Machine vision

Machine vision will be an important tool in the operation of unattended, fully automated renewable energy processes. Vision is the response of the eye and brain to light. Machine vision is the artificial response of a device to spectral radiation. Machine vision also extends the human range of the visible spectrum into the IR, UV, and x-ray regions. Machine vision can be used to make decisions faster or more accurately and precisely than a human can. Machine vision may combine online defect monitors with shade monitoring, which will be an important tool in optimizing the positioning of solar collectors (Table 3.128). 

Post-Oil Energy Technology After the Age of Fossil Fuels 

Type of Camera Cost ( ) Pixels Output Format Frame Grabber and Software Notes 

Linear PDA or CCD array (may have 3 arrays for RGB) 1,000-10,000 128-8,192 8-12 bits/pixel Serially transmitted pixel data Custom software 

Two-dimensional array monochrome or RGB, digital cameras 1,000-10,000 Up to 4,096 x 4,096 Digital, USB 2.0, IEEE 1394, Ethernet RS-170, NTSC, PC-based with custom software 

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Tsai, R.Y. A versatile camera calibration technique for high-accuracy 3D machine vision meterology using off-the-shell tv cameras and lenses. IEEE. 1.Robotics Automation, Vol. RA-3(4),August 1988, pp. 323-344. 

FIG. 21-Slc An intc ratcd-packii in line for a talilctcd product in lass vials diottlcsj. Machine vision inspection svstenis check kev varialiles. (Couiiesy of riuiniuiccvlioa] b Medica] Paokajiiriji News, Paoli, PA 19301.)... 

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How do we construct programs that aid us in reasoning as opposed to calculating AI is the underlying science. It has several sub-disciplines, including, for example, robotics, machine vision, natural language understanding and expert systems, each of which will make a contribution to the second computer age. My focus is on expert systems. 

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B4] Ivins, James P. and John Pomll, A deformable model of the human Ms for measuring small three-dimensional eye movements, Machine Vision and Applications, vol. 11, str. 42-51,1998... 

One important aspect of solar farm optimization is the tracking of the sun s trajectory while concentrating the sunlight, so that the mirror reflectors or troughs will be correctly rotated around both axes while concentrating the solar radiation. In order to maximize the collector efficiency, solar and position detectors (Sections 3.15 and 3.17 [Chapter 3]) are used. Some of the tools of positioning include the use of machine vision (Section 3.12) and a variety of positioning devices (Section 3.15). 

Gkoutos, G. V., H. Rzepa, R. M. Clark, O. Adjei, and H. Johal. 2003. Chemical machine vision automated extraction of chemical metadata from raster images. J. Chem. Inf. Comput. Sci. 43(5) 1342-1355. 

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Where is shown after the system investment, add 181,000 for an automated inspection system comprising an X-ray metal detection machine, and 3 machine vision units to verify closure in place and label and bar code are correct and in place. 

FIG. 21 -51 a Inspection system for an integrated-packaging line packing tablets into glass bottles. Machine vision, bar code technology, and sensor technology are linked together by a supervisory system. Courtesy of AGR International, Inc., Butler, PA 16003.)... 

Machine vision, also referred to as computer or robot vision, is a term that describes the many techniques by which machines visually sense the physical world. These techniques, used primarily for monitoring industrial manufacturing, are becoming increasingly popular as today s manufacturing environments become more automated and quality control standards increase. Whether the task is to sort and assemble a group of machined parts, to determine if a label has been placed properly on a soda bottle, or to check for microscopic defects in an automotive door panel, machine vision plays an essential role. 

Machine vision systems tend to mimic the human vision system. An optical sensor and electronic main processor typically act as the eyes and brain and, as in humans, they work together to interpret visual information. Also like their human counterparts, the sensor and processor are each somewhat responsible for filtering out the useless information within the scene before it is analyzed. This reduces the overall processing requirements and allows humans and well-designed machine vision systems to make decisions based on visual information very quickly. 

Filtering the information within a scene begins with matching the vision system to its industrial requirements. Just as humans can adjust to a variety of situations by dilating their pupils or by tuning themselves to look for a particular shape or color, machine vision systems must also be somewhat flexible. Typically, however, the most efficient system is one which is designed with only limited applications in mind. For this reason, machine vision designers have developed a variety of application-specific techniques and systems to meet the speed and accuracy standards that modem industry demands. 

The most common type of machine vision system is one which is responsible for examining situations two-dimensionally. These two-dimensional systems view a scene in much the same way that a person views a photograph. Cues such as shapes, shadows, textures, glares, and colors within the scene allow this type of vision system to be very good at making decisions based on what essentially amounts to a flat picture. 

Like humans, most machine vision systems are designed to use shape as the defining characteristic for an object. For these systems then, it is important to make an object s shape as easy to isolate as possible. Both proper illumination of the object and efficient computer processing of the image of that object are necessary. 

The most advanced machine vision systems typically involve acquisition and interpretation of three-dimensional information. These systems often require more sophisticated illumination and processing techniques than one- and two-dimensional systems, but their results can be riveting. These scanners can characterize an object s shape three-dimensionally to tolerances of far less than a millimeter. This allows them to do things such as identify three-dimensional object orientation (important for assembly applications), check for subtle surface deformations in high precision machined parts, and generate detailed surface maps used by computer-controlled machining systems to create clones of the scanned object. 

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