Search Results for author:
Found 4 papers, 1 papers with code
Deep Learning for Material Decomposition in Photon-Counting CT
In this work, we present a novel deep-learning solution for material decomposition in PCCT, based on an unrolled/unfolded iterative network.
Procrustes registration of two-dimensional statistical shape models without correspondences
Manually placed landmarks are often used for correspondence in the design of statistical shape models.
Model-order selection in statistical shape models
It requires choosing a model order, which determines how much of the variation seen in the training data is accounted for by the PDM.
A weighting strategy for Active Shape Models
Finding these targets is a critical step: some landmarks are more reliably detected than others, and some landmarks may not be within the field of view of their detectors.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
