Patterned fabrics are generally constructed from the periodic repetition of a primitive pattern unit. Repeat pattern segmentation of printed fabrics has a very significant impact on the pattern retrieval and pattern defect detection. In this paper, we propose a new approach for repeat pattern segmentation by employing the adaptive template matching method. In contrast to the traditional method for template matching, the proposed algorithm first selects an adaptive size template image in the repeat pattern image based on the size of the original image and its local maximum edge density. Then it uses the sum of absolute differences as the matching features to identify the matched regions in the original image, and the minimum envelope border of the primitive pattern, typically as a parallelogram, can be determined from the results of the four adjacent matched templates. Finally, image traversal base on the obtained parallelogram is implemented over the original image using minimum information loss theory to produce a well-segmented primitive pattern with a complete edge structure. The results from the experiments conducted using an extensive database of real fabric images show that the proposed algorithm has the advantage of rotation invariance and scaling invariance and will not be affected when the background or foreground color is changed.

中文翻译：

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基于自适应模板匹配的印花织物重复图案分割

有图案的织物通常由原始图案单元的周期性重复构成。印花织物的重复图案分割对图案检索和图案缺陷检测具有非常重要的影响。在本文中，我们提出了一种采用自适应模板匹配方法进行重复模式分割的新方法。与传统的模板匹配方法相比，该算法首先根据原始图像的大小及其局部最大边缘密度，在重复模式图像中选择自适应大小的模板图像。然后用绝对差之和作为匹配特征来识别原始图像中的匹配区域，以及原始图案的最小包络边界，通常为平行四边形，可以从四个相邻匹配模板的结果中确定。最后，使用最小信息损失理论在原始图像上实现基于获得的平行四边形的图像遍历，以产生具有完整边缘结构的分割良好的原始图案。使用大量真实织物图像数据库进行的实验结果表明，该算法具有旋转不变性和缩放不变性的优点，并且不会在背景或前景色发生变化时受到影响。