Reflectance and texture of real-world surfaces
ACM Transactions on Graphics (TOG)
Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons
International Journal of Computer Vision
The Effect of Illuminant Rotation on Texture Filters: Lissajous's Ellipses
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Classifying Images of Materials: Achieving Viewpoint and Illumination Independence
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Outex - New Framework for Empirical Evaluation of Texture Analysis Algorithms
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Recognizing Surfaces Using Three-Dimensional Textons
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
3D Texture Recognition Using Bidirectional Feature Histograms
International Journal of Computer Vision
A Statistical Approach to Texture Classification from Single Images
International Journal of Computer Vision - Special Issue on Texture Analysis and Synthesis
Class-Specific Material Categorisation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Journal of Mathematical Imaging and Vision
Editorial: Special issue on image and video retrieval evaluation
Computer Vision and Image Understanding
Extending morphological covariance
Pattern Recognition
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3D texture classification under varying viewpoint and illumination has been a vivid research topic, and many methods have been developed. It is crucial that these methods be compared using an unbiased evaluation methodology. The most frequently employed methodologies use images from the Columbia-Utrecht Reflectance and Texture Database. These methodologies construct the training and test sets to be disjoint in the imaging parameters, but do not separate them spatially because they use images of the same surface patch for both. We perform a series of experiments which show that such practice leads to overestimation of classifier performance and distorts experimental findings. To correct that, we accurately register the images across all imaging conditions and split the surface patches to parts. The training and testing is then done on spatially disjoint parts. We show that such methodology gives a more realistic assessment of classifier performance. The sample annotations for all images are publicly available.