Solid shape
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
Experiments in Curvature-Based Segmentation of Range Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
A robust method for registration and segmentation of multiple range images
Computer Vision and Image Understanding
NRC '97 Proceedings of the International Conference on Recent Advances in 3-D Digital Imaging and Modeling
Geometric matching of 3D objects: assessing the range of successful initial configurations
NRC '97 Proceedings of the International Conference on Recent Advances in 3-D Digital Imaging and Modeling
Fast global registration of 3D sampled surfaces using a multi-z-buffer technique
NRC '97 Proceedings of the International Conference on Recent Advances in 3-D Digital Imaging and Modeling
Registration of 3-D partial surface models using luminance and depth information
NRC '97 Proceedings of the International Conference on Recent Advances in 3-D Digital Imaging and Modeling
Surface registration by matching oriented points
NRC '97 Proceedings of the International Conference on Recent Advances in 3-D Digital Imaging and Modeling
A method for densitometric analysis of moving object tracking in medical images
Machine Graphics & Vision International Journal
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The ability to accurately register images of patients taken at different times is very important in many medical applications. In most cases, the main reason for registration is to measure changes, and therefore any automatic registration algorithm employed for this task must be able to cope when there are significant differences in the images. In this paper a new algorithm is presented which enables accurate surface registration by using a robust matching algorithm to calculate correspondences for all the points on the surface along with a measure of reliability. The basic idea is to move the point of interest to see if it always matches to the same point. This generates a scatter of "tentative corresponding points ", and a corresponding point and reliability are calculated from the geometric distribution of these points. This new algorithm is compared with two others. Experiments show that our algorithm is more accurate, consistent and converges faster than the others.