In computer vision and image processing, we often perform different processing on “objects” than on “texture.” In order to do this, we must have a way of localizing textured regions of an image. For this purpose, we suggest a working definition of texture: Texture is a substance that is more compactly represented by its statistics than by specifying the configuration of its parts. Texture, by this definition, is stuff that seems to belong to the local statistics. Outliers, on the other hand, seem to deviate from the local statistics, and tend to draw our attention, or “pop out”1, 2. This definition suggests that to find texture we first extract certain basic features and compute their local statistics. Then we compute a measure of saliency, or degree to which each portion of the image seems to be an outlier to the local feature distribution, and label as texture the regions with low saliency. We present a method, based upon this idea, for labeling points in natural scenes as belonging to texture regions. This method is based upon recent psychophysics results on processing of texture and popout.
The pdf included here is actually from SPIE '99, a similar extended abstract.