Modeling Appearance via the Object Class Invariant
Speaker: Matthew Toews , Harvard Medical School
Date: October 17 2008
Time: 2:00PM to 3:00PM
Location: 32-D507
Host: Polina Golland, CSAIL
Contact: Polina Golland, x38005, polina@csail.mit.edu
Relevant URL: As humans, we are able to identify, localize, describe and classify a
wide range of object classes, such as faces, cars or the human brain,
by their appearance in images. Designing a general computational model
of appearance with similar capabilities remains a long standing goal
in the research community. A major challenge is effectively coping
with the many sources of variability operative in determining image
appearance: illumination, noise, unrelated clutter, occlusion, sensor
geometry, natural intra-class variation and abnormal variation due to
pathology to name a few. Explicitly modeling sources of variability
can be computationally expensive, can lead to domain-specific
solutions and may ultimately be unnecessary for the computational
tasks at hand.
In this talk, I will show how appearance can instead be modeled in a
manner invariant to nuisance variations, or sources of variability
unrelated to the tasks at hand. This is done by relating spatially
localized image features (e.g. SIFT) to an object class invariant
(OCI), a reference frame which remains geometrically consistent with
the underlying object class despite nuisance variations. The resulting
OCI model is a probabilistic collage of local image patterns that can
be automatically learned from sets of images and robustly fit to new
images, with little or no manual supervision. Due to its general
nature, the OCI model can be used to address a variety of difficult,
open problems in the contexts of computer vision and medical image
analysis. I will show how the model can be used both as a
viewpoint-invariant model of 3D object classes in photographic imagery
and as a robust anatomical atlas of the brain in magnetic resonance
imagery.
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