IMPROVED ADENO ASSOCIATED VIRUS PURIFICATION

Not Available There is a current need for a dynamic target knowledge base for automatic target recognition to adapt to the variations in targets encountered in the real world. The proposed research will study methods to determine differences between 2E image and range data of a target and a library of 3D CAD model data, and update the CAD model to accurately represent the real world target. We will detect features, within the 2D data and attempt to detect corresponding features in the 3D CAD model. Creating a match quality metric to measure the difference between the features will allow us to determine which CAD model best matches the 2D data. The same metric will allow us to find areas of differences between the selected CAD model and the 2D data. The selected CAL model will be adapted to more closely match the real world 2D data, and the match quality metric will again be used to assure the quality of the adaptation. BENEFITS: The proposed research will allow rapid updating of target knowledge bases that would greatly enhance military automatic target recognition systems. Other commercial applications include assembly line quality assurance systems, robotic handling systems, and medical diagnosis through MRI imaging.