Journal of the Korean Society for Industrial and Applied Mathematics

  • 제27권1호
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  • Pages.37-55
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  • 2023
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  • 1226-9433(pISSN)
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  • 1229-0645(eISSN)
한국산업응용수학회 (Korean Society for Industrial and Applied Mathematics)
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DETECTION AND COUNTING OF FLOWERS BASED ON DIGITAL IMAGES USING COMPUTER VISION AND A CONCAVE POINT DETECTION TECHNIQUE

  • PAN ZHAO (DEPARTMENT OF MATHEMATICS, CHONNAM NATIONAL UNIVERSITY) ;
  • BYEONG-CHUN SHIN (DEPARTMENT OF MATHEMATICS, CHONNAM NATIONAL UNIVERSITY)
  • 투고 : 2022.12.26
  • 심사 : 2023.03.23
  • 발행 : 2023.03.25
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초록

In this paper we propose a new algorithm for detecting and counting flowers in a complex background based on digital images. The algorithm mainly includes the following parts: edge contour extraction of flowers, edge contour determination of overlapped flowers and flower counting. We use a contour detection technique in Computer Vision (CV) to extract the edge contours of flowers and propose an improved algorithm with a concave point detection technique to find accurate segmentation for overlapped flowers. In this process, we first use the polygon approximation to smooth edge contours and then adopt the second-order central moments to fit ellipse contours to determine whether edge contours overlap. To obtain accurate segmentation points, we calculate the curvature of each pixel point on the edge contours with an improved Curvature Scale Space (CSS) corner detector. Finally, we successively give three adaptive judgment criteria to detect and count flowers accurately and automatically. Both experimental results and the proposed evaluation indicators reveal that the proposed algorithm is more efficient for flower counting.

키워드

과제정보

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (2017R1E1A1A03070059).

