DATE
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TOPIC
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QUESTIONS
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REFERENCES
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9/28/06
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Introduction; organization; administration
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10/3
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Background material
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Any book on statistical decision theory; e.g. Duda-Hart
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10/5
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Local features and wide-baseline matching: SIFT, MSER,
Gabor Jets
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How can we tie the choice of feature to the task?
Currently features are designed for wide-baseline matching, but used
for category recognition. Can we devise better features for the latter
task?
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Lowe, Matas, Koenderink, Kadir, Sirovich-Kirby
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10/10*
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MURI Kickoff Meeting
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10/12
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Viewpoint invariance:
Affine invariance, scale-space, deformations
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Assuming statistics
on likely deformations are available, can we relax the notion of
invariance to "insensitivity" "to most likely deformations? When should
invariance/insensitivity be achieved by design, and when should it be
achieved as part of the matching process? (recognition via features vs.
recognition via reconstruction)
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Schmid, Vedaldi, Lindeberg
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10/17
(Meltzer, Voroninski)
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Illumination: Simple phenomenological models
(Phong, Torrance-Sparrow), Lambertian reflection, bas-relief
ambiguity. Global approximate models: Contrast functions, gradient
direction.
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General illumination invariants do not exist: Can we
design features that are insensitive to the most likely illuminations?
Can color help?
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Any book on computer graphics; Morel, Belhumeur
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10/19
(Nabil)
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Feature selection: Link to task (object vs. category
recognition)
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See 10/5, in the broader context of feature selection
in the AI literature.
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Koller
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10/24
(Raptis, Fontanelli)
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Dimensionality reduction: PCA, LDA, TCA, manifold
learning
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Mostly background material (tutorial)
|
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10/26
(Vedaldi)
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Information-theoretic approaches: Basics of
info-theory, Rate-Distortion theory; Rate-Recognition theory.
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How can we use information theory to guide the
selection of features? We want maximally insensitive to viewpoint and
illumination, but maximally informative with respect to the recognition
task. How do we characterize that? What are the tradeoffs?
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10/31
(Fulkerson)
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Computational Approaches: Complexity, Boosting, Bagging
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Some background (Adaboost); how do these approach
relate to those based on a model, or based on complexity?
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Geman, Viola, Freund,
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11/2
(Jurgens, Zhu)
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Grammars and Semantics
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Why is it necessary to "parse" a scene in order to
"understand" it?
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11/7*
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Illumination (Paul Debevec, USC)
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Guest lecture
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11/9
(Ling)
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Parts and Compositions
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What is a "part"? How can we model complex scenes as
the juxtaposition of simpler parts?
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11/14*
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Actions (Trevor Darrell, MIT)
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Guest lecture
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11/16
(Wnuk)
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Context
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What is context? Local vs.
Global?
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Torralba
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11/21
(Guidi, Sasai)
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Natural Statistics;
occlusions, textures, perceptual transitions
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How can we collect
statistics on the scene (rather than the image) from images? How do we
exploit such statistics? Shape statistics? Refleectance statistics?
Deformation statistics?
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Mumford
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11/23
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Thanksgiving Holiday
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Happy Turkey
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11/28*
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SLAM (Frank Dellaert,
GeorgiaTech)
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Guest lecture
|
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11/30
(Hong, Yi)
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Deformable Templates:
Sparse vs. Dense
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Grenander's tenet:
Variability in the domain of the image (group deformation) vs. range
(intensity)
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Grenander
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12/5
(Bissacco)
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Actions and events
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12/7
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Modeling vs. Learning,
Recognition vs. Segmentation
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How should we decide what
to "learn away" and what to model explicitly? What are fundamental
complexity bounds?
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