Supervised and Unsupervised Statistical Models for cephalometry
نویسندگان
چکیده
In this paper, we present an algorithm for aligning and matching unlabeled sets of points and a method for building a statistical model composed of a mean observation and associated variability. This model is used to solve the cephalometric problem. The main idea of this paper consists in using a dual step strategy: estimate the pose and then the correspondence alternatively. Correspondence being computed automatically, this process is applied to supervised and unsupervised model learning on unordered sets of points.
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تاریخ انتشار 2004