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Paper Details

"Learning never exhausts the mind"

Covariance based modeling of underwater scenes for fish detection

Abstract

In this paper we present an algorithm for visual object detection in a underwater real-life context which explicitly models both the background and the foreground for each frame - thus helping to avoid foreground absorption into similar background -, and integrates both colour and texture features (which have proved effective in overcoming the limitations of colour-only appearance descriptors) into a covariance-based model, which provides an elegant way to merge multiple features together and enforce structural relationships. A joint domain-range model combined to a post-processing approach based on Markov Random Field takes into account the spatial dependency between pixels in the classification process, unlike the classical pixel-oriented modeling techniques. Our results show the effectiveness of this approach in the underwater environment, which presents a lot of variety in scene conditions, objects' motion patterns, shapes and colouring, and background activity.

"Nature is the source of all true knowledge. She has her own logic, her own laws,
she has no effect without cause nor invention without necessity." L. Da Vinci."