Semantic segmentation assigns a class to every single pixel in an image, such as road, vehicle, person or sky. The result is a kind of colour-coded map of the image content. Unlike instance segmentation, objects of the same type are not distinguished: three cars are treated together as “car”. It is used, for example, in autonomous driving, in medical imaging to delineate tissue and in the analysis of aerial and satellite images.
Instance segmentation is a computer vision technique that detects every individual object in an image and outlines it pixel by pixel. Unlike semantic segmentation, it also distinguishes between objects of the same class: three people receive three separate masks rather than one shared area. It is used, for example, in quality inspection, medical imaging, robotics or counting products on a shelf.
Computer vision is a field of AI in which computers interpret images and video. Typical tasks include detecting and locating objects, reading text in images and measuring components. The technology is used, for example, in quality control in manufacturing, in driver assistance systems, in logistics and in medical imaging.
Image recognition is a field of computer vision in which software identifies what an image shows – objects, people, text, logos or scenes. It is usually based on neural networks trained on large numbers of labelled images. Applications range from quality control in manufacturing and sorting product photos to supporting medical imaging. With images of people, data protection and potential bias require particular attention.
A convolutional neural network (CNN) is a neural network architecture particularly suited to image data. It scans the input with small filters, detecting simple features such as edges in the first layers and more complex shapes in deeper ones. CNNs shaped image recognition for many years and are used, for example, to spot defects in product photos or in medical image analysis.
