Paper
10 May 2019 Fast focus of attention for corals from underwater images
Author Affiliations +
Abstract
Coral reef ecosystems is essential in healthy ocean and marine fishery. In the past decades, substantial of images and videos haven been collected from these cruises. These images are analyzed to quantify coral abundance in certain specific areas. However, the current manual analysis are time-consuming and labor intensive. In this paper, we proposes a fast automated tool for coral identification only based on sparse annotated labels by using deep learning method. There are two challenges to identify coral from such sparse labels and large images: one is to obtain denser labeled training data and the other is to improve the speed of testing on large images. In order to solves these problems, we propose a label augmentation algorithm to generate more labels and coarse-to-fine approach to find the location of corals quickly. Our methods were validated using the coral image dataset collected in Pulley Ridge region in the Gulf of Mexico, which substantial speed up the process of quantifying the corals while preserving accuracy.
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xi Yu, Bing Ouyang, Jose C. Principe, Stephanie Farrington, and John Reed "Fast focus of attention for corals from underwater images", Proc. SPIE 11014, Ocean Sensing and Monitoring XI, 1101408 (10 May 2019); https://doi.org/10.1117/12.2522669
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CITATIONS
Cited by 3 scholarly publications.
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KEYWORDS
Image segmentation

Image processing

RGB color model

Data modeling

Image processing algorithms and systems

Ecosystems

Image resolution

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