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The model shapes are given for some of the square plates with three kinds of thickness variations.
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The process for generating Skin Model Shapes is split into a prediction and an observation stage with respect to the available information and knowledge about expected geometric deviations.
In this regard, a Skin Model Shape is a specific outcome of the conceptual Skin Model and comprises deviations from manufacturing and assembly.
Using a detection difficulty metric, we found that model shape was consistently a significant predictor of model detection difficulty.
(Furthermore, ignoring the treatment effects in terms of shape, i.e. assuming a constant HR, implies that the uncertainty in model shape is not captured with the meta-analysis models).
In different computer models, shape is represented using different methodologies, to varying degrees of precision.
For this purpose, Skin Model Shapes, which are particular Skin Model representatives from a simulation perspective, are generated.
Three specific reference points - the estimated pelvic ring center, and two points on the identified femur model shapes - are then used to initialize ASM for pelvic ring detection.
In the simulations, several different flake sizes and shapes are modeled.
In the training phase, 30 landmarks are used to describe the pelvic ring; as with the femur model shape, these are a mixture of structural and auxiliary points.
Instead of learning a generative shape model, shape priors are incorporated on-the-fly through the sparse shape composition (SSC).
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