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

Figure6:Thenewmodelpositionandhandlerotationafteroneiterationofmodel tting.Newmatchestoimageedgesareshownwithheavylines.

Figures4–7showtheoperationofthesystemforoneframeofmotiontracking.However,duetothecomplexityofthemodel,thisversionrequiresabout6secondsofprocessingperframeonaSun3/260anddoesnotoperateinrealtime.Figure4showsanimageofahanddrillfromwhichedgesareextractedwithasimpli edversionoftheCannyedgedetector.InFigure5,themodelisshownsuperimposedontheseedgesfromthepreviousbestestimateofitscurrentviewpoint.Asimplematchingalgorithmisusedthat ndsimageedgesthatareclosetotheprojectedmodelcurvesoverthemaximumpossiblelengthoftheedge.Thesematchesarerankedaccordingtotheirlengthandaverageseparation,andthebestonesarechosenforminimization.TheselectedmatchesareshownwithheavylinesinFigure5alongwithperpendicularbarsmarkingtheerrorsbetweenmodelandimagecurvesthatareminimized.Afteroneiterationofmodel tting,thenewmodelpositionisshowninFigure6alongwithanewsetofimagematchesgeneratedfromthisposition.Notethattherotationofthehandleisafreeparameteralongwiththeviewpointparameters.Afterthisseconditerationofconvergence,the nalresultsofmodel ttingareshownsuperimposedontheoriginalimageinFigure7.Notethatduetoocclusionanderrorsinlow-leveledgedetection,this nalresultisbasedon

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