Detecting a vehicle to obtain traffic information at nighttime is difficult. This study proposes a vehicle detection algorithm, called the headlight extraction, pairing, and tracking (HLEPT) algorithm, which can acquire traffic information in the rain at nighttime by identifying vehicles through the location of their headlights and other indicative lights. A knowledge-based connected-component procedure, in which vehicles are located by grouping their lights and estimating their features, is proposed. The features of a complex nighttime traffic scene were also analyzed. The HLEPT algorithm includes a headlight extraction algorithm, as well as regulations for the pairing and grouping of lights and light tracking using a Kanade-Lucas-Tomasi tracker to measure traffic flow and velocity. Experimental results demonstrate the feasibility and effectiveness of the proposed approach on vehicle detection in the rain at nighttime.
Given the potential demonstrated by research into bone-tissue engineering, the use of medical image data for the rapid prototyping (RP) of scaffolds is a subject worthy of research. Computer-aided design and manufacture and medical imaging have created new possibilities for RP. Accurate and efficient design and fabrication of anatomic models is critical to these applications. We explore the application of RP computational methods to the repair of a pediatric skull defect. The focus of this study is the segmentation of the defect region seen in computerized tomography (CT) slice images of this patient's skull and the three-dimensional (3-D) surface rendering of the patient's CT-scan data. We see if our segmentation and surface rendering software can improve the generation of an implant model to fill a skull defect.