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Automatic scallop detection in benthic environments

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4 Author(s)
Dawkins, M. ; Dept. of Comput. Sci., RPI, Troy, NY, USA ; Stewart, C. ; Gallager, S. ; York, A.

As a multi-billion dollar industry, scallop fisheries world-wide rely on maintaining healthy off-shore populations. Recent developments in the collection of optical images from extended areas of the ocean floor has opened the possibility of assessing scallop populations from imagery. The shear volume of data - upwards of 20,000 images per hour - implies that automatic image analysis is necessary. This paper presents a computer vision software system to identify and count scallops. For each image, the system generates initial candidate regions of potential scallops, extracts image features in the candidate regions, and then applies one of several different trained Adaboost classifiers to determine the strength of each region as a scallop. In making the final classification decision, the strength of the scallop classifier output is compared to the output of other classifiers trained to detect sand dollars, clams and other “distractors”.

Published in:

Applications of Computer Vision (WACV), 2013 IEEE Workshop on

Date of Conference:

15-17 Jan. 2013