The term "video and scene analysis" refers to the semantic analysis of image data, often recorded outdoors. A simple example is traffic sign detection. Often, however, they are recordings from different sources.
One of the largest projects of the image processing department is the automatic detection of objects photographed with different mobile phones. For the purpose of the largely parameter-free recognition of different objects, current research algorithms are adapted as well as new algorithms developed.
For many years machine learning has been an integral part of many projects and research activities in the department. In the field of surface inspection, hybrids from the "classical" parameterizable methods (filters, morphology, edge detectors) and learning approaches are increasingly being used.
Learning methods, such as "deep learning", require a large number of annotated data, which in an industrial project is usually neither affordable nor practical. For this reason, it is necessary to model assumptions about the objects, you want to locate, and to use this modeling as a partial input for automated procedures. Because of this special data situation, the department's learning approaches are almost always model-based (model-based machine learning).