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Machine Vision Manufacturers and Suppliers

IQS Directory implements a thorough list of industrial machine vision manufacturers and suppliers. Utilize our listing to examine and sort top industrial machine vision manufacturers with previews of ads and detailed descriptions of each product. Any industrial machine vision company can design, engineer, and manufacture industrial machine vision systems to meet your companies specific qualifications. An easy connection to reach industrial machine vision companies through our fast request for quote form is provided on our website. The company information includes website links, company profile, locations, phone, product videos and product information. Customer reviews are available and product specific news articles. This source is right for you whether it's for a manufacturer of industrial machine visions, machine vision inspections, and machine vision systems.

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  • Small Business, Big Solutions: Decision Technology, LLC

    Machine Vision Decision Technology, LLC is a systems integration company that specializes in machine vision. We are a small business located in Indianapolis, Indiana. Our owner and chief engineer, Bob Rongo, has worked in the machine vision and robotics industry for over 25 years. Additionally, he holds a degree in electrical engineering from Polytechnic University of New York and has five patents. As a system integrator, we can engineer and build turn-key solutions or provide engineering services. Read more......

  • Fusion Systems Group: Innovative Automation Equipment

    Assembly Machines Fusion Systems Group is a supplier of high performance automation solutions with over 80 years of manufacturing experiences. Our accurate systems come in various configurations which are ideal for applications such as assembly, welding, brazing, soldering, inspection, material handling, robotic dispensing and more. Read More......

Industry Information
View A Video on Machine Vision - A Quick Introduction

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Machine vision is the industrial application of computer vision systems created by engineers to mimic human vision for the inspection, recognition and evaluation of parts in production. More than this, the capabilities of vision systems allow for the performance of a number of specific tasks such as bar code scanning, defect recognition, sorting, guiding and more all performed without destruction of product and to a high degree of precision and accuracy.

Machine vision processes can be undertaken by modular devices such as smart cameras or fully integrated machine vision systems such as those that might be present in robotic vision applications involving AGV. Though they perform many other tasks, quality control is one of the largest embodiments of machine vision as optical inspection systems, vision inspection systems and even laser inspection allow manufactures to assess products at higher-speed, consistency, repeatability and magnification, far exceeding the work period and capabilities of a human workforce. Vision sensors in conjunction with vision software emulate human sight by capturing images and then interpreting the collected data. With these interpretations optical sorting systems aid in the continued flow of production as products can be quickly sorted based on shape, size, material or any other pre-determined aspect. Though highly sophisticated, some limitations do exist as machine vision is not adaptable to the same degree as human processing. machine vision products nevertheless continue to grow in popularity and functionality as they consistently improve the productivity, reliability and quality of manufacturing across the industrial sector.

As aforementioned, machine vision systems can perform a large number of variable tasks and as such are incorporated in an equally extensive array of industries. Automotive and electronic fields are among the most visible applications as AGV equipment is commonly employed in the inspection and construction of components. This and other industries use machine vision to inspect not only the final product, but the components used to build them as well. Tool and die, casting and molding components are frequently inspected under high magnification to reduce the risk of running full production with inaccurate parts. Often multiple aspects of machine vision are employed as with pharmaceutical packaging where both counting and inspection is performed to ensure the proper filling of vital prescription medications. Human safety is one of the leading reasons for employing machine vision. In addition to medical applications, recycling and waste management facilities frequently employ machine vision technology to promote workforce safety when potentially dangerous or contaminated materials need sorting. The sensors are often required in the performance of tasks as well. Labeling machines, for example, commonly use smart vision technology to determine when a product is in proper position for application. Food processing, counterfeit identification, textile, pulp and paper and airport baggage sorting list just a handful of the many additional industrial applications for machine vision systems. Not all smart cameras or vision software are applied to industry, however, as one of the most common applications, for example is the facial recognition feature available in many handheld digital cameras.

The many industrial applications for machine vision coupled with the use of this technology in both commercial and residential sectors inherently necessitates a broad range of devices available for matrix matching, feature extraction or general purpose visibility applications. While machine vision products vary considerably, vision systems often employ the same basic components. These include vision sensors, digital camera or image capture device, lighting, computer processor and special image processing software. Often placed at a given point along a conveyor belt, machine vision begins with sensors that detect when a product or component is in position. This triggers the camera or imaging device and lighting which are often synchronized to produce a digital image which highlights features of interest. A digitizing device known as a frame grabber then converts the camera output and enters the data into the memory of the computer system. Vision software processes the image using several steps such as the removal of static within the image and mathematical algorithms to count, measure, identify or inspect the object. The interpretation of the given data is compared to predetermined criteria programmed by the developer. A simplified user interface then allows workers to view the progress and success rate of production.

With this basic order of events and necessary components in mind, engineers often develop application specific custom machine vision systems and products for industrial, commercial and residential applications. In addition to the specific system configuration, there are several operational specifications that should be considered in order to ensure the optimal use of machine vision in a given application. Inspection rate and product speed are both important factors and should be compatible to ensure that every part or object undergoes standard evaluation. The number of cameras and processor speed as well as data storage capacities are also variable and should be considered with a specific use in mind. Test samples or vision calibration targets are commonly used in order achieve the proper parameters for pre-programmed machine vision systems and smart cameras in order to avoid the possibility of distortion. Using such a prototype is especially useful in inspection systems as it can create the computerized model with exact surface and material features. Despite the many advantages apparent over human vision, machine vision is inherently limited. Even with nearly continual advancements of both hardware and software pertaining to this highly skilled field, computer vision programs lack the ability to adapt beyond pre-determined algorithms as human vision so aptly does. Machine vision works best when performing narrowly defined tasks, though continued advancements continually improve the performance possibilities of this technology.

