I. Image Acquisition Module
The image acquisition module is one of the most fundamental modules in a machine vision system. It is primarily responsible for acquiring image and video data and converting it into digital signals. The image acquisition module includes hardware devices such as cameras, lenses, and light sources, as well as digital signal processing devices such as acquisition cards and interface cards. This module is responsible for acquiring image or video data from external sensors or other sources and inputting it into the computer. These sensors can be cameras, depth cameras, LiDAR, etc.
II. Image Preprocessing Module
The image preprocessing module is a crucial component of a machine vision system. It is primarily responsible for preprocessing acquired image data to improve image quality and recognition accuracy. The image preprocessing module includes algorithms for image enhancement, filtering, denoising, and edge detection. This module processes the acquired images, including image enhancement, grayscale conversion, denoising, and morphological processing, to better facilitate subsequent processing.
III. Feature Extraction Module
The feature extraction module is a core module in a machine vision system. It is primarily responsible for extracting effective feature information from preprocessed image data for subsequent image recognition and classification. The feature extraction module includes algorithms such as Local Binary Pattern Recognition (LBP), Histogram of Oriented Gradients (HOG), and Convolutional Neural Networks (CNN). This module extracts useful features from images, such as edges, corners, feature points, and color histograms. These features can be used to identify target objects, scenes, and relationships between objects.
IV. Image Recognition Module
The image recognition module is another core module in a machine vision system, primarily responsible for classifying and recognizing extracted feature information. It includes algorithms such as Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN). This module is used to detect and recognize target objects in images or videos. It typically includes algorithms such as homography matrix estimation, color analysis, and edge detection. It is used to track the position of target objects in images or videos. This module typically includes algorithms such as image-based tracking, optical flow-based tracking, and deep learning-based tracking.
V. Decision-Making Module
The decision-making module is the last module in a machine vision system. It is primarily responsible for making decisions and judgments based on the recognition results to achieve automatic analysis and processing of image data. This module includes algorithms such as logical judgment, rule matching, and model evaluation. It analyzes and makes decisions based on the extracted features to ensure correct outcomes. This module typically includes algorithms for distance discrimination, optical flow discrimination, and probability statistics.
VI. Output Result Module
This module outputs the processed results to external devices or applications, such as image or video display, control actuators, etc.
The above outlines the main modules of a machine vision system. These modules work together to achieve various functions of the machine vision system, such as image segmentation, object detection, tracking, and analysis. The output module of a machine vision system generally includes the following parts:
Display module: Outputs the processed results to the computer screen via a monitor or other device.
Output module: Outputs the processed results to external devices via output devices (such as printers, barcode scanners, etc.).
Storage module: Stores the processed results to an external storage device for later use.
Communication module: Transmits the processed results to external devices or applications via a network.
Depending on the application scenario, the specific composition of the machine vision output module will vary. For example, in some industrial applications, it may be necessary to transmit the processed results to an automated control system, in which case a communication module is required to transmit the processed results to the control system. In some medical applications, it may be necessary to transmit the processed results to a medical imaging system, in which case a storage module is required to store the processed results in the medical imaging system.
In summary, the main modules of a machine vision system include an image acquisition module, an image preprocessing module, a feature extraction module, an image recognition module, and a decision-making module. These modules work together to automatically analyze and process image data, providing a solid technical foundation for realizing intelligent machine vision applications.
