A model-based method for rotation invariant texture classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reflectance and texture of real-world surfaces
ACM Transactions on Graphics (TOG)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Illuminant and gamma comprehensive normalisation in log RGB space
Pattern Recognition Letters - Special issue: Colour image processing and analysis
Distinctive Image Features from Scale-Invariant Keypoints
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
Radon Transform Orientation Estimation for Rotation Invariant Texture Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image retrieval measures based on illumination invariant textural MRF features
Proceedings of the 6th ACM international conference on Image and video retrieval
Material-specific adaptation of color invariant features
Pattern Recognition Letters
A multiscale representation including opponent color features for texture recognition
IEEE Transactions on Image Processing
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We propose an illumination invariant and rotation insensitive texture representation based on a Markovian textural model. A texture is aligned with its dominant orientation and textural features are derived from fast analytical estimates of Markovian statistics. We do not require any knowledge of illumination direction or spectrum. This makes our method suitable for computer analysis of real scenes, where appearance of materials depends on their orientation towards the illumination source. Our method is tested on the most realistic visual representation of natural materials - the bidirectional texture function (BTF), using data from the CUReT database, where it outperforms the alternative leading illumination invariant Local Binary Patterns (LBP) and texton MR8 methods, respectively.