Mon/Wed: 12:00-1:15 Winter 2014, Maths/Sciences 5203.
This course models vision as Bayesian Inference. It concentrates on visual tasks such as segmenting images, detecting objects in images, and recognizing objects. Its goal is to describe the state of the art techniques. The handouts consist of copies of the lecture notes and related papers.
3 homework assignments (25% each). Term project or review (25%).
Homework1Lecture | Date | Topics | Handouts | Supplements
|
Additional Readings |
1 | 01-06 | Introduction |
Lecture1 |
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2 |
01-08 | Images, Linear Filters, and
Statistical Edge Detection |
Lecture2 |
Notes2 |
LinearFilteringKokkinos
KonishiPaper |
3 |
01-13 |
Dictionaries, Super-Pixels,
and More Edge Detection |
|||
3 |
01-15 |
Guest Lecture: Dr. Boyan Bonev.
Hierarchical Super-pixels |
Lecture4 |
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4 |
01-20 |
Martin Luther King Holiday |
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5 |
01-22 |
Energy Functional Models |
Lecture5 |
TotalVariationExample |
|
6 |
01-27 |
Markov Random Fields: Gibbs
Sampling, MFT |
Lecture6 |
IntroGraphicalModels |
MRFsMFTBPGibbsSamping |
7 |
01-29 |
Belief Propagation |
Lecture7 |
BeliefPropagation |
|
8 |
02-03 |
Learning Distributions |
Lecture8 |
HandNotes |
Frame DellaPietra |
9 |
02-05 |
Learning Distributions with Hidden
Variables. |
Lecture9 |
HiddenMarkovModels |
|
10 |
02-10 |
Regression and Structural
Support Vector Machines |
Lecture9.5 |
YuilleHe |
|
11 |
02-12 |
Lambertian Lighting Models |
Lecture10 |
Basri1 |
Basri2 |
12 |
02-17 |
Preisdent's Day |
|||
13 |
02-19 |
The Geometry of Multiple Images | Lecture11 |
Notes |
|
14 |
02-24 |
Segmentation and Image Parsing | Lecture12 |
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15 |
02-26 |
AdaBoost/Regression: Face and Text detection | Lecture13 |
ChenYuille |
|
16 |
03-03 |
Deformable Template Models of Objects | Lecture14 |
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17 |
03-5 |
Deformable Part Models and
PASCAL |
Lecture15 |
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18 |
03-10 |
Deep Neural Network Models |
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19 |
03-12 |
Hierarchical Compositional Models | |||