2022, issue 1, p. 49-63
Received 28.12.2021; Revised 27.01.2022; Accepted 28.06.2022
Published 30.06.2022; First Online 03.08.2022
Recognition of Geometric Figures and Determination of Their Characteristics by Means of Computer Vision
V.M. Glushkov Institute of Cybernetics of the NAS of Ukraine, Kyiv
Introduction. Many computer vision applications often use procedures for recognizing various shapes and estimating their dimensional characteristics. The entire pipeline of such processing consists of several stages, each of which has no clearly defined boundaries. However, it can be divided into low, medium, and high-level processes. Low-level processes only deal with primitive operations such as preprocessing to reduce noise, enhance contrast, or sharpen images. The processes of this level are characterized by the fact that there are images at the input and output. Image processing at the middle level covers tasks such as segmentation, description of objects, and their compression into a form convenient for computer processing. Middle-level processes are characterized by the presence of images only at the input, and only signs and attributes extracted from images are received at the output. High-level processing involves “understanding” a set of recognized objects and recognizing their interactions.
Using the example of the developed software models for recognizing figures and estimating their characteristics, it is shown that the image processing process is reduced to transforming spatial image data into metadata, compressing the amount of information, which leads to a significant increase in the importance of data. This indicates that at the input of the middle level, the image should be as informative as possible (with high contrast, no noise, artifacts, etc.) because after the transformation of the spatial image data into metadata, no further the procedures are not able to correct the data obtained by the video sensors in the direction of improving or increasing the information content.
Recognition of figures in an image can be realized quite efficiently through the use of the procedure for determining the contours of figures. To do this, you need to determine the boundaries of objects and localize them in the image, often the first step for procedures such as separating objects from the background, image segmentation, detection and recognition of various objects, etc.
The purpose of the article is to study the image processing pipeline from the moment of image fixation to the recognition of a certain set of figures (for example, geometric shapes, such as a triangle, quadrilateral, etc.) in an image, the development of software models for recognizing figures in an image, determining the center of mass figures by means of computer vision.
Results. We proposed and tested some variants of nonlinear estimating problem. The properties of such problems depend on value of regulating parameter. The dependence of estimation on value of parameter was studied. It was defined a range for parameter's value for which estimating problem gives adequate result for initial task.
Numerical examples show how much volume of calculations reduces when using a dynamic branching tree.
Conclusions. The results obtained can be used in many applications of computer vision, for example, counting objects in a scene, estimating their parameters, estimating the distance between objects in a scene, etc.
Keywords: contour, segmentation, image binarization, computer vision, histogram.
Cite as: Golovin O. Recognition of Geometric Figures and Determination of Their Characteristics by Means of Computer Vision. Cybernetics and Computer Technologies. 2022. 1. P. 49–63. (in Ukrainian) https://doi.org/10.34229/2707-451X.22.1.6
1. Zhang D.S. Review of shape representation and description techniques. J. Pattern Recognition. 2004. P. 1–19. https://doi.org/10.1016/j.patcog.2003.07.008
