INRIA Sophia - Project Epidaure
AbstractThe recognition problem is probably one of the most studied in computer vision. However, most techniques were developed on point features and were not explicitly designed to cope with uncertainty in measurements.
The aim of this paper is to express recognition algorithms in terms of uncertain geometric features (such as points, lines, oriented points, or frames). In the first part we review the principal matching algorithms and adapt them to work with generic geometric features. Then we analyze some noise models on geometric features for recognition, and we consider how to cope with this uncertainty in the matching algorithms. We then identify four key problems for the implementation of these algorithms. Last but not least, we present a new statistical analysis of the probability of false positives that demonstrates a drastic improvement in confidence and complexity that we can obtain by using geometric features more complex than points.
Keywords: 3-D object recognition, invariants of 3-D objects, feature-matching, uncertain geometric features, generic features, matching error analysis