Texture Segmentation Using Area Morphology Local Granulometries

Texture segmentation based on local morphological pattern spectra provides an attractive alternative to linear scale spaces as the latter suffer from blurring and does not preserve the shape of image features. However, for successful segmentation, pattern spectra derived using a number of structuring elements, often at different orientations, are required. This paper addresses this problem by the use of area morphology to generate a single pattern spectra, consisting of a local granulometry and anti-granulometry, at each pixel position. As only one spectrum is produced, segmentation is performed by using the entire spectrum as the feature vector instead of taking pattern spectrum moments. Supervised and unsupervised segmentation results for a simulated image of Brodatz textures confirm the advantages of this new approach.