CMU VASC seminar : Probabilistic Models for Image Parsing

Title : Probabilistic Models for Image Parsing
Speaker : Xiaofeng Ren (TTI Chicago)

Abstract:

A grand challenge of computer vision is to understand and parse natural
images into boundaries, surfaces and objects. To solve this problem we
would inevitably need to work with visual entities and cues of
heterogeneous nature, such as brightness and texture at low-level, contour
and region grouping at mid-level, and shape recognition at high-level.
Learning to represent and incorporate these entities and cues, along with
the complexity of the visual world itself, calls for probabilistic models
for image parsing. Many previous efforts in this line suffer from issues
such as lack of a compact representation, lack of scale invariance or lack
of comprehensive experimentation. We describe a scale-invariant image
representation using piecewise linear approximations of contours and the
constrained Delaunay triangulation (CDT) for completing relentless gaps.
On top of the CDT graph we develop conditional random fields (CRF) for
contour completion, figure/ground organization as well as object
segmentation. Large datasets of human-annotated natural images are
utilized for both training and evaluation. Our quantitative results are
the first to demonstrate the working of mid-level visual cues in general
natural scenes. The CDT/CRF framework enables efficient representation and
inference of both bottom-up and top-down information, hence applicable to
various vision problems. We extend our work to joint object recognition
and segmentation, in particular finding people, in static images and
video.

Bio:

Xiaofeng Ren received his B.S. in computer science from Zhejiang
University, China, and his M.S. from Stanford University. In 2006 he
received his Ph.D. degree in computer science from University of
California at Berkeley, under the supervision of Jitendra Malik. He is
currently a research assistant professor at Toyota Technological Institute
at Chicago. His research interests lie broadly in the areas of computer
vision and artificial intelligence, and he has mainly worked on contour
completion, image segmentation, figure/ground labeling and human body pose
recovery.

Speaker's homepage : http://www.cs.berkeley.edu/~xren/

Appointments: Email Janice Brochetti

Origin : http://vasc.ri.cmu.edu/seminar/