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|Authors: ||Brown University Department of Computer Science|
|Issue Date: |
|Publisher: ||Brown University Department of Computer Science|
|Abstract: ||Date Lecture Description Readings Assignments Materials
What is vision good for? Why is it hard?ï¿½ Why is it interesting? How do you pose the problem computationally?
Case study 1 - depth perception.
ï¿½ ï¿½ Lecture slides
9/9 Continuing Introduction.
Ouchi illusion and perceptual organization
Ch 1.1, 1.4, Ch 4. Assignment 0 out
Ball and Shadow movie
Illusory motion from shadows
9/12 Continuing Introduction: Case study 2 - object recognition.
Begin Matlab tutorial (time permitting)
Assignment 0 due
Go through the Matlab tutorial before next class so you can come with questions.
Matlab tutorial (pdf)
Matlab tutorial code (pdf)
9/14 Matlab tutorial/overview.ï¿½ Leonid Sigal
* Matlab question and answer session.
Assignment 1 out
Matlab demo in class
* Gradual changes
* Person change 1
* Person change 2
9/16 Matlab continued Ch 7.4-7.7, 9.2 ï¿½
9/19 Convolution and linear filtering.
Ch 8 Lecture slides
9/21 Gradients, edges and Laplacian pyramids. Lecture slides
Matlab code - edges
Matlab code - derivatives of Gaussians
9/23 Guest lecture
Linear algebra tutorial. Leonid Sigal
Asgn1 - Problem 1 and 2 Due
Hand-in name: asgn1_p1_p2 Linear Algebra Review Slides
9/26 Image Features. Scale-Invariant Keypoints.
Distinctive Image Features from Scale-Invariant Keypoints (read Sections 1-3.1) Asgn1 (p1 and p2) Solution Lecture slides
9/28 Finish derivatives and filtering Ch 22.3 Lecture slides
9/30 PCA and matching Extra Chapter Asgn1 - All Problems Due
Hand-in name: asgn1all
Assignment 2 out
Assignment 1 solution (all parts)
10/3 PCA Ch 22.1, 22.2 ï¿½ Lecture slides
10/5 PCA Ch 15 ï¿½ Lecture slides
10/7 Probability, Classifiers, and Bayes. Moghaddam & Pentland Asgn2 - Problem 1 Due
Hand-in name: asgn2_p1 Lecture slides
10/10 University Holiday, No class ï¿½ ï¿½ ï¿½
Odds and ends. Motion Intro
Ch 15 and class notes Asgn2 (p1) Solution ï¿½Lecture slides
10/14 Motion Asgn2 - All Problems Due
Hand-in name: asgn2allï¿½
Assignment 3 out
Guest Lecture: Joe Mundy, Engineering.
Parametric motion estimation and tracking.
Hager and Belhemeur Lecture slides (doc)
10/19 No class. ï¿½ ï¿½
Guest lecture: Stuart Andrews
ï¿½Fergus et al ï¿½ Lecture slides
10/24 Affine Motion . ï¿½ Lecture notes
Cameras and projection
Begin dense optical flow.
Optimization - computing motion.
Ch 10.1, Ch 11 Lecture slides
10/28 Robust estimation.
Ch 11 Asgn1 - Problem 1 Due
Hand-in name: asgn3_p1
10/31 Robust estimation II, Optimization Asgn3 (p1) Solution Lecture slidesï¿½
11/2 Dense optical flow
Bayesian Tracking (Particle Filter)
ï¿½ Lecture slides
Asgn3 - All Problems Due
Hand-in name: asgn3allï¿½ Lecture slides
11/7 Particle filtering/tracking ï¿½ ï¿½Good overview article on sampling and particle filters
11/9 Particle filtering II and homework 4. Robots and Vision New: Assignment 4 out
New: Project handout
11/11 Stereo intro. ï¿½ Lecture slides
11/14 Guest lecture: Michael Tarr ï¿½ Asgn4 - problem 1 due.
Hand-in name: asgn4_p1
11/16 Multi-view stereo and space carving. Project proposals Due
Hand-in name: proposal
New: Asgn4 (p1) Solution
11/18 Adaboost and face detection ï¿½ Lecture slides
11/21 Odds and ends? ï¿½
11/22 Guest: Stefan Roth Fields of Experts Asgn4 - All Problems Due
Hand-in name: asgn4allï¿½ï¿½ Lecture slides
11/23 No class. ï¿½
11/25 Thanksgiving recess. No Class ï¿½ ï¿½ ï¿½
11/28 Guest: Leon Sigal Belief propagation. ï¿½ Leon's Lecture Slides
11/30 Adaboost wrapup ï¿½ Lecture slides
12/2 Big picture, open problems. ï¿½ ï¿½ Lecture slides
12/5 Guest ï¿½Greg Shakhnarovich Recognition from examples. ï¿½ Lecture slidesï¿½
12/7 Reading week.ï¿½ Work on project.ï¿½ No class ï¿½ ï¿½ ï¿½
12/9 Reading week.ï¿½ Work on project.ï¿½ No class ï¿½ ï¿½
Hand-in name: proj|
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