ERC FUNDED PROJECTS

Computational cameras go beyond 2D images and allow the extraction of more dimensions from the visual world such as depth, multiple viewpoints and multiple illumination conditions. They also allow us to overcome some of the traditional photography challenges such as defocus blur, motion blur, noise and resolution. The increasing variety of computational cameras is raising the need for a meaningful comparison across camera types. We would like to understand which cameras are better for specific tasks, which aspects of a camera make it better than others and what is the best performance we can hope to achieve. Our 2008 paper introduced a general framework to address the design and analysis of computational cameras. A camera is modeled as a linear projection in ray space. Decoding the camera data then deals with inverting the linear projection. Since the number of sensor measurements is usually much smaller than the number of rays, the inversion must be treated as a Bayesian inference problem accounting for prior knowledge on the world. Despite significant progress which has been made in the recent years, the space of computational cameras is still far from being understood. Computational camera analysis raises the following research challenges: 1) What is a good way to model prior knowledge on ray space? 2) Seeking efficient inference algorithms and robust ways to decode the world from the camera measurements. 3) Evaluating the expected reconstruction accuracy of a given camera. 4) Using the expected reconstruction performance for evaluating and comparing camera types. 5) What is the best camera? Can we derive upper bounds on the optimal performance? We propose research on all aspects of computational camera design and analysis. We propose new prior models which will significantly simplify the inference and evaluation tasks. We also propose new ways to bound and evaluate computational cameras with existing priors.

756 845 €

Start date: 2010-12-01, End date: 2015-11-30

As more and more economic activity is moving to the Internet, familiar economic mechanisms are being deployed at unprecedented scales of size, speed, and complexity. In many cases this new complexity becomes the defining feature of the deployed economic mechanism and the quantitative difference becomes a key qualitative one. A well-studied example of such situations is how the humble single-item auction suddenly becomes a billion-times repeated online ad auction, or even becomes a combinatorial auction with exponentially many possible outcomes. Similar complexity explosions occur with various markets, with information dissemination, with pricing structures, and with many other economic mechanisms. The aim of this proposal is to study the role and implications of such complexity and to start developing a coherent economic theory that can handle it. We aim to identify various measures of complexity that are crucial bottlenecks and study them. Examples of such complexities include the amount of access to data, the length of the description of a mechanism, its communication requirements, the cognitive complexity required from users, and, of course, the associated computational complexity. On one hand we will attempt finding ways of effectively dealing with complexity when it is needed, and on the other hand, attempt avoiding complexity, when possible, replacing it with "simple" alternatives without incurring too large of a loss.

2 026 706 €

Start date: 2017-05-01, End date: 2022-04-30

Current IT research does not respond to the world's multi-cultural reality. It could be argued that we are imposing the paradigms of our market-driven western culture also on IT and that current IT research results will only facilitate the access of a small part of the world’s information to a small part of the world's population. Most IT research is being carried out with a western centred approach and as a result, our data models, cognition models, user models, interaction models, ontologies, … are all culturally biased. This fact is quite evident in music information research, since, despite the world's richness in musical cultures, most of the research is centred on CDs and metadata of our western commercial music. CompMusic wants to break this huge research bias. By approaching musical information modelling from a multicultural perspective it aims at advancing our state of the art while facilitating the discovery and reuse of the music produced outside the western commercial context. But the development of computational models to address the world’s music information richness cannot be done from the West looking out; we have to involve researchers and musical experts immersed in the different cultures. Their contribution is fundamental to develop the appropriate multicultural musicological and cognitive frameworks from which we should then carry our research on finding appropriate musical features, ontologies, data representations, user interfaces and user centred approaches. CompMusic will investigate some of the most consolidated non-western classical music traditions, Indian (hindustani, carnatic), Turkish-Arab (ottoman, andalusian), and Chinese (han), developing the needed computational models to bring their music into the current globalized information framework. Using these music cultures as case studies, cultures that are alive and have a strong influence in current society, we can develop rich information models that can take advantage of the existing information coming from musicological and cultural studies, from mature performance practice traditions and from active social contexts. With this approach we aim at challenging the current western centred information paradigms, advance our IT research, and contribute to our rich multicultural society.

2 443 200 €

Start date: 2011-07-01, End date: 2017-06-30

PROBLEM: Despite extensive research on human-computer interaction (HCI), no method exists that guarantees the optimal or even a provably good user interface (UI) design. The prevailing approach relies on heuristics and iteration, which can be costly and even ineffective, because UI design often involves combinatorially hard problems with immense design spaces, multiple objectives and constraints, and complex user behavior. OBJECTIVES: COMPUTED establishes the foundations for optimizing UI designs. A design can be automatically optimized to given objectives and constraints by using combinatorial optimization methods that deploy predictive models of user behavior as objective functions. Although previous work has shown some improvements to usability, the scope has been restricted to keyboards and widgets. COMPUTED researches methods that can vastly expand the scope of optimizable problems. First, algorithmic support is developed for acquiring objective functions that cover the main human factors in a given HCI task. Second, formal analysis of decision problems in UI design allows combating a broader range of design tasks with efficient and appropriate optimization methods. Third, a novel interactive UI optimization paradigm for UI designers promotes fast convergence to good results even in the face of uncertainty and incomplete knowledge. IMPACT: Combinatorial UI optimization offers a strong complement to the prevailing design approaches. Because the structured search process has a high chance of finding good solutions, optimization could improve the quality of interfaces used in everyday life. Optimization can also increase cost-efficiency, because reference to optimality can eliminate fruitless iteration. Moreover, because no preknowledge of UI design is required, even novices will be able to design great UIs. Even in “messy,” less well-defined problems, it may support designers by allowing them to delegate the solving of well-known sub-problems.

1 499 790 €

Start date: 2015-04-01, End date: 2020-03-31