Human motion tracking for rehabilitation has been an active research topic since the 1980s.
It has been motivated by the increased number of patients who have suffered a stroke, or …

In this paper, we review the recently published work on deformable models. We have
chosen to concentrate on 2D deformable models and relate the energy minimization …

Although having achieved great success in medical image segmentation, deep learning-
based approaches usually require large amounts of well-annotated data, which can be …

There are two major types of uncertainty one can model. Aleatoric uncertainty captures
noise inherent in the observations. On the other hand, epistemic uncertainty accounts for …

Automatic segmentation is a fundamental task in computer-assisted medical image analysis.
Convolutional neural networks (CNNs) have been widely used for medical image …

Humans perceive the three-dimensional structure of the world with apparent ease. However,
despite all of the recent advances in computer vision research, the dream of having a …

The problem of tracking curves in dense visual clutter is challenging. Kalman filtering is
inadequate because it is based on Gaussian densities which, being unimo dal, cannot …

The ability to recognize humans and their activities by vision is key for a machine to interact
intelligently and effortlessly with a human-inhabited environment. Because of many …

In this paper, we propose using Gaussian processes to track an extended object or group of
objects, that generates multiple measurements at each scan. The shape and the kinematics …

While making a tremendous impact in various fields, deep neural networks usually require
large amounts of labeled data for training which are expensive to collect in many …