Bayesian recognition of targets by parts in second generation forward looking infrared images

摘要

This paper presents a system for the recognition of targets in second generation forward looking infrared images (FLIR). The recognition of targets is based on a methodology for recognition of two--dimensional objects using object parts. The methodology is based on a hierarchical, modular structure for object recognition. In the most general form, the lowest level consists of classifiers that are trained to recognize the class of the input object, while at the next level, classifiers are trained to recognize specific objects. At each level, the objects are recognized by their parts, and thus each classifier is made up of modules, each of which is an expert on a specific part of the object. Each modular expert is trained to recognize one part under different viewing angles and transformations. A Bayesian realization of the proposed methodology is presented in this paper, 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. Recognition relies on the sequential presentation of the parts to the system, without using any relational information between the parts. A new method to decompose a target into its parts and results obtained for target recognition in second generation FLIR images are also presented here.