Alignment is a common approach for recognizing 3-D objects in 2-D images. Current implementations handle image uncertainty in ad hoc ways. These errors, however, can propagate and magnify through the alignment computations, such that the ad hoc approaches may not work. The authors give a technique for tightly bounding the propagated error, which can be used to make the recognition robust while still being efficient. Previous analyses of alignment have demonstrated a sensitivity to false positives. But these analyses applied only to point features, whereas alignment systems often rely on extended features for verifying the presence of a model in the image. A new formula is derived for the selectivity of a line feature. It is experimentally demonstrated using the technique for computing error bounds that the use of line segments significantly reduces the expected false positive rate. The extent of the improvement is that an alignment system that correctly handles propagated error is expected to remain reliable even in substantially cluttered scenes.
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