Date |
Lecture topic |
New assignments |
Assignments due |
Reading |
Sept. 8 |
Introduction. What is Computer Vision?
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Assignment 1: Read Lightness Perception and Lightness Illusions Come up with five questions relevant to the paper. These can be things you didn't understand after a careful reading of the paper, or questions which the paper raises. Turn in the answer written up as a .pdf file. You will be graded on the depth of your questions and how much thought you were judged to have put into them. |
As. 1 due Sept. 15 |
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Sept. 10 |
Introduction to using
MATLAB
for Computer Vision. Matlab Session Transcript from Lecture
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Assignment 2:
Colorizing the Prokudin-Gorsky photo collection
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As. 2 due Sept. 17 |
Chap. 2 in Textbook (skim) |
Sept. 15 |
Formalizing the decision making process. Minimizing error. Maximizing utility. Review of basic probability theory. You will be responsible for all of the basic probability theory in this handout.
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Probability handout (see lecture description). |
Sept. 17 |
Introduction to supervised learning. Supervised learning for vision. Estimating distributions from data. Features of images.
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Sept. 22 |
Estimating distributions from data. Estimating joint distributions of multiple variables. Leveraging independence for better estimation. More applications of Bayes' rule.
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Sept. 24 | Alignment by
maximization of mutual information. Back to supervised learning using
probability estimates.
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Assignment 3. Aligning the Prokudin-Gorsky images automatically. | As. 3 due Oct. 6
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Mutual Information handout. |
Sept. 29 |
Naive Bayes. Assuming independence of features. Comparing the dependence of features using mutual information. Evaluating the value of features using mutual inforamtion.
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Oct. 1 |
Nearest neighbor classification
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Oct. 6 |
Nearest neighbor classification continued.
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Oct. 8 |
Alignment and classification.
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Assignment 4. Classification of handwritten digits. Training and test data file (digits.mat) | Due Oct. 22nd, before class (10\% penalty for lateness this time.)
| Handout: Supervised learning and estimation
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Oct. 13 |
Solving difficult alignment problems through joint alignment: congealing.
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Oct. 15 |
Congealing continued.
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Oct. 20 |
Image formation, lecture 1. Pinhole cameras, basic optics.
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Oct. 22 |
Image formation, lecture 2. Radiometry. Solid Angle, watts per unit area, etc.
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Assignment 5: Translation invariant classification | As. 4 due today. As. 5 extended to TUesday, Nov. 3, before class.
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Oct. 27 |
Illuminance, luminance, brightness, radiance, irradiance, cosine to the fourth law.
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Oct. 29 |
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Nov. 3 |
****************************************
In Class Mid-term
***************************************
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As. 5 due today! (before class)
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Nov. 17 |
Background subtraction
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Nov. 19 |
Background subtraction, continued.
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Nov. 24 |
Optical flow.
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Assignment 6: Background
subtraction. train_data.mat
test_data.mat |
Assignment 6 is due on Dec. 10, the last day of class
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Nov. 26 |
THANKSGIVING
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Dec. 1 |
Comparing Lukas-Kanade optical flow and Horn-Schunk Optical flow.
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| Assignment 7
gardenImages.mat
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Dec. 3 |
Mobil-eye and examples of real-world vision apps. Edges and lines as primitive vision features.
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Dec. 8 |
More on edges. Higher level features. SIFT features.
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Dec. 10 |
Last day of class.
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Friday Dec. 18 |
***FINAL EXAM ***: 10:30am, Computer Science 142
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Review of Radiometry
Lecture slides 1-30
Lecture slides 31-60
Lecture slides 61-90
Lecture slides 91-137
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