Dimensional Measurement using Vision Systems - NPL Publications ...

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<strong>Measurement</strong> Good Practice Guide No. 39 Figure 26: Normal and saturated image illumination. 7.3 OBTAINING THE PIXEL CALIBRATION Figure 27: Calibrating the image pixel size. All types of vision system software have calibration menus to relate the pixel size to units of length. A grid is useful for calibration of the system, as it can also be used to detect any gross image distortion (see Appendix A.1.1 for further information on calibration artefacts for vision systems). An appropriately sized square should be focused and imaged on the screen. The calibration menu is then selected and a variable frame is used to denote the line centres of the maximum pitch visible, as shown in Figure 27. (Some software packages use point-to-point markers instead of a variable frame). The calibrated value for the pitch is then entered and the system calculates the real world pixel size. This process is repeated 49
<strong>Measurement</strong> Good Practice Guide No. 39 with the scale oriented on the Y-axis, to check the squareness of the calibration, as the camera may not have square pixels. Failure to account for this may introduce significant errors into the measurements. Most systems can produce a software - generated grid. If this grid is superimposed on the image of the graticule grid, any large-scale image distortions will become evident. 7.4 SETTING THE CORRECT DETECTION THRESHOLD After calibrating the system, detecting objects will require setting a threshold. A vision system performs measurements by detecting changes in intensity and hence, the contrast of a feature e.g. a black spot against a white background. As each pixel has an 8-bit grey scale intensity value range, 0 being black and 255 white, the user can define a threshold on the grey scale so that regions with intensity above or below that value can be detected. In this way, a binary image is created, whereby pixels with a greyscale intensity value below the threshold are assigned a binary 0, and those above, a binary 1. Depending on whether bright or dark features are to be measured, adjacent detected pixels are grouped together to form a detected feature. Modern vision systems may have the facility to perform grey image processing of features, but for the purposes of this guide, only binary image processing will be covered. Figure 28 shows a typical intensity profile of a dark feature on a bright background. The maximum intensity range of the camera is 0-255 greyscale, but the actual dynamic range of the image may well be less, especially if there is low contrast. The correct threshold level should be calculated from the image grey scale range. This is called normalising the intensity threshold. For most vision systems, where the optical magnification is less than 20x, the 50% normalised threshold should be used. The effects of coherence from the illumination on the true edge position of a feature can be neglected at this scale, as it is well below the measurement uncertainty achievable. The formula for calculating the 50% threshold is given in equation 14: t = b + (a – b)/2 …..(14) Where ; t = Grey scale intensity threshold b = Mean dark level intensity a = Mean bright level intensity 50
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