Underwater stereo-video technology systems are used widely for measurement of fish. However the effectiveness of the stereo-video measurement has been limited because most operational systems still rely on a human operator. In this paper, an automated approach for fish detection using a shape-based level sets framework is presented. Shape knowledge of fish is modelled by Principal Component Analysis (PCA). The Haar classifier is used for precise position of the fish head and snout in the image, which is vital information for close proximity initialisation of the shape model. The approach has been tested on under-water images representing a variety of challenging situations typical of the underwater environment, such as background interference and poor contrast boundaries. The results obtained demonstrate that the approach is capable of overcoming these limitations and capturing the fish outline at sub-pixel accuracy.
['Mehdi Ravanbakhsh', 'Mark R. Shortis', 'Faisal Shaifat', 'Ajmal S. Mian', 'Euan S. Harvey', 'James Seager']
An Application of Shape-Based Level Sets to Fish Detection in Underwater Images