<div>Hi Charlotte</div> <div> </div> <div>If you only have Three points, you could rather use the class:<BR><BR> itkLandmarkBasedTransformInitializer<BR><BR><BR>if you do not have the correspondence between the points, well...<BR>this methods is so fast with only Three points that you could<BR>actually try all the combinations and settle for the one with<BR>the smallest final squared errors,otherwise your own suggestion of duplicating the landmarks should work.</div> <div> </div> <div>I hope it works.</div> <div>Alireza<BR><BR><B><I>Charlotte Curtis <curtisc@uoguelph.ca></I></B> wrote:</div> <BLOCKQUOTE class=replbq style="PADDING-LEFT: 5px; MARGIN-LEFT: 5px; BORDER-LEFT: #1010ff 2px solid">I'd like to use landmark registration to get a rough first guess prior to image registration. When I run IterativeClosestPoint2 with 4 landmarks, it complains about the number of
landmarks:<BR><BR>vnl_least_squares_function: WARNING: unknowns(6) > residuals(4) <BR>vnl_levenberg_marquardt: Number of unknowns(6) greater than number of data (4)<BR>Solution = [0, 0, 0, 0, 0, 0]<BR><BR>I'm just wondering if there's an optimizer that would let me get away with a minimum of three landmarks. Since 3 points would define a unique location in 3D space, it seems to me that there shouldn't need to be 6. Indeed, if I simply duplicate all of my landmarks and fool it into thinking it has 8 unique points, it comes up with a reasonable solution. This is my backup plan if nothing else works, but it seems a little messy. Any suggestions? Thanks, <BR><FONT color=#888888><BR>Charlotte</FONT> _______________________________________________<BR>Insight-users mailing list<BR>Insight-users@itk.org<BR>http://www.itk.org/mailman/listinfo/insight-users<BR></BLOCKQUOTE><BR><p> 
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