A Neural Network Approach to Objective Evaluation of Seam Pucker

—Seam pucker grade is one of the most important quality parameters in garments manufacturing industry. At present, seam pucker is usually evaluated by human inspectors, which is subjective, unreliable and time-consuming. Instead of subjective evaluation, this paper presents an objective method by using image analysis and pattern recognition. The evaluation system consists of image acquisition, image normalization, feature extraction and self organizing map classifier. Textural features of seam puckers are studied with a widely used statistical method, the co-occurrence matrix approach. The grades of seam puckers can be obtained from the trained self organizing map classifier and the results are very promising.

• Type: Conference
• Year: 2007
• Where: WCE
• Authors: K. L. Mak, Wei Li
• Keywords: MAP Classifier, Seam Puckers, Self Organizing Map, WCE 2007, WCE 2008