Marine Snow Detection for Real Time Feature Detection

Abstract

Underwater images are often degraded due to backscatter, light attenuation and light artifacts. One important aspect of it is marine snow, which are particles of varying shape and size. Computer vision technologies can be strongly affected by them and may therefore provide incorrect and biased results. In robotic applications, there is limited computational power for online processing. A method for real time marine snow detection is proposed in this paper based on a multi-step process of spatial-temporal data. The RGB colored images are converted to the YCbCr color space before they are decomposed to isolate the high frequency information using a guided filter for a first selection of candidates. Convolution with an uniform kernel is then applied for further analysis of the candidates. The method is demonstrated in two use cases, underwater feature detection and image enhancement.