Significantly Different Textures: A Computational Model of Pre-attentive Texture Segmentation

Abstract. Recent human vision research [1] suggests modelling preattentive texture segmentation by taking a set of feature samples from a local region on each side of a hypothesized edge, and then performing standard statistical tests to determine if the two samples differ significantly in their mean or variance. If the difference is significant at a specified level of confidence, a human observer will tend to pre-attentively see a texture edge at that location. I present an algorithm based upon these results, with a well specified decision stage and intuitive, easily fit parameters. Previous models of pre-attentive texture segmentation have poorly specified decision stages, more unknown free parameters, and in some cases incorrectly model human performance. The algorithm uses heuristics for guessing the orientation of a texture edge at a given location, thus improving computational efficiency by performing the statistical tests at only one orientation for each spatial location. 1 Pre-...

• Type: Conference
• Year: 2000
• Where: ECCV
• Authors: Ruth Rosenholz
• Keywords: Computer Vision, ECCV 2000, Human Observer, Pre-attentive Texture Segmentation, Preattentive Texture Segmentation, Segmentation Pre-attentive Texture, Texture Edge