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Authors: Brown University Department of Computer Science
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Publisher: Brown University Department of Computer Science
Abstract: Date Lecture Description Readings Assignments Materials 9/7 Introduction.� What is vision good for? Why is it hard?� Why is it interesting? How do you pose the problem computationally? Case study 1 - depth perception. � � 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 Lecture slides 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. Lecture slides Matlab tutorial (pdf) Matlab tutorial code (pdf) 9/14 Matlab tutorial/overview.� Leonid Sigal * Matlab question and answer session. Assignment 1 out Matlab demo in class Change blindness * Flicker * Gradual changes * Person change 1 * Person change 2 9/16 Matlab continued Ch 7.4-7.7, 9.2 � 9/19 Convolution and linear filtering. Pyramids Ch 8 Lecture slides Matlab code 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. Leonid Sigal 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) Lecture slides 10/3 PCA Ch 22.1, 22.2 � Lecture slides 10/5 PCA Ch 15 � 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 � � � 10/12 Odds and ends. Motion Intro Ch 15 and class notes Asgn2 (p1) Solution �Lecture slides 10/14 Motion Asgn2 - All Problems Due Hand-in name: asgn2all� Assignment 3 out Lecture slides 10/17 Guest Lecture: Joe Mundy, Engineering. Parametric motion estimation and tracking. Hager and Belhemeur Lecture slides (doc) 10/19 No class. � � 10/21 Guest lecture: Stuart Andrews Object Recognition �Fergus et al � Lecture slides 10/24 Affine Motion . � Lecture notes 10/26 Cameras and projection Begin dense optical flow. Optimization - computing motion. Ch 10.1, Ch 11 Lecture slides 10/28 Robust estimation. Project ideas Ch 11 Asgn1 - Problem 1 Due Hand-in name: asgn3_p1 Lecture slides 10/31 Robust estimation II, Optimization Asgn3 (p1) Solution Lecture slides� 11/2 Dense optical flow Bayesian Tracking (Particle Filter) � Lecture slides 11/4 Project ideas Asgn3 - All Problems Due Hand-in name: asgn3all� Lecture slides 11/7 Particle filtering/tracking � �Good overview article on sampling and particle filters Lecture slides 11/9 Particle filtering II and homework 4. Robots and Vision New: Assignment 4 out New: Project handout Lecture slides�� 11/11 Stereo intro. � Lecture slides 11/14 Guest lecture: Michael Tarr � Asgn4 - problem 1 due. Hand-in name: asgn4_p1 more info 11/16 Multi-view stereo and space carving. Project proposals Due Hand-in name: proposal New: Asgn4 (p1) Solution Lecture slides 11/18 Adaboost and face detection � Lecture slides 11/21 Odds and ends? � 11/22 Guest: Stefan Roth Fields of Experts Asgn4 - All Problems Due Hand-in name: asgn4all�� Lecture slides 11/23 No class. � 11/25 Thanksgiving recess. No Class � � � 11/28 Guest: Leon Sigal Belief propagation. � Leon's Lecture Slides Lecture slides 11/30 Adaboost wrapup � Lecture slides 12/2 Big picture, open problems. � � Lecture slides 12/5 Guest �Greg Shakhnarovich Recognition from examples. � Lecture slides� Paper link 12/7 Reading week.� Work on project.� No class � � � 12/9 Reading week.� Work on project.� No class � � 12/16 Projects due. Hand-in name: proj
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