Shape variability and spatial relationships modeling in statistical pattern recognition

摘要

We focus on the problem of shape variability modeling in statistical pattern recognition. We present a nonlinear statistical model invariant to affine transformations. This model is learned on an ordinate set of points. The concept of relations between model components is also taken in account. This model is used to find curves and points partially occulted in the image. We present its application on medical imaging in cephalometry.