Slippage Features

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Dateien:
Aufrufstatistik

URI: http://nbn-resolving.de/urn:nbn:de:bsz:21-opus-33880
http://hdl.handle.net/10900/49175
Dokumentart: Report (Bericht)
Date: 2008
Source: WSI ; 2008 ; 3
Language: English
Faculty: 7 Mathematisch-Naturwissenschaftliche Fakultät
9 Sonstige / Externe
Department: Informatik
Sonstige/Externe
DDC Classifikation: 004 - Data processing and computer science
Keywords: Computergraphik , Oberflächenanalyse , Registrierung
Other Keywords: Merkmalextraktion , Korrespondenzproblem
Geometryprocessing , Keypoints , Matching , Registration
License: Publishing license including print on demand
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Abstract:

In this report, we present a novel feature detection technique for unstructured point clouds. We introduce a generalized concept of geometric features that detects locally uniquely identifiable keypoints as centroids of area with locally minimal slippage. We extend the concept to multiple scales and extract features using multi-scale mean shift clustering. In order to validate matches between feature points, we employ a two stage technique that first sorts out unlikely matches, followed by an approximate alignment between remaining features by a rotational cross-correlation analysis and a local iterative closest point (ICP) registration. The resulting residuals are then used as final similarity measure. The proposed combination of techniques results in a robust and reliable correspondence detection technique that yields registration results in situations where previous techniques are not able to detect usable feature correspondences. We provide a detailed empirical analysis of the method, and apply the technique to global registration, symmetry detection and deformable matching problems.

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