Нечетко-логические методы в задаче детектирования границ объектов
Ключевые слова:
нечёткая логика, детектор Канни, выделение границ, оператор Собеля, центр тяжестиАннотация
Рассматривается задача уменьшения вычислительной сложности методов выделения контуров на изображениях. Решение поставленной задачи достигается модификацией детектора Канни двумя нечетко-логическими методами, позволяющими сократить число проходов по исходному изображению: в-первом случае, путем исключения двух проходов, связанных с определением наличия соседства претендующего на границу пикселя со смежными в рамке размером 3´3, а во-втором случае, исключением операции определения угла направления градиента путем формирования данной величины комбинацией нечетких правил. Целью работы является уменьшение времени детектирования границ объектов на фото- видео-изображениях, за счет уменьшения вычислительной сложности применяемых методов. Интеллектуализация процесса детектирования границ осуществляется частичным повтором вычислительных операций, используемых в детекторе Канни, с дальнейшей заменой наиболее сложных вычислительных процедур. В предлагаемых методах после определения величины градиента и угла его направления осуществляется фаззификация восьми входных переменных, в качестве которых используется разность градиентов между центральной и смежными ячейками в рамке размером 3´3. Затем строится база нечетких правил. В первом методе в зависимости от угла направления градиента используются четыре нечетких правила и исключается один проход. Во втором методе шестнадцать нечетких правил сами задают угол направления градиента, при этом исключается два прохода вдоль изображения. Разность градиентов между центральной ячейкой и смежными ячейками позволяет учитывать форму распределения градиента. Затем на основе метода центра тяжести осуществляется дефаззификация результирующей переменной. Дальнейшее использование нечетких a-срезов позволяет осуществить бинаризацию результирующего изображения с выделением на нем границ объектов. Для оценки вычислительной скорости работы предложенных нечетких методов детектирования границ в среде Microsoft Visual Studio было разработано программное обеспечение. Представленные экспериментальные результаты показали, что уровень шума зависит от величины a-среза и параметров меток трапециевидных функций принадлежности. Ограничением двух методов является использование кусочно-линейных функций принадлежности. Экспериментальные исследования работоспособности предложенных методов детектирования контуров показали, что время первого нечеткого метода на 18% быстрее по сравнению с детектором Канни и на 2 % по отношению ко второму нечеткому методу. Однако при визуальной оценке установлено, что второй нечеткий метод лучше определяет границы объектов.
Литература
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4. Bovik A. Handbook of image and video processing. New York: Academic; 2000.
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6. Sobel E. Camera Models and Machine Perception. Ph.D thesis. Stanford University, Stanford, California; 1970.
7. Chen G, Yang Y.H.H. Edge detection by regularized cubic B-spline fitting. IEEE Trans Syst, Man Cybern 1995; 25: 636–43.
8. Canny J. A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 1986; PAMI-8: 679–97.
9. Shen J., Castan S. An optimal linear operator for step edge detection. Graph Models Image Process 1992; 54(1): 112–33.
10. deSouza P. Edge detection using sliding statistical tests. Comput Vis, Graph Image Process 1983; 23(1).
11. Bhandarkar S.M., Zhang Y., Potter W.D. An edge detection technique using genetic algorithm based optimization. Pattern Recognit 1994; 27(9): 1159–80.
12. Srinivasan V., Bhatia P., Ong S.H. Edge detection using neural network. Pattern Recognit 1995; 27(12): 1653–62.
13. Chen M.H., Lee D., Pavlidis T. Residual analysis for feature detection. IEEE Trans Pattern Anal Mach Intell 1991; 13: 30 – 40.
14. Hebert T.J., Malagre D. Edge detection using a priori model. Int Conf Image Process 1994; 94: 303–7.
15. Mejias A., Romero S., Moreno F. A new algorithm to extract the lines and edges through orthogonal projections. Digital Signal Process. - 2012; 22(1): 147–52.
