Date 
Lecture topic 
New assignments 
Assignments due 
Reading 
Sept. 8 
Introduction. What is Computer Vision?

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 

Sept. 10 
Introduction to using
MATLAB
for Computer Vision. Matlab Session Transcript from Lecture

Assignment 2:
Colorizing the ProkudinGorsky photo collection

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.



Probability handout (see lecture description). 
Sept. 17 
Introduction to supervised learning. Supervised learning for vision. Estimating distributions from data. Features of images.




Sept. 22 
Estimating distributions from data. Estimating joint distributions of multiple variables. Leveraging independence for better estimation. More applications of Bayes' rule.




Sept. 24  Alignment by
maximization of mutual information. Back to supervised learning using
probability estimates.

Assignment 3. Aligning the ProkudinGorsky images automatically.  As. 3 due Oct. 6

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.




Oct. 1 
Nearest neighbor classification




Oct. 6 
Nearest neighbor classification continued.


 
Oct. 8 
Alignment and classification.

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

Oct. 13 
Solving difficult alignment problems through joint alignment: congealing.


 
Oct. 15 
Congealing continued.


 
Oct. 20 
Image formation, lecture 1. Pinhole cameras, basic optics.


 
Oct. 22 
Image formation, lecture 2. Radiometry. Solid Angle, watts per unit area, etc.

Assignment 5: Translation invariant classification  As. 4 due today. As. 5 extended to TUesday, Nov. 3, before class.
 
Oct. 27 
Illuminance, luminance, brightness, radiance, irradiance, cosine to the fourth law.


 
Oct. 29 


 
Nov. 3 
****************************************
In Class Midterm
***************************************


As. 5 due today! (before class)
 
Nov. 17 
Background subtraction


 
Nov. 19 
Background subtraction, continued.


 
Nov. 24 
Optical flow.

Assignment 6: Background
subtraction. train_data.mat
test_data.mat 
Assignment 6 is due on Dec. 10, the last day of class
 
Nov. 26 
THANKSGIVING


 
Dec. 1 
Comparing LukasKanade optical flow and HornSchunk Optical flow.

 Assignment 7
gardenImages.mat
 
Dec. 3 
Mobileye and examples of realworld vision apps. Edges and lines as primitive vision features.


 
Dec. 8 
More on edges. Higher level features. SIFT features.


 
Dec. 10 
Last day of class.



Friday Dec. 18 
***FINAL EXAM ***: 10:30am, Computer Science 142

Review of Radiometry
Lecture slides 130
Lecture slides 3160
Lecture slides 6190
Lecture slides 91137

 