In garment manufacturing, an automatic sewing machine is desirable to reduce cost. To accomplish this, a high-speed vision system is required to track fabric motions and recognize repetitive weave patterns with high accuracy, from a microperspective near a sewing zone. In this letter, we present an innovative framework for real-time texture tracking and weave pattern recognition. Our framework includes a module for motion estimation using blob detection and feature matching. It also includes a module for lattice detection to facilitate the weave pattern recognition. Our lattice-detection algorithm utilizes blob detection and template matching to assess pair-wise similarity in blobs' appearance. In addition, it extracts information of dominant orientations to obtain a global constraint in the topology. By incorporating both constraints in the appearance similarity and the global topology, the algorithm determines a lattice that characterizes the topological structure of the repetitive weave pattern, thus allowing for thread counting. In our experiments, the proposed thread-based texture tracking system is capable of tracking denim fabric with high accuracy (e.g., 0.03° rotation and 0.02 weave-thread translation errors) and high speed (3 frames per second), demonstrating its high potential for automatic real-time textile manufacturing.
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An innovative framework that integrates a lattice detectionmodule to accomplish fabric tracking in a thread-basedcoordinate system instead of a pixel-based system, to en-sure robustness to local fabric deformation.A novel algorithm for fast and efficient lattice detection forthread counting, achieved by constraining on both the ap-pearance similarity (through template learning and match-ing) and the global topology. An extensive comparative study evaluating various meth-ods of keypoint detection and description for their appli-cability to the fabric tracking problem of interest
Thread counting starts with low-level vision cues (e.g., blobs)and ends with high-level lattice models shown in Fig. 5. Wegenerate a representative blob template and seek a vector pairconsistent with geometric relationships between blobs.1) Template Learning, MSER gen-erates potential blobs, some of which blobs are connected, andothers are not. From attributes of each blob region (e.g., its areaand its intensity values), we group blobs into two clusters: in-dividual blobs and grouping blobs.
