Master of Science (MS)
Computer Science (Arts and Sciences)
Date of Defense
First Committee Member
Second Committee Member
Third Committee Member
The research of underwater creatures is a fascinating work to marine biologists and environmental experts. However, finding and segmenting underwater creatures in underwater images and videos is time consuming. Automated object detection and segmentation can be applied to such problems to accelerate the tedious process. This work describes an approach to detect fish from images or videos of benthic habitats, recorded in Virgin Islands, during the yellow-tail grouper spawning activities. The method involves first locating the horizon, separating the seawater and the seafloor, and then using different visual cues to detect fish both within the water column and over the seafloor. The detector can be applied to video data, or a single image (forgoing the visual motion cues). The precision and recall rates of 86.4% and 94.5% have been achieved.
fish detection; underwater video; benthic habitats
Wu, Nan, "Fish Detection in Underwater Video of Benthic Habitats in Virgin Islands" (2012). Open Access Theses. 348.