A Probabilistic Model to Learn, Detect, Localize and Classify Patterns in Arbitrary Images

This thesis presents a new, probabilistic model for describing image patterns arising from classes of visually similar objects, such as faces or brains. The model describes patterns in terms of a high level geometrical structure referred to as an object class invariant (OCI), which is invariant to nuisance parameters arising from the imaging process. The OCI it-self is not directly observed from images, but can be inferred via a probabilistic model based on generic, spatially localized image features. The OCI model can be learned from a large set of natural images containing pattern instances with minimal manual supervi-sion, in the presence of background clutter, illumination changes, partial pattern occlusion, multi-modal intra-pattern variation (e.g. faces with or without sunglasses), geometrical deformations (i.e. translations, rotations and magnifications) and viewpoint changes. In addition, it can be automatically fit to new images in similar difficult imaging conditions. Due to the general nature of the OCI model, it has a wide range of possible applications, and its importance is demonstrated in the research fields of computer vision and medical image analysis. In computer vision, the OCI model results in the first viewpoint-invarian