Interactive design of constrained variational curves

A constrained variational curve is a curve that minimizes some energy functional under certain interpolation constraints. Modeling curves using constrained variational principles is attractive, because the designer is not bothered with the precise representation of the curve (e.g. control points). Until now, the modeling of variational curves is mainly done by means of constraints. If such a curve of least energy is deformed locally (e.g. by moving its control points) the concept of energy minimization is lost. In this paper we introduce deform operators with built-in energy terms. We have tested our ideas in a prototype system for modeling uniform B-spline curves

Usability Test of Exercise Games Designed for Rehabilitation of Elderly Patients After Hip Replacement Surgery: Pilot Study

BACKGROUND: Patients who receive rehabilitation after hip replacement surgery are shown to have increased muscle strength and better functional performance. However, traditional physiotherapy is often tedious and leads to poor adherence. Exercise games, provide ways for increasing the engagement of elderly patients and increase the uptake o

Locating Human Interactions With Discriminatively Trained Deformable Pose+Motion Parts

We model dyadic (two-person) interactions by discriminatively training a spatio-temporal deformable part model of fine-grained human interactions. All interactions involve at most two persons. Our models are capable of localizing human interactions in unsegmented videos, marking the interactions of interest in space and time. Our contributions are as follows: First, we create a model that localizes human interactions in space and time. Second, our models use multiple pose and motion features per part. Third, we experiment with different ways of training our models discriminatively. When testing on the target class our models achieve a mean average precision score of 0.86. Cross dataset tests show that our models generalize well to different environments

Lend Me a Hand: Auxiliary Image Data Helps Interaction Detection

In social settings, people interact in close proximity. When analyzing such encounters from video, we are typically interested in distinguishing between a large number of different interactions. Here, we address training deformable part models (DPMs) for the detection of such interactions from video, in both space and time. When we consider a large number of interaction classes, we face two challenges. First, we need to distinguish between interactions that are visually more similar. Second, it becomes more difficult to obtain sufficient specific training examples for each interaction class. In this paper, we address both challenges and focus on the latter. Specifically, we introduce a method to train body part detectors from nonspecific images with pose information. Such resources are widely available. We introduce a training scheme and an adapted DPM formulation to allow for the inclusion of this auxiliary data. We perform cross-dataset experiments to evaluate the generalization performance of our method. We demonstrate that our method can still achieve decent performance, from as few as five training examples