Segmentation and outline detection in underwater video images using particle filters

Abstract

Recently we have been concerned with locating and tracking images of fish in underwater videos. While edge detection and region growing have assisted in obtaining some advances in this effort, a more extensive, non-linear approach appears necessary for improved results. In particular, the use of particle filtering applied to contour detection in natural images has met with some success. Following recent ideas in the literature, we are proposing to use a recursive Bayesian model which employs a sequential Monte Carlo approach, also known as the particle filter. This approach uses the corroboration between two scales of an image to produce various local features which characterize the different probability densities required by the particle filter. Since our data consist of video images of fish recorded by a stationary camera, we are capable of augmenting this process by means of background subtraction. Moreover, we are proposing a method that does not require the pre-computation of the distributions required by the particle filter. The above capabilities are applied to our dataset for the purpose of using contour detection with the aim of eventual segmentation of the fish images and fish classification. Although our dataset consists of fish images, the proposed techniques can be employed in applications involving different kinds of non-stationary underwater objects. We present results and examples of this analysis and discuss the particle filter application to our dataset.