Object segmentation by traversing a pose-shape manifold

Recent investigations in estimating object shape in images and leveraging knowledge of expected shapes to perform object segmentation have necessitated the formalization of a rigorous mathematical theory of shape. Most of the existing theory in nonlinear shape manifolds lacks physically meaningful parameterization of the shape components, for e.g., pose. We build a novel pose-shape manifold in which manifold parameters signify physically meaningful pose/shape deformation modes. Geodesic distances on this manifold estimate dissimilarities in pose and shape. The segmentation method initializes a template point on the pose-shape manifold and navigates the manifold to converge on the correct pose and shape of the object to be segmented. We show that this method is superior to traditional active contour methods in robustness to edges from clutter. Application of this method to cell delineation of vascular myocytes from phase-contrast microscopy gives reliable segmentation (within ±5% RMS pixel error) of cell boundaries and reliable estimates of geodesic object deformation.