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What is Machine Vision?
Machine vision (MV) is the application of computer vision to industry and manufacturing. Whereas computer vision is the general discipline of making computers see (understand what is perceived visually). Machine Vision is a subfield of engineering that is related to computer science, optics, mechanical engineering, and industrial automation. One of the most common applications of Machine Vision is the inspection of manufactured goods, automobiles, food and pharmaceuticals. Just as human inspectors working on assembly lines visually inspect parts to judge the quality of workmanship, so machine vision systems use digital cameras, smart cameras and image processing software to perform similar inspections.
ICS is an Instrumentation and Controls Company, specialising in Machine Vision solutions for use in industry and commerce applications.
Many industrial and commercial processes need to verify component and material manufacturing quality at all times. Providing statistical information to both the customers and their maintenance departments. It can perform roles as varied as quality checking, print or colour verification or system enhancement. Machine Vision systems are frequently used to gauge components, orient and align assemblies, inspect print or surface quality and identify faulty or missing elements.
Traditional methods rely on the manual inspection of the materials and sample checks. This relies largely on human resources and staff diligence.
At ICS we ensure our knowledge of the industry is completely up to date, we are able to draw on components from the world’s best vision system vendors to provide a uniquely tailored solution which is not only scalable but can take advantage of future technology advances.
We offer a bespoke vision systems, which we can tailor the system to you needs.
Fundamentally, cameras can be split into two categories – area scan and Line Scan. As their names suggest Area Scan cameras contain image sensors capable of scanning an area (multiple lines), historically with an aspect ratio of 4:3, while in line scan cameras the sensing devices only scan a single line before the image data is read out. Although many of the basic principles of the two cameras are similar we will treat each one separately because for all intents and purposes they are unique technologies.
Technologies and Processes
Camera sensor technologies occur in two forms – the CCD (Charge Coupled Device) and CMOS (Complimentary Metal Oxide Silicon) Sensors. Each have various advantages and disadvantages, so the choice of camera is driven by the application rather than any outright benefit enjoyed by a particular sensor type.
Colour information is obtained from a camera sensor through digital colour imaging and there are a multitude of methods for the transmission of video data
The most common type of shutter on a machine vision camera tends to be a global shutter where all pixels are exposed at the same time and their values are retrieved in unison. Global shuttering therefore allows for accurate capture of moving objects.
Rolling shutters come in a number of different forms. CMOS cameras are lower in cost but each pixel value is retrieved one after another. This results in distortion when viewing moving objects as the image may have changed in the time it takes to read one pixel value to the next. For full frame or frame transfer CCD cameras, the charge from each pixel is passed to the storage or shift register by one pixel to the next. This can lead to distortion if the light is not blocked from the active pixels while their charge is being retrieved.
The ability of a camera to distinguish between subtle differences in a field of view is determined by its dynamic range. Dynamic range is the term used to describe how many grey levels can be translated from a pixel value. It is expressed as the saturation voltage to total RMS noise of the camera output. If the contrast between features in an image is vast, a camera with a low dynamic range would be suited. A camera with a high dynamic range could better distinguish between subtle differences in features. The dynamic range of a camera can be increased by raising the saturation voltage or full well capacity of each pixel. The higher the capacity, the greater the dynamic range. Modern vision cameras vary in pixel capacity from 15Ke to 200Ke. The higher the full well capacity of a sensors pixels, the greater the physical size of the sensor. Better dynamic range can also be achieved by reducing the total RMS noise of the camera by using better quality components and reducing the sensors clock speeds. This has obvious repercussions on cost, size and performance.
A wider dynamic range not only improves definition of features but also allows simultaneous viewing of light and dark areas. Logarithmic sensors can be used to achieve higher sensitivity to light variations due to their logarithmic response to light levels as compared to near-liner responses of conventional sensors. True logarithmic sensors do result in higher levels of noise and require rolling shutters. However, there is new CMOS sensor technology which can achieve logarithmic responses with global shutters.
Non Contact Measurement
Non-contact measurement techniques using laser based gauging and machine vision have many advantages over the traditional contact gauging methods, like calipers, hand-held micrometers, and touch probes:
3 aspects of Machine Vision
CCD (Charged Coupled Device)
FOV (Field Of View)