2. Golovin O. Image Enhancement in Video Analytics Systems. Control Systems and Computers. 2020. 6. P. 3–17. https://doi.org/10.15407/csc.2020.06.003
3. Glasbey C.A. An analysis of histogram-based thresholding algorithms. CVGIP: Graphical Models and Image Processing. 1993. 55. P. 532–537. https://doi.org/10.1006/cgip.1993.1040
4. Sezgin M., Sankur B. Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging. 2004. 13 (1). P. 146–156. https://doi.org/10.1117/1.1631315
5. Otsu N. A threshold selection method from gray-level histogram, IEEE Transactions on System Man Cybernetics. 1979. Vol. SMC-9, No. 1. P. 62–66. https://doi.org/10.1109/TSMC.1979.4310076
6. Moghaddam R., Cheriet M. Adotsu: an adaptive and parameterless generalization of Otus’s method for document image binarization, Pattern Recogn. 2012. 45. P. 2419–2431. https://doi.org/10.1016/j.patcog.2011.12.013
7. Xu X., Xu S., Jin L., Song E. Characteristic analysis of Otsu threshold and its applications, Pattern Recogn. Lett. 2011. 32. P. 956–961. https://doi.org/10.1016/j.patrec.2011.01.021
8. Sirisha P., Raju C., Reddy R., An efficient fuzzy technique for detection of brain tumor. J. Softw. Eng. 2013. 7. https://doi.org/10.26634/jse.7.4.2316
9. Alsaeed D.H., Bouridane A., Elzaart A., Sammouda R., Two modified Otsu image segmentation methods based on lognormal and gamma distribution models, in: 2012 International Conference on Information Technology and e-Services (ICITeS), IEEE. 2012. P. 1–5. https://doi.org/10.1109/ICITeS.2012.6216680
10. Cai H., Yang Z., Cao X., Xia W., Xu X. A new iterative triclass thresholding technique in image segmentation, IEEE Trans. Image Process. 2014. 23. P. 1038–1046. https://doi.org/10.1109/TIP.2014.2298981
11. Lai Y.K., Rosin P.L. Efficient circular thresholding, IEEE Trans. Image Process. 2014. 23. P. 992–1001. https://doi.org/10.1109/TIP.2013.2297014
12. Xue J., Titterington D. t-tests, f-tests and Otus’s methods for image thresholding, IEEE Trans. Image Process. 2011. 20. https://doi.org/10.1109/TIP.2011.2114358
13. Arora S., Acharya J., Verma A., Panigrahi P.K. Multilevel thresholding for image segmentation through a fast statistical recursive algorithm. Pattern Recognition Letters. 2008. 29. P. 119–125. https://doi.org/10.1016/j.patrec.2007.09.005
14. Dong L.J., Yu G., Ogunbona P., Li W.Q. An efficient iterative algorithm for image thresholding. Pattern Recognition Letters. 2008. 29. P. 1311–1316. https://doi.org/10.1016/j.patrec.2008.02.001
15. Huang D.Y., Wang C.H. Optimal multi-level thresholding using a two-stage Otsu optimization approach. Pattern Recognition Letters. 2009. 30. P. 275–284. https://doi.org/10.1016/j.patrec.2008.10.003
16. Liao P.S., Chen T.S., Chung P.C. A fast algorithm for multilevel thresholding. Journal of Information Science and Engineering. 2001. 17. P. 713–727.
17. Papamarkos N., Gatos B. A new approach for multilevel threshold selection. Graphics Models Image Process. 1994. 56. P. 357–370. https://doi.org/10.1006/cgip.1994.1033
18. Flusser J., Suk T., Zitová B. What are moments? Moments and Moment Invariants in Pattern Recognition. John Wiley & Sons Ltd, 2009. P. 6. ISBN 978-0-470-69987-4 https://doi.org/10.1002/9780470684757
19. Pouli T., Reinhard E., Cunningham D. Image Statistics in Visual Computing. CRC Press, 2014. P. 35. ISBN 978-1-4665-3982-2
20. Farin G. E. Curves and Surfaces for Computer Aided Geometric Design: A Practical Guide. Academic Press, Boston, Massachusetts, 4th edition. 1996.
21. Douglas D.H., Peucker T.K. Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. The Canadian Cartographer, 1973. 10 (2). P. 112–122. https://doi.org/10.1002/9780470669488.ch2
22. Ramer U. An Iterative Procedure for the Polygonal Approximation of Plane Curves. Computer Graphics and Image Processing. 1972. 1 (3). P. 244–256. https://doi.org/10.1016/S0146-664X(72)80017-0
23. Ramer–Douglas–Peucker algorithm. http://en.wikipedia.org/wiki/Ramer-Douglas-Peucker_algorithm (accessed: 28.12.2021)
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