In this paper, a neural tree-based approach for classifying range images Into a set of nonoverlapping regions is presented. An Innovative procedure is applied to extract invariant surface features from each pixel of the range image. These features are 1) robust to noise, and 2) invariant to scale, shift, rotations, curvature variations, and direction of the normal. Then, a generalized neural tree is used to classify each image point as belonging to one of the six surface models of differential geometry, i.e., peak, ridge, valley, saddle, pit, and flat Comparisons with other methods and experiments on both synthetic and real three-dimensional range images have been proposed.

Invariant feature extraction and neural trees for range surface classification

FORESTI, Gian Luca
2002

Abstract

In this paper, a neural tree-based approach for classifying range images Into a set of nonoverlapping regions is presented. An Innovative procedure is applied to extract invariant surface features from each pixel of the range image. These features are 1) robust to noise, and 2) invariant to scale, shift, rotations, curvature variations, and direction of the normal. Then, a generalized neural tree is used to classify each image point as belonging to one of the six surface models of differential geometry, i.e., peak, ridge, valley, saddle, pit, and flat Comparisons with other methods and experiments on both synthetic and real three-dimensional range images have been proposed.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11390/686778
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