Classification of Biological Objects using Active Appearance Modelling and Color Cooccurrence Matrices |
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| 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 | Classification, Active Appearance Models, AAM, Cooccurrence Matrices |
| Type | Conference paper [With referee] |
| Conference | SCIA 2007 |
| Editors | |
| Year | 2007 Month June Vol. 15 pp. 938-947 |
| Publisher | Springer |
| Series | Lecture Notes in Computer Science |
| ISBN / ISSN | 0302-9743 |
| Electronic version(s) | [pdf] |
| Publication link | http://springer.com |
| BibTeX data | [bibtex] |
| IMM Group(s) | Image Analysis & Computer Graphics |