Multiscale model-based feature extraction in structural texture images

Abstract

We deal with the problem of time-efficient extraction of structural features in a large class of structural texture images. The proposed approach of multiscale morphological texture modeling describes explicitly and concisely both shape and intensity parameters in the structural texture model. The modeling is based on a morphological skeletal representation of structural texture cells as objects of interest and the genomic growth of a texture region starting from a seed cell. This representation offers the advantage of concise description of texture cells as compared to the existing edge-based or contour-based approaches. A computationally efficient estimation of the structural texture parameters for texture segmentation tasks is proposed. The model parameter estimation and subsequent feature extraction rely on cell localization and scale-based locally adaptive binarization of the localized cells using isotropic matched filtering. The multiscale isotropic matched filter (MIMF) provides a scale- and orientation-invariant detection of structural cells regarded as multiple objects of interest in texture regions. Results of experiments pertaining to the parameter estimation from synthetic and real texture images as well as the segmentation of texture regions based on structural features are also provided.