Full text: XVIIth ISPRS Congress (Part B3)

  
  
image. However, each method may provide a subset 
of the information necessary to produce a more 
meaningful interpretation of the scene [Chu,90,91] 
[Hsieh] [Shufelt]. It is reasonable to expect that there 
will be complication in fusing the result from edge 
detection and region-based segmentation. Most 
research on this subject falls into four categories: (1) 
pixel-wise logical operations; (2) algorithm for 
specific imaging modalities or processing techniques; 
(3) theoretical approaches using a priori information 
and probabilistic models; and (4) techniques using 
high-level knowledge. Our future scheme will be 
oriented on using high-level constraints to enhance 
the quality of segmentation. 
5. CONCLUSIONS 
In this article, we have proposed an architecture for 
integrated image analysis which enable us to carry 
out image analysis from low-level to high-level and 
from high-level to low-level interactively. We are 
especially interested in the problems of: 1) integrate 
high-level knowledge into image segmentation; 2) 
use structural information for stereo matching; 3) 
integrate image segmentation and stereo matching; 
4) combine the edge-based and region-based 
segmentation. One of major theoretic obstacles is on 
how to combine the knowledge from different 
sources, which has partially answered in this paper. 
ACKNOWLEDGEMENT 
Without the consistent support from Prof.dr.ir. 
M.J.M. Bogaerts, the work presented here would be 
not possible. This research is jointly supported by 
CCGM (Centre for Computer Graphics and 
Mapping), FRANK project and Photogrammetry 
Laboratory, Faculty of Geodesy, TU Delft. FRANK 
is a registered trademark of FRANK system 
supported by Geeris Holding Netherlands BV. 
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