About Implicit and Explicit Shape Representation

  • Fiora Pirri
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4155)


We present a composite analysis of shapes based on form and features. We discuss how form and features are two facets of object representation and how similarity measures are used to understand the relation between two objects’ images. We present a novel approach to approximate a shape that can still make use of Procrustes distance, leading to a relaxed notion of similarity measure. We introduce also a study on the similarity measures for non-parametric kernel densities. Finally we briefly discuss how these distance measures can be combined and represented into a Bayesian network, to learn the parameters of the defined similarity function.


Similarity Measure Bayesian Network Pattern Anal Object Representation Zernike Moment 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Fiora Pirri
    • 1
  1. 1.Dipartimento di Informatica e SistemisticaRomaItaly

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