Detecting fabric defects with computer vision and fuzzy rule generation part II: Defect identification by a fuzzy expert system

A new method for a fabric defect identifying system uses fuzzy inference in multicondition approximate reasoning and is capable of defect identificati on. The system uses fuzzy inference rules, and the membership function for these rules adopts a neural network approach. Only a small number of fuzzy inference rules are required to make the identifications of nondefect, slub (warp direction), slub (weft direction), nep, and composite defect. One fu zzy inference rule can replace many crisp rules. With this method, we can d esign a reliable system for identifying fabric defects. Experimental result s with this approach have demonstrated an identification ability comparable to that of a human inspector.