It characterizes the defective sub-regions and defect-free background sub-regions, and highlights the critical defect regions in the image analysis. Their approach has two key techniques for UAV-based rail images: image enhancement and surface defect segmentation. The inspection of a PCB (printed circuit board) is a kind of surface defect detection problem with visual image in manufacturing. [PERSON] et al. [19] extracted robust features in the visual image with various types of defect patterns such as scratches and improper etching, applying a random forest method. They also used probabilistic kernel density estimation to improve the detection performance. Visual inspection technology in the manufacturing process in the iron and steel industry has been investigated by [PERSON] et al. [PERSON] et al. (2019). Coverage of cameras or light source information can differentiate the hardware selection of visual inspection. The inspection or detection algorithm depends on filtering, statistics and learning methods. Basic theories and foundations of image processing highly contribute to the visual inspection technologies, according to this review paper. The following part introduces medical applications of intelligent imaging and analysis techniques on various types of image datasets including magnetic resonance images (MRI), computed tomography (CT), optical scan data, photographic images and digital videos. Segmentation of medical images is one of the most important tasks for many medical image analyses. However, the boundaries of the object segmentation have not been well addressed so far. [PERSON] et al. [PERSON] et al. (2019) proposed a method to overcome the poor performance issues encountered on the boundaries of objects. Their boundary-specific U-net improved the segmentation performance on the boundaries of the intervertebral discs in MR spine images. Human lumbar spine diagnosis can be assisted by accurate segmentation of the vertebral bodies. [PERSON] et al. [PERSON] et al. (2019) also proposed a semi-automatic segmentation technique for vertebral bodies in MR images which can reduce the user's role while achieving good segmentation accuracy. A data balancing problem is an important issue to address for some deep learning applications, especially when there are not enough datasets for training. It is often noticed that non-balanced datasets lead to a biased estimation model. [PERSON] et al. [PERSON] et al. (2019) proposed a pre-training strategy to address this problem encountered in the liver segmentation from computed tomography (CT) scans. [PERSON] et al. proposed an intelligent evaluation system for strabismus diagnosis with a sequence of digital video analyses [PERSON] et al. (2019). Each image in the recorded video is analyzed to localize important features, which are combined to evaluate the strabismus. Updating and automatic training can be an important step for the practical application and subsequence maintenance of an AI-based system. [PERSON] (2019) discussed the overall accuracy of additional learning and automatic training system for CT classification task. Intelligent design and manufacturing can help medical procedures. [PERSON] et al. [PERSON] et al. (2019) proposed a computer-aided design and manufacturing technology for patient-specific optimization of the Nuss procedure for minimally invasive surgery of pectus excavatum. This can be an example of how the intelligent imaging and analysis techniques can change the shape of the medical procedures in the future. Automatic alignment of images and volumes in medical imaging data is an important step for further subsequent analysis. [PERSON] and [PERSON] [PERSON] and [PERSON] (2019) proposed an efficient automatic midsagittal plane extraction in brain MRI images. This method can be useful for image registration, asymmetric analysis and tilt correction encountered in the analysis of brain images. Registration between volume data and surface data is also an important application in several medical procedures. [PERSON] et al. [PERSON] et al.