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An Approach to Perceptual Shape Matching

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Visual Information and Information Systems (VISUAL 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3736))

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Abstract

In contrast to geometric similarity, perceptual similarity is manifold and much more difficult to handle. Relatively few work is known towards capturing the perceptual similarity. In this work we introduce the concept of local shape width and show that it can be applied to measure the perceptual significance of shape parts. Given such a measure, one can define the influence of the parts as a function of their significance. By doing so, we tend to base the shape matching on perceptually more meaningful parts and reduce the matching relevance of other parts. As an application, we propose a shape evolution approach, which synthesizes a series of new shapes from an input shape. They have the property that perceptually less significant parts smoothly vanish while other parts remain unchanged. This tool can be used in combination with any shape matching algorithm. A second application is proposed to perform non-uniform shape sampling. Experimental results will be given to show the practical usefulness of the local shape width concept.

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References

  1. Avrithis, Y., Xirouhakis, Y., Kollias, S.: Affine-invariant curve normalization for object shape representation, classification, and retrieval. Machine Vision and Applications 13, 80–94 (2001)

    Article  Google Scholar 

  2. Berretti, S., Del Bimbo, A., Pala, P.: Retrieval by shape similarity with perceptual distance and effective indexing. IEEE Trans. on Multimedia 2(4), 225–239 (2000)

    Article  Google Scholar 

  3. Chuang, G., Kuo, C.-C.: Wavelet descriptor of planar curves: Theory and applications. IEEE Trans. on Image Processing 5, 56–70 (1996)

    Article  Google Scholar 

  4. Gdalyahu, Y., Weinshall, D.: Flexible syntactic matching of curves and its application to automatic hierarchical classification of silhouettes. IEEE Trans. on PAMI 21(12), 1312–1328 (1999)

    Google Scholar 

  5. Jiang, X., Bunke, H., Abegglen, K., Kandel, A.: Curve morphing by weighted mean of strings. In: Proc. of 16th ICPR, vol. IV, pp. 192–195 (2002)

    Google Scholar 

  6. Kindratenko, V.: On using functions to describe the shape. Journal of Mathematical Imaging and Vision 18, 225–245 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  7. Latecki, L.J., Lakmper, R., Wolter, D.: Optimal partial shape similarity. Image and Vision Computing 23, 227–236 (2005)

    Article  Google Scholar 

  8. Mokhtarian, F., Bober, M.: Curvature Scale Space Representation: Theory, Applications, and MPEG-7 Standardization. Kluwer Academic Publishers, Dordrecht (2003)

    MATH  Google Scholar 

  9. Nixon, M., Aguado, A.: Feature Extraction & Image Processing. Newnes (2002)

    Google Scholar 

  10. Sinclair, D., Blake, A.: Isoperimetric normalization of planar curves. IEEE Trans. on PAMI 16(8), 769–777 (1994)

    Google Scholar 

  11. Veltkamp, R.C., Hagedoorn, M.: State of the art in shape matching. In: Lew, M.S. (ed.) Principles of Visual Information Retrieval. Springer, Heidelberg (2001)

    Google Scholar 

  12. Zhang, D., Lu, G.: Review of shape representation and description techniques. Pattern Recognition 37, 1–19 (2004)

    Article  MATH  Google Scholar 

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© 2006 Springer-Verlag Berlin Heidelberg

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Jiang, X., Lewin, S. (2006). An Approach to Perceptual Shape Matching. In: Bres, S., Laurini, R. (eds) Visual Information and Information Systems. VISUAL 2005. Lecture Notes in Computer Science, vol 3736. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11590064_10

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  • DOI: https://doi.org/10.1007/11590064_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-30488-3

  • Online ISBN: 978-3-540-32339-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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