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Understanding Designing and Analyzing Computational Cameras

Objectif

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.

Champ scientifique

  • /ingénierie et technologie/génie électrique, génie électronique, génie de l’information/ingénierie électronique/capteurs/capteurs optiques
  • /sciences naturelles/mathématiques/mathématiques appliquées/statistique et probabilité/statistique bayésienne

Appel à propositions

ERC-2010-StG_20091028
Voir d’autres projets de cet appel

Régime de financement

ERC-SG - ERC Starting Grant

Institution d’accueil

WEIZMANN INSTITUTE OF SCIENCE
Adresse
Herzl Street 234
7610001 Rehovot
Israël
Type d’activité
Higher or Secondary Education Establishments
Contribution de l’UE
€ 756 845
Chercheur principal
Anat Levin (Dr.)
Contact administratif
Gabi Bernstein (Ms.)

Bénéficiaires (1)

WEIZMANN INSTITUTE OF SCIENCE
Israël
Contribution de l’UE
€ 756 845
Adresse
Herzl Street 234
7610001 Rehovot
Type d’activité
Higher or Secondary Education Establishments
Chercheur principal
Anat Levin (Dr.)
Contact administratif
Gabi Bernstein (Ms.)