Segmentation of nonrandom textures using zero-crossings

A method for unsupervised segmentation of textured regions is presented. Rather than identifying boundaries between texture patches, this method detects regions of uniform texture in real images. No a priori knowledge regarding the image, the texture types, or their scales is assumed. The images may contain an unknown number of texture regions including regions with no texture at all. The method is most useful for identifying textures in which sharp intensity changes constitute the most distinctly perceived characteristic. Texture features are computed over image subregions from the distributions of local orientations and the separations of zero-crossing points. The segmentation algorithm establishes the existence of texture regions by finding neighboring subregions that share one or more nonaccidental properties of the computed features, e.g., a distribution of local orientations with a significant peak. Regions' accurate boundaries are identified by extending the seed regions using a region-growing technique that is applied to the computed texture features. The growth is directed by a region-specific self-adaptive thresholding scheme. No assumption is made regarding the texture scale, and the feature analysis is performed across multiple neighborhood (window) sizes. As a result different textures in the image may be segmented using different window sizes.<>