Spectral Clustering with Spatial Coherence Property Jointing to Active Contour Model for Image Local SE Gmentation
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References
- M.N. Do and M. Vetterli, “The contourlet transform: an efficient directional multiresolution image representation”,IEEE Transactions on Image Processing, December, 2005, pp.2091-2106.
- E.L. Pennec and S. Mallat, “Sparse geometric image representations with bandelets”, IEEE Transactions on Image Processing, April, 2005, pp. 423-438.
- J. Ferber, “Multi-Agent Systems”, New York: Addisin-Wesley Longman, Inc 1999.
- R. O. Duda, P. E. Hart and D. G, “Stork. Pattern Classification”, New York: A Wiley- Interscience Publication, 2000.
- A. Y. Ng, M. L. Jordan and Y. Weiss, “On spectral clustering: analysis and an algorithm”, Advances in Neural Information Processing System, 2001, pp. 849-856.
- H. N. El, K. M. Ait and Hammouch A, “Unsupervised approach data analysis based on fuzzy possibilistic clustering application to medical image MRI”, Computational intelligence and neuroscience, October, 2013, pp. 435-497.
- J. Mille, “Narrow band region-based active contours and surfaces for 2D and 3D segmentation”, Computer Vision and Image Understanding, September 2009, pp. 946-965.
- Y. H. Hu, “Sub-localizing region-based active contours”, Communications Technology, February, 2010, pp. 68-70. (In Chinese)
- X. Y. Cai, G. Z. Dai and L. B.Yang, “Survey on spectral clustering algorithms”, Computer Science, July, 2008, pp. 14-18. (In Chinese)
- U. Von Luxbug, “A tutorial on spectral clustering”, Statistics and Computing. April (2007), pp. 395-416.
- Z. Wu and R. Leahy, “An optimal graph theoretic approach to data clustering: theory and its application to image segmentation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, November, 1993, pp. 1101-1113.
- L. Hagen and A. B. Kahng, “New spectral methods for ratio cut partitioning and clustering”, IEEE Transactions on Computer Aided Design, September,1992, pp.1075-1085.
- S. Sarkar and P. Soundararajan, “Supervised learning of large perceptual organization: Graph spectral partitioning and learning automata”, IEEE Transactions on Pattern Analysis and Machine Intelligence, May, 2000, pp. 504-525.
- J. Shi and J. Malik, “Normalized cuts and image segmentation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, August, 2000, pp. 888-905.
- W. E. Donatha and A. J. Hoffman, “Lower bounds for the partitioning of graphs”, IBM Journal of Research and Development, May,1973, pp. 420-425.
- M. Fiedler, “Algebraic connectivity of graphs”, Journal of Czechoslovak Mathematical, August, 1973, pp.298-305.
- M. Kass and A. Witkin, “Terzopoulos D. Snakes: active contour models”, International Journal of Computer Vision, April (1987), pp. 321-331.
- V. Caselles, F. Catte and T. Coll, “A geometric model for active contours in image processing”, Numerische Mathematik, Jenuary,1993, pp. 1-31.
- T. H. Kim, K. M. Lee and S. U. Lee, “Nonparametric higher-order learning for interactive segmentation”, Proceedings of the IEEE Society Conference on Computer Vision and Pattern Recognition, April, 2010, pp. 20-24: Morgan Kaufmann, Palo Alto, Calif, USA.
- T. Riklin-Raviv, N. Kiryati and N. Sochen, “Segmentation by Level Sets and Symmetry”, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, April, 2006, pp.3-6: New York, USA.
DOI: https://doi.org/10.21307/ijssis-2017-937 | Journal eISSN: 1178-5608
Language: English
Page range: 1731 - 1749
Submitted on: Jan 6, 2016
Accepted on: Oct 4, 2016
Published on: Dec 1, 2016
Published by: Professor Subhas Chandra Mukhopadhyay
In partnership with: Paradigm Publishing Services
Publication frequency: 1 times per year
Keywords:
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© 2016 Guang Hu, Shengzhi Yuan, published by Professor Subhas Chandra Mukhopadhyay
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.