참고문헌

  1. C. Okinda, I. Nyalala, T. Korohou, C. Okinda, J. Wang, T. Achieng, P. Wamalwa, T. Mang, M. Shen,A review on computer vision systems in monitoring of poultry: A welfare perspective, Artificial Intelligence in Agriculture, 4 (2020), 184-208. https://doi.org/10.1016/j.aiia.2020.09.002
  2. A. McBratney, B. Whelan, T. Ancev, J. Bouma. Future directions of precision agriculture, Precision agriculture, 6 (2005), 7-23. https://doi.org/10.1007/s11119-005-0681-8
  3. A. Gongal, S. Amatya, M. Karkee, Q. Zhang, K. Lewis, Sensors and systems for fruit detection and localization: A review, Computers and Electronics in Agriculture, 116 (2015), 8-19. https://doi.org/10.1016/j.compag.2015.05.021
  4. W. Shen, Y. Wu, Z. Chen, H. Wei, Grading Method of Leaf Spot Disease Based on Image Processing, 2008 International Conference on Computer Science and Software Engineering, IEEE, 6 (2008), 491-494.
  5. A. Wang, W. Zhang, X. Wei, A review on weed detection using ground-based machine vision and image processing techniques, Computers and Electronics in Agriculture, 158 (2019), 226-240. https://doi.org/10.1016/j.compag.2019.02.005
  6. E. Hamuda, B. Mc Ginley, M. Glavin, E. Jones, Automatic crop detection under field conditions using the HSV color space and morphological operations, Computers and Electronics in Agriculture, 133 (2017), 97-107. https://doi.org/10.1016/j.compag.2016.11.021
  7. K. Kapach, E. Barnea, R. Mairon, Y. Edan, O. Ben-Shahar, Computer vision for fruit harvesting robots-state of the art and challenges ahead, International Journal of Computational Vision and Robotics, 3 (2012), 4-34. https://doi.org/10.1504/IJCVR.2012.046419
  8. R. S. Sarkate, N. V. Kalyankar, P. B. Khanale, Application of computer vision and color image segmentation for yield prediction precision, International Conference on Information Systems and Computer Networks, IEEE 2013.
  9. N. Bairwa, N. Agrawal, S. Gupta, Development of counting algorithm for overlapped agricultural products, IJCA Proceedings on Recent Advances in Wireless Communication and Artificial Intelligence RAWCAI 2014.
  10. N. Bairwa, N. K. Agrawal, Counting of flowers using image processing, International Journal of Engineering Research and Technology, 3 (2014), 775-779.
  11. V. S. Sundar, and J. Bagyamani, Flower counting in yield approximation using digital image processing techniques, International Journal of Advance Research in Science and Engineering, 4 (2015), 97-106.
  12. D. Oppenheim, Y. Edan, G. Shani, Detecting tomato flowers in greenhouses using computer vision, International Journal of Computer, Electrical, Automation, Control and Information Engineering, 11 (2017), 104-109.
  13. M. P. Diago, A. Sanz-Garcia, B. Millan, J. Blasco, J. Tardaguila, Assessment of flower number per inflorescence in grapevine by image analysis under field conditions, Journal of the Science of Food and Agriculture, 94 (2014), 1981-1987. https://doi.org/10.1002/jsfa.6512
  14. N. Otsu, A threshold selection method from gray-level histograms, IEEE transactions on systems, man, and cybernetics 9 (1979), 62-66. https://doi.org/10.1109/TSMC.1979.4310076
  15. S. Zhang, S. Huang, Z. Zhang, H. Wang, J. Ma, P. Li, Corner detection based on tangent-to-point distance accumulation technique, Multimedia Tools and Applications, 78 (2019), 25685-25706. https://doi.org/10.1007/s11042-019-07792-x
  16. S. Zhu, D. Zhou, Review on Image Corner Detection, Computer Systems and Applications, 29 (2020), 22-28.
  17. E. Rachmawati, I. Supriana, M. L. Khodra, FAST corner detection in polygonal approximation of shape, International Conference on Science in Information Technology (ICSITech), IEEE 2017.
  18. F. Mokhtarian, M. Bober, Robust image corner detection through curvature scale space, Curvature Scale Space Representation: Theory, Applications, and MPEG-7 Standardization. Computational Imaging and Vision, Springer, Dordrecht, 2003.
  19. M. Awrangjeb, G. Lu, Robust image corner detection based on the chord-to-point distance accumulation technique, IEEE transactions on multimedia 10 (2008), 1059-1072. https://doi.org/10.1109/TMM.2008.2001384
  20. W. Zhang, F. Wang, L. Zhu, Z. Zhou, Corner detection using Gabor filters, IET Image Processing, 8 (2014), 639-646. https://doi.org/10.1049/iet-ipr.2013.0641
  21. X. He, N. H. C. Yung, Curvature scale space corner detector with adaptive threshold and dynamic region of support, Proceedings of the 17th International Conference on Pattern Recognition (ICPR), IEEE 2004.
  22. X. He, N. H. C. Yung, Corner detector based on global and local curvature properties, Optical engineering, 47 (2008), 057008.
  23. X. Zhang, H. Wang, M. Hong, L. Xu, D. Yang, B. C. Lovell, Robust image corner detection based on scale evolution difference of planar curves, Pattern Recognition Letters, 30 (2009) , 449-455. https://doi.org/10.1016/j.patrec.2008.11.002
  24. A. Rosenfeld, J. S. Weszka, An improved method of angle detection on digital curves, IEEE Transactions on Computers, 24 (1975), 940-941.
  25. D. M. Tsai, H. T. Hou, H. J. Su, Boundary-based corner detection using eigenvalues of covariance matrices, Pattern Recognition Letters, 20 (1999), 31-40. https://doi.org/10.1016/S0167-8655(98)00130-5
  26. S. Zhang, D. Yang, S. Huang, X. Zhang, L. Tu, Z. Ren, Robust corner detection using the eigenvector-based angle estimator, Journal of Visual Communication and Image Representation, 45 (2017), 181-193. https://doi.org/10.1016/j.jvcir.2017.01.020
  27. C. H. Yeh, Wavelet-based corner detection using eigenvectors of covariance matrices, Pattern Recognition Letters, 24 (2003), 2797-2806.  https://doi.org/10.1016/S0167-8655(03)00124-7
  28. Online Color Converter (RGB-space to HSV-space). https://www.peko-step.com/en/tool/ hsvrgb_en.html.
  29. S. Kapur, Computer Vision with Python 3, Packt Publishing Ltd, UK, 2017.
  30. B. G. Batchelor, F. M. Waltz, Morphological image processing, Machine vision handbook, Springer, London, 2012.
  31. W. Wang, Binary image segmentation of aggregates based on polygonal approximation and classification of concavities, Pattern Recognition, 31 (1998), 1503-1524. https://doi.org/10.1016/S0031-3203(97)00145-3
  32. X. Bai, C. Sun, F. Zhou, Splitting touching cells based on concave points and ellipse fitting, Pattern recognition, 42 (2009), 2434-2446. https://doi.org/10.1016/j.patcog.2009.04.003
  33. R. Mukundan, K. R. Ramakrishnan, Moment functions in image analysis: theory and applications, World Scientific Publishing Co. Pte. Ltd, Singapore, 1998.
  34. W. Zhang, X. Jiang, Y. Liu, A method for recognizing overlapping elliptical bubbles in bubble image, Pattern Recognition Letters, 33 (2012), 1543-1548.
  35. P. J. Ramos, F. A. Prieto, E. C. Oliveros, Automatic fruit count on coffee branches using computer vision, Computers and Electronics in Agriculture, 137 (2017), 9-22. https://doi.org/10.1016/j.compag.2017.03.010
  36. D. Hearn, M. Pauline Baker, Computer Graphics with OpenGL (3rd Edition), Pearson Prentice Hall, Pearson Education, Inc, America, 2004.
  37. X. Song, C. Cheng, C. Zhou, An Analysis and Investigation of Algorithms for Identifying Convexity-Concavity of a Simple Polygon, Remote Sensing for Land and Resources, 3 (2011), 25-31.
  38. F. Feito, J.C. Torres, A. Urena, Orientation, simplicity, and inclusion test for planar polygons, Computers and Graphics, 19 (1995), 595-600. https://doi.org/10.1016/0097-8493(95)00037-D
  39. J. Zhao, G. Zhang, S. Qu, Orientation and Convexity-Concavity Identification for Polygons Using Extremity Vertices Sequence, Journal of Engineering Graphics, 28 (2007), 595-600.
  40. T. Lewiner, J. D. Gomes, H. Lopes, M. Craizer, Arc-length based curvature estimator, Proceedings. 17th Brazilian Symposium on Computer Graphics and Image Processing. IEEE 2004.
  41. N. Chernov, C. Lesort, Statistical efficiency of curve fitting algorithms, Computational statistics and data analysis, 47 (2004), 713-728. https://doi.org/10.1016/j.csda.2003.11.008
  42. N. Chernov, C. Lesort, Least squares fitting of circles, Journal of Mathematical Imaging and Vision, 23 (2005), 239-252. https://doi.org/10.1007/s10851-005-0482-8
  43. M. Grossetete, Y. Berthoumieu, J. P. Da-Costa, C. Germain, O. Lavialle, G. Grenier, Early estimation of vineyard yield: site specific counting of berries by using a smartphone, International Conference of Agricultural Engineering-CIGR-AgEng 2012.
  44. Coefficient of determination, expanded September 2019. https://en.wikipedia.org/wiki/Coefficient_of_determination.