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Machine vision systems are systems that use computer technology to analyze and process images or videos. They simulate the way human eyes and brains process visual information by obtaining images or videos through cameras or other image acquisition devices and then analyzing and understanding them through algorithms and models. This article will detail the components of industrial machine vision systems.
Image acquisition devices are the foundation of industrial machine vision systems, primarily responsible for obtaining image or video data. Common image acquisition devices include cameras, scanners, and radars. Cameras are the most commonly used image acquisition devices, converting optical signals into electrical signals through Charge-Coupled Devices (CCD) or Complementary Metal-Oxide-Semiconductor (CMOS) sensors.
Cameras can be divided into black-and-white cameras and color cameras, with color cameras being able to acquire RGB signals from three color channels, while black-and-white cameras can only obtain grayscale signals.
Image preprocessing is an important part of industrial machine vision systems, usually including steps like image denoising, image enhancement, and image segmentation. Image denoising is to remove noise interference in images, with common methods including mean filtering, median filtering, and Gaussian filtering.
Image enhancement aims to improve the quality and visual effect of images, with common methods including histogram equalization, grayscale transformation, and filtering. Image segmentation is dividing the image into different regions or objects, with common methods including threshold segmentation, edge detection, and region growing.
Feature extraction and representation are key steps in industrial machine vision systems, aimed at extracting representative features from images to describe them. Common features include texture features, color features, and shape features. Texture features describe the texture information in an image, with common methods including Gray-Level Co-occurrence Matrix (GLCM) and wavelet transform.
Color features describe the color distribution in an image, with common methods including color histograms and color moments. Shape features describe the shape information in an image, with common methods including edge histograms and contour descriptors.
Object detection and recognition are core tasks of industrial machine vision systems, targeting the recognition and detection of objects in images. Object detection determines whether objects are present in an image and obtains their position information, with common methods including sliding window method and Regional Convolutional Neural Networks (R-CNN).
Object recognition determines the category to which the objects in an image belong, with common methods including Support Vector Machines (SVM) and Convolutional Neural Networks (CNN).
Motion estimation and tracking are important tasks in industrial machine vision systems, primarily used to analyze and understand the motion information of objects in image sequences. Motion estimation estimates the motion of objects through pixel differences between consecutive frames, with common methods including optical flow and block matching. Motion tracking, given an initial tracking box, achieves continuous tracking of an object by predicting and updating its position, with common methods including Kalman filtering and particle filtering.
In summary, the components of industrial machine vision systems include image acquisition devices, image preprocessing, feature extraction and representation, object detection and recognition, and motion estimation and tracking. Image acquisition devices are responsible for obtaining image or video data, image preprocessing is used for denoising, enhancement, and segmentation of images.
Feature extraction and representation is the process of extracting representative features from images, object detection and recognition is used for detecting and recognizing objects in images, and motion estimation and tracking is used to analyze and understand the motion information of objects in image sequences. Industrial machine vision systems have wide applications in industries, medical fields, and security surveillance, with significant application prospects.
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