Date 
Lecture topic 
New assignments 
Assignments due 
Reading 
Sept. 5 
UNIT 1: Introduction. What is Computer Vision? What are the goals of computer vision? What can we learn by studying the human vision system?
Introduction to MATLAB.

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 Sep. 12 
Introduction to computer vision 
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. 12 
Introduction to color. The electromagnetic spectrum. The multifrequency nature of light.
The relative absorption of light by surfaces. The basic anatomy of the eye. Rods and cone cells
in the retina. The relative sensitivity of rods and short, medium, and long wavelengh cone cells.
Computing the response of cone cells to a particular distribution of light. Lecture slides
David Heeger's slides on trichromacy



Beau Lotto Ted Talk on optical illusions

Sept. 17 
Finish discussion of metamers. See this document.
UNIT 2: Probability, Statistics, and Learning
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. 19 
Basic probability continued. Joint probability. Marginalization. Conditional Probablity. Bayes rule.




Sept. 24 
Introduction to supervised learning. Supervised learning for vision. Estimating distributions from data. Features of images.



Handout: Supervised learning and estimation

Sept. 26 
Estimating distributions from data. Estimating joint distributions of multiple variables. Leveraging independence for better estimation. More applications of Bayes' rule.

Assignment
3. Classification of handwritten digits.
Training and test data file (digits.mat)



Oct. 1 
Independence of features. Implications for estimating distributions. Consequences of assuming independence when variables are not independent.
Comparing the dependence of features using mutual information. Evaluating the value of features using mutual information.




Oct. 3 
UNIT 3: Alignment
Basic transformation classes: translation, rotation, rigid, similarity, affine, perspective, diffeomorphisms, arbitrary.
Applying transformations to images: Forward versus inverse transformations.
Optimization of alignment criteria: exhaustive, keypoint, and local.
Gradient descent for local alignment.
Alignment by
maximization of mutual information.

Assignment 4. Aligning the ProkudinGorsky images automatically.  Due October 17

Mutual Information handout. 
Oct. 9 
Alignment continued.


 
Oct. 10 
More alignment.
Alignment by correlation demo in Matlab.
Intro to alignment slides.


 
Oct. 15 
Solving difficult alignment problems through joint alignment: congealing. Introduction to the "self likelihood", and its relation to entropy. The selflikelihood as a criterion of joint alignment.
Joint alignment slides.


 
Oct. 17 
Congealing continued.
The congealing page.


 
Oct. 22 
Finish congealing. Finish Alignment Unit.
UNIT 4: Features
What are features? Principles of feature engineering.
Optimal features. Commonly used features. Features vs. representations.


 
Oct. 24 
Features continued.

Assignment
5. Problem Set due Wednesday, October 31st, by class time.
 
Oct. 29 
NO CLASS: Hurricane SANDY cancelled class.


 
Oct. 31 
Features: Linear filters and convolution.
Exam review


 
Nov. 5 
****************************************
In Class Midterm: Covers material up to and including UNIT 3 (Oct. 22nd and earlier)
***************************************


 
Nov. 7 
Linear filters: Correlation filters for matching and tracking.


 
Nov. 12 
VETERAN'S DAY: NO CLASS


 
Nov. 14 
Keypoint matching: Scale Invariant Feature Transforms (SIFT)


 
Nov. 19 
Michal Erel's SIFT slides used in class
Matlab scale space code from class
SIFT features, continued. Matlab topics: Gaussians, Gaussian convolution,
building a scale space, building a DifferenceofGaussian (DOG) scale
space. Also, finding local maxima and minima in DOG scale space. Calculating
gradients in an image, magnitudes of gradients, and angles of gradients.

Assignment: Correlation tracking  Due, December 3rd (revised from November 28th).
 MATLAB solution to correlation tracking 
Nov. 21 
Using SIFT for panorama stitching. RANdom Sample Consensus (RANSAC). Slides from lecture can be found here. See lecture number 13 on RANSAC.

  
THURSDAY Nov. 22 
THANKSGIVING


 
Nov. 26 
UNIT 5: Faces
Intro to face recognition.
Intro to face slides


 
Nov. 28 
ViolaJones face detection. Haar features. Boosting. Integral images.


 
Dec. 3 
ViolaJones continued. The Boosting Cascade.
ViolaJones face detection. Slides by Lana Lazebnik, adapted from Paul Viola.


 
Dec. 5  Principal Components Analysis and Morphable models. Morphable models for acromegaly
detection.
Acromegaly slides



MONDAY, DECEMBER 10, 1:303:30, Room 142 Computer Science building  FINAL EXAM

Review for FINAL 
