Abstract
Graphs are useful tools for modeling problems that occur in a variety of fields. In machine vision graph based solutions have been successfully applied to many image processing problems e.g. quad trees for image compression and region adjacency graphs for segmentation. The application of graphs to machine vision problems poses special problems due to the underlying size of the image e.g. a graph representing the base level of a 512x512 image has over 200.000 nodes. The large size of the graphs make issues of both space and time complexity important when designing algorithms for machine vision problems. We present an implementation under LEDA (Library of Efficient Data structures and Algorithms) of DGC (Dual Graph Contraction) for irregular pyramids. In the first section we present the theory behind DGC, in the second an algorithmic specification is derived, and in the third an implementation under LEDA is given followed by a short conclusion.
This work was supported by the Austrian Science Foundation under grant number S7002-MAT.
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© 1998 Springer-Verlag Wien
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Kropatsch, W.G., Burge, M., Ben Yacoub, S., Selmaoui, N. (1998). Dual Graph Contraction with LEDA. In: Jolion, JM., Kropatsch, W.G. (eds) Graph Based Representations in Pattern Recognition. Computing Supplement, vol 12. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6487-7_11
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DOI: https://doi.org/10.1007/978-3-7091-6487-7_11
Publisher Name: Springer, Vienna
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