# Principles of EasyGauge ## Finding Transition Points All gauges are based on finding the position of all the *transition* points, called ***sample points***, along a line segment probe, called the ***sample path***, that cross one or several object edges and selecting the most relevant ones. To do that, **EasyGauge** extracts the pixel values along the ***sample path*** (the red curve) then uses the *peak analysis* to determine the transition location. All the pixel values in the peak area are used to compute the transition location.  On a sample path extracted from an image: - An edge appears as a transition from dark to light (or vice versa). - When plotting the pixel values along the sample path, this transition appears as an S-shaped curve. - The first derivative of this curve exhibits a peak around the transition point. #### HINT * The better the contrast, the sharper the transition and the higher the peak. * Sub-pixel accuracy is only possible if the transition is surrounded by almost uniform regions of at least 2 pixels wide. ## Measuring Complex Shapes Except for the [point gauge](gauge_types.html.md#easygauge-point-gauge), all gauges try to find and measure *complex shapes* with the following algorithm: 1. Place *sample paths* along the nominal shape at regular interval 2. Measure each *sample path* to find *sample points* on this path. 3. Select one *sample point* for each *sample path*. 4. Fit the shape to the selected *sample points*. * Fitting the shape means adjusting its parameters to minimize the error residue (average distance of the *sample points* to the measured shape). * The fitting algorithm can include an iterative **outlier rejection** process, that will reject *sample points* that may harm the location accuracy: 1. Apply the model fitting to the *valid* *sample points*. 2. The *sample points* that are too far away from the fitted model and may harm the location accuracy are made *invalid*. 3. Repeat the proccess.