Seeing through clutter: Snake computation with dynamic programming for particle segmentation

State-of-the-art snake methods for object segmentation fail in the presence of strong clutter. Here, we present a dynamic programming (DP) setup to combat strong clutter. Our solution maximizes a score function known as gradient inverse coefficient of variation (GICOV). GICOV cannot be directly used in DP, because it does not have an additive form. We derive a set of DP-friendly necessary conditions for maximization of the GICOV. Our experiments illustrate that while other snake methods are thwarted by clutter, the proposed method finds particle object boundaries rejecting clutter and distracters.