TY - GEN
T1 - Linear approximation of mean curvature
AU - Gong, Yuanhao
AU - Xie, Yuan
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - Mean curvature has been shown a good regularization for many image processing tasks. Computing mean curvature, however, usually requires the image at least twice differentiable, which is an issue for discrete images, especially at edges. In this paper, we present several linear schemes to approximate the mean curvature of discrete images, based on Euler Theorem from differential geometry. We further compare these schemes with the traditional formula in terms of accuracy, computational efficiency, convexity, etc. The experiments confirm that these schemes are good approximations to the mean curvature of discrete images.
AB - Mean curvature has been shown a good regularization for many image processing tasks. Computing mean curvature, however, usually requires the image at least twice differentiable, which is an issue for discrete images, especially at edges. In this paper, we present several linear schemes to approximate the mean curvature of discrete images, based on Euler Theorem from differential geometry. We further compare these schemes with the traditional formula in terms of accuracy, computational efficiency, convexity, etc. The experiments confirm that these schemes are good approximations to the mean curvature of discrete images.
KW - Convex
KW - Curvature filter
KW - Linear approximation
KW - Mean curvature
KW - Weighted mean curvature
UR - https://www.scopus.com/pages/publications/85045295091
U2 - 10.1109/ICIP.2017.8296345
DO - 10.1109/ICIP.2017.8296345
M3 - 会议稿件
AN - SCOPUS:85045295091
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 570
EP - 574
BT - 2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings
PB - IEEE Computer Society
T2 - 24th IEEE International Conference on Image Processing, ICIP 2017
Y2 - 17 September 2017 through 20 September 2017
ER -