The primary goal of a vehicular headlight is to improve safety in low-light and poor weather conditions. The typical
headlight however has very limited flexibility - switching between high and low beams, turning off beams toward
the opposing lane or rotating the beam as the vehicle turns - and is not designed for all driving environments. Thus,
despite decades of innovation in light source technology, more than half of the vehicular accidents still happen at
night even with much less traffic on the road. We will describe a new DMD-based design for a headlight that can be
programmed to perform several tasks simultaneously and that can sense, react and adapt quickly to any environment
with the goal of increasing safety for all drivers on the road. For example, we will be able to drive with high-beams
without glaring any other driver and we will be able to see better during rain and snowstorms when the road is most
treacherous to drive. The headlight can also increase contrast of lanes, markings and sidewalks and can alert drivers
to sudden obstacles. In this talk, we will lay out the engineering challenges in building this headlight and share our
experiences with the prototypes developed over the past two years.
Many modern forms of segmentation and registration require manual input, making them tedious and time-consuming processes. There have been some successes with automating these methods, but these tend to be unreliable due to inherent variations in anatomical shapes and image quality. It is toward this goal that we have developed methods of identifying correspondences in two images between medial nodes; image features related to anatomical structures. Medial based image features are used because they have proven robust against image noise and shape variation, and provide rotationally invariant properties of dimensionality and scale, while preserving orientation information independently. We have introduced several novel metrics for comparing the medial and geometric relationships between medial nodes and different cliques of medial nodes (a clique is a set of multiple medial nodes). These metrics overcome problems introduced by symmetry between cliques and provide increasing discriminability with the size of the clique. In this paper, we demonstrate medial-based correspondences and validate their specificity with standard Receiver Operator Characteristic (ROC) analysis. It is believed that our method of locating corresponding medial features may be useful for automatically locating anatomical structures or generating landmarks for registration.