by Libor Král
Vision is a key factor in natural evolution, whether aiding a jellyfish to detect light or providing the supra-human ability of an eagle to spot its prey from miles away. The desire to enhance human vision through artificial means dates back several centuries, but it is not until the last twenty years or so that machine and computer vision took off. Today, as evidenced by the contributions to this ERCIM edition, it is a vibrant research field.
Why should the European Union be interested in image technology? There are two main reasons. Firstly, vision systems represent an important socio-economic activity. Sales by European suppliers of sophisticated (e.g. application-specific or reconfigurable) vision systems and of products like smart cameras, software and components reached the €2 billion mark according to a 2012 survey by the European Machine Vision Association. Vision systems are an enabling technology with a multiplier effect on sectors important to the seventh EU research framework programme (FP7), like health, agriculture, space and security. Robotics, with a projected annual economic impact of several trillion euros worldwide by 2025 , is a prime example of a future sector whose growth will depend heavily on image understanding. According to the 2020 EUROP (European technology platform for robotics) Strategic Research Agenda (SRA), sensing is what sets robots apart from other types of machines. The SRA technology targets include increasing the resolution and range of 3D sensors and exploiting novel sensing mechanisms.
Secondly, the EU has focussed on the scientific potential of computer vision for just over one decade, marked by the launch of the ECVISION coordination network in March 2002 and of a small batch of research projects in cognitive vision. The term ''cognitive vision'' encapsulates an attempt to achieve more robust, resilient, and adaptable computer vision systems by endowing them with a cognitive faculty: the ability to learn, adapt, weigh alternative solutions, and develop new strategies for analysis and interpretation. This initiative broadened to cognitive systems and robotics in 2004.
Since then, FP7 has invested some €700 million into over 100 cognitive systems and robotics projects. The main scientific and technical research challenges for these projects lie in innovative methods of perception, understanding and acting in real world situations. Autonomous moving robots set radical new challenges for computer vision, e.g. for on-the-fly scene analysis and for person and object recognition in real-life environments, where only few prior assumptions can be made by the vision tools. Projects address topics such as urban scene recognition, modelling a city pedestrian's viewshed (line of sight), robust sensor deployment in the ocean for seabed mapping or in hazardous terrains for search & rescue, low-cost sensors for navigation or for shape and gesture recognition, networked vision, e.g. continuous tracking of a moving object or person from one set of cameras to another, and compact or modular and fast image processing for real-time inspection of
large installations by mobile platforms like minicopters or crawling robots.
What is next at the EU level? Proposed research in the coming Horizon 2020 EU research framework programme will aim to increase significantly the scale of deployment and technological readiness of industrial and service robotics through research projects and through new innovation-promoting use cases. This will put extra demands of robustness and cost-effectiveness on vision systems. Research opportunities will not only exist in robotics. Proposed research into digital content will promote unprecedented access to very large data sets (“big data”), putting new demands on image analysis, modelling and visualization tools. Simulation and testing systems will become far more realistic and more closely integrated with the hardware they simulate. 3D and augmented reality technologies will need to evolve even further for digital content creation, virtual learning or digital gaming.
In conclusion, the role of image understanding as an enabling technology has been highly productive in FP7 and should certainly continue in Horizon 2020, not only pursuing scientific and technical goals in a wide variety of research topics but also contributing to innovation through deployment projects.
 “Disruptive technologies: Advances that will transform life, business, and the global economy” (McKinsey, 2013), available at: http://www.mckinsey.com/insights/business_technology/ disruptive_technologies)
Head of Unit A2 – Robotics in Directorate General Communication Networks, Content & Technology, European Commission