The features that make up a pattern can be of different sizes, from features a few pixels in size to features up to 50 or 100 pixels in size. Most patterns contain features with a range of sizes.
PatMax uses features of different sizes to locate the desired features in the run-time images you acquire. In general, PatMax uses large features to find an approximate pattern match in a run-time image quickly, and small features to determine the pattern location precisely.
The particular features that PatMax can detect in an image are determined by the granularity the PatMax software is currently using. Large granularity settings allow the software to detect only large features in an image, while a smaller granularity setting allows the tool to locate smaller features.
Granularity is expressed as the radius of interest, in pixels, within which features are detected. Figure 6 illustrates two important characteristics of pattern granularity.
- Large features are detected at both small and large granularity settings.
- Smaller features are present or absent from the image depending on the granularity setting.
In some cases, however, a feature might be present at a fine granularity and at a coarse granularity, but not at an intermediate granularity. The following figure shows the effect of different granularity settings on the features that are detected in a single image.
In addition to affecting the features that are trained as part of the pattern, pattern granularity also affects the spacing of boundary points along a feature boundary. In general, the spacing of feature boundary points is approximately equal to the pattern granularity.
PatMax uses a range of pattern granularities when it trains a pattern from an image; PatMax automatically determines the optimum granularity settings when it trains a pattern. The smallest granularity used to detect features in the training image or shape description is called the fine granularity limit. The largest granularity used to detect features is called the coarse granularity limit.
You can display the actual features and feature boundary points trained using the coarse and fine granularity limits.