Publications in Scientific Journals:

T. Melzer:
"Non-parametric segmentation of ALS point clouds using mean shift";
Journal of Applied Geodesy, 1 (2007), 3; 159 - 170.

English abstract:
Segmentation is a key task in the processing
of 3D point clouds as obtained from airborne
laser scanners (ALS). However, most of the segmentation
techniques currently employed require prior
gridding of the data and thus do not respect the inherently
three-dimensional geometry of more intricate
structures such as power lines. By contrast, the
mean shift procedure, a filtering and clustering
approach which has recently found much interest in
the image processing community, works directly on
the original 3D point cloud; also, mean shift is a
non-parametric technique (i.e., it does not depend on
any geometric model assumptions) and can thus also
be applied to vegetation structures. In this paper, we
will give a self-contained derivation of the mean shift
procedure, and discuss how it can be used to obtain a
classification or segmentation of an unstructured 3D
point cloud. Two application examples shall further
illustrate its usefulness to ALS data processing.

Segmentation, classification, clustering, airborne laser scanning, mean shift

"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)

Created from the Publication Database of the Vienna University of Technology.