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6 December 2002 Matching of a 3D model into a 2D image using a hypothesize-and-test alignment method
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This paper presents three novel matching algorithms, where a hypothesis of a 3D object is matched into a 2D image. The three algorithms are compared with respect to speed and precision on some examples. A hypothesis consists of the object model and its six degrees of freedom. The hypothesis is projected into the image plane using a pinhole camera model. The model of the used object is a feature-attributed 3D geometric model. It contains various local features and their rules of visibility. After the projection into the image plane the local environment of the projected features is searched for the best match value of the various features. There exists a trade-off between the rigidity of the object and the best-match position of the local features in the image. After the matching a 2D-3D pose estimation is run to get an updated pose from the matching. Three novel algorithms for matching the local features under the consideration of their geometric formation are decribed in this paper. The first algorithm combines the local features into a graph. The graph is viewed as a network of springs, where the spring forces constraint the object's rigidity. The quality of the local best matches is represented by additional forces introduced into the nodes of the graph. The second matching algorithm decouples the local features from each other for moving them independently. This does not impose constraints on the rigidity of the object and does not consider the feature quality. The third matching method takes into account the feature quality by using it within the pose estimation.
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Thorsten Koelzow and Lars Krueger "Matching of a 3D model into a 2D image using a hypothesize-and-test alignment method", Proc. SPIE 4791, Advanced Signal Processing Algorithms, Architectures, and Implementations XII, (6 December 2002);

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