Objective evaluation of seam pucker using an adaptive neuro-fuzzy inference system

Seam pucker evaluation plays a very important role in the garments manufacturing industry. At present, seam puckers are usually evaluated by human inspectors, which is subjective, unreliable and time-consuming. With the developments of image processing and pattern recognition technologies, an automatic vision-based seam pucker evaluation system becomes possible. This paper presents a new approach based on adaptive neuro-fuzzy inference system (ANFIS) to establish the relationship between seam pucker grades and textural features of seam pucker images. The evaluation procedure is performed in two stages: features extraction with the co-occurrence matrix approach, and classification with ANFIS. Experimental results demonstrate the validity and effectiveness of the proposed ANFIS-based method.