Graph Cut Segmentation with Nonlinear Shape Priors

Abstract

Graph cut image segmentation with intensity information alone is prone to fail for objects with weak edges, in clutter, or under occlusion. Existing methods to incorporate shape are often too restrictive for highly varied shapes, use a single fixed shape which may be prone to misalignment, or are computationally intensive. In this note we show how highly variable nonlinear shape priors learned from training sets can be added to existing iterative graph cut methods for accurate and efficient segmentation of such objects. Using kernel principle component analysis, we demonstrate how a shape projection pre-image can induce an iteratively refined shape prior in a Bayesian manner. Examples of natural imagery show that both single-pass and iterative segmentation fail without such shape information.