Machine vision systems are a set of integrated components that are designed to use information extracted from digital images to automatically guide manufacturing and production operations such as go/no testing and quality control processes. These systems can also play a role in automated assembly verification and inspection operations through their ability to guide material handling equipment to position products or materials as needed in a given process. They have wide applications across different industries and can be used to automate any mundane, repetitive tasks that would become tiring to a human inspector or operator. The use of automatic vision measuring machine allows for 100% inspection of products or parts in a process, resulting in improved yields, reductions in defect rates, increased quality, lower costs, and greater consistency of process results.
How Machine Vision Systems Work
To understand how a manual vision measuring machine works, it may be helpful to envision it performing a typical function, such as product inspection. First, the sensor detects if a product is present. If there is indeed a product passing by the sensor, the sensor will trigger a camera to capture the image, and a light source to highlight key features. Next, a digitizing device called a frame-grabber takes the camera’s image and translates it into digital output, which is then stored in computer memory so it can be manipulated and processed by software.
In order to process an image, computer software must perform several tasks. First, the image is reduced in gradation to a simple black and white format. Next, the image is analyzed by system software to identify defects and proper components based on predetermined criteria. After the image has been analyzed, the product will either pass or fail inspection based on the machine vision system’s findings.
Measurement functions of the large-stroke coordinate measuring instrument are done through the comparison of a recorded dimension from a digital image against a standard valve to establish a tolerance or to determine if the observed value of the dimension is within acceptable levels of tolerance as called for in the design specification for that part.
Video Motion Quality (VMQ) evaluates the relative motion quality of the distorted video generated from the reference video based on all the frames from the two videos. VMQ uses any frame-based metric to compare frames from original video and distorted video. It uses the timestamp of each frame to measure the intersection value. VMQ measuring machine combines the comparison value with the intersection value in the aggregate function to produce the final result. To explore the efficiency of VMQ, we used a set of uncompressed original videos to generate a new set of encoded videos. These encoded videos are then used to generate a new set of distorted videos that have the same video bit rate and frame size, but the frame rate is reduced.
