Abstract
Flow fields are determined from image sequences obtained in an experiment in which benthic
macrofauna, Arenicola marina, causes water flow and the images depict the distribution of a
tracer that is carried with the flow. The experimental setup is such that flow is largely twodimensional, with a localized region where the Arenicola resides, from which flow originates.
Here, we propose a novel parametric framework that quantifies such flow that is dominant
along the image plane. We adopt a Bayesian framework so that we can impart certain physical
constraints on parameters into the estimation process via prior distribution. The primary aim
is to approximate the mean of the posterior distribution to present the parameter estimate
via Markov Chain Monte Carlo (MCMC). We demonstrate that the results obtained from the
proposed method provide more realistic flows (in terms of divergence magnitude) than those
computed from classical approaches such as the multi-resolution Horn-Schunk method. This
highlights the usefulness of our approach if motion is largely constrained to the image plane
with localized fluid sources.