Stochastic Templates for Aquaculture Images and a Parallel Pattern Detector

A general statistical approach is presented for the identification of objects in digital images, motivated by an application in aquaculture involving underwater images of fish. Using Procrustes analysis, a point distribution model is fitted on a set of training images and used as a prior distribution for the shape of a deformable template. The likelihood of a proposed template is calculated in terms of the response from a feature detector along the boundary of the template. The posterior distribution of template variables is examined by using Markov chain Monte Carlo analysis. A key challenge in the aquaculture application is the variable nature of edges arising from the surface curvature of fish and the low contrast between the foreground and background. Conventional gradient‐based edge detection proves inadequate, but a parallel pattern detector copes much better. Results are presented for a fully automated analysis of the database. The strengths and weaknesses of this approach are discussed and future developments are outlined.