Abstract
We focus on the segmentation of sonar images for the aim of underwater object detection. Speckle noise and intensity inhomogeneity may cause false detections, and complex seabed textures like sand-ripples and sea-grass often lead to false segmentation. To tackle these problems, we propose a new method to incorporate the possible spatial correlation between neighboring pixels in the sonar image for improved segmentation. Our method modifies the expectation-maximization (EM) algorithm by adding an intermediate step (I-step) between the expectation (E-step) and maximization (M-step). Results show that our proposed method achieves improved segmentation performance over the state-of-the-art and is also robust to different seabed texture and for intensity inhomogeneity.
Original language | English |
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Title of host publication | OCEANS 2017 - Aberdeen |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1-5 |
Number of pages | 5 |
ISBN (Electronic) | 9781509052783 |
DOIs | |
State | Published - 25 Oct 2017 |
Event | OCEANS 2017 - Aberdeen - Aberdeen, United Kingdom Duration: 19 Jun 2017 → 22 Jun 2017 |
Publication series
Name | OCEANS 2017 - Aberdeen |
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Volume | 2017-October |
Conference
Conference | OCEANS 2017 - Aberdeen |
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Country/Territory | United Kingdom |
City | Aberdeen |
Period | 19/06/17 → 22/06/17 |
Bibliographical note
Publisher Copyright:© 2017 IEEE.
Keywords
- Expectation-maximization (EM)
- gamma distribution
- object detection
- sand ripples
- sonar image segmentation
- speckle noise
ASJC Scopus subject areas
- Instrumentation
- Computer Networks and Communications
- Oceanography
- Acoustics and Ultrasonics
- Automotive Engineering