Probabilistic Methods for 3–D Object Recognition

A new Bayesian framework for 3–D object classification and localization is introduced. Objects are represented as probability density functions, and observed features are treated as random variables. These probability density functions turn out a non geometric nature of models and characterize the statistical behavior of local object features like points or lines. The parameterization of model densities covers several terms of object recognition: locations and instabilities of features, rotation and translation, projection, the assignment of image and model features, as well as relations. This paper treats especially the probabilistic modeling of relational dependencies between single features. The mathematical framework, the training algorithms, as well as the localization and classification modules are discussed in detail. The experimental evaluation shows the usefulness of the introduced concepts on real image data.