Active shape models—their training and application
Computer Vision and Image Understanding
Group Actions, Homeomorphisms, and Matching: A General Framework
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: Part II
Analysis of Planar Shapes Using Geodesic Paths on Shape Spaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour Inferences for Image Understanding
International Journal of Computer Vision
Active Contour External Force Using Vector Field Convolution for Image Segmentation
IEEE Transactions on Image Processing
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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.