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Pattern matching algorithms are some of the most important functions in machine vision because of their use in varying applications. You can use pattern matching in the following three general applications:
Pattern matching provides your application with the number of instances and the locations of template matches within an inspection image. For example, you can search an image containing a printed circuit board (PCB) for one or more fiducials. The machine vision application uses the fiducials to align the board for chip placement from a chip mounting device. Figure 12-1a shows part of a PCB. Figure 12-1b shows a common fiducial used in PCB inspections or chip pick-and-place applications.
Gauging applications first locate and then measure, or gauge, the dimensions of an object in an image. If the measurement falls within a tolerance range, the object passes inspection. If it falls outside the tolerance range, the object is rejected.
Searching for and finding image features is the key processing task that determines the success of many gauging applications, such as inspecting the leads on a quad pack or inspecting an antilock-brake sensor. In real-time applications, search speed is critical.
In general, pattern matching works well on images where the template is primarily characterized by grayscale information. Templates containing texture, or that have dense, intricate data with no discernible pattern, are the most successful.