3D object recognition using scale space of curvatures

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2011
Akagündüz, Erdem
In this thesis, a generic, scale and resolution invariant method to extract 3D features from 3D surfaces, is proposed. Features are extracted with their scale (metric size and resolution) from range images using scale-space of 3D surface curvatures. Different from previous scale-space approaches; connected components within the classified curvature scale-space are extracted as features. Furthermore, scales of features are extracted invariant of the metric size or the sampling of the range images. Geometric hashing is used for object recognition where scaled, occluded and both scaled and occluded versions of range images from a 3D object database are tested. The experimental results under varying scale and occlusion are compared with SIFT in terms of recognition capabilities. In addition, to emphasize the importance of using scale space of curvatures, the comparative recognition results obtained with single scale features are also presented.

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Citation Formats
E. Akagündüz, “3D object recognition using scale space of curvatures,” Ph.D. - Doctoral Program, Middle East Technical University, 2011.