Research on Underwater Polarization Image Segmentation Inspired by Biological Optic Nerve

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.

• Periodical: Applied Mechanics and Materials (Volumes 347-350)
• Pages: 2178-2184
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.347-350.2178
• Online since: August 2013
• Authors: Hui Bin Wang, Yu Rong Wu, Jie Shen, Zhe Chen
• Keywords: Feedback Neural Network Model, Optic Nerve of Mantis Shrimps, Parameter Optimization, Underwater Polarization Image Segmentation

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