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20. Cho S.M., Cho J.H. Thresholding for Edge Detection using Fuzzy Reasoning Technique. IEEE Int. Conf. Computational Sci. Proc. 1994; p. 1121–1124.
21. Xiao W., Hui X. An Improved Canny Edge Detection Algorithm Based on Predisposal Method for Image Corrupted by Gaussian Noise. IEEE World Automation Congr. 2010; p. 113–116.
22. Wang H.R., Yang J.L., Sun H.J., Chen D., Liu X.L. An improved Region Growing Method for Medical Image Selection and Evaluation Based on Canny Edge Detection. IEEE Int. Conf. Manage. and Service Sci. 2011; p. 1–4, DOI: 10.1109/ICMSS.2011.5999180.
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26. D. Xu, W. Ouyang, X. Alameda-Pineda, E. Ricci, X. Wang, and N. Sebe, “Learning deep structured multi-scale features using attention gated crfs for contour prediction,” in Conference on Neural Information Processing Systems, 2017, pp. 3964–3973.
27. J. He, S. Zhang, M. Yang, Y. Shan, and T. Huang, “Bi-directional cascade network for perceptual edge detection,” in IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 3828-3837.
28. Jianzhong He et al. “Bi-directional cascade network for perceptual edge detection”. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019, pp. 3828–3837.
29. Yang Liu, Zongwu Xie, and Hong Liu. “An Adaptive and Robust Edge Detection Method Based on Edge Proportion Statistics”. In: IEEE Transactions on Image Processing 29 (2020), pp. 5206–5215.
30. S. Yun, J. Choi, Y. Yoo, K. Yun, and J. Young Choi, “Action-decision networks for visual tracking with deep reinforcement learning,” in IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 2711-2720.
31. K.H. Choi and J.E. Ha, “Edge detection based-on U-Net using edge classification CNN,” Journal of Institute of Control, Robotics and Systems, vol. 25, no. 8, pp. 684-689, 2019 (in Korean).
32. Yun Liu et al. “Richer convolutional features for edge detection”. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017, pp. 3000–3009.
33. Xavier Soria Poma, Edgar Riba, and Angel Sappa. “Dense extreme inception network: Towards a robust cnn model for edge detection”. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2020, pp. 1923–1932.
34. Raihan F., Ce W. “PCB defect detection USING OPENCV with image subtraction method”. In International Conference on Information Management and Technology, 2017, pp. 204-209.
35. Lee D.H., Chen P.Y., Yang F.J., et al. “High-Efficient Low-Cost VLSI Implementation for Canny Edge Detection”. Journal of Information Science & Engineering, vol. 36, no. 3, 2020, pp. 34-57.
36. François Chollet. “Xception: Deep learning with depthwise separable convolutions”. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017, pp. 1251–1258.
37. Debotosh Bhattacharjee and Hiranmoy Roy. “Pattern of local gravitational force (PLGF): A novel local image descriptor”. In: IEEE Transactions on Pattern Analysis and Machine Intelligence 43.2 (2019), pp. 595–607.
38. Mengtian Li et al. “Photo-sketching: Inferring contour drawings from images”. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE. 2019, pp. 1403–1412
39. D. Dhillon and R. Chouhan. “Noise-aided Edge preserving Image Denoising using Non-Local Means with Stochastic Resonance”. In: 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP). 2018, pp. 21–25.
40. Animesh Sengupta et al. “Edge information based image fusion metrics using fractional order differentiation and sigmoidal functions”. In: IEEE Access 8 (2020), pp. 88385–88398.
41. Benoit Brummer and Christophe De Vleeschouwer. “Natural image noise dataset”. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2019, pp. 139-151.
