[1] Caselles V, Kimmel R, Sapiro G. Geodesic active contours. International Journal of Computer Vision, 1997, 22(1):61-79 doi: 10.1023/A:1007979827043
[2] Chan T F, Vese L A. Active contours without edges. IEEE Transactions on Image Processing, 2001, 10(2):266-277 http://dl.acm.org/citation.cfm?id=2320071
[3] Bernard O, Friboulet D, Thévenaz P, Unser M. Variational b-spline level-set:a linear filtering approach for fast deformable model evolution. IEEE Transactions on Image Processing, 2009, 18(6):1179-1191 doi: 10.1109/TIP.2009.2017343
[4] Shi Y G, Karl W C. A real-time algorithm for the approximation of level-set-based curve evolution. IEEE Transactions on Image Processing, 2008, 17(5):645-656 doi: 10.1109/TIP.2008.920737
[5] Li C M, Huang R, Ding Z H, Gatenby J C, Metaxas D N, Gore J C. A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI. IEEE Transactions on Image Processing, 2011, 20(7):2007-2016 doi: 10.1109/TIP.2011.2146190
[6] Min H, Jia W, Wang X F, Zhao Y, Hu R X, Luo Y T, Xue F, Lu J T. An intensity-texture model based level set method for image segmentation. Pattern Recognition, 2015, 48(4):1547-1562 doi: 10.1016/j.patcog.2014.10.018
[7] Wang X F, Min H, Zou L, Zhang Y G. A novel level set method for image segmentation by incorporating local statistical analysis and global similarity measurement. Pattern Recognition, 2015, 48(1):189-204 doi: 10.1016/j.patcog.2014.07.008
[8] Zhao Y Q, Wang X H, Wang X F, Shih F Y. Retinal vessels segmentation based on level set and region growing. Pattern Recognition, 2014, 47(7):2437-2446 doi: 10.1016/j.patcog.2014.01.006
[9] 周则明, 孟勇, 黄思训, 胡宝鹏.基于能量最小化的星载SAR图像建筑物分割方法.自动化学报, 2016, 42(2):279-289 http://www.aas.net.cn/CN/abstract/abstract18817.shtml

Zhou Ze-Ming, Meng Yong, Huang Si-Xun, Hu Bao-Peng. Building segmentation of spaceborne SAR images based on energy minimization. Acta Automatica Sinica, 2016, 42(2):279-289 http://www.aas.net.cn/CN/abstract/abstract18817.shtml
[10] 张迎春, 郭禾.基于粗糙集和新能量公式的水平集图像分割.自动化学报, 2015, 41(11):1913-1925 http://www.aas.net.cn/CN/abstract/abstract18766.shtml

Zhang Ying-Chun, Guo He. Level set image segmentation based on rough set and new energy formula. Acta Automatica Sinica, 2015, 41(11):1913-1925 http://www.aas.net.cn/CN/abstract/abstract18766.shtml
[11] Adalsteinsson D, Sethian J A. A fast level set method for propagating interfaces. Journal of Computational Physics, 1995, 118(2):269-277 doi: 10.1006/jcph.1995.1098
[12] Li C M, Kao C Y, Gore J C, Ding Z H. Minimization of region-scalable fitting energy for image segmentation. IEEE Transactions on Image Processing, 2008, 17(10):1940-1949 doi: 10.1109/TIP.2008.2002304
[13] Li C M, Xu C Y, Gui C F, Fox M D. Distance regularized level set evolution and its application to image segmentation. IEEE Transactions on Image Processing, 2010, 19(12):3243-3254 doi: 10.1109/TIP.2010.2069690
[14] Lemoine G, Delannay L, Idrissi H, Colla M S, Pardoen T. Dislocation and back stress dominated viscoplasticity in freestanding sub-micron Pd films. Acta Materialia, 2016, 111:10-21 doi: 10.1016/j.actamat.2016.03.038
[15] Nagasako N, Asahi R, Isheim D, Seidman D N, Kuramoto S, Furuta T. Microscopic study of gum-metal alloys:a role of trace oxygen for dislocation-free deformation. Acta Materialia, 2016, 105:347-354 doi: 10.1016/j.actamat.2015.12.011
[16] Guiu F. The stress dependence of the dislocation velocity in molybdenum. Physica Status Solidi, 2016, 25(1):203-207 http://www.researchgate.net/publication/243143363_The_stress_dependence_of_the_dislocation_velocity_in_molybdenum
[17] Yuan R, Beyerlein I J, Zhou C Z. Statistical dislocation activation from grain boundaries and its role in the plastic anisotropy of nanotwinned copper. Acta Materialia, 2016, 110:8-18 doi: 10.1016/j.actamat.2016.02.064
[18] Luo Y S, Chung A C S. Nonrigid image registration with crystal dislocation energy. IEEE Transactions on Image Processing, 2013, 22(1):229-243 http://ieeexplore.ieee.org/document/6220249/
[19] Xiang Y, Chung A C S, Ye J. An active contour model for image segmentation based on elastic interaction. Journal of Computational Physics, 2006, 219(1):455-476 doi: 10.1016/j.jcp.2006.03.026
[20] Badakhshannoory H, Saeedi P. A model-based validation scheme for organ segmentation in CT scan volumes. IEEE Transactions on Biomedical Engineering, 2011, 58(9):2681-2693 doi: 10.1109/TBME.2011.2161987
[21] Heimann T, van Ginneken B, Styner M A, Arzhaeva Y, Aurich V, Bauer C, et al. Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Transactions on Medical Imaging, 2009, 28(8):1251-1265 doi: 10.1109/TMI.2009.2013851
[22] Zhang K H, Zhang L, Yang M H. Fast compressive tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(10):2002-2015 doi: 10.1109/TPAMI.2014.2315808