Research on Underwater Polarization Image Segmentation Inspired by Biological Optic Nerve

DOI

10.2991/iccsee.2013.660How to use a DOI?

Keywords

underwater polarization image segmentation, optic nerve of mantis shrimps, feedback neural network model, parameters optimization

Abstract

Due to effects of the light by water and other particles, the quality of underwater image will degrade. The traditional underwater image segmentation methods based on intensity and spectrum have difficulty in determining boundary. Inspired by the visual system of mantis shrimps, this paper constructed a feedback neural network model, in which the parameters were optimized using machine learning method. Based on this model, we combine the polarization and intensity information to achieve the underwater polarization image segmentation. The results of experiment prove that the neural network model designed in this paper can improve the accuracy of underwater image segmentation.

Copyright

© 2013, 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  - Huibin Wang
AU  - Yurong Wu
AU  - Jie Shen
AU  - Zhe Chen
PY  - 2013/03
DA  - 2013/03
TI  - Research on Underwater Polarization Image Segmentation Inspired by Biological Optic Nerve
BT  - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
PB  - Atlantis Press
SP  - 2646
EP  - 2650
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.660
DO  - 10.2991/iccsee.2013.660
ID  - Wang2013/03
ER  -