Shape in images
Ian L. Dryden, Kanti V. Mardia · Wiley series in probability and statistics · 2016
An important area of study is the interpretation of images, and shape is a very important component. In image analysis the registration parameters will usually need to be modelled, although a partition of variables into shape and registration parameters is often helpful. Since the early 1980s statistical approaches to image analysis using the Bayesian paradigm have proved to be very successful. An appropriate method for high-level Bayesian image analysis is the use of deformable templates. The key to the successful inclusion of prior knowledge in high level Bayesian image analysis is through specification of the prior distribution. Many approaches have been proposed, including methods based on outlines, landmarks and geometric parameters. Images can be deformed using deformations and this process is called image warping. Warping or morphing of images is used in a wide set of applications, including in the cinema industry and medical image registration.