Subject Areas : Computer Engineering
1 -
Keywords:
Abstract :
References:
[1] M. Chen, Y. Tang, X. Zou, K. Huang, L. Li, and Y. He, “High-accuracy multi-camera reconstruction enhanced by adaptive point cloud correction algorithm,” Opt. Lasers Eng., vol. 122, pp. 170–183, 2019, doi: 10.1016/j.optlaseng.2019.06.011.
[2] P. Rathore, J. C. Bezdek, S. M. Erfani, S. Rajasegarar, and M. Palaniswami, “Ensemble Fuzzy Clustering Using Cumulative Aggregation on Random Projections,” IEEE Trans. Fuzzy Syst., vol. 26, no. 3, pp. 1510–1524, 2018, doi: 10.1109/TFUZZ.2017.2729501.
[3] K. Chen and X. Chen, “Fuzzy C-Means Clustering Image Segmentation Algorithm with Local Spatial Information Based on ELM,” Shuju Caiji Yu Chuli/Journal Data Acquis. Process., vol. 34, no. 1, pp. 100–110, 2019, doi: 10.16337/j.1004-9037.2019.01.011.
[4] A. Sabbaghi, M. R. Keyvanpour, and S. Parsa, “FCCI: A fuzzy expert system for identifying coincidental correct test cases,” J. Syst. Softw., vol. 168, 2020, doi: 10.1016/j.jss.2020.110635.
[5] Z. Li, K. Kamnitsas, and B. Glocker, “Analyzing Overfitting under Class Imbalance in Neural Networks for Image Segmentation,” IEEE Trans. Med. Imaging, vol. 40, no. 3, pp. 1065–1077, 2021, doi: 10.1109/TMI.2020.3046692.
[6] M. Diwakar and M. Kumar, “A review on CT image noise and its denoising,” Biomed. Signal Process. Control, vol. 42, pp. 73–88, 2018, doi: 10.1016/j.bspc.2018.01.010.
[7] F. Zhang, H. Liu, C. Cao, Q. Cai, and D. Zhang, “RVLSM: Robust variational level set method for image segmentation with intensity inhomogeneity and high noise,” Inf. Sci. (Ny)., vol. 596, pp. 439–459, 2022, doi: 10.1016/j.ins.2022.03.035.
[8] J. Cai, S. Gu, and L. Zhang, “Learning a deep single image contrast enhancer from multi-exposure images,” IEEE Trans. Image Process., vol. 27, no. 4, pp. 2049–2062, 2018, doi: 10.1109/TIP.2018.2794218.
[9] B. Sasmal and K. G. Dhal, “A survey on the utilization of Superpixel image for clustering based image segmentation,” Multimed. Tools Appl., vol. 82, no. 23, pp. 35493–35555, 2023, doi: 10.1007/s11042-023-14861-9.
[10] P. Peng et al., “Application of Semi-supervised Fuzzy Clustering Based on Knowledge Weighting and Cluster Center Learning to Mammary Molybdenum Target Image Segmentation,” Interdiscip. Sci. – Comput. Life Sci., vol. 16, no. 1, pp. 39–57, 2024, doi: 10.1007/s12539-023-00580-0.
[11] K. G. Dhal, A. Das, S. Ray, J. Gálvez, and S. Das, “Nature-Inspired Optimization Algorithms and Their Application in Multi-Thresholding Image Segmentation,” Arch. Comput. Methods Eng., vol. 27, no. 3, pp. 855–888, Jul. 2020, doi: 10.1007/s11831-019-09334-y.
[12] H. Y. Yalic and A. B. Can, “Automatic object segmentation on RGB-D data using surface normals and region similarity,” VISIGRAPP 2018 - Proc. 13th Int. Jt. Conf. Comput. Vision, Imaging Comput. Graph. Theory Appl., vol. 4, pp. 379–386, 2018, doi: 10.5220/0006617303790386.
[13] H. Zhang, Q. Wang, W. Shi, and M. Hao, “A novel adaptive fuzzy local information C-means clustering algorithm for remotely sensed imagery classification,” IEEE Trans. Geosci. Remote Sens., vol. 55, no. 9, pp. 5057–5068, 2017, doi: 10.1109/TGRS.2017.2702061.
[14] Y. Zhang, X. Bai, R. Fan, and Z. Wang, “Deviation-sparse fuzzy C-means with neighbor information constraint,” IEEE Trans. Fuzzy Syst., vol. 27, no. 1, pp. 185–199, 2019, doi: 10.1109/TFUZZ.2018.2883033.