42. Arash Akbarinia and C. Alejandro Parraga. “Feedback and surround modulated boundary detection”. In: International Journal of Computer Vision 126.12 (2018), pp. 1367–1380
43. Y.X. Wang and J.M. Chen, “Iris edge detection algorithm based on adaptive canny operator and multi-directional Sobel operator,” Computer and Digital Engineering, Vol. 11, No. 4, pp. 2744–2749, 2020.
44. C.W. Tian, X.C. Wang, and J.N. Yang, “Research on parallelization of kirsch operator edge detection algorithm for geological image,” (in Chinese), Journal of Xinjiang University, vol. 38, No. 1, pp. 54–60, 2021.
45. J.H. Zeng and S.J. Huang, “Comparison and analysis on typical image edge detection operators,”Journal of Hebei Normal University (Natural Science), vol. 44, No. 1, pp. 295–300, 2020.
46. S.J. Chen, X.H. Wang, Y.P. Ge, C. Li, and Y.C. Li., “Application of image edge extraction algorithm in the third land survey,” Computer Technology and Development, vol. 30, No. 10, pp. 161–166, 2020.
47. Cadena, Luis, Franklin Cadena, Nikolai Espinosa, Anna Korneeva, Alexy Kruglyakov, Alexander Legalov, Alexey Romanenko, and Alexander Zotin. (2017) “Brain's tumor image processing using shearlet transform.” Proc. SPIE 10396, Applications of Digital Image Processing XL, 103961B, doi: 10.1117/12.2272792; in United States.
48. Yuksel M.E., Borlu M. Accurate Segmentation of Dermoscopic Images by Image Thresholding Based on Type-2 Fuzzy Logic. IEEE Trans. Fuzzy Syst. 2009; vol. 17, no. 4, p. 976–982.
49. M. Bobyr, A. Arkhipov, A. Yakushev, Shade recognition of the color label based on the fuzzy clustering, Inform. Autom. 20(2) (2021) 407–434, http://dx.doi.org/10.15622/ia.2021.20.2.6.
50. Bobyr M.V., Emelyanov. S.G., A nonlinear method of learning neuro-fuzzy models for dynamic control systems, Appl. Soft Comput. J. 88 (2020) 106030, http://dx.doi.org/10.1016/j.asoc.2019.106030.
51. Bobyr M.V., Milostnaya N.A., Kulabuhov S.A., A method of defuzzification based on the approach of areas’ ratio, Appl. Soft Comput. 59 (2017) 19–32, http://dx.doi.org/10.1016/j.asoc.2017.05.040.
52. M.V. Bobyr, A.S. Yakushev, A.A. Dorodnykh, Fuzzy devices for cooling the cutting tool of the CNC machine implemented on FPGA, Meas.: J. Int. Meas. Confed. 152 (2020) http://dx.doi.org/10.1016/j.measurement.2019.107378.
53. Bobyr M.V., Milostnaya N.A., Bulatnikov V.A. The fuzzy filter based on the method of areas' ratio. Applied Soft Computing. 117 (2022) 108449, https://doi.org/10.1016/j.asoc.2022.108449
54. Bobyr M.V., Kulabukhov S.A. Simulation of control of temperature mode in cutting area on the basis of fuzzy logic. Journal of Machinery Manufacture and Reliability, 2017, 46(3), стр. 288–295. http://dx.doi.org/10.3103/S1052618817030049
55. Sala, F.A. Design of false color palettes for grayscale reproduction. Displays, 2017, 46, 9–15. https://doi.org/10.1016/j.displa.2016.11.005
56. Abdou, I.E., & Pratt, W.K. Quantitative design and evaluation of enhance-ment/thresholding edge detectors. Proceedings of the IEEE, 1979, 67(5), 753–763. doi:10.1109/proc.1979.11325
2. Kang C.C, Wang W.J. A novel edge detection method based on the maximizing objective function. Pattern Recogn 2007; 40(2): 609–18. http://dx.doi.org/10.1016/j.patcog.2006.03.016.
