Digital image processing technology, broadly speaking, can be considered a general term for various image processing techniques. It encompasses a series of tasks performed using computers and other electronic devices, such as image acquisition, encoding, storage, and transmission; image synthesis and generation; image display, rendering, and output; image transformation, enhancement, restoration, and reconstruction; feature extraction and measurement; target detection, representation, and description; image sequence correction; and image classification, representation, and recognition. These processing techniques are interconnected, and a practical image processing system often combines multiple techniques to obtain the desired results. Digital image processing technology offers advantages such as good reproducibility, high processing accuracy, high flexibility, and wide applicability.

Typical applications of machine vision technology include:

Image recognition applications: Image recognition utilizes machine vision to process, analyze, and understand images in order to identify targets and objects of different patterns. The most typical application of image recognition in the machine vision industry is QR code recognition, as QR codes are one of the most common types of barcodes.

Object sorting: In machine vision applications, object sorting is based on recognition and detection. It uses machine vision systems to process images and combines them with robotic arms to sort products.

Facial recognition: Facial recognition technology has been widely applied in industries such as healthcare, finance, education, industry, power, aerospace, government, border control, public security, military, and the judiciary. Furthermore, China has a strong demand for facial recognition technology. Currently, the technology is ready for large-scale commercial application and is expected to experience rapid growth in the next three to five years.

Video/Surveillance Analytics: Artificial intelligence technology can quickly search and query structured video content information such as people, vehicles, and objects. This application makes it possible for public security systems to find criminals in complex surveillance videos. This technology is also widely used in crowd analysis, crime prevention and early warning systems, and other applications in transportation hubs.

Security protection: By monitoring and identifying personnel within a specific range using machine vision, unauthorized personnel can be prevented from entering. Once a specific person is identified, an alarm message can be issued, which can greatly improve the efficiency of monitoring and management.

Product quality inspection: By analyzing high-resolution photographs of products, product quality can be analyzed and screened. Machine vision can more effectively control quality. This quality includes traditional production processes that require human inspection, such as circuit board design, solder joints, screw installation, crack detection, and color difference detection.

Industrial visual inspection: Machine vision can quickly acquire and automatically process large amounts of information. In automated production processes, machine vision systems are widely used for condition monitoring, finished product inspection, and quality control. The key features of machine vision systems are increased production flexibility and automation. They are applicable to hazardous working environments or situations where human vision is insufficient. Furthermore, machine vision inspection can significantly improve production efficiency and automation.