Object recognition and localization via pose clustering
Computer Vision, Graphics, and Image Processing
Ten lectures on wavelets
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Iterative point matching for registration of free-form curves and surfaces
International Journal of Computer Vision
Partial Surface and Volume Matching in Three Dimensions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Separable and nonseparable multiwavelets in multiple dimensions
Journal of Computational Physics
Simplification and Repair of Polygonal Models Using Volumetric Techniques
IEEE Transactions on Visualization and Computer Graphics
Integrated Registration and Visualization of Medical Image Data
CGI '98 Proceedings of the Computer Graphics International 1998
Matching of 3-D curves using semi-differential invariants
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
The steerable pyramid: a flexible architecture for multi-scale derivative computation
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 3)-Volume 3 - Volume 3
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
SIFT and shape context for feature-based nonlinear registration of thoracic CT images
CVAMIA'06 Proceedings of the Second ECCV international conference on Computer Vision Approaches to Medical Image Analysis
Point fingerprint: a new 3-D object representation scheme
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Image Processing
Isotropic polyharmonic B-splines: scaling functions and wavelets
IEEE Transactions on Image Processing
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Rapid, three-dimensional, shape-based registration is a key aspect of current challenges in applications from automated surface inspection to cancer detection and surgery. Fast, automatic registration of three-dimensional objects is an area of active research. Two main approaches exist: registration using geometric features and registration using voxel comparison. In general, registration using geometric features is faster but less accurate, while registration using voxel comparison is very accurate but requires an initial positioning as the methods tend to settle into local minima. In addition, most geometric feature methods are not designed for use on voxelized data. We present a fast, automatic, rigid registration method using wavelet features which is designed for voxelized data and which provides an excellent initial positioning for further non-rigid registration using a voxel comparison method. The efficacy of the algorithm is demonstrated through examples from solid modeling and biomedical applications.