3. Lopez-Molina C., De Baets B., Bustince H. Generating fuzzy edge images from gradient magnitudes. Comput Vision Image Understand 2011; 115(11): 1571–80. http://dx.doi.org/10.1016/j.cviu.2011.07.003.
4. Bovik A. Handbook of image and video processing. New York: Academic; 2000.
5. Canny J. A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 1986; 8(6): 679–98.
6. Sobel E. Camera Models and Machine Perception. Ph.D thesis. Stanford University, Stanford, California; 1970.
7. Chen G, Yang Y.H.H. Edge detection by regularized cubic B-spline fitting. IEEE Trans Syst, Man Cybern 1995; 25: 636–43.
8. Canny J. A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 1986; PAMI-8: 679–97.
9. Shen J., Castan S. An optimal linear operator for step edge detection. Graph Models Image Process 1992; 54(1): 112–33.
10. deSouza P. Edge detection using sliding statistical tests. Comput Vis, Graph Image Process 1983; 23(1).
11. Bhandarkar S.M., Zhang Y., Potter W.D. An edge detection technique using genetic algorithm based optimization. Pattern Recognit 1994; 27(9): 1159–80.
12. Srinivasan V., Bhatia P., Ong S.H. Edge detection using neural network. Pattern Recognit 1995; 27(12): 1653–62.
13. Chen M.H., Lee D., Pavlidis T. Residual analysis for feature detection. IEEE Trans Pattern Anal Mach Intell 1991; 13: 30 – 40.
14. Hebert T.J., Malagre D. Edge detection using a priori model. Int Conf Image Process 1994; 94: 303–7.
15. Mejias A., Romero S., Moreno F. A new algorithm to extract the lines and edges through orthogonal projections. Digital Signal Process. - 2012; 22(1): 147–52.
16. Rakesh R.R., Chaudhuri P., Murthy C.A. Thresholding in edge detection: a statistical approach. IEEE Trans Image Process 2004; 13(7): 927–36. http://dx.doi.org/10.1109/TIP.2004.828404.
17. S. Uguz, U. Sahin, F. Sahin. Edge detection with fuzzy cellular automata transition function optimized by PSO. Computers and Electrical Engineering. - 2015; 43. – pp.180–192
18. Alexander Zotin, Konstantin Simonov, Mikhail Kurako, Yousif Hamad, Svetlana Kirillova Edge detection in MRI brain tumor images based on fuzzy C-means clustering. Procedia Computer Science. – 2018; 126. – pp. 1261–1270
19. Er-sen L., Shu-long Z., Bao-shan Z., Yong Z., Chao-gui X., Li-hua S. An Adaptive Edge Detection Method Based on The Canny Operator. IEEE Int. Conf. Environmental Sci. and Inform. Applicat. Technology 2009; p. 265–269.
20. Cho S.M., Cho J.H. Thresholding for Edge Detection using Fuzzy Reasoning Technique. IEEE Int. Conf. Computational Sci. Proc. 1994; p. 1121–1124.
21. Xiao W., Hui X. An Improved Canny Edge Detection Algorithm Based on Predisposal Method for Image Corrupted by Gaussian Noise. IEEE World Automation Congr. 2010; p. 113–116.
22. Wang H.R., Yang J.L., Sun H.J., Chen D., Liu X.L. An improved Region Growing Method for Medical Image Selection and Evaluation Based on Canny Edge Detection. IEEE Int. Conf. Manage. and Service Sci. 2011; p. 1–4, DOI: 10.1109/ICMSS.2011.5999180.
23. Ranita Biswas, Jaya Sil An Improved Canny Edge Detection Algorithm Based on Type-2 Fuzzy Sets. Procedia Technology. 2012: 4. – pp. 820 – 824
24. Shah, Hemang J. Detection of Tumor in MRI Images using Image Segmentation. International Journal of Advance Research in Computer Science and Management Studies. – 2014; 2(6).- pp. 53-56.
