TY - GEN
T1 - Multivariate self-dual morphological operators
AU - Lei, Tao
AU - Fan, Yangyu
AU - Guo, Zhe
AU - Wei, Feng
AU - Liu, Weihua
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/9/3
Y1 - 2014/9/3
N2 - Self-dual morphological operators (SDMO) do not rely on whether one starts the sequence with erosion or dilation, they treat the image foreground and background identically. Nevertheless, it is difficult to extend SDMO to multi-channel images. Based on the self-duality property of traditional morphological operators and the theory of extremum constraint, this paper gives a complete characterization for the construction of multivariate SDMO. We introduce a pair of symmetric vector orderings (SVO) to construct multivariate dual morphological operators. Utilizing extremum constraint to optimize multivariate morphological operators, we further establish methods for the construction of multivariate SDMO. Finally, we illustrate the importance and effectiveness of the multivariate SDMO by an application of noise removal in color images. The experimental results show that the proposed multivariate SDMO provide better results, they can suppress noises efficiently while maintaining image details compared with other operators.
AB - Self-dual morphological operators (SDMO) do not rely on whether one starts the sequence with erosion or dilation, they treat the image foreground and background identically. Nevertheless, it is difficult to extend SDMO to multi-channel images. Based on the self-duality property of traditional morphological operators and the theory of extremum constraint, this paper gives a complete characterization for the construction of multivariate SDMO. We introduce a pair of symmetric vector orderings (SVO) to construct multivariate dual morphological operators. Utilizing extremum constraint to optimize multivariate morphological operators, we further establish methods for the construction of multivariate SDMO. Finally, we illustrate the importance and effectiveness of the multivariate SDMO by an application of noise removal in color images. The experimental results show that the proposed multivariate SDMO provide better results, they can suppress noises efficiently while maintaining image details compared with other operators.
KW - extremum constrain
KW - Multivariate mathematical morphology
KW - SDMO (self-dual morphological operators)
KW - vector ordering
UR - https://www.scopus.com/pages/publications/84929400141
U2 - 10.1109/ChinaSIP.2014.6889264
DO - 10.1109/ChinaSIP.2014.6889264
M3 - 会议稿件
AN - SCOPUS:84929400141
T3 - 2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings
SP - 359
EP - 363
BT - 2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014
Y2 - 9 July 2014 through 13 July 2014
ER -