Classification of Rotated and Scaled Textured Images Using Gaussian Markov Random Field Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Rough surface classification using point statistics from photometric stereo
Pattern Recognition Letters
Factorial Markov Random Fields
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
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
Recognizing Surfaces Using Three-Dimensional Textons
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Affine-Invariant Local Descriptors and Neighborhood Statistics for Texture Recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast separation of direct and global components of a scene using high frequency illumination
ACM SIGGRAPH 2006 Papers
Clustering Appearance for Scene Analysis
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Photometric Stereo via Expectation Maximization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visibility subspaces: uncalibrated photometric stereo with shadows
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Divergence measures based on the Shannon entropy
IEEE Transactions on Information Theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D Geometric Scale Variability in Range Images: Features and Descriptors
International Journal of Computer Vision
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The most defining characteristic of texture is its underlying geometry. Although the appearance of texture is as dynamic as its illumination and viewing conditions, its geometry remains constant. In this work, we study the fundamental characteristic properties of texture geometry—self similarity and scale variability—and exploit them to perform surface normal estimation, and geometric texture classification. Textures, whether they are regular or stochastic, exhibit some form of repetition in their underlying geometry. We use this property to derive a photometric stereo method uniquely tailored to utilize the redundancy in geometric texture. Using basic observations about the scale variability of texture geometry, we derive a compact, rotation invariant, scale-space representation of geometric texture. To evaluate this representation we introduce an extensive new texture database that contains multiple distances as well as in-plane and out-of plane rotations. The high accuracy of the classification results indicate the descriptive yet compact nature of our texture representation, and demonstrates the importance of geometric texture analysis, pointing the way towards improvements in appearance modeling and synthesis.