Feb. 17, 11:45, Wei Ji Ma. Optimality and Probabilistic Computation in Visual Categorization

Yotam Gingold

Computer Science, Rutgers and Columbia

Perceptual Micro Human Computation for Visual Tasks

Human computation (HC) uses humans to solve problems or carry out
tasks that are hard for pure computational algorithms. Many graphics
and vision problems have such tasks. Previous HC approaches mainly
focus on generating data in batch, to gather benchmarks or perform
surveys demanding non-trivial interactions. We advocate a tighter
integration of human computation into online, interactive algorithms.
We aim to distill the differences between humans and computers and
maximize the advantages of both in one algorithm. Our key idea is to
decompose such a problem into a massive number of very simple,
carefully designed, human micro-tasks that are based on perception,
and whose answers can be combined algorithmically to solve the
original problem.
We present three specific examples for the design of Perceptual
Micro-HC algorithms to extract depth layers and image normals from a
single photograph, and to augment an image with high-level semantic
information such as symmetry.


Yotam Gingold is a post-doctoral researcher in the computer science
departments of Rutgers and Columbia, supervised by Andrew Nealen and Eitan Grinspun.  His research interests include interactive geometric modeling, topology for computation, human computation, and game design.  Yotam earned his Ph.D. in Computer Science from New York University in 2009 under the supervision of Denis Zorin, and a B.Sc. in Computer Science and Mathematics from Brown University in 2002.  From 2010 to 2011, Yotam was a post-doctoral researcher at Tel-Aviv University and the Herzliya Interdisciplinary Center.