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Underwater Object Segmentation Based on Optical Features.

Zhe Chen1, Zhen Zhang2, Yang Bu3

  • 1College of Computer and Information, Hohai University, Nanjing 211100, China. chenzhe@hhu.edu.cn.

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|January 13, 2018
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Summary
This summary is machine-generated.

This study introduces a novel two-phase method for underwater object segmentation. It effectively uses artificial light features to identify targets, improving image clarity and outperforming existing techniques.

Keywords:
artificial light guidancelevel-set-based object segmentationoptical featuresunderwater object segmentation

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Area of Science:

  • Computer Vision
  • Underwater Imaging

Background:

  • Underwater optical environments are complex, influenced by natural and artificial light.
  • Sky light and scattered light hinder underwater object segmentation by obscuring targets and distorting images.

Purpose of the Study:

  • To develop an effective method for underwater object segmentation despite challenging optical conditions.
  • To leverage artificial light characteristics for improved target identification and segmentation.

Main Methods:

  • A two-phase approach: 1) Optical feature extraction to identify artificial light regions as candidate object areas. 2) Level set method for precise segmentation within candidate regions.
  • The method focuses on recognizing the collimation of artificial light to pinpoint potential objects.

Main Results:

  • The proposed method successfully removes background noise, enhancing the visibility of underwater objects.
  • It effectively highlights the outlines of target objects, improving segmentation accuracy.
  • Performance evaluation on diverse datasets shows superior results compared to previous methods.

Conclusions:

  • The developed two-phase segmentation strategy offers a significant advancement in underwater image analysis.
  • By utilizing artificial light, the method overcomes limitations posed by natural light scattering.
  • This technique provides a robust solution for accurate underwater object segmentation.