COMPCAMERAANALYZ
Understanding Designing and Analyzing Computational Cameras
| Coordinatore | WEIZMANN INSTITUTE OF SCIENCE
Spiacenti, non ci sono informazioni su questo coordinatore. Contattare Fabio per maggiori infomrazioni, grazie. |
| Nazionalità Coordinatore | Israel [IL] |
| Totale costo | 756˙845 € |
| EC contributo | 756˙845 € |
| Programma | FP7-IDEAS-ERC
Specific programme: "Ideas" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013) |
| Code Call | ERC-2010-StG_20091028 |
| Funding Scheme | ERC-SG |
| Anno di inizio | 2010 |
| Periodo (anno-mese-giorno) | 2010-12-01 - 2015-11-30 |
Partecipanti
| # | ||||
|---|---|---|---|---|
| 1 |
WEIZMANN INSTITUTE OF SCIENCE
Organization address
address: HERZL STREET 234 contact info |
IL (REHOVOT) | hostInstitution | 756˙845.00 |
| 2 |
WEIZMANN INSTITUTE OF SCIENCE
Organization address
address: HERZL STREET 234 contact info |
IL (REHOVOT) | hostInstitution | 756˙845.00 |
Mappa
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Obiettivo del progetto (Objective)
'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.'