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Characterizing 3D Shapes Using Fractal Dimension

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNIP,volume 6419)

Abstract

Developments in techniques for modeling and digitizing have made the use of 3D models popular to a large number of new applications. With the diffusion and spreading of 3D models employment, the demand for efficient search and retrieval methods is high. Researchers have dedicated effort to investigate and overcome the problem of 3D shape retrieval. In this work, we propose a new way to employ shape complexity analysis methods, such as the fractal dimension, to perform the 3D shape characterization for those purposes. This approach is described and experimental results are performed on a 3D models data set. We also compare the technique to two other known methods for 3D model description, reported in literature, namely shape histograms and shape distributions. The technique presented here has performed considerably better than any of the others in the experiments.

Keywords

  • Fractal dimension
  • complexity
  • 3D shape descriptor

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Backes, A.R., Eler, D.M., Minghim, R., Bruno, O.M. (2010). Characterizing 3D Shapes Using Fractal Dimension. In: Bloch, I., Cesar, R.M. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2010. Lecture Notes in Computer Science, vol 6419. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16687-7_7

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  • DOI: https://doi.org/10.1007/978-3-642-16687-7_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-16686-0

  • Online ISBN: 978-3-642-16687-7

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