Full text: XVIIIth Congress (Part B3)

  
Figure 3: Aerial image 
formula, the computational effort depends only from the size 
of the model and is independent from the size of the seg- 
mented region. 
The homogeneity predicate is used in a region growing 
scheme, but it can also be used in other control algorithms for 
image segmentation or clustering. Experiments with synthe- 
tical images have shown, that the most important parameter 
of our approach is the variance of the noise in the image. For 
segmenting aerial images, this variance is estimated using an 
algorithm from the literature. Initial seed regions and the 
model type to use is extracted from map data. The segmen- 
tation results are good for non-textured areas and for areas 
with regular texture. For irregular textured surfaces experi- 
ments with higher order MRF-models will be performed. 
ACKNOWLEDGMENT 
This work is funded by the Deutsche Forschungsgemeinschaft 
(DFG). 
References 
Besl, P. (1988). Surfaces in range image understanding. 
Springer, New York. 
Brügelmann, R. and Fórstner, W. (1992). Noise estima- 
tion for color edge extraction. In Fôrstner, W. and 
Ruwiedel, S., editors, Robust computer vision, pages 
90-106. Wichmann, Karlsruhe. 
Cohen, F. and Fan, Z. (1992). Maximum likelihood unsuper- 
vised textured image segmentation. Computer Vision, 
Graphics and Image Processing, 54:239-251. 
Haralick, R. M. and Shapiro, L. G. (1985). Survey, image 
segmentation techniques. Computer Vision, Graphics 
and Image Processing, 29:100-132. 
Herlin, l., Bereziat, D., Giraudon, G., Nguyen, C., and Graf- 
figne, C. (1994). Segmentation of echocardiographic 
668 
International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B3. Vienna 1996 
Figure 4: Segmentation result 
images with Markov random fields. In Eklundh, J.-O., 
editor, Computer Vision — ECCV '94, pages 201-206, 
Berlin. Springer. 
Landes, S. (1995). Entwicklung eines Fláchenwachstumsver- 
fahrens zur Segmentierung von Luftbildern. Master's 
thesis, IPF, Universitat Karlsruhe. 
LaValle, S. and Hutchinson, S. (1995). A Bayesian segmen- 
tation methodology for parametric image models. /EEE 
Transactions on Pattern Analysis and Machine Intelli- 
gence, 17:211-218. 
Pal, N. R. and Pal, S. K. (1993). A Review on Image Segmen- 
tation Techniques. Pattern Recognition, 26(9):1277— 
1294. 
Pavlidis, T. (1977). Structural Pattern Recognition. Springer, 
Berlin. 
Pearl, J. (1986). Fusion, propagation and structuring in belief 
networks. Artificial Intelligence, 29:241-288. 
Quint, F. (1994). Bildsegmentierung mit einem Bayesschen 
Ansatz. Technischer Bericht IPF-FQ-9/94, IPF, Univer- 
sitat Karlsruhe. 
Quint, F. and Sties, M. (1995). Map-based semantic mo- 
deling for the extraction of objects from aerial images. 
In Grün, A., Kübler, O., and Agouris, P., editors, Auto- 
matic Extraction of Man-Made Objects from Aerial and 
Space Images, pages 307-316. Birkhäuser, Basel. 
Silverman, J. and Cooper, D. (1988). Bayesian clustering for 
unsupervised estimation of surface and texture models. 
IEEE Transactions on Pattern Analysis and Machine In- 
telligence, 10:482-495. 
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