Create a Suitable Pattern Model in TrainPatMaxPattern

One problem with a circular feature is that it is very ambiguous when considering rotation angle. Similarly, a simple edge feature is ambiguous when considering scale. A robust pattern is one in which the vertical, horizontal, rotation, and scale attributes are unambiguous.

The circular pattern below provides X, Y, and scale information which is unambiguous, but there is no detectible rotation information.

By modifying the pattern as follows, there is no longer any ambiguity in rotation.

The following patterns have good X, Y, and Rotation information, but very little scale information. There is a 2X difference in scale, but FindPatMaxPattern cannot reliably detect it. The previous image also serves to illustrate how corner features, when combined with other features, alleviate the scale ambiguity.

Another attribute of a reliably detectible pattern is that it should not be cluttered with unnecessary or unwanted details. For example, suppose the smaller connector in the first image below, may have a variable number of pins. If we want to find the angle of the keyed connector fitting, regardless of the number or position of pins in the connector, we must train a model consisting of just the connector outline without the pins, as shown in the left image. Note that this can be done by either removing the unwanted clutter features physically, if possible, or by utilizing an EditCompositeRegion function with a Subtract zone to mask the pins.

Note: In the right image, the pattern of the connector outline is found at a 45-degree angle, even though the pins were not masked in this image. That is because the FindPatMaxPattern function was configured for this image to match only features that were trained and ignores everything else. (Because it is also possible to configure it to not ignore untrained features.)

Every feature trained in the TrainPatMaxPattern model is considered as an equally important part of the model. For more information, see Optimize TrainPatMaxPattern. If the pins in the above example or other unwanted features such as surface texture are included in the model, they are outlined in green. FindPatMaxPattern attempts to match every green feature with equal vigor, so if the unwanted features provide a better "match" than the actual desired features, FindPatMaxPattern returns an incorrect result. Consider the following images in which the feature we care about is the group of three dots. The pattern model was created excessively large in the left image for this example so that it includes other features that are of no interest.

The FindPatMaxPattern function returns a successful "Pass" result in the right image even though 1/3 of the desired feature is missing. That is because the missing feature only constitutes a tiny percentage of the total model. Unless the minimum Accept parameter in FindPatMax Pattern is set to a very high value this is considered a good match. The problem with setting a very high Accept parameter value is that the first step of the FindPatMaxPattern algorithm (the initial Coarse search) is more likely to miss good candidates and cause unnecessary "Fail" results.

Although the TrainPatMaxPatterns Pattern Region and the FindPatMaxPatterns Find Region parameters can use any of the standard internal or external Region structures, the FindPatMaxPattern function runs faster and consumes the least amount of memory if the regions are simple rectangles and not rotated or curved. In other words, use rectangular regions aligned to the pixel grid unless there is a compelling need to rotate, curve, or use composite shapes.