  Congealing is a flexible nonparametric data-driven framework for the joint
alignment of data. It has been successfully applied to the joint alignment of
binary images of digits, binary images of object silhouettes, grayscale MRI
images, color images of cars and faces, and 3D brain volumes. This research
enhances congealing to practically and effectively apply it to curve data. We
develop a parameterized set of nonlinear transformations that allow us to apply
congealing to this type of data. We present positive results on aligning
synthetic and real curve data sets and conclude with a discussion on extending
this work to simultaneous alignment and clustering.
