Machine vision refers to the technology that uses computers to perceive and understand images or videos. It mainly involves image processing, pattern recognition, computer vision, deep learning and other fields.
The goal of machine vision is to enable computers to simulate the human visual system and achieve advanced understanding and analysis of images or videos. It can be used in many application fields such as real-time monitoring, target detection and tracking, image classification and recognition, face recognition, and video analysis.
The basic steps of machine vision include image acquisition, preprocessing, feature extraction, feature matching and classification, etc. Among them, image acquisition can be carried out through cameras, sensors and other equipment. Preprocessing includes operations such as denoising and enhancement to improve image quality. Feature extraction refers to extracting key features in images, such as color, texture, shape, etc. Feature matching matches the extracted features with existing libraries for identification or classification. Finally, classification is the classification of images into different categories or tasks such as target detection and tracking.
Deep learning plays an important role in machine vision. It uses a large number of labeled samples for model training and can automatically learn and extract features in images. Well-known deep learning models include convolutional neural network (CNN), recurrent neural network (RNN), etc.
Machine vision has broad application prospects in industrial, medical, security and other fields. For example, in industrial production, machine vision can realize product quality inspection and automated control; in the medical field, machine vision can be used for medical image analysis and disease diagnosis; in the security field, machine vision can be used for face recognition, target tracking, etc. Task.