17. A tangible, non-transitory, computer readable medium comprising machine-readable instructions to: perform a semi-supervised least-squares-based alignment of an image ensemble using an inverse warping technique; determine estimated warping parameters of unlabeled images in the image ensemble based on known warping parameters of labeled images of the image ensemble using results of the semi-supervised least-squares-based alignment; reduce outliers of the estimated warping parameters by partitioning a mean shape space of the semi-supervised least-squares-based alignment, wherein partitioning the means shape space includes iteratively partitioning an initially identified patch corresponding to an image of the image ensemble into child patches; and refine landmark estimations based on resultant patch appearance.
17. A tangible, non-transitory, computer readable medium comprising machine-readable instructions to: perform a semi-supervised least-squares-based alignment of an image ensemble using an inverse warping technique; determine estimated warping parameters of unlabeled images in the image ensemble based on known warping parameters of labeled images of the image ensemble using results of the semi-supervised least-squares-based alignment; reduce outliers of the estimated warping parameters by partitioning a mean shape space of the semi-supervised least-squares-based alignment, wherein partitioning the means shape space includes iteratively partitioning an initially identified patch corresponding to an image of the image ensemble into child patches; and refine landmark estimations based on resultant patch appearance. 19. The computer readable medium of claim 17 , wherein the instructions generate output data including an output set of images.