Omnidirectional Multi-scale Generalized Blur Minimization Mathematical Morphology Edge Detection Algorithm
Shubin Yang, Qianwen Qiu, Sai Zhang, Jinpeng Li
Available Online June 2014.
- https://doi.org/10.2991/icemct-14.2014.84How to use a DOI?
- Mathematical Morphology, Edge Detection, Omnidirectional Mulit-scale, Blur Minimization
- Owing to image noise and differ in thousands ways of edge shape, traditional morphological edge detection operators can’t detect the edge well and the edge detected by it is very blur. In order to gain the good edge, characteristic of morphological operators are used. Generalized morphological detection operator is used to conquer noise influence, Omnidirectional edge operator is used to detect different direction edge information and multi-scale edge operator is used to gain different scale and detail edge information and wipe off noise more. After that, according to suitable weights combining edge result to make up another edge image which can gain final edge through blur-minimization edge detection. Experiment proved that the proposed edge detection algorithm can detect edge better than traditional detection algorithm and can not only detect image edge effectively with upper detection precision but also restrain noise efficaciously.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Shubin Yang AU - Qianwen Qiu AU - Sai Zhang AU - Jinpeng Li PY - 2014/06 DA - 2014/06 TI - Omnidirectional Multi-scale Generalized Blur Minimization Mathematical Morphology Edge Detection Algorithm BT - 2014 International Conference on Education, Management and Computing Technology (ICEMCT-14) PB - Atlantis Press SN - 1951-6851 UR - https://doi.org/10.2991/icemct-14.2014.84 DO - https://doi.org/10.2991/icemct-14.2014.84 ID - Yang2014/06 ER -