## 1 Introduction  
Product surface defect detection is an important application field of machine vision, and it has been widely used in various industries, including the steel industry [1], textile industry [2], semiconductor manufacturing industry [3, 4], and printing industry [5, 6]. For surface defect detection systems based on machine vision, one key technology is image registration. Image registration is an image processing technique that aligns two images spatially. Machine vision inspection systems ensure that each detected image captured by the camera is aligned with the standard template image in a spatial position [7]. Selecting the appropriate registration features for different detection contents is a critical in improving registration accuracy. At present, image registration algorithms can be roughly divided into three types: pixel grayscale-, image feature-, and transform domain-based image registration algorithms [8]. The pixel grayscale-based image registration algorithm usually finds the optimal match through a grayscale similarity measure, such as the normalized cross-correlation registration algorithm [9] and the sequential similarity detection algorithm [10]. This type of algorithm is stable and usually uses full grayscale information to measure the similarity of two images. However, pixel grayscale-based registration methods are often computationally intensive and thus achieve poor real-time performance. The image feature-based registration algorithm mainly uses image features such as corner, edge, texture, and shape. This method extracts the features of two images and uses a similarity measure to determine the spatial transformation relationship [11]. The extracted feature types determine the accuracy of image registration. The existing feature extraction operators mainly include the Harris operator [12], Forstner operator [13], SIFT operator [14], SURF operator [15], FAST operator [16], PCA-SIFT [17], SAR-SIFT [18], AB-SIFT [19], and other operators. This type of algorithm entails a minimal amount of computation and achieves strong robustness. However, for images with inconspicuous features, this type of algorithm seems to be ineffective [17]. The transform domain-based registration algorithm transforms images from the spatial domain to the frequency domain and then analyzes the image in the frequency domain to determine the registration parameters. The commonly used frequency domain transform-based image registration algorithms mainly include wavelet transform registration technology [20; 21], Fourier transform registration technology [22; 23; 24] and composite registration algorithm combined with the space and frequency domain [25; 26]. This type of image registration method has a strong anti-noise ability, but the calculation amount is relatively large. Pixel grayscale-based and shape feature-based image registration algorithms are widely used in the field of printing defect detection systems based on machine vision. However, as mentioned previously, the pixel grayscale-based image registration algorithm entails a large amount of calculation, achieves low real-time performance, and is greatly influenced by illumination. In addition, the registration feature regions based on pixel grayscale are difficult to establish and are not robust because the image contents of different prints vary widely. Therefore, the current study adopts the shape feature-based image registration algorithm to align captured printed images with a standard reference template image. In existing printing defect detection systems, feature regions are selected manually. However, manual selection results in inconsistent selected regions and low efficiency. These drawbacks affect the precision of registration and cause systems to fail to meet the requirements of the highly automated visual inspection of printed matter. Hence, this study proposes an automatic feature region searching algorithm for image registration without manual marking to improve the real-time results and accuracy of the registration process.