## 4 Conclusions  
Automatic methods for searching feature regions in printed image registration are studied in this work, and the existing problems of the manual marker feature regions are addressed. The automatic feature region searching method for image registration without manual marking is proposed. First, the characteristics of different shapes are analyzed. The characteristics of feature region shapes for image registration are proposed. Second, the contour point distribution information and the edge gradient direction are combined. The decision conditions of the corresponding shape searching algorithm are given. The experimental results show that the proposed method can extract the ideal shape, such as a quasi-rectangle region and a quasi-ellipse region, and use it as the image registration feature region in a printing defect detection system.  
The contributions of the proposed automatic feature region searching algorithms are as follows. First, despite the real-time requirements of printing defect inspection systems, the proposed algorithm for the automatic searching of registration feature regions is not particularly complicated and solves the problem in printing defect online detection systems. It also solves the problem of automatically, efficiently, and accurately establishing the registration feature regions in printing defect detection systems. The method can establish a detection standard reference template image automatically and quickly during online printing defect detection. The problem of low efficiency and unreliability of registration regions in manual searching is overcome by the proposed method, which provides an effective solution for image registration feature region searching in printing defect detection systems. Second, the description of a good shape region for image registration for printing defect inspection is proposed. Lastly, the proposed feature region extraction method for subregion registration and the subregion-associated feature region searching method solve the problem of local registration errors for the registration of entire printed images caused by the deformation and rotation of printed images. The proposed method is successfully applied to an actual online printing defect detection system for a certain enterprise.  
[PERSON] guided the experiments and wrote the manuscript. [PERSON] carried out the measurements and analyzed the experimental data. [PERSON] designed the automatic feature region searching algorithms and wrote the program code. [PERSON] conceived this study and proposed some valuable suggestions.  
This work is supported by the National Key R&D Program of China under Grant No. 2018 YFD0700400-2018 YFD0700403, the National Natural Science Foundation of China under Grant No. 61671374, the Key Research and Development Program of Shaanxi under Grant No. 2019 GY-080, the Key Program of Natural Science Foundation of Shaanxi Province of China under Grant No. 2017 JZ020, the Project of Xi'an University of Technology of China under Grant No. 108-451418006, and the PhD Stand-up Fund of Xi'an University of Technology of China under Grant No. 108-256081702.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \(\mathbf{T_{n}}\) & \(\mathbf{T_{g}}\) & \(\mathbf{d_{min}}\) & \(\mathbf{d_{max}}\) & \(\mathbf{T_{m}}\) \\ \hline
25 & 0.035 & 0.01 & 0.07 & 32 \\ \hline \end{tabular}
\end{table}
Table 1: Selected parameter values for shape search.