Date 
Lecture topic 
New assignments 
Assignments due 
Reading 
Jan. 19 
Lecture Slides 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 Jan. 26 
Handout: Introduction to Computer Vision

Jan. 24 
Introduction to using
MATLAB
for Computer Vision.
MM's candy image used in lecture
Matlab Session 1 Transcript from Lecture
Matlab Session 2 Transcript from Lecture




Jan. 26 
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.

Assignment 2:
Colorizing the ProkudinGorsky photo collection

As. 2 due Feb. 2 
Probability handout (see lecture description). 
Jan. 31 
Probability review continued.




Feb. 2 
SNOW DAY.




Feb. 7 
Bayes rule. Minimizing probability of error. Utility.
Introduction to supervised learning. Supervised learning for vision. Estimating distributions from data. Features of images.



Supervised learning handout. 
Feb. 9 
Classification of handwritten digits with simple features.
Diary from lecture




Feb. 14 
Estimating joint distributions of multiple
variables. Leveraging independence for better estimation. More
applications of Bayes' rule.




Feb. 16 
More on independence. Alignment issues in computer vision.

Assignment 3: Single pixel classification of digits. Download digits here.  Due, Monday, February 21 by end of day.


Feb. 22 
The three parts of alignment: Similarity and difference functions, sets of
transformations, and optimization methods


 
Feb. 23 
Alignment continued and how to transform an image.

Assignment 4: Multiple pixel classification of digits. Download digits here.  Due, Wednesday March 2 end of day.


Feb. 28 
Transforming images in matlab. Avoiding "holes" in images by
inverse transforming. Minimizing difference function over transformations.
Three matlab files from class:
rotate1.m
rotate2.m
demo.m


 Assignment
3 solution: likFromTraining.m Assignment
3 solution: BayesRule.m Assignment
3 solution: classifyTestData.m 
Mar. 2 


 
Mar. 7 
Midterm review.

 
Assignment4 solution: likFromTraining_multi.m
Assignment4 solution: BayesRule_multi.m
Assignment4 solution: classifyTestData_multi.m 
Mar. 9 
****************************
IN CLASS MIDTERM
************************


 
Mar. 14 
No Class  Spring break.


 
Mar. 16 
No Class  Spring break.


 
Mar. 21 
The role of optics and photogrammetry in computer vision.
Electromagnetic spectrum. Visible light. Composition of
visible light.


 
Mar. 23 
Point light sources. Steradians. Solid angle. Watts of a light
source. Inverse square law. Reflection, scattering.


 Light sources and camera models handout. 
Mar. 28 


 
Mar. 30  Background substraction



Background modeling (subtraction) introduction 
Apr 4  Background substraction continued.

Assignment 5: Cameras and light.
 Due, Monday
April 18 end of day.


Apr. 6 
Background on convolution, delta functions, using convolution to
help in density estimation.


 Convolution (wikipedia
link)

Apr. 11 
Overview of Face recognition. Detection, alignment, recognition.


 
Apr. 18  No class. HOLIDAY


 
Apr. 20  More on distribution fields. Finish background subtraction.

Assignment 6: Background subtraction train_data.mat
test_data.mat  

Apr. 25  Slides on edges


 
Apr. 27  Slides on SIFT 


 
May 2  LAST DAY OF CLASS.
Review for FINAL.


 Assignment5 solution: Pinhole camera problem

May 9 1:30 PM 
***FINAL EXAM ***: 1:30pm, Computer Science building room 140



Exam 1 review handout
Final Exam review handout
