Vision measuring machines

Automated inspection equipment Coordination measuring machines (CMM), machine vision systems, etc. 

There has been much progress in the development of vision systems towards highly accurate measurement devices in recent years. The support and replacement of mechanical 3-D measurement machines (CMM) by vision systems is well on its way. However, when performing absolute accuracy tests or when comparing the performance of both systems with each other one should always remember a fairly trivial fact an object feature (e.g. an edge) cannot be measured more accurately than it is physically defined. 

The necessary data are usually obtained by means of external devices such as vision devices [3], the laser tracker [4] or the coordinate measuring machine. Then, the following step is to identify the kinematic parameters that provide the optimum value of an objective function. This function can be formulated in terms of a linear minimum-square. 

The flow chart of the color acquisition procedure is given in Figure 3. The color machine vision system should be white balanced before taking measurement of samples. This can be accomplished by first measuring a white reference target to establish a base line. The R,G,B values of a sample are then acquired. To compare the machine vision measured tri-stimulus values against that of published standard color plates, R,G,B values from CMVS were transformed to L a b values. This transformation required two steps first, R,G,B is converted to X,Y,Z by Eq. 3. Next, Eq. 4 is used to convert X,Y,Z to L, a, b color coordinates. 

A CMVS was designed and evaluated on colors perceived by human. The machine vision measured tri-stimulus values in R,G,B color coordinate were transformed to L a b color space for better representation of human perceived colors. The CMVS measurements were evaluated using Munsell standard color chips, a colorimeter, and a sensory panel. Excellent correlations were found between the CMVS and the colorimeter, which is designed to quantify human perceived colors. The CMVS s ability in quantifying human perceived colors was evidenced by accurate measurement of Munsell standard color chips and the excellent correlations with the sensory panel perceived colors (R>0.90). From the experiments, it was possible to resolve up to 0.7 CIE unit color difference of the tested colors. 

A - GMF M-lOO ROBOT W1T KAREL CONTROLLER B - GMr S-IO ROBOT WITH A KAREL CONTROLLER C - PRATT VHITNEY MACHINING CENTER VlTH FANUC CONTROL D - PRATT VHITNEY LATHE WITH FAPT AND FANUC CONTROL E - ALLEN-BRADLEY expert VISION SYSTEM WITH TVG CAMERAS F - MITUTOYO COORDINATE MEASURING MACHINE G - ALLEN-BRADLEY PLC 5/25 H - MILL AND LATHE LIFT STATIONS I - palletized mill and LATHE STATIONS... 

There is great interest in the electrical and optical properties of materials confined within small particles known as nanoparticles. These are materials made up of clusters (of atoms or molecules) that are small enough to have material properties very different from the bulk. Most of the atoms or molecules are near the surface and have different environments from those in the interior—indeed, the properties vary with the nanoparticle s actual size. These are key players in what is hoped to be the nanoscience revolution. There is still very active work to learn how to make nanoscale particles of defined size and composition, to measure their properties, and to understand how their special properties depend on particle size. One vision of this revolution includes the possibility of making tiny machines that can imitate many of the processes we see in single-cell organisms, that possess much of the information content of biological systems, and that have the ability to form tiny computer components and enable the design of much faster computers. However, like truisms of the past, nanoparticles are such an unknown area of chemical materials that predictions of their possible uses will evolve and expand rapidly in the future. 

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... 

Gaze-Controlled Stereo Vision to Measure Position and Track a Moving Object Machine Vision for Crane Control... 

Non-contact 3D digitizing systems are mostly used in the field of reverse engineering, in which numerical models are reconstructed from clouds of points, as described in the literature [6], They are also used in pattern recognition of machine vision applications, online measurement systems and dimensional control systems. With these systems, the coordinates of a large number of points can be obtained in a few seconds, but they require further treatment as they form discrete images of objects [7]. 

Viscorex, Discharge gear pump for viscous polymer melts, Maag Pump Systems Textron Visi-Pack, Machine vision systems, PTl Packaging Technologies Inspection Vision Bottle Gauge, Dimensions measurement on extrusion blow bottles,TopWave International, Inc. 

Machine vision (Jerome H. Lemelson) Machine vision allows a computer to move and measure products and to inspect them for quality control. 

When we hear or read the word spectroscopy, we tend to imag e rooms full of expensive machines measuring away. It s quite an inhuman vision. Yet we are spectrometers ourselves. All of our sensory experiences, most obviously vision, are examples of spectroscopic measurements. Those machines in our imaginations are mere extensions of our biological, in-house equipment. When you see a red apple among a basket of green ones, and decide that it is the ripe red one you will eat, you are acting on a spectroscopic measurement. Let s examine vision a litde bit here, because it depends on polyene chemistry. 

Accurate machine vision color measurement is a non-trivial task and every step in a color quantification process can affect the accuracy of the measurement. Major areas of the color machine vision system (CMVS) development task consists of acquisition of color images, low-level image processing to compensate for aberrations caused by qitical impurity and noisy electronic devices in a system, color quality index selection/develqiment, and defming relationships between instrumental and sensory evaluation results. Successes or failures in developing each of these CMVS building blocks will affect the overall system performance. 

Regardless of which color solid is used, color signals (li) generated by an ideal machine vision color measurement system at a spatial location jt,y can be in general described by an expression given by Horn (1986) ... 

Figure 4. The color measurement stability of the color machine vision system. The CMVS has a range of color resolution in measuring various color due to various level of noise in measuring individual colors. The graph shows the magnitude of measurement deviation of each color from 100 repeated measurements.

Correlation was established between the colors of food samples measured by the colorimeter color and the color machine vision system. The correlation coefficients between L a b color parameters for beef and carrots obtained from the two instruments can be seen in Table 2. For all the data collected, a high correlation was observed (R > = 0.98). The established correlation makes it possible to verify CMVS s color measurement accuracy of colors that are not available from published color standards. Furthermore, the correlations between the CMVS and colorants in food systems can also be inferenced based on established correlations between colorimeter measurements and colorant contents. 

Figure 6. A distribution of the color machine vision system measured colors in Lab color system. There are seven major groups with major color differences. Each group consists of six or seven colors with minor color differences.

Figure 8. Excellent agreement was found between the color machine vision and the colorimeter measured carrot puree colors. The largest disagreement was less than 2 CIE units, while most differences are less than 0.5 CIE unit.

In-line/on-line feedback control of color of food during processing can improve not only color quality but also color related quality such as texture and appearance. To do this, there are three major aspects development of an in-line/on-line color sensor understanding of color change kinetics and establish correlations between instrumental measured and sensory panel perceived colors of foods. In this research, we have chosen color machine vision technology for the measurement of colors of food due to its superior spatial resolution over conventional instruments such as colorimeter or spectrophotometer. Relationships between measured colors and corresponding principal chemical markers were established for the model food systems. We have also found excellent correlations between the color machine vision system (CMVS) measured and a sensory panel determined colors of food samples (Ling and Tepper, 1995). We believed that a CMVS can be used for food process control to ensure color quality as perceived by consumers. 

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