Free-form 3-d object recognition at multiple scales

The recognition of free-form 3-D objects using multi-scale features recovered from 3-D models, and based on the geometric hashing al-gorithm and global verication is presented. The feature points on the object are detected by smoothing its surface after construction of semigeodesic coordinates at each point (mesh vertex). This technique is the generalisation of the CSS method which is a powerful shape de-scriptor expected to be in the MPEG-7 standard. Smoothing is used to remove noise and to select multi-scale feature points to add to the eÆciency and robustness of the system. The local maxima of Gaussian and mean curvatures are selected as feature points. Furthermore the torsion maxima of the zero-crossing contours of Gaussian and mean curvatures are also selected as feature points. Recognition results are demonstrated for rotated and scaled as well as partially occluded ob-jects. In order to conrm the match, 3D translation, rotation and scaling parameters are used for verication and results indicate that our technique is invariant to those transformations.