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Figure 9: Results of edge extraction: Upper row) aerial im 
age, lower row left) using intensity alone (s — 2.0, t = 2.0J, 
right) using the Laplacian box ( s x = 0.7, h = 2.0, m = 
3, S2 = 4.0, t2 = 3.0,). 
REFERENCES 
A. C. Bovik, M. C. and Geisler, W. S., 1990. Multichannel 
texture analysis using localized spatial filters. IEEE Trans 
action on PAMI 12, pp. 55-73. 
Andrey, P. and Tarroux, P, 1996. Unsupervised texture 
segmentation using selectionist relaxation. Proc. of ECCV, 
pp. 483-491. 
Ballard, D. H. and Rao, R. P. N., 1994. Seeing behind oc 
clusions. In: Lecture Notes in Computer Scienes, Vol. 800, 
Springer Verlag, Heidelberg, pp. 274-285. 
Bigün, J. and du Buf, J. M. H., 1992. Geometric image 
primitives by complex moments in Gabor space and the ap 
plication to texture segmentation. In: Proceedings of Con 
ference on Computer Vision and Pattern Recognition, IEEE 
Computer Society Press, pp. 648-649. 
Burt, P. J. and Adelson, E. H., 1983. The Laplacian pyramid 
as a compact image code. IEEE Transactions on Commu 
nications COM-31 (4), pp. 532-540. 
Carlucci, L. A., 1976. A formal system for texture lan- 
gagues. Pattern Recognition 4(1), pp. 53-72. 
de Beuaville, J.-R, Bi, D. and Langlais, L., 1994. Texture 
segmentation using grey level rank-vectors. Graphics and 
Vision 3(4), pp. 667-674. 
Derin, H. and Cole, W. S., 1986. Segmentation of tex 
tured images using Gibbs random fields. Computer Vision, 
Graphics, and Image Processing 35 (1), pp. 72-98. 
Förstner, W., 1991. Statistische Verfahren für die automa 
tische Bildanalyse und ihre Bewertung bei der Objekterken 
nung und -Vermessung. München. DGK bei der Bay 
erischen Akademie der Wissenschaften, Reihe C, Heft 370. 
Fuchs, C., 1998. Extraktion polymorpher Bildstrukturen 
und ihre topologische und geometrische Gruppierung. 
München. DGK bei der Bayerischen Akademie der Wis 
senschaften, Reihe C, Heft 502. 
Figure 10: Results of edge extraction: Upper row) image 
’’toys”, lower row left) using intensity alone (s — 2.0, t = 
2.0), right) using level the Laplacian box ( si = 0.7, t } = 
2.0, tii = 2, S2 — 2.0, ¿2 = “2.0). 
Haralick, R., 1979. Statistical and structural approaches to 
texture. In: Proceedings of IEEE, Vol. 67, No. 5, pp. 786- 
804. 
Haralick, R. M. and Shapiro, L. G., 1992. Computer and 
Robot Vision, Volume I/ll. 1st edition, Addison-Wesley Pub 
lishing Company. 
Jahne, B., 1989. Digitale Bildverarbeitung. Springer. 
Julez, B. and Bergen, J. R., 1983. Textons, the fundamental 
elements in preattentive vision and perception of textures. 
Bell SystemTechnology Journal 62(6), pp. 1619-1645. 
Malik, J. and Perona, R, 1990. Preattentive texture dis 
crimination with early vision mechanism. Journal of Optical 
Society of America, pp. 923-932. 
Papoulis, A., 1984. Probability, Random Variables, and 
Stochastic Processes. Electrical Engineering, 2nd edition, 
McGraw-Hill. 
Reed, T. R. and du Buf, J. M. H., 1993. A review of 
recent texture segmentation and feature extraction tech 
niques. CVGIP: Image Understanding 57(3), pp. 359-372. 
Shao, J. and Forstner, W., 1994. Gabor wavelets for texture 
edge extraction. In: Proceedings of Symposium on Spatial 
Information from Digital Photogrammetry and Computer Vi 
sion, ISPRS Commission III, Munich, pp. 745-751. 
Weidner, U., 1994. Information preserving surface restora 
tion and feature extraction for digital elevation models. 
In: Proceedings of Symposium on Spatial Information 
from Digital Photogrammetry and Computer Vision, ISPRS 
Comm. Ill, Munich, pp. 908-915.