Abstract
The problem of shape analysis has played an important role in the area of image analysis, computer vision and pattern recognition. In this paper, we present a new method for shape decomposition. The proposed method is based on a refined morphological shape decomposition process.We provide two more analysis for morphological shape decomposition. The first step is scale invariant analysis. We use a scale hierarchy structure to find the invariant parts in all different scale level. The second step is noise deletion. We use graph energy analysis to delete the parts which have minor contribution to the average graph energy. Our methods can solve two problems for morphological decomposition - scale invariant and noise. The refined decomposed shape can then be used to construct a graph structure. We experiment our method on shape analysis.
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References
Bai, X., Latecki, L.J., Liu, W.Y.: Skeleton pruning by contour partitioning with discrete curve evolution. IEEE Trans. PAMI 29(3), 449–462 (2007)
Luo, B., Wilson, R.C., Hancock, E.R.: A spectral approach to learning structural variations in graphs. Pattern Recognition 39, 1188–1198 (2006)
Chung, F.R.K.: Spectral graph theory. American Mathematical Society, Reading (1997)
Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407, p. 484. Springer, Heidelberg (1998)
Lowe, D.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 1, 91–110 (2004)
Gutman, I., Zhou, B.: Laplacian energy of a graph. Linear Algebra and its Applications 44, 29–37 (2006)
Kim, D.H., Yun, I.D., Lee, S.U.: A new shape decomposition scheme for graph-based representation. Pattern Recognition 38(5), 673–689 (2005)
Kimia, B.B., Tannenbaum, A.R., Zucker, S.W.: Shapes, shocks, and deformations. Int. J. Computer Vision 15, 189–224 (1995)
Klassen, Srivastava, A., Mio, W., Joshi, S.H.: Analysis of planar shapes using geodesic paths on shape spaces. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 372–383 (2004)
Latecki, L.J., Lakamper, R.: Convexity rule for shape decomposition based on discrete contour evolution. Computer Vision and Image Understanding 77, 441–454 (1999)
Lee, C.G., Small, C.G.: Multidimensional scaling of simplex shapes. Pattern Recognition 32, 1601–1613 (1999)
Murase, H., Nayar, S.K.: Illumination planning for object recognition using parametric eigenspaces. IEEE Transactions on Pattern Analysis and Machine Intelligence 16, 1219–1227 (1994)
Trinh, N., Kimia, B.B.: A symmetry-based generative model for shape. In: International Conference on Computer Vision (2007)
Pitas, I., Venetsanopoulos, A.N.: Morphological shape decomposition. IEEE Trans. Pattern Anal. Mach. Intell. 12(1), 38–45 (1990)
Shokoufandeh, A., Dickinson, S., Siddiqi, K., Zucker, S.: Indexing using a spectral encoding of topological structure. In: International Conference on Computer Vision and Pattern Recognition, pp. 491–497 (1999)
Torsello, A., Hancock, E.R.: A skeletal measure of 2d shape similarity. Computer Vision and Image Understanding 95(1), 1–29 (2004)
Xiao, B., Hancock, E.R.: Clustering shapes using heat content invariants, pp. 1169–1172 (2005)
Xiao, B., Hancock, E.R.: A spectral generative model for graph structure. In: SSPR/SPR, pp. 173–181 (2006)
Xiao, B., Song, Y.-Z., Hall, P.M.: Learning object classes from structure. In: British Machine Vision Conference, Warwich, vol. 1407, pp. 207–217 (2007)
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Luyuan, C., Meng, Z., Shang, L., Xiaoyan, M., Xiao, B. (2010). Shape Decomposition for Graph Representation. In: Lee, R., Ma, J., Bacon, L., Du, W., Petridis, M. (eds) Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2010. Studies in Computational Intelligence, vol 295. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13265-0_1
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DOI: https://doi.org/10.1007/978-3-642-13265-0_1
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