Hierarchical, modular architectures for object recognition by parts

We present a methodology for object recognition by parts. The methodology is based on a hierarchical, modular structure for object recognition. Recognition is performed at different levels in the hierarchy, and the type of recognition performed differs from level to level. Each level is made up of modules, where each module is an expert on a particular part of an object, that is, each module is specifically trained to recognize one part of an object. We present a Bayesian system in which the expert modules represent the probability density functions of each part, modeled as a mixture of densities to incorporate different views (aspects) of each part. Results obtained for object recognition in second generation forward looking infrared (FLIR) images are also presented in this paper.