Machine Vision Metrology

Machine Vision comprises technologies and methods for automatic sensor-based inspection or robot guidance and is mainly used in industry for quality assurance and automation.
BeispielbildMarkus Ulrich

Machine vision is a key component in the ever increasing automation worldwide and is therefore also often referred to as the "eye of Industry 4.0". Although not always obvious, machine vision is ubiquitous today: It can be assumed, for example, that every manufactured smartphone has been extensively inspected with machine vision. This development was recently fuelled by the use of increasingly powerful graphics cards, which opened up new areas of application, especially in the field of machine learning, and by the emergence of industrial 3D sensors, which led to the increased use of 3D machine vision, especially in robotics.

Research in the field of machine vision is strongly influenced by influences from the fields of computer vision, machine learning, photogrammetry, and robotics. Important technology drivers, especially for machine learning, are currently automotive engineering, communication and consumer electronics, medicine, and logistics. As a result, the research landscape has recently undergone major changes: Cutting-edge research in the fields of computer vision and machine learning is not only carried out at universities and research institutes, but increasingly also by large tech companies, whose immense research budgets make them attractive employers for young academics.

Machine Vision Metrology can benefit from these developments at the interface between research, innovation, and application and at the same time make important contributions. In particular, the development of precise and accurate methods, such as the measurement or localization of electronic components during the manufacturing process, can be considered a core competence. The research objective is not only to develop scientific methods in an industrial context. It is also the aim to observe research results from neighbouring disciplines, to illuminate them against a geodetic background, and make them accessible to Machine Vision Metrology, and to communicate them to graduates in the course of research-oriented teaching.