Automated Vision Inspection Machines – View Online..

Automated Vision Inspection Machines – View Online..

Machine vision (MV) is the technology and techniques used to provide imaging-based automatic inspection and analysis for such applications as automatic inspection, process control, and robot guidance, usually in industry. Machine vision refers to many technologies, hardware and software products, integrated systems, actions, methods and expertise. Machine vision as a systems engineering discipline can be considered distinct from computer vision, a kind of computer science. It attempts to integrate existing technologies in new ways and apply them to solve real life problems. The phrase is the prevalent one for these functions in industrial automation environments but can also be used for these functions in other environments such as security and vehicle guidance.

The general Top Machine Vision Inspection System Manufacturer includes planning the specifics from the requirements and project, and after that making a solution. During run-time, this process begins with imaging, followed by automated research into the image and extraction of the required information.

Definitions in the term “Machine vision” vary, but all include the technology and techniques utilized to extract information from a picture on an automated basis, instead of image processing, where the output is an additional image. The data extracted can become a simple good-part/bad-part signal, or maybe more an intricate set of web data like the identity, position and orientation of every object within an image. The information can be used for such applications as automatic inspection and robot and process guidance in industry, for security monitoring and vehicle guidance. This industry encompasses a lot of technologies, software and hardware products, integrated systems, actions, methods and expertise. Machine vision is actually the only real saying used for these functions in industrial automation applications; the phrase is less universal for these particular functions in other environments including security and vehicle guidance. Machine vision as being a systems engineering discipline can be regarded as distinct from computer vision, a form of basic computer science; machine vision tries to integrate existing technologies in new ways and apply these to solve real life problems in a way that meets the prerequisites of industrial automation and other application areas. The word is also used in a broader sense by trade events and trade groups like the Automated Imaging Association and also the European Machine Vision Association. This broader definition also encompasses products and applications usually related to image processing. The key ways to use machine vision are automatic inspection and industrial robot/process guidance. See glossary of machine vision.

Imaging based automatic inspection and sorting

The primary uses for machine vision are imaging-based automatic inspection and sorting and robot guidance.;:6-10 in this particular section the former is abbreviated as “automatic inspection”. The overall process includes planning the specifics of the requirements and project, and then creating a solution. This section describes the technical process that occurs during the operation of the solution.

Methods and sequence of operation

The first step in the automatic inspection sequence of operation is acquisition of an image, typically using cameras, lenses, and lighting which has been designed to give you the differentiation required by subsequent processing. MV software applications and programs created in them then employ various digital image processing methods to extract the necessary information, and frequently make decisions (such as pass/fail) based on the extracted information.


The components of an automatic inspection system usually include lighting, a camera or some other imager, a processor, software, and output devices.3


The imaging device (e.g. camera) can either be apart from the primary image processing unit or coupled with it where case a combination is normally known as a smart camera or smart sensor When separated, the bond may be made to specialized intermediate hardware, a custom processing appliance, or even a frame grabber in a computer using either an analog or standardized digital interface (Camera Link, CoaXPress) MV implementations also have digital camera models capable of direct connections (with no framegrabber) to a computer via FireWire, USB or Gigabit Ethernet interfaces.

While conventional (2D visible light) imaging is most commonly used in MV, alternatives include multispectral imaging, hyperspectral imaging, imaging various infrared bands,line scan imaging, 3D imaging of surfaces and X-ray imaging. Key differentiations within MV 2D visible light imaging are monochromatic vs. color, frame rate, resolution, and if the imaging process is simultaneous on the entire image, rendering it appropriate for moving processes.

Though the vast majority of machine vision applications are solved using two-dimensional imaging, Automated Vision Inspection Machines utilizing 3D imaging really are a growing niche in the industry. By far the most frequently used way of 3D imaging is scanning based triangulation which utilizes motion from the product or image during the imaging process. A laser is projected on the surfaces nefqnm an object and viewed from a different angle. In machine vision this can be accomplished with a scanning motion, either by moving the workpiece, or by moving the digital camera & laser imaging system. The line is viewed with a camera coming from a different angle; the deviation in the line represents shape variations. Lines from multiple scans are assembled into a depth map or point cloud. Stereoscopic vision is utilized in special cases involving unique features found in both views of a pair of cameras. Other 3D methods used for machine vision are period of flight and grid based.One method is grid array based systems using pseudorandom structured light system as employed by the Microsoft Kinect system circa 2012.