Automatic texture defect detection is highly important for many fields of visual inspection.We introduce novel, geometrical
texture features for this task, which are Euclidean motion invariant and texture adaptive: An algebraic function (rational,
Padé, or polynomial) is integrated over intensities in local, circular neighborhoods on the image including an anisotropical
texture analysis. Adaptiveness is achieved through the optimization of this feature kernel and further coefficients based on a
simplex energy minimization, constrained by a measure of texture discrimination (Fisher criterion). A backpropagation trained,
multilayer perceptron network classifies the textures locally. Our approach contains new properties, usually not common in
feature theories: Theoretically implicit, multiple invariances and an automatic and specific adaptation of the features to the
texture images. Experiments with a fabric data set and Brodatz textures reveal up to 98.6% recognition accuracy.
