Thyroid-SPOT is a mobile application that allows users to be able to analyse thyroid function tests laboratory reports by computing the homeostatic euthyroid set point based on regressing thyroid function data according to a patented algorithm to define the parameters that maximize the goodness-of-fit to a negative exponential model for target-centric individualized treatment.
We focus on the problem of still image-based human action recognition, which essentially involves making prediction by analyzing human poses and their interaction with objects in the scene. Besides image-level action labels (e.g., riding, phoning), during both training and testing stages, existing works usually require additional input of human bounding boxes to facilitate the characterization of the underlying human–object interactions. We argue that this additional input requirement might severely discourage potential applications and is not very necessary.
Pose estimation, tracking, and action recognition of articulated objects from depth images are important and challenging problems, which are normally considered separately. In this paper, a unied paradigm based on Lie group theory is proposed, which enables us to collectively address these related problems. Our approach is also applicable to a wide range of articulated objects. Empirically it is evaluated on lab animals including mouse and sh, as well as on human hand. On
these applications, it is shown to deliver competitive results compared to the state-of-the-arts, and non-trivial