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Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects
Baltsavias, Emmanuel P.

International Archives of Photogrammetry and Remote Sensing,Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999
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,).
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-
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,
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.