Omnidirectional Multi-scale Generalized Blur Minimization Mathematical Morphology Edge Detection Algorithm

DOI

10.2991/icemct-14.2014.84How to use a DOI?

Keywords

Mathematical Morphology, Edge Detection, Omnidirectional Mulit-scale, Blur Minimization

Abstract

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.

Copyright

© 2014, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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  - Proceedings of the 2014 International Conference on Education, Management and Computing Technology
PB  - Atlantis Press
SP  - 359
EP  - 362
SN  - 1951-6851
UR  - https://doi.org/10.2991/icemct-14.2014.84
DO  - 10.2991/icemct-14.2014.84
ID  - Yang2014/06
ER  -