Martin Sengel

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In this paper, we derive an analytic representation of the eigenvectors and coefficients for in-plane rotated images. This, on the one hand, allows an efficient PCA-basis calculation in the learning stage and on the other hand a direct computation of the rotation angle in the recognition stage. In the experimental section, we demonstrate that the new method(More)
Our work addresses the problem of fast object recognition and pose determination of segmented objects. It combines the well-studied parametric eigenspace method with statistical moments of image signatures resulting in a computationally and memory efficient algorithm. The approach is suited for time or memory critical applications. e.g. in embedded systems.(More)
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