Classifying Materials from Their Reflectance Properties

  • Peter Nillius
  • Jan-Olof Eklundh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3024)


We explore the possibility of recognizing the surface material from a single image with unknown illumination, given the shape of the surface. Model-based PCA is used to create a low-dimensional basis to represent the images. Variations in the illumination create manifolds in the space spanned by this basis. These manifolds are learnt using captured illumination maps and the CUReT database. Classification of the material is done by finding the manifold closest to the point representing the image of the material. Testing on synthetic data shows that the problem is hard. The materials form groups where the materials in a group often are mis-classifed as one of the other materials in the group. With a grouping algorithm we find a grouping of the materials in the CUReT database. Tests on images of real materials in natural illumination settings show promising results.


Recognition Rate Close Manifold High Recognition Rate Matte Material Zernike Polynomial 
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 2004

Authors and Affiliations

  • Peter Nillius
    • 1
  • Jan-Olof Eklundh
    • 1
  1. 1.Computational Vision & Active Perception Laboratory (CVAP), Department of Numerical Analysis and Computer ScienceRoyal Institute of Technology (KTH)StockholmSweden

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