Detecting man-made objects in unconstrained subsea videos

Abstract

We present a system detecting the presence of unconstrained man-made objects in unconstrained subsea videos. Classification is based on contours, which are reasonably stable features in underwater imagery. First, the system determines automatically an optimal scale for contour extraction by optimising a quality metric. Second, a two-feature Bayesian classifier determines whether the image contains man-made objects. The features used capture general properties of man-made structures using measures inspired by perceptual organisation. The system classified correctly approximately 85% of 1390 test images from five different underwater videos, in spite of the varying image contents, poor quality and generality of the classification task.