Event Location: NSH 1507
Bio: Sam Hasinoff received the BSc degree in computer science from the
University of British Columbia in 2000. He is currently a PhD candidate
at the University of Toronto. In 2006, he received an honorable mention
for the Longuet-Higgins Best Paper Award at the European Conference on
Computer Vision. His interests include cross-country skiing, spicy
foods, and amateur photography.

Abstract: In this talk I will describe three projects that harness the power
of variable-aperture photography — capturing multiple photos by
manipulating basic lens controls such as aperture and focus. I will show
that by combining such photos, the information encoded in defocus can be
exploited to achieve a variety of goals.

First, I will describe a new method for computing highly detailed 3D
shape by controlling both the aperture and focus of a lens. This method
is particularly well-suited for scenes with high geometric complexity,
for which standard reconstruction methods can break down.

Second, I will show that we can exploit “aperture bracketing” — a
one-button operation on most digital SLR’s — to allow refocusing and
other effects in post-capture, all with increased dynamic range. To
achieve this, we compute a layered scene model that simultaneously
accounts for defocus, high dynamic range exposure, and noise in the
input images.

Finally, I will talk about our current work on “light-efficient”
photography, whose goal is to capture photos with the desired level of
defocus in the shortest time possible.