Traditional machine vision relies on analytical, rule-based algorithms to detect and parameterize defects that can be mathematically defined. In such applications, highly skilled systems developers and engineers evaluate each problem, apply a series of rules that can accomplish the task, and then program the system. To streamline the process, many vendors offer low-code and no-code solutions that help ease the process of tuning a set of analytical pattern matching, blob, edge, caliper, or other machine vision tools to meet application requirements. Despite these advances, rule-based solutions reach their limit when defects are difficult to define numerically or their appearance varies significantly.
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