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Invariant Greyscale Features for 3D Sensordata Marc Schael and Sven Siggelkow
 

Summary: Invariant Grey­scale Features for 3D Sensor­data
Marc Schael and Sven Siggelkow
Institute for Pattern Recognition and Image Processing
Computer Science Department
University of Freiburg, D­79110 Freiburg i. Br., Germany
fschael,siggelkowg@informatik.uni­freiburg.de
Abstract
In this paper a technique for the construction of invari­
ant features of 3D sensor­data is proposed. Invariant grey­
scale features are characteristics of grey­scale sensor­data
which remain constant if the sensor­data is transformed ac­
cording to the action of a transformation group. The pro­
posed features are capable of recognizing 3D objects in­
dependent of their orientation and position, which can be
used e. g. in medical image analysis. The computation of
the proposed invariants needs no preprocessing like filter­
ing, segmentation, or registration. After the introduction of
the general theory for the construction of invariant features
for 3D sensor­data, the paper focuses on the special case
of 3D Euclidean motion which is typical for rigid 3D ob­

  

Source: Albert-Ludwigs-Universität Freiburg, Institut für Informatik,, Lehrstuhls für Mustererkennung und Bildverarbeitung

 

Collections: Computer Technologies and Information Sciences