Feature matching of fancy weft knitted stitch based on modified SURF algorithm

Abstract

In this paper, based on Hessian invariant moments, a new approach for classification of fancy weft knitted stitch was proposed. The speeded up robust features (SURF) algorithm was used to identify fancy weft knitted samples including cable stitch and tuck stitch. Weft knitted stitch images generally contain many repeated features, wrong matches are easily encountered when the SURF algorithm is applied to match images. To resolve this problem, the inflection point of misjudgment was found out. The general regularity of faults and the key threshold were concluded by analyzing a number of classification experiments. The results show that wrong matches can be removed by locking the inflection point and feature matching of fancy weft knitted stitch is proved to be feasible for assigning an unknown image to one of a set of known texture classes.