גיא רוסמן (מדעי המחשב, טכניון)
יום שלישי, 9.7.2013, 11:30
חדר 337, בניין טאוב למדעי המחשב
One of the most important aspects of solving a problem is that of choosing an
appropriate parameterization. This trivial observation can be seen in many
forms in image processing and computer vision. Global parametrizations
include the Hough and Fourier transforms, whereas local parameterizations
include sparsity-based patch models and over-parameterized approaches. My
research explores important cases in motion analysis and 3D reconstruction
where a careful choice of the parameterization matters. It leads, in these
cases, to simple and yet generic formulations that can be efficiently
The first part of my talk relates to 3D motion understanding, where I
reformulate articulated motion as edge-preserving smoothing of Lie-group-
valued images. The resulting generic algorithm obtains results comparable to
those of domain specific tools, on 3D range data, at real-time speeds.
Furthermore, it applies also to other inverse problems such as diffusion tensor
imaging reconstruction, and direction diffusion.
In the second part I show how structured light reconstruction can be
formulated as probability maximization with respect to the range image. This
allows us to incorporate sparse priors for the surface into the non-linear
reconstruction process itself. These priors, resulting from the data, have a
natural and intuitive interpretation. Furthermore, they help us obtain 3D
reconstruction that is robust to low sensor exposure and motion artifacts.
Ph.D. research under the supervision of prof. Ron Kimmel.