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This paper proposes a novel training model based on shape and appearancefeatures for object segmentation in images and videos. Whereas most such modelsrely on two-dimensional appearance templates or a finite set of descriptors,our appearance-based feature is a one-dimensional function, which isefficiently coupled with the object's shape by integrating intensities alongthe object's iso-contours. Joint PCA training on these shape and appearancefeatures further exploits shape-appearance correlations and the resultingtraining model is incorporated in an active-contour-type energy functional forrecognition-segmentation tasks. Experiments on synthetic and infrared imagesdemonstrate how this shape and appearance training model improves accuracycompared to methods based on the Chan-Vese energy.

An Efficiently Coupled Shape and Appearance Prior for Active Contour Segmentation