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