[15] A. Sabbaghi, H. Rashidy Kanan, and M. R. Keyvanpour, “FSCT: A new fuzzy search strategy in concolic testing,” Inf. Softw. Technol., vol. 107, pp. 137–158, 2019, doi: 10.1016/j.infsof.2018.11.006.
[16] M. Gholizade, M. Rahmanimanesh, H. Soltanizadeh, and S. S. Sana, “Hesitant triangular fuzzy FlowSort method: the multi-criteria decision-making problems,” Int. J. Syst. Sci. Oper. Logist., vol. 10, no. 1, 2023, doi: 10.1080/23302674.2023.2259293.
[17] M. Gong, Y. Liang, J. Shi, W. Ma, and J. Ma, “Fuzzy C-means clustering with local information and kernel metric for image segmentation,” IEEE Trans. Image Process., vol. 22, no. 2, pp. 573–584, 2013, doi: 10.1109/TIP.2012.2219547.
[18] H.-J. Xing and M.-H. Ha, “Further improvements in Feature-Weighted Fuzzy C-Means,” Inf. Sci. (Ny)., vol. 267, pp. 1–15, May 2014, doi: 10.1016/j.ins.2014.01.033.
[19] D. Hallac, J. Leskovec, and S. Boyd, “Network lasso: Clustering and optimization in large graphs,” Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., vol. 2015-Augus, pp. 387–396, 2015, doi: 10.1145/2783258.2783313.
[20] L. Guo, L. Chen, X. Lu, and C. L. P. Chen, “Membership affinity lasso for fuzzy clustering,” IEEE Trans. Fuzzy Syst., vol. 28, no. 2, pp. 294–307, 2020, doi: 10.1109/TFUZZ.2019.2905114.
[21] E. Ghadimi, A. Teixeira, I. Shames, and M. Johansson, “Optimal parameter selection for the Alternating Direction Method of Multipliers (ADMM): Quadratic problems,” IEEE Trans. Automat. Contr., vol. 60, no. 3, pp. 644–658, 2015, doi: 10.1109/TAC.2014.2354892.
[22] N. R. Pal, K. Pal, J. M. Keller, and J. C. Bezdek, “A possibilistic fuzzy c-means clustering algorithm,” IEEE Trans. Fuzzy Syst., vol. 13, no. 4, pp. 517–530, 2005, doi: 10.1109/TFUZZ.2004.840099.
[23] T. Lei, X. Jia, Y. Zhang, S. Liu, H. Meng, and A. K. Nandi, “Superpixel-Based Fast Fuzzy C-Means Clustering for Color Image Segmentation,” IEEE Trans. Fuzzy Syst., vol. 27, no. 9, pp. 1753–1766, Sep. 2019, doi: 10.1109/TFUZZ.2018.2889018.
[24] S. Zhou, D. Li, Z. Zhang, and R. Ping, “A New Membership Scaling Fuzzy C-Means Clustering Algorithm,” IEEE Trans. Fuzzy Syst., vol. 29, no. 9, pp. 2810–2818, Sep. 2021, doi: 10.1109/TFUZZ.2020.3003441.
[25] T. Lei, X. Jia, Y. Zhang, L. He, H. Meng, and A. K. Nandi, “Significantly Fast and Robust Fuzzy C-Means Clustering Algorithm Based on Morphological Reconstruction and Membership Filtering,” IEEE Trans. Fuzzy Syst., vol. 26, no. 5, pp. 3027–3041, 2018, doi: 10.1109/TFUZZ.2018.2796074.
[26] K. D. Koutroumbas, S. D. Xenaki, and A. A. Rontogiannis, “On the Convergence of the Sparse Possibilistic C-Means Algorithm,” IEEE Trans. Fuzzy Syst., vol. 26, no. 1, pp. 324–337, 2018, doi: 10.1109/TFUZZ.2017.2659739.
[27] S. Chen and D. Zhang, “Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure,” IEEE Trans. Syst. Man, Cybern. Part B Cybern., vol. 34, no. 4, pp. 1907–1916, 2004, doi: 10.1109/TSMCB.2004.831165.
[28] A. Sodiqin, “Fuzzy C-means as a regularization and maximum entropy approach,” J. Chem. Inf. Model., vol. 53, no. 9, pp. 1–21, 2013.
[29] J. Huang, F. Nie, and H. Huang, “A new simplex sparse learning model to measure data similarity for clustering,” IJCAI Int. Jt. Conf. Artif. Intell., vol. 2015-Janua, pp. 3569–3575, 2015.
[30] G. Liu, Y. Zhang, and A. Wang, “Incorporating adaptive local information into fuzzy clustering for image segmentation,” IEEE Trans. Image Process., vol. 24, no. 11, pp. 3990–4000, 2015, doi: 10.1109/TIP.2015.2456505.
