Defect detection of capacitive touch panel using a nonnegative matrix factorization and tolerance model.

Abstract

Capacitive touch panels (CTPs), as a medium of information interactions, have become essential parts in many consumer electronics. However, current methods such as image edge matching and frequency notch filter cannot suit the defect detection for the new-type complex CTP patterns, which have neither basic primitives nor periodicity. For solving the issues, we proposed a nonnegative matrix factorization (NMF)-based large-size image registration method, and combined it with image tolerance models to detect defects in such CTP patterns. The NMF-based image registration method can fast extract each CTP from a large image. And then, any three of registered images are selected as reference images, which are further processed by threshold processing and simple mathematical morphological operation to obtain tolerance models. Afterward, we can use the tolerance models to obtain a nondefective template. In the normal inspection stage, the defects in CTP patterns can be identified as long as comparing the tolerance models of the template and sensed images. The experimental results show that the proposed method can efficiently and accurately detect various types of defects in CTP patterns. Moreover, the detection results are robust under different illuminations. Therefore, this algorithm can be reliably applied in actual inspection of such new-type CTP patterns.