Abstract

Detection of repetitive patterns in texture images is a longstanding problem in texture analysis. In the textile industry, this is particularly useful in isolating repeats in woven fabric designs. Based on repetitive patterns, textile designers can identify and classify complex textures. In this paper, we propose a new method for detecting, locating, and grouping the repetitive patterns, particularly for near regular textures (NRT) based on a mid-level patch descriptor. A NRT is parameterized as a vector-valued function representing a texton unit together with a set of geometric transformations. We perform shape alignment by image congealing and correlation matching. Our experiments demonstrate that our patch-based method significantly improves the performance and the versatility of repetitive pattern detection in NRT images.

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