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Shape Recognition Via an a Contrario Model for Size Functions

  • Andrea Cerri
  • Daniela Giorgi
  • Pablo Musé
  • Frédéric Sur
  • Federico Tomassini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4142)

Abstract

Shape recognition methods are often based on feature comparison. When features are of different natures, combining the value of distances or (dis-)similarity measures is not easy since each feature has its own amount of variability. Statistical models are therefore needed. This article proposes a statistical method, namely an a contrario method, to merge features derived from several families of size functions. This merging is usually achieved through a touchy normalizing of the distances. The proposed model consists in building a probability measure. It leads to a global shape recognition method dedicated to perceptual similarities.

Keywords

False Alarm Background Model Perceptual Information Size Function Fourier Descriptor 
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

  • Andrea Cerri
    • 1
  • Daniela Giorgi
    • 1
  • Pablo Musé
    • 2
  • Frédéric Sur
    • 3
  • Federico Tomassini
    • 1
  1. 1.Dipartimento di MatematicaUniversità di BolognaBolognaItaly
  2. 2.Centre de Mathématiques et de Leurs ApplicationsÉcole Normale Supérieure de CachanCachanFrance
  3. 3.Loria & INPL, LoriaVandoeuvre-lès-NancyFrance

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