An Automatic Thresholding Approach to Gravitation-Based Edge Detection in Grey-Scale Images
Subject Areas : Image ProcessingHamed Agahi 1 * , Kimia Rezaei 2
1 - Department of Electrical Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran
2 - Department of Electrical and Electronic Engineering, University College Cork, Ireland
Keywords: Auto-Thresholding, Edge Detection, Force Feature, The Law of Universal Gravity, The Grasshopper Optimization Algorithm,
Abstract :
This paper presents an optimal auto-thresholding approach for the gravitational edge detection method in grey-scale images. The goal of this approach is to enhance the performance measures of the edge detector in clean and noisy conditions. To this aim, an optimal threshold is automatically found, according to which the proposed method dichotomizes the pixels to the edges and non-edges. First, some pre-processing operations are applied to the image. Then, the vector sum of the gravitational forces applied to each pixel by its neighbors is computed according to the universal law of gravitation. Afterwards, the force magnitude is mapped to a new characteristic called the force feature. Following this, the histogram representation of this feature is determined, for which an optimal threshold is aimed to be discovered. Three thresholding techniques are proposed, two of which contain iterative processes. The parameters of the formulation used in these techniques are adjusted by means of the metaheuristic grasshopper optimization algorithm. To evaluate the proposed system, two standard databases were used and multiple qualitative and quantitative measures were utilized. The results confirmed that the methodology of our work outperformed some conventional and recent detectors, achieving the average precision of 0.894 on the BSDS500 dataset. Moreover, the outputs had high similarity to the ideal edge maps.
[1] R. C. Gonzalez and R. E. Woods, "Digital image processing," ed: Prentice hall Upper Saddle River, 2002.
[2] D. Marmanis, K. Schindler, J. D. Wegner, S. Galliani, M. Datcu, and U. Stilla, "Classification with an edge: Improving semantic image segmentation with boundary detection," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 135, pp. 158-172, 2018.
[3] I. Sobel, "Camera models and machine perception [PhD Dissertation]," ed: Stanford, CA: Stanford University, 1970.
[4] J. M. Prewitt, "Object enhancement and extraction," Picture processing and Psychopictorics, vol. 10, no. 1, pp. 15-19, 1970.
[5] J. Canny, "A computational approach to edge detection," IEEE Transactions on pattern analysis and machine intelligence, no. 6, pp. 679-698, 1986.
[6] L. S. Davis, "A survey of edge detection techniques," Computer graphics and image processing, vol. 4, no. 3, pp. 248-270, 1975.
[7] L. Bin and M. S. Yeganeh, "Comparison for image edge detection algorithms," IOSR Journal of Computer Engineering, vol. 2, no. 6, pp. 1-4, 2012.
[8] L. Romani, M. Rossini, and D. Schenone, "Edge detection methods based on RBF interpolation," Journal of Computational and Applied Mathematics, vol. 349, pp. 532-547, 2019.
[9] S. Eser and A. Derya, "A new edge detection approach via neutrosophy based on maximum norm entropy," Expert Systems with Applications, vol. 115, pp. 499-511, 2019.
[10] Y. He and L. M. Ni, "A novel scheme based on the diffusion to edge detection," IEEE Transactions on Image Processing, vol. 28, no. 4, pp. 1613-1624, 2018.
[11] R. K. Bhogal and A. Agrawal, "Image Edge Detection Techniques Using Sobel, T1FLS, and IT2FLS," in Information and Communication Technology for Intelligent Systems: Springer, 2019, pp. 303-317.
[12] A. Banharnsakun, "Artificial bee colony algorithm for enhancing image edge detection," Evolving Systems, vol. 10, no. 4, pp. 679-687, 2019.
[13] O. P. Verma and A. S. Parihar, "An optimal fuzzy system for edge detection in color images using bacterial foraging algorithm," IEEE Transactions on Fuzzy Systems, vol. 25, no. 1, pp. 114-127, 2016.
[14] M. D. Ansari, A. R. Mishra, and F. T. Ansari, "New divergence and entropy measures for intuitionistic fuzzy sets on edge detection," International Journal of Fuzzy Systems, vol. 20, no. 2, pp. 474-487, 2018.
[15] W. Shen, X. Wang, Y. Wang, X. Bai, and Z. Zhang, "Deepcontour: A deep convolutional feature learned by positive-sharing loss for contour detection," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 3982-3991.
[16] S. Xie and Z. Tu, "Holistically-nested edge detection," in Proceedings of the IEEE international conference on computer vision, 2015, pp. 1395-1403.
[17] 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 Advances in Neural Information Processing Systems, 2017, pp. 3961-3970.
[18] R. P. Olenick, T. M. Apostol, D. L. Goodstein, and A. Arons, "The Mechanical Universe: Introduction to Mechanics and Heat," ed: AAPT, 1986.
[19] E. Rashedi and H. Nezamabadi-Pour, "A stochastic gravitational approach to feature based color image segmentation," Engineering Applications of Artificial Intelligence, vol. 26, no. 4, pp. 1322-1332, 2013.
[20] G. Sun, A. Zhang, Y. Yao, and Z. Wang, "A novel hybrid algorithm of gravitational search algorithm with genetic algorithm for multi-level thresholding," Applied Soft Computing, vol. 46, pp. 703-730, 2016.
[21] J. J. D. M. S. Junior, A. R. Backes, and P. C. Cortez, "Color texture classification based on gravitational collapse," Pattern Recognition, vol. 46, no. 6, pp. 1628-1637, 2013.
