The term „pattern recognition pipelines“ originates from the fields of Artificial Intelligence, automation, and Big Data and Smart Data, and describes a process where computers help to identify specific recurring patterns within large datasets.
A pattern recognition pipeline functions like an assembly line: first, raw data is collected, for example, images, texts or sensor data. This data is then processed step-by-step – for instance, images are first enhanced, then searched for specific features, and finally, software is used to automatically recognise the desired pattern.
A practical example from automation: In a modern factory, cameras are used to detect faulty products on a conveyor belt. The captured images run through a pattern recognition pipeline, which automatically checks if, for example, a scratch or a dent is visible. If this is the case, the product is sorted out.
Pattern recognition pipelines are important for companies that want to analyse large datasets or automate processes, as they help to make faster and more reliable decisions. They thus provide valuable insights for optimising operations.
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