  A maximally stable extreme region (MSER) analysis based convolutional neural
network (CNN) for unified defect detection framework is proposed in this paper.
Our proposed framework utilizes the generality and stability of MSER to
generate the desired defect candidates. Then a specific trained binary CNN
classifier is adopted over the defect candidates to produce the final defect
set. Defect datasets over different categories \blue{are used} in the
experiments. More generally, the parameter settings in MSER can be adjusted to
satisfy different requirements in various industries (high precision, high
recall, etc). Extensive experimental results have shown the efficacy of the
proposed framework.
