Date 
Lecture topic 
New assignments 
Assignments due 
Reading 
Sep. 4 
UNIT 1: 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. Lecture slides

Assignment 1:
Probabilistic classification. Digit data for assignment 1 Due by midnight on Sept 11. Please
email the solution to Rae as a zipped tar file of all necessary files. 

Readings 1, 2, and 3 from the "Readings" list above by Monday, September 11. 
Sep. 9 
UNIT 2: Probability, Statistics, and Learning
Basics. Review of basic discrete probability. Samples
spaces. Events. Joint Probability. Conditional
Probability. Marginalization. Role of Probability and Statistics in
Computer Vision.
Bayes' rule. Likelihoods, priors, and posteriors. Estimating
likelihoods, priors, and posteriors. Lecture slides

 

Sep. 11 
MAP classification. Optimality of MAP classification with
true posteriors. Feature selection. Which pixel is best?
Which two pixels are best?
Statistical independence. Mutual information. Information gain.
Lecture slides



Assignment 1 Due by midnight 
Sep. 16 
Feature selection. Maximum information gain and
greedy maximum information gain.
Lecture slides


Reading number 4, for next lecture. 
Assignment 2:
Greedy Feature Selection. Due on Sept. 30, midnight. 
Sept. 18 
UNIT 3: Alignment Alignment: motivation and
definitions. Image to image, image to model, and joint alignment.
The core matching problem: aligning Patch J to Image I. Families of
transformations. Translations. Rigid. Similarity. Affine. Linear. Homographies
or perspective. Diffeomorphisms. Implementing transformations as
"looking back" to original image using transform inverse.


Reading number 5, SECTIONS 2.0, 2.1, 3.0, 3.1 

Sept. 23 
Tranformations continued. Mechanics of forward and backward transformations.
Alignment criteria, briefly.
Optimization of alignment criteria. Exhaustive search, keypoint methods,
gradient descent.
Mutual information based aligment of medical images.




Sep. 25 
Alignment continued.
Brief detour: High level anatomy of the eye: pupil, iris,
retina, rods and cone cells.
3color vision (tristimulus theory) and the human cone cells.
Duplicating cone responses is good enough to give any visual
percept about color.
ProkudinGorski photographs.
Lecture slides from last 3 lectures.

 

Sept. 30 
Congealing: Joint alignment. Lecture 1.

Assignment 3: Automatic alignment of ProkudinGorsky plates. Plates
Due by midnight on Oct.14. 


Oct. 2 
Congealing continued. Complex image congealing. Congealing on 3D arrays. Congealign with brightness transformations.
Lecture slides from last 2 lectures.

 

Oct. 7 
Convolution.
Convolution and nonparametric density estimation slides.

 

Oct. 9 
Maximum likelihood estimation and nonparametric density estimation (also known as Parzen window estimation, kernel density estimation).
Some simple matlab examples.

 

Oct. 15 
Distribution fields. Exploding an image. Convolving with a Gaussian. Basin of attraction with distribution fields. Likelihood match. Sharpening match.

 

Oct. 16 
Distribution fields continued.
Slides from last few lectures on distribution fields and image comparison functions.

 

Oct. 21 
Finish distribution fields. Start Features Unit.




Oct. 23 
Features




Oct. 28 
PROJECT DISCRIPTIONS. CHOOSE PROJECT BY Nov. 4. Turn in short description of proposed project to Prof. LearnedMiller as a single page pdf by Nov. 4.
More features. SIFT. SIFT keypoints. Difference of Gaussian scale space.
Extrema of difference of Gaussians. Unstable keypoints: low contrast and low minimum curvature points.
SIFT slides used in class. NOTE: See the notes on the slides at the bottom of this link. It gives a short explanation of each slide.
Project descriptions from class.




Oct. 30 
SIFT continued. More technical detail on low minimum
curvature keypoints. Orientation of a keypoint: maxima of anglefrequency
distribution.




Nov. 4 
SIFT descriptors.




Nov. 6 
Image formation: Light, cameras, and signal transduction.




Nov. 11 
HOLIDAY, NO CLASS.




Nov. 13 
Guest Lecture: Andrew Kae. Topic: Conditional Random Fields, Restricted Boltzmann machines, and face segmentation.
Andrew Kae guest lecture slides.




Nov. 18 
Image formation: lecture 2




Nov. 20 
Image formation: lecture 3
Slides from last 3 lectures
Study Guide, Part 1
Study Guide, Part 2




Nov. 25 
TEST IN CLASS




Nov. 27 




Dec. 2 




Dec. 4 
LAST DAY of class.



