Investigation on the Classification of Weave Pattern Based on an Active Grid Model

In this paper, a new method based on the active grid model (AGM) is used to identify the weave pattern of woven fabrics. The two-dimensional geometrical weaving structure of the woven fabrics could be described mathematically using the concept of active grid alignment, so that the analysis of the fabric weave pattern could be implemented in the field of an AGM model of a fabric. This proposed method utilizes dual-side scanning technology to merge the dual-side images of a fabric at the yarn level. It contains a four-step method to construct an AGM. First, a yarn-detecting algorithm is applied on the dual-side scan images to initialize the AGM. Second, the AGM self-adjustment scheme is used to adjust the AGM accurately. Then, the types of the yarn interlacing are classified based on the edge map and the result is refined using the neighboring information of yarns. Finally, the color pattern is determined by using color clustering and matching; error correction is also made based on the color configuration. Some preliminary experiments show that the AGM is effective for the classification of fabric weaving patterns.