[1] M. Chen, Y. Tang, X. Zou, K. Huang, L. Li, and Y. He, “High-accuracy multi-camera reconstruction enhanced by adaptive point cloud correction algorithm,” Opt. Lasers Eng., vol. 122, pp. 170–183, 2019, doi: 10.1016/j.optlaseng.2019.06.011.
[2] P. Rathore, J. C. Bezdek, S. M. Erfani, S. Rajasegarar, and M. Palaniswami, “Ensemble Fuzzy Clustering Using Cumulative Aggregation on Random Projections,” IEEE Trans. Fuzzy Syst., vol. 26, no. 3, pp. 1510–1524, 2018, doi: 10.1109/TFUZZ.2017.2729501.
[3] K. Chen and X. Chen, “Fuzzy C-Means Clustering Image Segmentation Algorithm with Local Spatial Information Based on ELM,” Shuju Caiji Yu Chuli/Journal Data Acquis. Process., vol. 34, no. 1, pp. 100–110, 2019, doi: 10.16337/j.1004-9037.2019.01.011.
[4] A. Sabbaghi, M. R. Keyvanpour, and S. Parsa, “FCCI: A fuzzy expert system for identifying coincidental correct test cases,” J. Syst. Softw., vol. 168, 2020, doi: 10.1016/j.jss.2020.110635.
[5] Z. Li, K. Kamnitsas, and B. Glocker, “Analyzing Overfitting under Class Imbalance in Neural Networks for Image Segmentation,” IEEE Trans. Med. Imaging, vol. 40, no. 3, pp. 1065–1077, 2021, doi: 10.1109/TMI.2020.3046692.
[6] M. Diwakar and M. Kumar, “A review on CT image noise and its denoising,” Biomed. Signal Process. Control, vol. 42, pp. 73–88, 2018, doi: 10.1016/j.bspc.2018.01.010.
[7] F. Zhang, H. Liu, C. Cao, Q. Cai, and D. Zhang, “RVLSM: Robust variational level set method for image segmentation with intensity inhomogeneity and high noise,” Inf. Sci. (Ny)., vol. 596, pp. 439–459, 2022, doi: 10.1016/j.ins.2022.03.035.
[8] J. Cai, S. Gu, and L. Zhang, “Learning a deep single image contrast enhancer from multi-exposure images,” IEEE Trans. Image Process., vol. 27, no. 4, pp. 2049–2062, 2018, doi: 10.1109/TIP.2018.2794218.
[9] B. Sasmal and K. G. Dhal, “A survey on the utilization of Superpixel image for clustering based image segmentation,” Multimed. Tools Appl., vol. 82, no. 23, pp. 35493–35555, 2023, doi: 10.1007/s11042-023-14861-9.
[10] P. Peng et al., “Application of Semi-supervised Fuzzy Clustering Based on Knowledge Weighting and Cluster Center Learning to Mammary Molybdenum Target Image Segmentation,” Interdiscip. Sci. – Comput. Life Sci., vol. 16, no. 1, pp. 39–57, 2024, doi: 10.1007/s12539-023-00580-0.
[11] K. G. Dhal, A. Das, S. Ray, J. Gálvez, and S. Das, “Nature-Inspired Optimization Algorithms and Their Application in Multi-Thresholding Image Segmentation,” Arch. Comput. Methods Eng., vol. 27, no. 3, pp. 855–888, Jul. 2020, doi: 10.1007/s11831-019-09334-y.
[12] H. Y. Yalic and A. B. Can, “Automatic object segmentation on RGB-D data using surface normals and region similarity,” VISIGRAPP 2018 - Proc. 13th Int. Jt. Conf. Comput. Vision, Imaging Comput. Graph. Theory Appl., vol. 4, pp. 379–386, 2018, doi: 10.5220/0006617303790386.
[13] H. Zhang, Q. Wang, W. Shi, and M. Hao, “A novel adaptive fuzzy local information C-means clustering algorithm for remotely sensed imagery classification,” IEEE Trans. Geosci. Remote Sens., vol. 55, no. 9, pp. 5057–5068, 2017, doi: 10.1109/TGRS.2017.2702061.
[14] Y. Zhang, X. Bai, R. Fan, and Z. Wang, “Deviation-sparse fuzzy C-means with neighbor information constraint,” IEEE Trans. Fuzzy Syst., vol. 27, no. 1, pp. 185–199, 2019, doi: 10.1109/TFUZZ.2018.2883033.
[15] A. Sabbaghi, H. Rashidy Kanan, and M. R. Keyvanpour, “FSCT: A new fuzzy search strategy in concolic testing,” Inf. Softw. Technol., vol. 107, pp. 137–158, 2019, doi: 10.1016/j.infsof.2018.11.006.
