Research Group Projects and Descriptions

The Camera Culture group is building new tools to better capture and share visual information. What will a camera look like in ten years? How should we change the camera to improve mobile photography? How will a billion networked and portable cameras change the social culture? We exploit unusual optics, novel illumination, and emerging sensors to build new capture devices and develop associate algorithms.

Computational photography is an emerging multi-disciplinary field that is at the intersection of optics, signal processing, computer graphics and vision, electronics, art, and online sharing in social networks. The first phase of computational photography was about building a super-camera that has enhanced performance in terms of the traditional parameters, such as dynamic range, field of view, or depth of field. We call this 'Epsilon Photography.' The next phase of computational photography is building tools that go beyond capabilities of this super-camera. We call this 'Coded Photography.' We can code exposure, aperture, motion, wavelength and illumination. By blocking light over time or space, we can preserve more details about the scene in the recorded single photograph.

Our goal is to exploit the finite speed of light to improve image capture and scene understanding. New theoretical analysis coupled with emerging ultra-high-speed imaging techniques can lead to a new source of computational visual perception. We are developing the theoretical foundation for sensing and reasoning using transient light transport, and experimenting with scenarios in which transient reasoning exposes scene properties that are beyond the reach of traditional machine vision.

We are creating a portable and high-speed tomography machine. We record all the data in a single snapshot without mechanical movement or time-division multiplexing of emitters.

We are building a wearable fabric to support millimeter-accurate location and bio-parameter tracking at thousands of points on the body. Such a fabric can compute and predict 3-D representations of human activity and use them for a closed-loop control to augment human performance. The goal is to support a detailed analysis and control of higher-level human activity. The basic technology uses a new, optical motion-capture method we have recently developed. The first phase of the project involves building next-generation optical communication tools.