Constructing Stochastic Pyramids by MIDES — Maximal Independent Directed Edge Set
We present a new method (MIDES) to determine contraction kernels for the construction of graph pyramids. Experimentally the new method has a reduction factor higher than 2.0. Thus, the new method yields a higher reduction factor than the stochastic decimation algorithm (MIS) and maximal independent edge set (MIES), in all tests. This means the number of vertices in the subgraph induced by any set of contractible edges is reduced to half or less by a single parallel contraction. The lower bound of the reduction factor becomes crucial with large images.
Keywordsirregular graph pyramids maximal independent set maximal independent directed edge set topology preserving contraction
Unable to display preview. Download preview PDF.
- 2.M. Borowy and J. Jolion. A pyramidal framework for fast feature detection. In Proc. of 4th Int. Workshop on Parellel Image Analysis, pages 193–202, 1995.Google Scholar
- 3.M. Burge and W. G. Kropatsch. Run Graphs and MLPP Graphs in Line Image Encoding. In W. G. Kropatsch and J.-M. Jolion, editors, 2nd IAPR-TC-15 Workshop on Graph-based Representation, pages 11–19. OCG-Schriftenreihe, Österreichische Computer Gesellschaft, 1999. Band 126.Google Scholar
- 4.K. Cho and P. Meer. Image Segmentation from Consensus Information. CVGIP: Image Understanding, 68(1):72–89, 1997.Google Scholar
- 6.R. Glantz, R. Englert, and W. G. Kropatsch. Representation of Image Structure by a Pair of Dual Graphs. In W. G. Kropatsch and J.-M. Jolion, editors, 2nd IAPR-TC-15 Workshop on Graph-based Representation, pages 155–163. OCG-Schriftenreihe, Österreichische Computer Gesellschaft, 1999. Band 126.Google Scholar
- 7.Y. Haxhimusa, R. Glantz, M. Saib, Langs, and W. G. Kropatsch. Logarithmic Tapering Graph Pyramid. In L. van Gool, editor, Proceedings of 24th DAGM Symposium, pages 117–124, Swiss, 2002. Springer Verlag LNCS 2449.Google Scholar
- 8.Y. Haxhimusa and W. G. Kropatsch. Experimental Results of MIS, MIES, MIDES and D3P. Technical Report No.78, PRIP, Vienna University of Technology. http://www.prip.tuwien.ac.at/ftp/pub/publications/trs, 2003.Google Scholar
- 11.J.-M. Jolion and A. Rosenfeld. A Pyramid Framework for Early Vision. Kluwer Academic Publishers, 1994.Google Scholar
- 12.P. Kammerer and R. Glantz. Using Graphs for Segmenting Crosshatched Brush Strokes. In J.-M. Jolion, W. G. Kropatsch, and M. Vento, editors, 3nd IAPR-TC-15 Workshop on Graph-based Representation, pages 74–83. CUEN, 2001.Google Scholar
- 14.W. G. Kropatsch and M. Burge. Minimizing the Topological Structure of Line Images. In A. Amin, D. Dori, P. Pudil, and H. Freeman, editors, Advances in Pattern Recognition, Joint IAPR International Workshops SSPR’98 and SPR’98, volume Vol. 1451 of Lecture Notes in Computer Science, pages 149–158, Sydney, Australia, August 1998. Springer, Berlin Heidelberg, New York.CrossRefGoogle Scholar
- 15.W. G. Kropatsch and H. Macho. Finding the structure of connected components using dual irregular pyramids. In Cinquième Colloque DGCI, pages 147–158. LLAIC1, Université d’Auvergne, ISBN 2-87663-040-0, September 1995.Google Scholar
- 16.C. Mathieu, I. E. Magnin, and C. Baldy-Porcher. Optimal stochastic pyramid: segmentation of MRI data. Proc. Med. Imaging VI: Image Processing, SPIE Vol.1652:pp.14–22, Feb. 1992.Google Scholar
- 18.P. Meer, D. Mintz, A. Montanvert, and A. Rosenfeld. Consensus vision. In AAAI-90 Workshop on Qualitative Vision, Boston, Massachusetts, USA, July 29 1990.Google Scholar