Date 
Lecture topic 
New assignments 
Assignments due 
Reading 
Jan. 19 
Introduction. What is Computer Vision? What are the goals of computer vision? What should we take from humans? Also, Intro to Matlab. See Matlab Diary of in class session under "Resources" above.

 
Read A Review of Basic Probability by Tuesday, January 26, at class time. 
Jan. 21 
Review of basic probability. Samples spaces. Events. Joint Probability. Conditional Probability. Marginalization. Role of Probability and Statistics in Computer Vision.

 

Jan. 26 
Bayes rule. Likelihoods, priors, and posteriors. Estimating likelihoods, priors, and posteriors.

 
Read Supervised Learning and Bayesian Classification by Tuesday, February 2, at class time. 
Jan. 28 
A few more Matlab tricks: dotm files and Matlab calling conventions, the image toolbox, avoiding for loops, repmat, dottimes, dotslash, dotpower, etc. What features to use in classification? Modeling the entire joint distribution of images in a class. Assuming feature independence. More on estimation and smoothing.

Assignment 1:
Probabilistic classification. Digit data for assignment 1 Due by class time on Feb. 4. Please
email me the solution as a zipped tar file of all necessary files. 


Feb. 2 
Euclidean distance functions in 1, 2, 3, and more dimensions.
Nonparametric density estimators.

 

Feb. 4 
The statistics of changing coordinates. Translation,
rotation, and other image movements as changing coordinates. Nearest
neighbor classification. KNearest neighbor
classification. Consistency of Knearest neighbors.

 

Feb. 9 
ProkudinGorski photographs. Correlation alignment.
Mutual information alignment. Other schemes of aligment.
Problems of alignment and solutions to alignment problems.

Assignment 2: Automatic alignment of ProkudinGorsky plates. Plates
Due by 11:15 on Feb. 16. 

Read Entropy and mutual information by Thursday, Feb. 11 at
class time. 
Feb. 11 
Alignment continued.

 

Feb. 16 
MONDAY CLASS SCHEDULE. NO CLASS.

 

Feb. 18 
Types of alignment: exhaustive search, gradient
descent, coordinate descent, gradient descent with restarts. Issues in
alignment. Local minima. The zerogradient problem. Computational
complexity. Criteria for pairwise alignment: mutual information,
correlation, norms (L2, L1, Linfinity). Introduction to joint
alignment.

 

Feb. 23 
Joint alignment and congealing. The minimum entropy
criterion. Nonparametric maximum likelihood. Joint gradient descent
(or joint coordinate descent). Smoothing of the optimization landscape
without destroying information.

Next assignment: Congealing implementation, due on March 9th, before class. 

Lecture slides on congealing 
Feb. 28 




Mar. 2 
Start light and optics. Electromagnetic spectrum. Pinhole cameras. Thin lenses.




Mar. 4 
Point sources, extended sources. Radiance, irradiance, luminance, illuminance, brightness.



Handout on radiometry. 
Mar. 9 




Mar. 11 
Learning to classify textures.

Written problem set. Due Mar. 25 


Mar. 16 
SPRING BREAK




Mar. 18 
SPRING BREAK




Mar. 23 
Guest Lecture: Vidit Jain




Mar. 25 
IN CLASS TEST




Mar. 30 
Motion in images. Part 1. Backgrounding.




Apr. 1 
More on backgrounding. Modeling the background, the foreground, and the prior.




Apr. 6 
Finish backgrounding. Start optical flow.




Apr. 8 
Finish optical flow.



Final problem set. Due Apr. 20
train_data.mat
test_data.mat gardenImages.mat