Machine Vision
Machine Vision
Machine Vision
Machine Vision – Cincinnati Automation Ltd.
Machine Vision – Cincinnati Automation Ltd.
Machine Vision – Cincinnati Automation Ltd.
Machine Vision
Machine Vision
Machine Vision – Cincinnati Automation Ltd.
Machine Vision – Cincinnati Automation Ltd.

Machine Vision Types

  • CCD cameras are cameras that use CCD (charge coupled device) chips to convert photons to electrical or digital information. Instead of the image being recorded on film, the CCD image is placed in an image file on the computer.
  • Computer vision is an amalgamation of computer science, optics, mechanical engineering and industrial automation aimed at the mechanized extrapolation of the information present in visual images.
  • Laser inspection involves the use of a specific type of photoelectric sensor that employs a highly focused beam of light to detect surface defects at a microscopic level, count parts as they move through production and scan codes or serial numbers as needed for a given application.
  • Laser technology is often used to create 3-D images of a recorded surface. Lasers produce high powered light with electrical properties.
  • Machine vision products encompass all systems and components designed and used for the application of computer vision in industrial and manufacturing proceedings.
  • Machine vision systems mimic human vision in machinery.
  • Magnetic imaging uses the magnetic properties of material to create a visual slice or image via an x-ray type sensor. 
  • Optical inspection systems ensure quality by inspecting products with machine vision.
  • Optical sorting systems use machine vision to accurately sort products.
  • Robotic vision is the practical application of computer vision providing semi-autonomous machines and devices with a limited visual capacity as needed in industrial and manufacturing settings.
  • Smart cameras have image processing circuitry and software within them. While smart cameras are highly mobile, they lack large storage capacity, and so are often connected to a central computer to alleviate this problem.
  • Vision inspection systems use machine vision in assembly lines to search for surface defects, count products, and scan serial numbers, among other things.
  • Vision sensors are integral to the field of machine vision as they allow for the capture of precision imagery and the subsequent analysis of the information present in that image.
  • Vision software is the computer or processor programming that extrapolates and extracts information from imagery and converts it into usable data.
  • Vision systems use imaging technology to sense, sort and guide without making contact.

Machine Vision Terms

3-D Imaging – A technology that provides 3D images from a large quantity of 2D cross-sectional images. These images are assembled in a computer from pictures or scans taken of a desired piece.
Acquisition – The manner in which outside information is brought into an analysis system.
Aperture – The diameter of the lens, which controls the amount of light that reaches the photoconductive image sensor.
Attenuation – Reduction of signal strength.
Chroma – Quality of color, which includes both hue and saturation.
Decompression – The restoration of original information from compressed data.
Depth of Focus – The range of the distance from the sensor to the object at which the lens is focused.
Digital Imaging – Conversion of a video picture into pixels by means of an A/D converter, where the level of each pixel can be stored in a computer.
Dichroic Filter – A filter used to transmit light based on wavelength rather than on the plane of vibration. Dichroic filters will transmit one color while reflecting a second, when illuminated with white light.
Fiber Optics – Light source or optical image delivery via long, flexible fibers of transparent material, usually bundled together. Light is transmitted via internal reflection inside each fiber; coherent fiber optics are spatially organized so images can be relayed.
Focal Plane – The plane perpendicular to the lens axis at the point of focus, usually found at the image sensor.
Frame Rate – The amount of frames that are displayed per unit of time.
Gauging – The non-contact dimensional examination of an object.
Gray Scale – Variations of values from white through shades of gray to black in a digitized image with black assigned the value of zero and white the value of one.
Halogen Lamp – An incandescent lamp inside which is a gas similar to iodine that is constantly evaporated and then redeposited on the filament.
Image Analysis – The process of identifying objects and shapes in a photograph, drawing, video or other visual image. Image analysis is used for everything from colorizing classic motion pictures to piloting cruise missiles.
Image Plane – The plane surface of the imaging sensor, perpendicular to the viewing direction, at which the optics are focused.
Infrared – The region of the electromagnetic spectrum adjacent to the visible spectrum, just beyond red with longer wavelengths.
Pattern Recognition – Classification of images into predetermined categories, usually using statistical methods.
Pixel – Acronym for picture element. Pixels are the individual elements in a digitized image array.
Process Imaging – The process of manufacturing at both the design and production stage. Process imaging is a generic term for quality control imaging.
Sharpening – An image processing operation that enhances edges. A non-sharp mask adds a low-pass, filtered image to the original, resulting in edge enhancement.
Shutter – An electrical or mechanical device used to control the amount of time the imaging surface is exposed to light. The shutter is often used to stop the blur created by moving objects.
Spatial Filtering – Enhancing an image by changing its spatial frequencies.
Zoom Lens – A compound lens that remains in focus as the image size is continuously varied. A zoom lens may be motorized or manually operated.

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