A picture paints a thousand words? Yes – if the data it contains is correctly identified and evaluated. For us humans, this is quite natural; we can, for example, easily assign different versions of a category, such as a fir and an oak as a “tree”. The idea of using images to collect information in larger contexts is therefore obvious.
Image recognition makes this possible: The artificial intelligence (AI)-based technology converts image information into code. This allows the information contained to be processed automatically.
In this way, knowledge that is particularly difficult to access can be broken down, whether in very large dimensions such as wooded areas or in invisibly small ones such as body cells.
In image recognition, AI algorithms learn to “see,” compare, and interpret images using various techniques such as image augmentation. This eliminates the intermediate human description step. Image recognition can be used to assess the condition of buildings, for example, but also the development of bacterial strains in laboratory tests. This is how you quickly get the optimal overview!
An algorithm is unconsciously influenced by the skill and worldview of the programmers. Image recognition AI, on the other hand, trains on images – and learns with each new image. It writes its own code based on this information and becomes more objective.
If people have to first translate information from what they see into text or numbers to feed a data set, it’s more laborious and very error-prone. If, on the other hand, the artificial intelligence works directly with the image information, it can generate unfiltered information from it.
When we see, our brain hides information that is unimportant to us, adds others, and picks up small deviations. In this way, it protects us from sensory overload. Image recognition, on the other hand, knows neither stimulus overload nor fatigue – and can, thus, compare the smallest details in thousands of images.
Image recognition means the automatic recognition and analysis of images via an algorithm. Since transferring image data into code is very complex, artificial intelligence (AI) is usually used for image recognition. For example, the AI recognizes the target object in the original image and then extracts it for further processing (image segmentation).
Artificial intelligence does not “see” anything itself, which means that all data present in the image must be transferred by it into code. For this purpose, as many images of target objects as possible are first collected in order to train the AI with them. Via image augmentation – which is the modification of images, for example by mirroring and color changes – the number of images for training can be increased. In order to later be able to recognize individual animals, e.g. wild animals, the expressions of certain characteristics are transferred into code. This code represents the individual. The AI can also tolerate slight deviations, for example, due to lighting conditions or other viewing angles, and still identify the object. To do this, the AI is offered an “anchor” image of the object, along with another image of the object and the image of another individual. Thus, the AI develops a suitable code for itself to identify the individual it is looking for. With each new image, the AI continues to adapt the code, becoming more and more accurate.
Four main techniques are used for image recognition:
“One of our projects is funded by the German Federal Ministry of Education and Research (BMBF) and deals with the bark beetle. We can use satellite images to map the forest ecosystem and analyze how the bark beetle spreads. From this, our solution makes predictions about the areas at risk. These help foresters take appropriate measures to protect trees. As soon as these solutions are ready for the market, they will be deployed throughout Germany.”
Dr. Gerhard Heinzerling
Senior Data Scientist, Arineo GmbH
Our core is the development of artificial intelligence. To this end, our experts repeatedly collaborate with university researchers. The German Federal Ministry of Education and Research (BMBF) is funding two of our current research projects on improving work processes through AI.
From extensive projects such as the detection of leaf diseases, our experts bring the expertise to implement smart image recognition for you, even in complex environments.
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