Edge-based cuing for detection of benthic camouflage

Abstract

Locating marine organisms in their natural habitats is important for understanding ocean biodiversity. Many species are often camouflaged in their surroundings, rendering them hard to detect. Our increasing ability to image large areas of the ocean floor produces millions of images, which must be inspected to spot the occasional organism. This calls for automation of camouflage detection. We investigate reliable detectability of marine camouflage by looking for structural regularities as cues to locating organisms in their natural settings. We study skates and flounder, which use different mechanisms to avoid detection. We introduce a simple edge-based criterion for detecting local structural regularity to reduce the image area to be inspected for likely presence of camouflaged organisms. This sets the stage for efficient use of more complex algorithms to confirm detections and aid in marine census. We also study the possibility of detecting octopuses based on a simple measure of texture applied to a hierarchical segmentation of octopus images.