Active contour model based on local Kullback–Leibler divergence for fast image segmentation

Biswas, 2021, A level set model by regularizing local fitting energy and penalty energy term for image segmentation, Signal Process., 183, 10.1016/j.sigpro.2021.108043 Caselles, 1993, A geometric model for active contours in image processing, Numer. Math., 66, 1, 10.1007/BF01385685 Chan, 2001, Active contours without edges, IEEE Trans. Image Process., 10, 266, 10.1109/83.902291 Chen, 2023, An overview of intelligent image segmentation using active contour models, Intell. Robot., 3, 10.20517/ir.2023.02 Fu, 1981, A survey on image segmentation, Pattern Recognit., 13, 3, 10.1016/0031-3203(81)90028-5 Ge, 2022, A hybrid active contour model based on pre-fitting energy and adaptive functions for fast image segmentation, Pattern Recognit. Lett., 158, 71, 10.1016/j.patrec.2022.04.025 Ge, 2022, An active contour model driven by adaptive local pre-fitting energy function based on jeffreys divergence for image segmentation, Expert Syst. Appl., 210, 10.1016/j.eswa.2022.118493 Han, 2018, A novel active contour model driven by J-divergence entropy for SAR river image segmentation, Pattern Anal. Appl., 21, 613, 10.1007/s10044-018-0702-7 Han, 2020, Active contour model for inhomogenous image segmentation based on Jeffreys divergence, Pattern Recognit., 107, 10.1016/j.patcog.2020.107520 Haralick, 1985, Image segmentation techniques, Comput. Vis. Graph. Image Process., 29, 100, 10.1016/S0734-189X(85)90153-7 Hoffman, 1987, Segmentation and classification of range images, IEEE Trans. Pattern Anal. Mach. Intell., 608, 10.1109/TPAMI.1987.4767955 Jeffreys, 1946, An invariant form for the prior probability in estimation problems, Proc. R. Soc. Lond. Ser. A Math. Phys. Sci., 186, 453 Kass, 1988, Snakes: Active contour models, Int. J. Comput. Vis., 1, 321, 10.1007/BF00133570 Klemenčič, 1998, Automated segmentation of muscle fiber images using active contour models, Cytometry: J. Int. Soc. Anal. Cytol., 32, 317, 10.1002/(SICI)1097-0320(19980801)32:4<317::AID-CYTO9>3.0.CO;2-E Kumar, 2016, Medical image segmentation based on minimization of region-scalable fitting energy, Asian J. Res. Soc. Sci. Humanit., 6, 830 Land, 1964, The retinex, Am. Sci., 52, 247 Li, 2016, Active contours driven by divergence of gradient vector flow, Signal Process., 120, 185, 10.1016/j.sigpro.2015.08.020 Li, 2011, A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI, IEEE Trans. Image Process., 20, 2007, 10.1109/TIP.2011.2146190 Li, 2008, Minimization of region-scalable fitting energy for image segmentation, IEEE Trans. Image Process., 17, 1940, 10.1109/TIP.2008.2002304 Li, 2010, Distance regularized level set evolution and its application to image segmentation, IEEE Trans. Image Process., 19, 3243, 10.1109/TIP.2010.2069690 Liu, 2017, An improved edge-based level set method combining local regional fitting information for noisy image segmentation, Signal Process., 130, 12, 10.1016/j.sigpro.2016.06.013 Mumford, 1989, Optimal approximations by piecewise smooth functions and associated variational problems, Comm. Pure Appl. Math., 10.1002/cpa.3160420503 Nguyen, 2017, Supervised distance metric learning through maximization of the Jeffrey divergence, Pattern Recognit., 64, 215, 10.1016/j.patcog.2016.11.010 Park, 2003, The generalized Kullback–Leibler divergence and robust inference, J. Stat. Comput. Simul., 73, 311, 10.1080/0094965021000033477 Rao, 2021, Multi-semantic CRF-based attention model for image forgery detection and localization, Signal Process., 183, 10.1016/j.sigpro.2021.108051 Ronfard, 1994, Region-based strategies for active contour models, Int. J. Comput. Vis., 13, 229, 10.1007/BF01427153 Treves, 1970, Applications of distributions to PDE theory, Amer. Math. Monthly, 77, 241, 10.1080/00029890.1970.11992463 Vese, 2002, A multiphase level set framework for image segmentation using the Mumford and Shah model, Int. J. Comput. Vis., 50, 271, 10.1023/A:1020874308076 Wang, 2013, An adaptive level set evolution equation for contour extraction, Appl. Math. Comput., 219, 11420 Wang, 2009, Active contours driven by local Gaussian distribution fitting energy, Signal Process., 89, 2435, 10.1016/j.sigpro.2009.03.014 Wang, 2010, An efficient local Chan–Vese model for image segmentation, Pattern Recognit., 43, 603, 10.1016/j.patcog.2009.08.002 Wang, 2009, Active contours driven by local and global intensity fitting energy with application to brain MR image segmentation, Comput. Med. Imaging Graph., 33, 520, 10.1016/j.compmedimag.2009.04.010 Wang, 2023, An active contour model based on local pre-piecewise fitting bias corrections for fast and accurate segmentation, IEEE Trans. Instrum. Meas., 72, 1 Weng, 2021, A level set method based on additive bias correction for image segmentation, Expert Syst. Appl., 185, 10.1016/j.eswa.2021.115633 Yan, 2022, Hybrid active contour model driven by optimized local pre-fitting image energy for fast image segmentation, Appl. Math. Model., 101, 586, 10.1016/j.apm.2021.09.002 Yang, 2022, An active contour model based on retinex and pre-fitting reflectance for fast image segmentation, Symmetry, 14, 2343, 10.3390/sym14112343 Yu, 2013, A novel active contour model for image segmentation using distance regularization term, Comput. Math. Appl., 65, 1746 Zhang, 2017, A novel active contour model for image segmentation using local and global region-based information, Mach. Vis. Appl., 28, 75, 10.1007/s00138-016-0805-3 Zhang, 2010, Active contours driven by local image fitting energy, Pattern Recognit., 43, 1199, 10.1016/j.patcog.2009.10.010 Zhang, 2018, Level set evolution driven by optimized area energy term for image segmentation, Optik, 168, 517, 10.1016/j.ijleo.2018.04.046 Zhao, 2020