| Summary: | 碩士 === 臺灣大學 === 資訊工程學研究所 === 95 === In this thesis, we present a descriptor based approach for augmented reality by using a 3D background feature model (3DBFM). 3DBFM contains 3D positions of scene objects and their image appearance distributions. To describe image appearances, we use a new descriptor, contrast context histogram (CCH), which has been shown high matching accuracies but less computation time. By matching the image features with the features in the 3DBFM, we can get 3D-2D correspondences. Then, we adopt iterated closet point (ICP) based algorithm to estimate the camera pose. According to the camera pose, new scene points, which are not in the 3DBFM, can be learned. The experiments showed that our approach can match features under significant changes of illumination and scales. Even long term occlusion occurs; the system can still work after matching feature without any additional penalty.
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