Initialization of deformable templates using weighted Gaussian approximations

Segmentation followed by shape descriptors represents a common and fundamental approach used in many image processing systems. Active contour models have been used for shape description as a promising method. In particular, the deformable template, which is a kind of active contour model, has been used for various shape description problems. But active contour models, including the deformable template model, suffer from some common difficulties. We propose new approaches in which we represent an object using a weighted Gaussian approximation to find the best candidate template and minimize an appropriately designed cost function to deform the template after finding a best fit candidate in the multiscale representation of the image. This framework can be applied to many real-time applications such as object based video coding, and the estimation of facial features in face recognition.