Statistical learning, localization, and identification of objects

This work describes a statistical approach to deal with learning and recognition problems in the field of computer vision. An abstract theoretical framework is provided, which is suitable for automatic model generation from examples, identification, and localization of objects. Both, the learning and localization stage are formalized as parameter estimation tasks. The statistical learning phase is unsupervised with respect to the matching of model and scene features. The general mathematical description yields algorithms which can even treat parameter estimation problems from projected data. The experiments show that this probabilistic approach is suitable for solving 2D and 3D object recognition problems using grey-level images. The method can also be applied to 3D image processing issues using range images, i.e. 3D input data.<>