[22] G. Sun, Q. Liu, Q. Liu, C. Ji, and X. Li, "A novel approach for edge detection based on the theory of universal gravity," Pattern Recognition, vol. 40, no. 10, pp. 2766-2775, 2007.
[23] C. Lopez-Molina, H. Bustince, J. Fernández, P. Couto, and B. De Baets, "A gravitational approach to edge detection based on triangular norms," Pattern Recognition, vol. 43, no. 11, pp. 3730-3741, 2010.
[24] O. P. Verma and R. Sharma, "An optimal edge detection using universal law of gravity and ant colony algorithm," in 2011 World Congress on Information and Communication Technologies, 2011: IEEE, pp. 507-511.
[25] O. P. Verma, R. Sharma, M. Kumar, and N. Agrawal, "An optimal edge detection using gravitational search algorithm," Lecture Notes on Software Engineering, vol. 1, no. 2, p. 148, 2013.
[26] F. Deregeh and H. Nezamabadi-Pour, "A new gravitational image edge detection method using edge explorer agents," Natural Computing, vol. 13, no. 1, pp. 65-78, 2014.
[27] G. Sun et al., "Gravitation-based edge detection in hyperspectral images," Remote Sensing, vol. 9, no. 6, p. 592, 2017.
[28] D. Wang, J. Yin, C. Tang, X. Cheng, and B. Ge, "Color edge detection using the normalization anisotropic Gaussian kernel and multichannel fusion," IEEE Access, vol. 8, pp. 228277-228288, 2020.
[29] J. He, S. Zhang, M. Yang, Y. Shan, and T. Huang, "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.
[30] K. Li, Y. Tian, B. Wang, Z. Qi, and Q. Wang, "Bi-Directional Pyramid Network for Edge Detection," Electronics, vol. 10, no. 3, p. 329, 2021.
[31] Y. Lu, C. He, Y. F. Yu, G. Xu, H. Zhu, and L. Deng, "Vector co‐occurrence morphological edge detection for colour image," IET Image Processing, 2021.
[32] S. Anand and G. Sangeethapriya, "Gaussian modulated hyperbolic tangent high pass filter for edge detection in noisy images," arXiv preprint arXiv:2005.11432, 2020.
[33] S. Saremi, S. Mirjalili, and A. Lewis, "Grasshopper optimisation algorithm: theory and application," Advances in Engineering Software, vol. 105, pp. 30-47, 2017.
[34] C. Lopez-Molina, B. De Baets, and H. Bustince, "Generating fuzzy edge images from gradient magnitudes," Computer Vision and Image Understanding, vol. 115, no. 11, pp. 1571-1580, 2011.
[35] N. Otsu, "A threshold selection method from gray-level histograms," IEEE transactions on systems, man, and cybernetics, vol. 9, no. 1, pp. 62-66, 1979.
[36] M. Mafarja, I. Aljarah, H. Faris, A. I. Hammouri, A.-Z. Ala’M, and S. Mirjalili, "Binary grasshopper optimisation algorithm approaches for feature selection problems," Expert Systems with Applications, vol. 117, pp. 267-286, 2019.
[37] X. Zhang, Q. Miao, H. Zhang, and L. Wang, "A parameter-adaptive VMD method based on grasshopper optimization algorithm to analyze vibration signals from rotating machinery," Mechanical Systems and Signal Processing, vol. 108, pp. 58-72, 2018.
[38] U. SIPI, "The usc-sipi image database," ed, 2016.
[39] P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik, "Contour detection and hierarchical image segmentation," IEEE transactions on pattern analysis and machine intelligence, vol. 33, no. 5, pp. 898-916, 2010. [40] S. E. Umbaugh, Computer imaging: digital image analysis and processing. CRC press, 2005.
[41] C. E. Shannon, "A mathematical theory of communication," Bell system technical journal, vol. 27, no. 3, pp. 379-423, 1948.
[42] R. Xiaofeng and L. Bo, "Discriminatively trained sparse code gradients for contour detection," in Advances in neural information processing systems, 2012, pp. 584-592.
[43] K.-K. Maninis, J. Pont-Tuset, P. Arbeláez, and L. Van Gool, "Convolutional oriented boundaries," in European Conference on Computer Vision, 2016: Springer, pp. 580-596.
[44] I. Kokkinos, "Pushing the boundaries of boundary detection using deep learning," arXiv preprint arXiv:1511.07386, 2015.
[45] P. Isola, D. Zoran, D. Krishnan, and E. H. Adelson, "Crisp boundary detection using pointwise mutual information," in European Conference on Computer Vision, 2014: Springer, pp. 799-814.
[46] G. Bertasius, J. Shi, and L. Torresani, "High-for-low and low-for-high: Efficient boundary detection from deep object features and its applications to high-level vision," in Proceedings of the IEEE International Conference on Computer Vision, 2015, pp. 504-512.
[47] Y. Liao, S. Fu, X. Lu, C. Zhang, and Z. Tang, "Deep-learning-based object-level contour detection with CCG and CRF optimization," in 2017 IEEE International Conference on Multimedia and Expo (ICME), 2017: IEEE, pp. 859-864.
[48] W. Yupei, Z. Xin, and K. Huang, "Deep crisp boundaries," 2017: CVPR.
[49] S. Hallman and C. C. Fowlkes, "Oriented edge forests for boundary detection," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 1732-1740.