Model-based recognition and motion tracking depends upon the ability to solve for projection and model parameters that will best fit a 3-D model to matching 2-D image features. This paper extends current methods of parameter solving to handle objects with

Figure5:EdgesextractedfromtheimageofFigure4usingtheCannyedgedetector.Super-imposedontheseedgesarethemodelfromitspreviousestimatedviewpoint,nearbymatchingedges,andperpendicularerrorstobeminimized.

aSun3/260,wheretheedgesarelinkedintolistsonthebasisoflocalconnectivity.Afairlysimplematchingtechniqueisusedtoidentifytheimageedgesthatareclosesttothecurrentprojectedcontoursofa3-Dmodel.Thefewbestinitialmatchesareusedtoperformoneit-erationoftheviewpointsolution,thenfurthermatchesaregeneratedfromthenewviewpointestimate.Upto5iterationsofthisprocedureareperformed,withagraduallynarrowingrangeofimagelocationswhicharesearchedforpotentialmatches(thishelpstoeliminateanyfalseoutliermatches).Forsimplemodelswithstraightedges,allofthesestepscanbeperformedinlessthan1second,resultinginasystemthatcanperformrobustbutratherslowreal-timemo-tiontracking.Wehaverunthissystemforthousandsofframesatatimebyholdinganobjectinfrontofthevideocameraandslowlymovingit.Correctnessofthemotiontrackingcanbeeasilyjudgedinrealtimebywatchingawire-framemodelsuperimposedontheimagefromthecurrentsetofparameterestimates.Wearecurrentlyexploringtheuseofparallelarchitec-turesthatcouldgreatlyspeedtheoperationofthissystemsothatitperformsatvideoratesforcomplexobjectmodels.

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