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Related Concept Videos

Region of Convergence01:17

Region of Convergence

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The z-transform is a powerful mathematical tool used in the analysis of discrete-time signals and systems. It is a crucial tool in the analysis of discrete-time systems, but its convergence is limited to specific values of the complex variable z. This range of values, known as the Region of Convergence (ROC), is fundamental in determining the behavior and stability of a system or signal. The ROC defines the region in the complex plane where the z-transform converges, which can take various...
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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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A Robust and Fast Method for Sidescan Sonar Image Segmentation Based on Region Growing.

Xuyang Wang1, Luyu Wang1, Guolin Li2

  • 1School of Integrated Circuits, Tsinghua University, Beijing 100084, China.

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|November 13, 2021
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This study introduces a new region growing segmentation method (RGLT) for sonar images. It significantly improves accuracy and speed for underwater target detection by reducing processing time.

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fast and accurateregion growingsegmentationsonar images

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Area of Science:

  • Marine technology
  • Acoustic imaging
  • Image processing

Background:

  • Accurate segmentation of high-resolution side scan sonar images is vital for underwater target detection.
  • Existing methods often lack real-time performance due to high complexity and iterative processes.
  • Low signal-to-noise ratio (SNR) and environmental noise in sonar images pose significant challenges.

Purpose of the Study:

  • To develop an accurate and fast segmentation method for sonar images.
  • To improve the real-time performance of underwater target detection and recognition systems.
  • To address the limitations of existing iterative segmentation techniques.

Main Methods:

  • A novel region growing based segmentation using likelihood ratio testing (RGLT) is proposed.
  • Likelihood ratio testing is used to identify seed points in highlight and shadow regions.
  • Standard deviation filtering (STDF) is introduced for SNR improvement and speckle noise reduction.

Main Results:

  • The RGLT method demonstrated significantly improved quantitative metrics, including accuracy and speed.
  • Segmentation time was greatly reduced by avoiding seabed reverberation regions.
  • Experiments on three sonar databases validated the method's effectiveness, achieving 95.90% accuracy and 0.44s running time for 100x400 images.

Conclusions:

  • The proposed RGLT method offers a robust and efficient solution for sonar image segmentation.
  • The STDF pre-processing enhances image quality, improving segmentation outcomes.
  • This approach provides a substantial advancement in real-time underwater target detection capabilities.