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Computer Vision simulates the human capability of perceiving the environment, differentiating between various objects and naming them with a certain probability. These days almost exclusively artificial neuronal networks are used. Based upon the labelled training data sets, these networks learn to recognise various objects from all possible perspectives. This training is one of the main problems in the Computer Vision Division.
Similarly to the human who can already differentiate between various objects as a child, a neuronal network requires gigantic data sets in order to identify an object with a high probability. Therefore, more and more companies are relying on artificially-generated data sets in order to train their neuronal networks. For image recognition, today, primarily convolutional neural networks (CNN) are used. They are able to generate information from image data. However, these networks are not able to correlate the image data chronologically. Thus, in order to evaluate video material, one uses recurring neural networks (RNN) which can correlate various frames with each other.
Owing to the enormous progress in this field, it is today not just possible to place a box around an object in the image (object detection), but rather to categorise each pixel from an image to an object (semantic divisioning). In this case, fully-convolutional networks (FCN) are used. Mask R-CNN goes still one step further. It breaks down image data into their most-fundamental blocks and is thus able, for example, to differentiate between all individual persons in a group of persons.
Computer Vision is probably one of the most exciting fields in Artificial Intelligence. Analogous to the human sense of vision, a computer learns to automatically find, process and interpret useful information in photos or video materials. Particularly by using neuronal networks and the advances in Deep Learning, more and more companies are relying on Computer Vision in order to solve a wide array of problems. A technology driver is the automobile industry which requires the technology in order to develop self-driving cars. But also in quality assurance at production companies or the inspection of industrial facilities, etc., Computer Vision is already being used today.
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