Optimal Geometric Model Matching

Model based object recognition systems have rarely dealt directly with D perspective while matching models to images The algorithms presented here use D pose recovery during matching to explicitly and quantitatively account for changes in model appearance associated with D per spective These algorithms use random start local search to nd with high probability the globally optimal correspondence between model and image features in spaces containing over possible matches Three speci c algorithms are compared on robot landmark recognition problems A full perspective algorithm uses the D pose algorithm in all stages of search while two hybrid algorithms use a computationally less demanding weak perspective procedure to rank alternative matches and updates D pose only when moving to a new match These hybrids successfully solve problems involving perspective and in less time than required by the full perspective algorithm This work was supported by the Defense Advanced Research Projects Agency under contract DAAE C R monitored by TACOM contract DAAH G monitored by ARO and by the National Science Foundation under grant CDA To Appear in CVGIP IU in