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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