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Comparative analysis of reflection symmetry detection methods in binary raster images with skeletal and contour representations
O.S. Seredin 1, O.A. Kushnir 1, S.A. Fedotova 1

1 Tula State University, 300012, Tula, Russia, Lenin Ave 92

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DOI: 10.18287/2412-6179-CO-1115

Страницы: 921-928.

Язык статьи: English.

Аннотация:
The study is a comparative analysis of two fast reflection symmetry axis detection methods: an algorithm to refine the symmetry axis found with a chain of skeletal primitives and a boundary method based on the Fourier descriptor. We tested the algorithms with binary raster images of plant leaves (FLAVIA database). The symmetry axis detection quality and performance indicate that both methods can be used to solve applied problems. Neither method demonstrated any significant advantage in terms of accuracy or performance. It is advisable to integrate both methods for solving real-life problems.

Ключевые слова:
binary raster image, reflection symmetry, Jaccard measure, Fourier descriptor.

Благодарности
This study was supported by the Russian Science Foundation, Grant No. 22-21-00575, https://rscf.ru/project/22-21-00575/.

Цитирование:
Текст. Текст.

Citation:
Seredin OS, Kushnir OA, Fedotova SA. Comparative analysis of reflection symmetry detection methods in binary raster images with skeletal and contour representations. Computer Optics 2022; 46(6): 921-928. DOI: 10.18287/2412-6179-CO-1115.

References:
  1. Kushnir O, Fedotova S, Seredin O, Karkishchenko A. Reflection symmetry of shapes based on skeleton primitive chains. In Book: Analysis of Images, Social Networks and Texts. AIST 2016. Communications in Computer and Information Science. Cham: Springer, 2016: 293-304. DOI: 10.1007/978-3-319-52920-2_27.
  2. Kushnir OA, Seredin OS, Fedotova SA. Algorithms for adjustment of symmetry axis found for 2D shapes by the skeleton comparison method. Int Arch Photogramm Remote Sens Spat Inf Sci 2019; XLII-2/W12: 129-136. DOI: 10.5194/isprs-archives-XLII-2-W12-129-2019.
  3. Mestetskiy L, Zhuravskaya A. Method for assessing the symmetry of objects on digital binary images based on Fourier descriptor. Int Arch Photogramm Remote Sens Spat Inf Sci 2019; XLII-2/W12: 143-148. DOI: 10.5194/isprs-archives-XLII-2-W12-143-2019.
  4. Jaccard P. Étude comparative de la distribution florale dans une portion des Alpes et des Jura. Bull Soc Vaudoise Sci Nat 1901; 37: 547-579.
  5. Fedotova S, Seredin O, Kushnir O. The Parallel Implementation of Algorithms for Finding the Reflection Symmetry of the Binary Images. Int Arch Photogramm Remote Sens Spat Inf Sci 2017; XLII-2/W4: 179-184. DOI: 10.5194/isprs-archives-XLII-2-W4-179-2017.
  6. Van Otterloo PJ. A contour-oriented approach to digital shape analysis. Technische Universiteit Delft; 1988.
  7. Sheynin S, Tuzikov A, Volgin D. Computation of symmetry measures for polygonal shapes. In Book: CAIP '99: Proceedings of the 8th international conference on computer analysis of images and patterns. Berlin, Heidelberg: Springer-Verlag; 1999: 183-190.
  8. Yang X, et al. Symmetry of shapes via self-similarity. In Book: Advances in visual computing. 4th International Symposium, ISVC 2008, Las Vegas, NV, USA, December 1-3, 2008, Proceedings, Part II. Berlin, Heidelberg: Springer-Verlag; 2008: 561-570. DOI: 10.1007/978-3-540-89646-3_55.
  9. Li Z, et al. Robust symmetry detection for 2D shapes based on electrical charge distribution. J Inf Comput Sci 2014; 11(9): 2887-2894. DOI: 10.12733/jics20103838.
  10. Niu D, et al. a novel approach for detecting symmetries in two-dimensional shapes. J Inf Comput Sci 2015; 12(10): 3915-3925. DOI: 10.12733/jics20106437.
  11. Sun C, Si D. Fast reflectional symmetry detection using orientation histograms. Real-Time Imaging 1999; 5(1): 63-74. DOI: 10.1006/rtim.1998.0135.
  12. Nguyen TP. Projection based approach for reflection symmetry detection. 2019 IEEE Int Conf on Image Processing (ICIP) 2019: 4235-4239. DOI: 10.1109/ICIP.2019.8803575.
  13. Wu SG, Bao FS, Xu EY, Wang YX, Chang YF, Xiang QL. A leaf recognition algorithm for plant classification using probabilistic neural network. 2007 IEEE Int Symp on Signal Processing and Information Technology 2007: 11-16. DOI: 10.1109/ISSPIT.2007.4458016.
  14. Sadovnichy V, Tikhonravov A, Voevodin V, Opanasenko V. "Lomonosov": Supercomputing at Moscow state university. In Book: Vetter JS, ed. Contemporary high performance computing: from petascale toward exascale. New York: Chapman and Hall/CRC Computational Science; 2013: 283-307.

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