Ski jumping has continuously raised major public interest since the early 70s of the last century, mainly in Europe and
Japan. The sport undergoes high-level analysis and development, among others, based on biodynamic measurements
during the take-off and flight phase of the jumper. We report on a vision-based solution for such measurements that
provides a full 3D trajectory of unique points on the jumper's shape. During the jump synchronized stereo images are
taken by a calibrated camera system in video rate. Using methods stemming from video surveillance, the jumper is
detected and localized in the individual stereo images, and learning-based deformable shape analysis identifies the
jumper's silhouette. The 3D reconstruction of the trajectory takes place on standard stereo forward intersection of
distinct shape points, such as helmet top or heel. In the reported study, the measurements are being verified by an
independent GPS measurement mounted on top of the Jumper's helmet, synchronized to the timing of camera exposures.
Preliminary estimations report an accuracy of +/-20 cm in 30 Hz imaging frequency within 40m trajectory. The system is
ready for fully-automatic on-line application on ski-jumping sites that allow stereo camera views with an approximate
base-distance ratio of 1:3 within the entire area of investigation.
A shape matching framework designed for an industrial application is presented. The task of the proposed system is to identify and sort plastic teeth, used by dentists, based on their 2D shape only. A sorting machine puts each tooth on a predefined location under a camera which is equipped with a telecentric lens. From the resulting image the contour of the object is extracted and compared with a database of reference teeth. In order to cope with the problem that a tooth may rest on several (typically 4 - 15) stable positions when placed under the camera, all its proper contours are stored as valid tooth representations. In total the tooth database used for tests contained 171 teeth represented by 1257 contours of 1000 points each. Under the constraint that one contour out of 1257 has to be identified in less than one second, we describe the algorithmic approach which has successfully led to the implementation of the system. A fast pre-selection of shapes and the repeated calculation of point transforms to match them with the reference contours makes up the underlying principle of the proposed system. In addition to the decisions actually made during the design of the system we describe several possible enhancements which can further improve the speed and generality of our matching approach.