[1] 1吴一全, 孟天亮, 吴诗婳. 图像阈值分割方法研究进展20年(1994—2014)[J]. 数据采集与处理, 2015, 30(1): 1-23. doi: 10.16337/j.1004-9037.2015.01.001WUY Q, MENGT L, WUS H. Research progress of image thresholding methods in recent 20 years (1994-2014)[J]. Journal of Data Acquisition & Processing, 2015, 30(1): 1-23.(in Chinese). doi: 10.16337/j.1004-9037.2015.01.001
[2] 2袁小翠, 吴禄慎, 陈华伟. 基于Otsu方法的钢轨图像分割[J]. 光学 精密工程, 2016, 24(7): 1772-1781. doi: 10.3788/ope.20162407.1772YUANX C, WUL S, CHENH W. Rail image segmentation based on Otsu threshold method[J]. Opt. Precision Eng., 2016, 24(7): 1772-1781.(in Chinese). doi: 10.3788/ope.20162407.1772
[3] 3姜鑫, 陈武雄, 聂海涛, 等. 航空遥感影像的实时舰船目标检测[J]. 光学 精密工程, 2020, 28(10): 2360-2369. doi: 10.37188/ope.20202810.2360JIANGX, CHENW X, NIEH T, et al. Real-time ship target detection based on aerial remote sensing images[J]. Opt. Precision Eng., 2020, 28(10): 2360-2369.(in Chinese). doi: 10.37188/ope.20202810.2360
[4] F NIE, P ZHANG, J LI et al. A novel generalized entropy and its application in image thresholding. Signal Processing, 134, 23-34(2017).
[5] Z Z WANG, J J XIONG, Y M YANG et al. A flexible and robust threshold selection method. IEEE Transactions on Circuits and Systems for Video Technology, 28, 2220-2232(2017).
[6] H YAZID, S N BASAH, S A RAHIM et al. Performance analysis of entropy thresholding for successful image segmentation. Multimedia Tools and Applications, 81, 6433-6450(2022).
[7] 7罗希平, 田捷. 用最大熵原则作多阈值选择的条件迭代算法[J]. 软件学报, 2000, 11(3): 379-385. doi: 10.1038/sj.cr.7290056LUOX P, TIANJ. The ICM algorithm for multi-level threshold selection by maximum entropy criterion[J]. Journal of Software, 2000, 11(3): 379-385.(in Chinese). doi: 10.1038/sj.cr.7290056
[8] C I CHANG, Y DU, J WANG et al. Survey and comparative analysis of entropy and relative entropy thresholding techniques. IEE Proceedings-Vision, Image, and Signal Processing, 153, 837(2006).
[9] T Pun. A new method for grey-level picture thresholding using the entropy of the histogram. Signal Processing, 2, 223-237(1980).
[10] JN KAPUR, PK SAHOO, AK WONG. A new method for gray-level picture thresholding using the entropy of the histogram. Computer Vision, Graphics, and Image Processing, 29, 273-285(1985).
[11] AS ABUTALEB. Automatic thresholding of gray-level pictures using two-dimensional entropy. Computer Vision, Graphics, and Image Processing, 47, 22-32(1989).
[12] A K C WONG, P K SAHOO. A gray-level threshold selection method based on maximum entropy principle. IEEE Transactions on Systems, Man, and Cybernetics, 19, 866-871(1989).
[13] MP DE ALBUQUERQUE, IA ESQUEF, AG MELLO. Image thresholding using tsallis entropy. Pattern Recognition Letters, 25, 1059-1065(2004).
[14] P SAHOO, C WILKINS, J YEAGER. Threshold selection using Renyi’s entropy. Pattern Recognition, 30, 71-84(1997).
[15] S Wang, FL Chung. Note on the equivalence relationship between Renyi-entropy based and Tsallis-entropy based image thresholding. Pattern Recognition Letters, 26, 2309-2312(2005).
[16] PK SAHOO, G ARORA. A thresholding method based on two-dimensional Renyi’s entropy. Pattern Recognition, 37, 1149-1161(2004).
[17] S SAHOO, S S SAHOO, S KUMAR et al. Optimized Entropy Based Image Segmentation, 1-6(13).
[18] S ABDEL-KHALEK, AB ISHAK, OA OMER et al. A two-dimensional image segmentation method based on genetic algorithm and entropy. Optik, 131, 414-422(2017).
[19] 19刘松涛, 刘振兴, 姜康辉. 基于模糊Renyi熵和区域增长的图像目标分割方法[J]. 系统工程与电子技术, 2018, 40(8): 1693-1701. doi: 10.3969/j.issn.1001-506X.2018.08.04LIUS T, LIUZ X, JIANGK H. Image target segmentation method based on fuzzy Renyi entropy and region growing[J]. Systems Engineering and Electronics, 2018, 40(8): 1693-1701. (in Chinese). doi: 10.3969/j.issn.1001-506X.2018.08.04
[20] Y WANG, X XU. An improved level set method to image segmentation tation based on saliency. Journal of Information Processing Systems, 15, 7-21(2019).
[21] C M HE, X B WANG, L Z DENG et al. Image Threshold Segmentation Based on GLLE Histogram, 410-415(14).
[22] B LEI, J FAN. Adaptive granulation Renyi rough entropy image thresholding method with nested optimization. Expert Systems With Applications, 203, 117378(2022).
[23] G P NASON, B W SILVERMAN. The Stationary Wavelet Transform and Some Statistical Applications. Wavelets and Statistics, 281-299(1995).
[24] 24薛婷, 钟麦英. 基于SWT与等价空间的LDTV系统故障检测[J]. 自动化学报, 2017, 43(11): 1920-1930. doi: 10.16383/j.aas.2017.c160479XUET, ZHONGM Y. SWT and parity space based fault detection for linear discrete time-varying systems[J]. Acta Automatica Sinica, 2017, 43(11): 1920-1930. (in Chinese). doi: 10.16383/j.aas.2017.c160479
[25] A RAMÍREZ-REYES, A HERNÁNDEZ-MONTOYA, G HERRERA-CORRAL et al. Determining the entropic index q of tsallis entropy in images through redundancy. Entropy, 18, 299(2016).
[26] X H CAO, T H LI, H L LI et al. A robust parameter-free thresholding method for image segmentation. IEEE Access, 7, 3448-3458(2018).
[27] M T N TRUONG, S KIM. Automatic image thresholding using Otsu's method and entropy weighting scheme for surface defect detection. Soft Computing, 22, 4197-4203(2018).
[28] X H JIA, T LEI, X G DU et al. Robust self-sparse fuzzy clustering for image segmentation. IEEE Access, 8, 146182-146195(2020).
[29] T WEI, X WANG, X LI et al. Fuzzy subspace clustering noisy image segmentation algorithm with adaptive local variance & non-local information and mean membership linking. Engineering Applications of Artificial Intelligence, 110, 104672(2022).
[30] H G XIAO, B L ZHANG, R H LIU et al. Accurate image segmentation based on adaptive distance regularization level set method. International Journal of Wavelets, Multiresolution and Information Processing, 20, 2250033(2022).
[31] J FANG, H LIU, J LIU et al. Fuzzy region-based active contour driven by global and local fitting energy for image segmentation. Applied Soft Computing, 100, 106982(2021).
[32] D CHICCO, G JURMAN. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics, 21, 6(2020).