ITM Web Conf.
Volume 12, 2017The 4th Annual International Conference on Information Technology and Applications (ITA 2017)
|Number of page(s)||7|
|Section||Session 2: Bioinformatics|
|Published online||05 September 2017|
- M. Kass, A. Witriw, and D. Terzopoilos, “Snakes: active contour models,” International Journal of Computer Vision, 1988, 1:321–369 [CrossRef] [Google Scholar]
- V. Caselles, F. Catte, T. Coll, and F. Dibos, “A geometric model for active contours in image processing,” Numerische Mathmatik., 1993, 66:1–31. [CrossRef] [Google Scholar]
- T. Chan, and L. Vese, “Active contours without edge,” IEEE Transactions on Image Processing, 2001, 10(2):266–277. [NASA ADS] [CrossRef] [Google Scholar]
- C. Li, C. Kao, C. Gore, and Z. Ding, “Implicit active contours driven by local binary fitting energy,” IEEE Computer Vision and Pattern Recognition, 2007, 6:17–22. [Google Scholar]
- C. Li, C. Xu, C. Gui, and M. D. Fox, “Level set evolution without re-initialization; a new variational formulation,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005, 1:430–436 [Google Scholar]
- D. Gabor, “Information theory in electron microscopy,” Laboratory Investigation, 1965, 14:801–807. [Google Scholar]
- A. K. Jain, “Partial differential equations and finite-difference methods in image processing, part 1: Image representation,” Optimization Theory and Applications, 1997, 23: 65–91. [CrossRef] [Google Scholar]
- J. J. koenderink, “The structure of images,” Biological Cybernetics, 1984, 50:363–370. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
- A. P. Witkin, “Scale-space filtering,” Proceedings of the 8th International Joint Conference Artificial Intelligence (IJCAI), Karlsruhe Germany, 1983, 2:1019–1021. [Google Scholar]
- L. Alvarez, F. Guichard, P. L. Lions, and J. M. Morel, et.al. “Axioms and fundamental euations of image processing,” Archive for Rational Mechanics and Analysis, 1993, 16(9):200–257. [Google Scholar]
- C. Li, C. Kao, J. C. Gore, and Z. Ding, “Minimization of region-scalable fitting energy for image segmentation,” IEEE Transactions on Image Processing, 2008, 17(10):1940–1949. [CrossRef] [MathSciNet] [Google Scholar]
- L. Wang, C. Li, Q. Sun, D. Xia, and C. Kao, “Active contours driven by local and global intensity fitting energy with application to brain MR image segmentation,” Computerized Medical Imaging and Graphics, 2009, 33:520–531. [CrossRef] [Google Scholar]
- B. R. Frieden, “Restoring with maximum likelihood and maximum entropy,” Journal of the Optical Society of America, 1982, 62(4):511–518. [NASA ADS] [CrossRef] [EDP Sciences] [PubMed] [Google Scholar]
- M. Sezgin, and B. Sankur, “Survey over image thresholding techniques and quantitative performance evaluation,” Journal of Electronic Imaging, 2004, 13(1):146–165. [CrossRef] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.