When: Monday, September 26, 3:30 p.m.- 4:45 p.m.

Where: 1305 Newell-Simon Hall

Yoav Y. Schechner, Faculty

VASC Seminar

Abstract:
Computer vision typically regards images as given entities to be processed. However, richer information can be extracted by modifying and analyzing the imaging process itself. This modification includes the sensor or the illumination, in conjunction to carefully tailored algorithms. This hybridization exploits the advantages of both the sensor and the algorithmic components of a vision system. We describe our recent results in this approach, which apply to the full observation setup: illumination of the object, scattering media between the object and the camera, optical phenomena in the camera, and multi-sensor computational processing. In particular, the talk shows new results in the development of multiplexing for enhanced imaging under varying illumination directions. We then describe denoising that is tailored to vision in scattering media. In addition, we describe a method for blindly estimating simultaneous spatio-temporal inconsistencies of sensors (gain, vignetting, radiometric response). Finally, we explore audio-visual interaction, whereby a vision algorithm using a sparsity prior uniquely pinpoints the pixels that correspond to sound sources, with high definition.

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