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Classification of Biological Objects Using Active Appearance Modelling and Color Cooccurrence Matrices

  • Anders Bjorholm Dahl
  • Henrik Aanæs
  • Rasmus Larsen
  • Bjarne K. Ersbøll
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4522)

Abstract

We use the popular active appearance models (AAM) for extracting discriminative features from images of biological objects. The relevant discriminative features are combined principal component (PCA) vectors from the AAM and texture features from cooccurrence matrices. Texture features are extracted by extending the AAM’s with a textural warp guided by the AAM shape. Based on this, texture cooccurrence features are calculated. We use the different features for classifying the biological objects to species using standard classifiers, and we show that even though the objects are highly variant, the AAM’s are well suited for extracting relevant features, thus obtaining good classification results. Classification is conducted on two real data sets, one containing various vegetables and one containing different species of wood logs.

Keywords

Face Recognition Object Recognition Biological Object Discriminative Feature Canonical Discriminant Analysis 
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 Berlin Heidelberg 2007

Authors and Affiliations

  • Anders Bjorholm Dahl
    • 1
    • 2
  • Henrik Aanæs
    • 1
  • Rasmus Larsen
    • 1
  • Bjarne K. Ersbøll
    • 1
  1. 1.Informatics and Mathematical Modelling, Technical University of Denmark 
  2. 2.Dralle A/S - Cognitive Systems, CopenhagenDenmark

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