Papari G, Petkov N. Edge and line oriented contour detection: State of the art. Image and Vision Computing 2011; 29(2–3): 79–103. doi: 10.1016/j.imavis.2010.08.009

27. Hough PVC. Method and Means for Recognizing Complex Patterns. U.S. Atomic Energy Commission No. 3069654, 18 December 1962.

28. Trubitsyn A, Grachev EY. Switching median filter for suppressing multi-pixel impulse noise. Computer Optics 2021; 45(4): 580–588. doi: 10.18287/2412-6179-CO-841

29. Lin JA, Fuh CS. 2D barcode image decoding. Mathematical Problems in Engineering 2013; 2013: 848276. doi: 10.1155/2013/848276

30. Dynamsoft. Barcode reader SDK. Available online: www.dynamsoft.com/Products/Dynamic-BarcodeReader.aspx (accessed on 25 May 2023).

31. Mikolajczyk K, Schmid C. A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence 2005; 27(10): 1615–1630. doi: 10.1109/TPAMI.2005.188




DOI: https://doi.org/10.32629/jai.v6i2.597

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Copyright (c) 2023 Andrey Trubitsyn, Maksim Shadrin, Andrey Serezhin

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