Abstract: An implant may include a body including a ring, the ring further defining an opening, and the body defining a transverse plane dividing the implant into a superior half and an inferior half. The ring may also define a radial direction and a circumferential direction. The implant may further include a first outer member attached to the ring, wherein the first outer member extends radially from the ring, wherein the first outer member defines one or more arches, and wherein the ring further defines a central channel extending through the implant.

Abstract: A Bayesian treatment of mixture models is based on individual components having Student distributions, which have heavier tails compared to the exponentially decaying tails of Gaussians. The mixture of Student distribution components is characterized by a set of modeling parameters. Tractable approximations of the posterior distributions of individual modeling parameters are optimized and used to generate a data model for a set of input data.

Abstract: A discriminative machine learning system for labels text and non-text strokes in digital ink. The learning system considers stroke features and the context of the strokes, such as temporal information about one or more strokes, in a probabilistic framework. The learning system can also consider gap features within the probabilistic framework to label associated strokes.

Abstract: A low spatial frequency video sequence is enhanced to provide a higher spatial frequency video sequence using a super-resolution process. Patches of higher frequency image data are inferred from the images of the lower frequency video sequence and a dictionary containing a training set of higher resolution image data. An inference module selects result patches from the training set to preserve spatial consistency within each image frame and to preserve temporal consistency, between image frames of the resulting video sequence. Temporal consistency can be preserved for static portions and/or moving portions of each frame.

Abstract: An image super resolution system computes a high resolution image of a target from multiple low resolution images of the same target. Each low resolution image differs slightly in perspective from each of the other low resolution images. A coarse registration operation determines initial estimates of registration parameters (e.g., representing shifts and rotation in perspective) associated with each low resolution image. A fine registration operation improves the initial estimates using Bayesian analysis to infer the registration parameters and an acuity parameter. As such, a marginal likelihood of the low resolution images is optimized to determine the improved estimates of the registration parameters and the acuity parameter, which are used to solve for the high resolution image.

Abstract: A low spatial frequency video sequence is enhanced to provide a higher spatial frequency video sequence using a super-resolution process. Patches of higher frequency image data are inferred from the images of the lower frequency video sequence and a dictionary containing a training set of higher resolution image data. An inference module selects result patches from the training set to preserve spatial consistency within each image frame and to preserve temporal consistency, between image frames of the resulting video sequence. Temporal consistency can be preserved for static portions and/or moving portions of each frame.

Abstract: An image super resolution system computes a high resolution image of a target from multiple low resolution images of the same target. Each low resolution image differs slightly in perspective from each of the other low resolution images. A coarse registration operation determines initial estimates of registration parameters (e.g., representing shifts and rotation in perspective) associated with each low resolution image. A fine registration operation improves the initial estimates using Bayesian analysis to infer the registration parameters and an acuity parameter. As such, a marginal likelihood of the low resolution images is optimized to determine the improved estimates of the registration parameters and the acuity parameter, which are used to solve for the high resolution image.

Abstract: The invention is of an “elastic matching” method for comparing one signature against another, comprising the operations of creating a mapping between corresponding points in two signatures to be compared measured at different times after the commencement of writing the signatures which maximizes the correlation between the local spatial neighborhood of the measured points and simultaneously minimizes the curvature of the elastic spatial distortion from the mapping, providing quantitative measures of both the degree of the spatial correlations and the degree of the non-uniform spatial distortions in the mapping, thereby providing measures of the similarity between the signatures.