25. Zotin Alexander, Konstantin Simonov, Fedor Kapsargin, Tatyana Cherepanova, Alexey Kruglyakov, and Luis Cadena. Techniques for Medical Images Processing Using Shearlet Transform and Color Coding”, in Favorskaya M. and Jain L. (eds) Computer Vision in Control Systems-4. Intelligent Systems. – 2018: 136, Springer, Cham.
26. D. Xu, W. Ouyang, X. Alameda-Pineda, E. Ricci, X. Wang, and N. Sebe, “Learning deep structured multi-scale features using attention gated crfs for contour prediction,” in Conference on Neural Information Processing Systems, 2017, pp. 3964–3973.
27. J. He, S. Zhang, M. Yang, Y. Shan, and T. Huang, “Bi-directional cascade network for perceptual edge detection,” in IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 3828-3837.
28. Jianzhong He et al. “Bi-directional cascade network for perceptual edge detection”. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019, pp. 3828–3837.
29. Yang Liu, Zongwu Xie, and Hong Liu. “An Adaptive and Robust Edge Detection Method Based on Edge Proportion Statistics”. In: IEEE Transactions on Image Processing 29 (2020), pp. 5206–5215.
30. S. Yun, J. Choi, Y. Yoo, K. Yun, and J. Young Choi, “Action-decision networks for visual tracking with deep reinforcement learning,” in IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 2711-2720.
31. K.H. Choi and J.E. Ha, “Edge detection based-on U-Net using edge classification CNN,” Journal of Institute of Control, Robotics and Systems, vol. 25, no. 8, pp. 684-689, 2019 (in Korean).
32. Yun Liu et al. “Richer convolutional features for edge detection”. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017, pp. 3000–3009.
33. Xavier Soria Poma, Edgar Riba, and Angel Sappa. “Dense extreme inception network: Towards a robust cnn model for edge detection”. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2020, pp. 1923–1932.
34. Raihan F., Ce W. “PCB defect detection USING OPENCV with image subtraction method”. In International Conference on Information Management and Technology, 2017, pp. 204-209.
35. Lee D.H., Chen P.Y., Yang F.J., et al. “High-Efficient Low-Cost VLSI Implementation for Canny Edge Detection”. Journal of Information Science & Engineering, vol. 36, no. 3, 2020, pp. 34-57.
36. François Chollet. “Xception: Deep learning with depthwise separable convolutions”. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017, pp. 1251–1258.
37. Debotosh Bhattacharjee and Hiranmoy Roy. “Pattern of local gravitational force (PLGF): A novel local image descriptor”. In: IEEE Transactions on Pattern Analysis and Machine Intelligence 43.2 (2019), pp. 595–607.
38. Mengtian Li et al. “Photo-sketching: Inferring contour drawings from images”. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE. 2019, pp. 1403–1412
39. D. Dhillon and R. Chouhan. “Noise-aided Edge preserving Image Denoising using Non-Local Means with Stochastic Resonance”. In: 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP). 2018, pp. 21–25.
40. Animesh Sengupta et al. “Edge information based image fusion metrics using fractional order differentiation and sigmoidal functions”. In: IEEE Access 8 (2020), pp. 88385–88398.
41. Benoit Brummer and Christophe De Vleeschouwer. “Natural image noise dataset”. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2019, pp. 139-151.
42. Arash Akbarinia and C. Alejandro Parraga. “Feedback and surround modulated boundary detection”. In: International Journal of Computer Vision 126.12 (2018), pp. 1367–1380
43. Y.X. Wang and J.M. Chen, “Iris edge detection algorithm based on adaptive canny operator and multi-directional Sobel operator,” Computer and Digital Engineering, Vol. 11, No. 4, pp. 2744–2749, 2020.