[16] M. Gholizade, M. Rahmanimanesh, H. Soltanizadeh, and S. S. Sana, “Hesitant triangular fuzzy FlowSort method: the multi-criteria decision-making problems,” Int. J. Syst. Sci. Oper. Logist., vol. 10, no. 1, 2023, doi: 10.1080/23302674.2023.2259293.
[17] M. Gong, Y. Liang, J. Shi, W. Ma, and J. Ma, “Fuzzy C-means clustering with local information and kernel metric for image segmentation,” IEEE Trans. Image Process., vol. 22, no. 2, pp. 573–584, 2013, doi: 10.1109/TIP.2012.2219547.
[18] H.-J. Xing and M.-H. Ha, “Further improvements in Feature-Weighted Fuzzy C-Means,” Inf. Sci. (Ny)., vol. 267, pp. 1–15, May 2014, doi: 10.1016/j.ins.2014.01.033.
[19] D. Hallac, J. Leskovec, and S. Boyd, “Network lasso: Clustering and optimization in large graphs,” Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., vol. 2015-Augus, pp. 387–396, 2015, doi: 10.1145/2783258.2783313.
[20] L. Guo, L. Chen, X. Lu, and C. L. P. Chen, “Membership affinity lasso for fuzzy clustering,” IEEE Trans. Fuzzy Syst., vol. 28, no. 2, pp. 294–307, 2020, doi: 10.1109/TFUZZ.2019.2905114.
[21] E. Ghadimi, A. Teixeira, I. Shames, and M. Johansson, “Optimal parameter selection for the Alternating Direction Method of Multipliers (ADMM): Quadratic problems,” IEEE Trans. Automat. Contr., vol. 60, no. 3, pp. 644–658, 2015, doi: 10.1109/TAC.2014.2354892.
[22] N. R. Pal, K. Pal, J. M. Keller, and J. C. Bezdek, “A possibilistic fuzzy c-means clustering algorithm,” IEEE Trans. Fuzzy Syst., vol. 13, no. 4, pp. 517–530, 2005, doi: 10.1109/TFUZZ.2004.840099.
[23] T. Lei, X. Jia, Y. Zhang, S. Liu, H. Meng, and A. K. Nandi, “Superpixel-Based Fast Fuzzy C-Means Clustering for Color Image Segmentation,” IEEE Trans. Fuzzy Syst., vol. 27, no. 9, pp. 1753–1766, Sep. 2019, doi: 10.1109/TFUZZ.2018.2889018.
[24] S. Zhou, D. Li, Z. Zhang, and R. Ping, “A New Membership Scaling Fuzzy C-Means Clustering Algorithm,” IEEE Trans. Fuzzy Syst., vol. 29, no. 9, pp. 2810–2818, Sep. 2021, doi: 10.1109/TFUZZ.2020.3003441.
[25] T. Lei, X. Jia, Y. Zhang, L. He, H. Meng, and A. K. Nandi, “Significantly Fast and Robust Fuzzy C-Means Clustering Algorithm Based on Morphological Reconstruction and Membership Filtering,” IEEE Trans. Fuzzy Syst., vol. 26, no. 5, pp. 3027–3041, 2018, doi: 10.1109/TFUZZ.2018.2796074.
[26] K. D. Koutroumbas, S. D. Xenaki, and A. A. Rontogiannis, “On the Convergence of the Sparse Possibilistic C-Means Algorithm,” IEEE Trans. Fuzzy Syst., vol. 26, no. 1, pp. 324–337, 2018, doi: 10.1109/TFUZZ.2017.2659739.
[27] S. Chen and D. Zhang, “Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure,” IEEE Trans. Syst. Man, Cybern. Part B Cybern., vol. 34, no. 4, pp. 1907–1916, 2004, doi: 10.1109/TSMCB.2004.831165.
[28] A. Sodiqin, “Fuzzy C-means as a regularization and maximum entropy approach,” J. Chem. Inf. Model., vol. 53, no. 9, pp. 1–21, 2013.
[29] J. Huang, F. Nie, and H. Huang, “A new simplex sparse learning model to measure data similarity for clustering,” IJCAI Int. Jt. Conf. Artif. Intell., vol. 2015-Janua, pp. 3569–3575, 2015.
[30] G. Liu, Y. Zhang, and A. Wang, “Incorporating adaptive local information into fuzzy clustering for image segmentation,” IEEE Trans. Image Process., vol. 24, no. 11, pp. 3990–4000, 2015, doi: 10.1109/TIP.2015.2456505.
