Machine vision is a subject that studies how computers simulate and understand human visual system. It covers many fields such as image processing, pattern recognition, computer vision and deep learning. The goal of machine vision is to analyze and interpret images and videos through computers, so as to obtain information in images, and to understand, identify and process them.
Image acquisition and preprocessing: machine vision first needs to obtain image data from various sensors or cameras. Then, the image is preprocessed, such as denoising, enhancement and geometric correction. In order to improve the effect of subsequent processing.
Feature extraction and representation: Machine vision uses image processing and pattern recognition technology to extract and represent features in images. These features can include edges, textures, shapes, colors, etc. By extracting and representing features, the computer can better understand the content of the image.
Target detection and recognition: An important task of machine vision is to detect and recognize the target in the image. By training and using classifiers, deep learning networks and other methods, machines can automatically identify objects, faces, people and so on in images.
Three-dimensional reconstruction and analysis: Machine vision can use image data from multiple perspectives for three-dimensional reconstruction and analysis. Through the registration and fusion of multi-views, the three-dimensional model and spatial information of the object can be obtained, and then it can be visualized, measured and analyzed.
Video analysis and tracking: Machine vision can not only process static images, but also analyze and process video data. For example, motion detection can track and analyze the motion of objects in video, and behavior recognition can identify the actions and behavior patterns of people or animals.
The role of machine vision
With the rapid development of deep learning and computer hardware, machine vision has made remarkable progress in recent years. The convolutional neural network architecture of deep learning has made remarkable achievements in image recognition, target detection and semantic segmentation. Machine vision is widely used in many fields. For example, automatic driving, intelligent monitoring, medical image analysis, industrial quality inspection, robot navigation and other fields are inseparable from the technical support of machine vision.
2. The development of machine vision is of great significance to improve the perception and intelligence of computers. It has broad application prospects in the fields of industry, medical care, transportation, security and so on, and will bring more convenience and benefits to people's lives and work. Machine vision is also combined with other technical fields, such as augmented reality, virtual reality and natural language processing, to achieve more intelligent and interactive applications.