Invariant texture segmentation with reduced illumination sensitivity

A method for rotation and scale-invariant texture segmentation is proposed, which can also be employed for object recognition based on pattern analysis in noisy images. The segmentation scheme is based on a supervised rotation and scale-invariant texture recognition using multi-channel polar logarithmic Gabor filters for feature extraction. The polar logarithmic arrangement works like a Fourier-Mellin descriptor providing orientation and scale invariance. The classification of the features is carried out by symmetric phase-only matched filtering. The classification accuracy is about 90% at arbitrary rotation angle and for scale factors between 0.25 and 4.0. Rotation angle and scale factor can be determined with high precision by the classification scheme. Prior to the segmentation, a normalization scheme as preprocessing step is used to reduce illumination gradients, which is also able to treat illumination, edges like shades. (C) 2001 Elsevier Science B.V. All rights reserved.