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.