6.881
Representation and Modeling for Image Analysis
Spring 2005
Instructor: Polina Golland
TA: Stanley M Bileschi
Lecture:
MW 1-2.30
(36-144)
Description:
Most algorithms in computer vision and image analysis can
be
understood in terms of two important components: a representation
and
a modeling/estimation algorithm. The representation defines
what
information is important about the objects and is used to
describe
them. The modeling techniques extract the information from images
to
instantiate the representation for the particular objects present
in
the scene. In this seminar, we will discuss popular
representations
(such as contours, level sets, deformation fields) and useful
methods
that allow us to extract and manipulate image information,
including
manifold fitting, markov random fields, expectation
maximization,
clustering and others.
For each concept -- a new representation or an estimation
algorithm --
a lecture on the mathematical foundations of the concept will
be
followed by a discussion of two or three relevant research papers
in
computer vision, medical and biological imaging, that use the
concept
in different ways. We will aim to understand the fundamental
techniques and to recognize situations in which these
techniques
promise to improve the quality of the analysis.
Announcements
Room change
The new room is 36-144
Announced on
07 February 2005
10:21
a.m.
by Polina Golland
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