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Increasing the Power of Shape Descriptor Based Object Analysis Techniques

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Perspectives in Shape Analysis

Part of the book series: Mathematics and Visualization ((MATHVISUAL))

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Abstract

An advantage of shape based techniques, for object analysis tasks, is that shape allows a large number of numerical characterizations. Some of these have an intuitively clear meaning, while others do not, but they are still very useful because they satisfy some desirable properties (e.g. invariance with respect to a set of certain transformations). In this chapter we focus on numerical shape characteristics that have a clear intuitive interpretation – i.e. based on such numerical values, we can predict, to some extent, what the considered object looks like. This is beneficial, since it enables a priori appraisal of whether certain shape characteristics have suitable discriminative potential that make them appropriate for the intended task. By their nature, the number of such methods cannot be as large as the number of methods to allocate shape/object characteristics based on some formalism (algebraic, geometric, probabilistic, etc.). Because of that, some other possibilities to increase the discriminative capacity of the methods based on numerical shape characteristics, with an intuitively predictable meaning, are considered. Herein, we observe two such possibilities: the use of tuning parameters to obtain a family of shape characteristics, and the use of multiple shapes derived from the objects analyzed.

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Notes

  1. 1.

    For an extension to the circularity measures with α ∈ (−1, 0), see [45].

  2. 2.

    We note that our results for \(\mathcal{C}_{st}(S)\) listed in Table 5.1 do not match Rangayyan et al.’s [28] reported accuracies for \(\mathcal{C}_{st}(S)\). This can be attributed to several factors: (i) different classifiers were used, and also (ii) different methods for estimating perimeter may have been used.

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Acknowledgements

This work is partially supported by the Ministry of Science of the Republic of Serbia.

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Authors and Affiliations

  1. Mathematical Institute, Serbian Academy of Sciences and Arts, Belgrade, Serbia

    Joviša Žunić

  2. School of Computer Science & Informatics, Cardiff University, Cardiff, CF24 3AA, UK

    Paul L. Rosin

  3. Computer Science, Toros University, Mersin, Turkey

    Mehmet Ali Aktaş

Authors
  1. Joviša Žunić
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  2. Paul L. Rosin
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  3. Mehmet Ali Aktaş
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Corresponding author

Correspondence to Joviša Žunić .

Editor information

Editors and Affiliations

  1. Institute for Applied Mathematics and Scientific Computing, Brandenburg University of Technology, Cottbus, Germany

    Michael Breuß

  2. Israel Institute of Technology Technion, Haifa, Israel

    Alfred Bruckstein

  3. School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece

    Petros Maragos

  4. INRIA, Grenoble Rhône-Alpes, Saint Ismier, France

    Stefanie Wuhrer

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Žunić, J., Rosin, P.L., Aktaş, M.A. (2016). Increasing the Power of Shape Descriptor Based Object Analysis Techniques. In: Breuß, M., Bruckstein, A., Maragos, P., Wuhrer, S. (eds) Perspectives in Shape Analysis. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-319-24726-7_5

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