Image recognition – What it's all about
With the help of image recognition computers can identify objects in pictures. For humans, this seems like a simple task but when it comes to computers it becomes a complex project that needs lots of specifications. Thanks to highly efficient computers it is now possible to recognize objects with artificial intelligence. The key to this solution are big data sets that “teach” the computer how different objects look. This teaching proceeds with the help of a so-called “algorithm”, an exceptionally large mathematical equation.
AI does not equal AI
Pixel-precise segmentation – the silver bullet
Computer vision using bounding boxes
Pixel-precise computer vision of the visioncheckout
The areas analyzed during the recognition are the frames with a pink background. The recognition considers everything inside the box on the 2nd image. It often works fine, since the object to be recognized makes up the largest part of the box. However, there are also many cases in which the boxes do not supply a satisfying food recognition result.
That is why we use pixel-precise segmentation. In this type of computer vision, no rigid box surrounds the articles. Instead, the AI calculates precisely fitting masks that frame the items to be recognized. This way, no overlaps occur, and each article is recognized accurately and reliably. A few examples will illustrate the differences between the two types of image processing and their consequences.
You are in a rush, you have only one plate or you simply forgot something. The situation where a customer wants to check out without a tray is daily business and shouldn’t be a problem for an autonomous self-checkout. But the thing is: The more background areas are considered, the more important it is, that they look the same in every picture. As the pixel-precise segmentation does not consider any background for the recognition, these cases are no problem for the visioncheckout.