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Increasing the Power of Shape Descriptor Based Object Analysis Techniques

  • Joviša ŽunićEmail author
  • Paul L. Rosin
  • Mehmet Ali Aktaş
Conference paper
Part of the Mathematics and Visualization book series (MATHVISUAL)

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.

Keywords

Spiral Galaxy Shape Descriptor Moment Invariant Shape Measure Polygonal Approximation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

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

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Joviša Žunić
    • 1
    Email author
  • Paul L. Rosin
    • 2
  • Mehmet Ali Aktaş
    • 3
  1. 1.Mathematical InstituteSerbian Academy of Sciences and ArtsBelgradeSerbia
  2. 2.School of Computer Science & InformaticsCardiff UniversityCardiffUK
  3. 3.Computer ScienceToros UniversityMersinTurkey

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