<title>Deformable models for object recognition in aerial images</title>

Abstract

A new type of deformable model is presented that is able to combine some of the characteristics of both snakes and templates. It can be used to segment and recognize two dimensional objects when only vague prior knowledge about their shapes is available. A jump-diffusion process is used to fit the template to the image. The jumps allows the template to undergo abrupt discontinuous changes in shape and position and to decide among multiple target models. The diffusion process allows the template to perform continuous flowing deformations like a snake. A prior shape model is described that uses the local and global characteristics of each different target class. An efficient form for the image likelihood is given that extends to multiple attributes and multiple images. The jump transition kernel defines the probabilities of the template jumping to a new state. This is difficult to generate and sample in practice though. To allow for this a method is described where a marginal transition kernel is generated by integrating over the continuous internal parameters for subsets of jumps. This makes the sampling problem much easier while still providing effective inferencing. The relation of this approach to active contours and region competition is discussed. It is shown that with the appropriate choice of prior and likelihood that snakes can easily be modelled within the deterministic part of the diffusion process. The method is demonstrated with the detection of buildings and planes in infrared and optical images and a comparison with an active contour is also given.