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.
|Title of host publication||OCEANS 2017 - Aberdeen|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|State||Published - 25 Oct 2017|
|Event||OCEANS 2017 - Aberdeen - Aberdeen, United Kingdom|
Duration: 19 Jun 2017 → 22 Jun 2017
|Name||OCEANS 2017 - Aberdeen|
|Conference||OCEANS 2017 - Aberdeen|
|Period||19/06/17 → 22/06/17|
Bibliographical noteFunding Information:
Our second recommendation relates to financial support of students/trainees, from the undergraduate to postdoctoral levels. Programs should set aside training and travel monies for ethnic minority students/trainees. Such funds could cover tuition, stipends, and professional development to allow for students/trainees to attend national conferences, since these students may derive particular benefit from connecting with ethnic minority students in other programs and from connecting with a nationwide network of ethnic minority neuropsychologists present at these meetings.
© 2017 IEEE.
- Expectation-maximization (EM)
- gamma distribution
- object detection
- sand ripples
- sonar image segmentation
- speckle noise
ASJC Scopus subject areas
- Computer Networks and Communications
- Acoustics and Ultrasonics
- Automotive Engineering