Abstract
Thin nets are the lines where the grey level function is locally extremum in a given direction. Recently, we have shown that it is possible to characterize the thin nets using differential properties of the image surface. However, the method failed when these structures present different widths. In this paper we show that, the extraction process of the thin nets, having different width, requires a multi-scale analysis of the image. To design the fusion process of the multi-scale information, we will study the behavior of the differential properties of the image surface, in particular the curvatures, in scale space. We illustrate the efficiency of the proposed multi-scale approach by extracting roads of different widths in satellite images.
We wish to thank R. Deriche for stimulating discussions about the multi-resolution analysis methods. Thanks also to P. soille for his advice on this paper.
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© 1997 Springer-Verlag Berlin Heidelberg
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Armande, N., Montesinos, P., Monga, O. (1997). Thin nets extraction using a multi-scale approach. In: ter Haar Romeny, B., Florack, L., Koenderink, J., Viergever, M. (eds) Scale-Space Theory in Computer Vision. Scale-Space 1997. Lecture Notes in Computer Science, vol 1252. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63167-4_72
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DOI: https://doi.org/10.1007/3-540-63167-4_72
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