UNCERTAINTY PROPAGATION IN MODEL-BASED RECOGNITION

Robust recognition systems require a careful understanding of the effe cts of error in sensed features. In model-based recognition, matches b etween model features and sensed image features typically are used to compute a model pose and then project the unmatched model features int o the image. The error in the image features results in uncertainty in the projected model features. We first show how error propagates when poses are based on three pairs of 3D model and 2D image points. In pa rticular, we show how to simply and efficiently compute the distribute d region in the image where an unmatched model point might appear, for both Gaussian and bounded error in the detection of image points, and for both. scaled-orthographic and perspective projection models. Next , we provide geometric and experimental analyses to indicate when this linear approximation will succeed and when it will fail. Then, based on the linear approximation, we show how we can utilize Linear Program ming to compute bounded propagated error regions for any number of ini tial matches. Finally, we use these results to extend, from two-dimens ional to three-dimensional objects, robust implementations of alingmen t, interpretation-tree serach, and transformation clustering.