We have developed a new feature descriptor relying on statistics of second order curvature. We have applied this method in two competitions within medical image analysis and have achieved good results.
A small demo consisting of two computers connected over Ethernet. We create a virtual environment were Microsoft Kinect cameras allow people to communicate and feed fish together!
Using off-the-shelf computer vision methods we demonstrate a system for recognizing meat cuts at different points along a slaughterhouse production line. Our results shows that the suggested approach is a promising alternative to the more intrusive methods currently available.
We develop a local feature descriptor using higher order image derivatives (aka. image jets). Our main finding is that this type of description is very competitive with traditional local feature description based on gradient orientation histograms (e.g. SIFT).
We present a system for articulated human tracking and show that unscented Kalman filtering (UKF) allows for less likelihood evaluations compared to particle filtering (PF). Moreover, we achieve a smoother tracking with lower errors using UKF rather than PF.