Abstract

A probabilistic system for recognition of individual ob jects is presented. The ob jects to recognize are composed of constellations of features, and features from a same ob ject share the common reference frame of the image in which they are detected. Features appearance and pose are modeled by probabilistic distributions, the parameters of which are shared across features in order to allow training from few examples. In order to avoid an expensive combinatorial search, our recognition system is organized as a cascade of well-established, simple and inexpen- sive detectors. The candidate hypotheses output by our algorithm are evaluated by a generative probabilistic model that takes into account each stage of the matching process. We apply our ideas to the problem of individual ob ject recognition and test our method on several data-sets. We compare with Lowe’s algorithm [7] and demonstrate significantly better performance.