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Performance analysis of compression algorithms for noisy multispectral underwater images of small targets
Title
Performance analysis of compression algorithms for noisy multispectral underwater images of small targets.
Creator
Schmalz, Mark S., Ritter, G. X., Caimi, F. M., Harbor Branch Oceanographic Institute
Date Issued
1997
PURL
http://purl.flvc.org/FCLA/DT/3180413
Subject Headings
Image compression, Underwater vision
Format
Document (PDF)
Computer vision techniques for quantifying, tracking, and identifying bioluminescent plankton
Title
Computer vision techniques for quantifying, tracking, and identifying bioluminescent plankton.
Creator
Kocak, D. M., da Vitoria Lobo, N., Widder, Edith A., Harbor Branch Oceanographic Institute
Date Issued
1999
PURL
http://purl.flvc.org/FCLA/DT/3183711
Subject Headings
Underwater imaging systems, Computer vision
Format
Document (PDF)
A Deep Learning Approach To Target Recognition In Side-Scan Sonar Imagery
Title
A Deep Learning Approach To Target Recognition In Side-Scan Sonar Imagery.
Creator
Einsidler, Dylan, Dhanak, Manhar R., Florida Atlantic University, College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
Abstract/Description
Automatic target recognition capabilities in autonomous underwater vehicles has been a daunting task, largely due to the noisy nature of sonar imagery and due to the lack of publicly available sonar data. Machine learning techniques have made great strides in tackling this feat, although not much research has been done regarding deep learning techniques for side-scan sonar imagery. Here, a state-of-the-art deep learning object detection method is adapted for side-scan sonar imagery, with...
Show moreAutomatic target recognition capabilities in autonomous underwater vehicles has been a daunting task, largely due to the noisy nature of sonar imagery and due to the lack of publicly available sonar data. Machine learning techniques have made great strides in tackling this feat, although not much research has been done regarding deep learning techniques for side-scan sonar imagery. Here, a state-of-the-art deep learning object detection method is adapted for side-scan sonar imagery, with results supporting a simple yet robust method to detect objects/anomalies along the seabed. A systematic procedure was employed in transfer learning a pre-trained convolutional neural network in order to learn the pixel-intensity based features of seafloor anomalies in sonar images. Using this process, newly trained convolutional neural network models were produced using relatively small training datasets and tested to show reasonably accurate anomaly detection and classification with little to no false alarms.
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Date Issued
2018
PURL
http://purl.flvc.org/fau/fd/FA00013025
Subject Headings
Deep learning, Sidescan sonar, Underwater vision
Format
Document (PDF)
Underwater applications of solid-state laser technology
Title
Underwater applications of solid-state laser technology.
Creator
Tusting, Robert F., Harbor Branch Oceanographic Institute
Date Issued
1995
PURL
http://purl.flvc.org/FCLA/DT/3338513
Subject Headings
Solid-state lasers, Semiconductor lasers, Underwater vision
Format
Document (PDF)