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AI image recognition

 

 

 

In the deep learning computer vision approach, the system learns how to recognize a target by itself. Based on hundreds of raw pictures of the given target, deep learning algorithms can build their own optimal set of visual features to recognize this target. The more pictures, the more accurate the system.

For visual product recognition — the kind we perform with NT Adaptive controllers — deep learning has several advantages that make it an ideal approach. The system can be fed with many good examples of the targets under all sorts of conditions — products on shelves. Over time, the system learns to self-adjust and recognize new examples of products it has never seen. Without the need for explicit rules, the system can recognize products even with low-quality inputs. It’s a generic system, fast to roll out for new product bases, adaptive, and robust to real conditions to get truly accurate results.

 
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