This paper presents generalizations to the gray scale and rotation invariant texture classification method based on local binary patterns that we have recently introduced. We derive a generalized presentation that allows for realizing a gray scale and rotation invariant LBP operator for any quantization of the angular space and for any spatial resolution, and present a method for combining mult...

This paper presents a theoretically very simple yet efficient multiresolution approach to gray scale and rotation invariant texture classification based on local binary patterns and nonparametric discrimination of sample and prototype distributions. The method is based on recognizing that certain local binary patterns termed ‘uniform’ are fundamental properties of local image texture, and their...

We propose a “signature” for rotation-invariant representation of local image structure. The signature is a complex-valued vector constructed analytically from the projections of the image onto a set of oriented basis kernels. The components of the signature form an overcomplete set of algebraic invariants, but are chosen to avoid instabilities associated with previously developed algebraic inv...

One of basic difficulties of machine learning is handling unknown rotations of objects, for example in image recognition. A related problem is evaluation of similarity of shapes, for example of two chemical molecules, for which direct approach requires costly pairwise rotation alignment and comparison. Rotation invariants are useful tools for such purposes, allowing to extract features describi...

We propose a \signature" for rotation-invariant representation of local image structure. The signature is a complex-valued vector constructed analytically from the projections of the image onto a set of oriented basis kernels. The components of the signature form an over-complete set of algebraic invariants, but are chosen to avoid instabilities associated with previously developed algebraic in...