44. C.W. Tian, X.C. Wang, and J.N. Yang, “Research on parallelization of kirsch operator edge detection algorithm for geological image,” (in Chinese), Journal of Xinjiang University, vol. 38, No. 1, pp. 54–60, 2021.
45. J.H. Zeng and S.J. Huang, “Comparison and analysis on typical image edge detection operators,”Journal of Hebei Normal University (Natural Science), vol. 44, No. 1, pp. 295–300, 2020.
46. S.J. Chen, X.H. Wang, Y.P. Ge, C. Li, and Y.C. Li., “Application of image edge extraction algorithm in the third land survey,” Computer Technology and Development, vol. 30, No. 10, pp. 161–166, 2020.
47. Cadena, Luis, Franklin Cadena, Nikolai Espinosa, Anna Korneeva, Alexy Kruglyakov, Alexander Legalov, Alexey Romanenko, and Alexander Zotin. (2017) “Brain's tumor image processing using shearlet transform.” Proc. SPIE 10396, Applications of Digital Image Processing XL, 103961B, doi: 10.1117/12.2272792; in United States.
48. Yuksel M.E., Borlu M. Accurate Segmentation of Dermoscopic Images by Image Thresholding Based on Type-2 Fuzzy Logic. IEEE Trans. Fuzzy Syst. 2009; vol. 17, no. 4, p. 976–982.
49. M. Bobyr, A. Arkhipov, A. Yakushev, Shade recognition of the color label based on the fuzzy clustering, Inform. Autom. 20(2) (2021) 407–434, http://dx.doi.org/10.15622/ia.2021.20.2.6.
50. Bobyr M.V., Emelyanov. S.G., A nonlinear method of learning neuro-fuzzy models for dynamic control systems, Appl. Soft Comput. J. 88 (2020) 106030, http://dx.doi.org/10.1016/j.asoc.2019.106030.
51. Bobyr M.V., Milostnaya N.A., Kulabuhov S.A., A method of defuzzification based on the approach of areas’ ratio, Appl. Soft Comput. 59 (2017) 19–32, http://dx.doi.org/10.1016/j.asoc.2017.05.040.
52. M.V. Bobyr, A.S. Yakushev, A.A. Dorodnykh, Fuzzy devices for cooling the cutting tool of the CNC machine implemented on FPGA, Meas.: J. Int. Meas. Confed. 152 (2020) http://dx.doi.org/10.1016/j.measurement.2019.107378.
53. Bobyr M.V., Milostnaya N.A., Bulatnikov V.A. The fuzzy filter based on the method of areas' ratio. Applied Soft Computing. 117 (2022) 108449, https://doi.org/10.1016/j.asoc.2022.108449
54. Bobyr M.V., Kulabukhov S.A. Simulation of control of temperature mode in cutting area on the basis of fuzzy logic. Journal of Machinery Manufacture and Reliability, 2017, 46(3), стр. 288–295. http://dx.doi.org/10.3103/S1052618817030049
55. Sala, F.A. Design of false color palettes for grayscale reproduction. Displays, 2017, 46, 9–15. https://doi.org/10.1016/j.displa.2016.11.005
56. Abdou, I.E., & Pratt, W.K. Quantitative design and evaluation of enhance-ment/thresholding edge detectors. Proceedings of the IEEE, 1979, 67(5), 753–763. doi:10.1109/proc.1979.11325
Опубликован
2022-03-15
Как цитировать
Бобырь, М. В., Архипов, А. Е., Горбачев, С. В., Цао, Ц., & Бхаттачарья, С. (2022). Нечетко-логические методы в задаче детектирования границ объектов. Информатика и автоматизация, 21(2), 376-404. https://doi.org/10.15622/ia.21.2.6
Раздел
Искусственный интеллект, инженерия данных и знаний
Copyright (c) Максим Владимирович Бобырь, Александр Евгеньевич Архипов, Сергей Викторович Горбачев, Цзиньдe Цао, Сиддхартха Бхаттачарья
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