COS 429 - Computer Vision

Fall 2019

Course home Outline and Lecture Notes Assignments


The optional textbook for the course is Computer Vision: Algorithms and Applications by Richard Szeliski. There is a PDF version of the book available at In addition, we will be reading a few recent vision papers.


The lecture slides are provided for reference but are subject to change. We will update the pdf posted here with any major changes, but otherwise expect minor changes between what's posted here and the lecture.

Date Lecture (click for slides) Readings Assignments
Thu, Sep 12 Introduction to computer vision Ch. 1.1  
Tue, Sep 17 Image formation and capture (slide 59 updated 10/16) Ch. 2.2.3, 2.3  
Thu, Sep 19 Convolution and filtering Ch. 3.2, 3.4, 4.2 Assignment 0 due
Tue, Sep 24 Feature detectors and descriptors Ch. 4, Trucco & Verri: Ch. 4.1 – 4.3, SIFT paper
Thu, Sep 25 Fitting, Hough transforms, RANSAC Ch. 4.3.2, 6.1.4, COS 324 notes on linear regression  
Tue, Oct 1 Image alignment and stitching Ch. 6.1.1 – 6.1.4, 9, Multires blending paper  
Thu, Oct 3 Intro to recognition and machine learning (slides updated 10/5 based on lecture content) COS324 notes on least squares, regularization, cross-validation Assignment 1 due
Tue, Oct 8 Face and pedestrian detection(slides updated 10/8 post-lecture) Ch. 14.1, Viola & Jones paper, Dalal & Triggs paper, COS324 notes on SVM optimization, GenderShades paper
Thu, Oct 10 Object classification Ch. 14.4.1, 14.5, 14.6, Caltech 101 paper, SPM paper, COS324 notes on kmeans  
Tue, Oct 15 Object detection Ch. 14.4.2, Deformable Parts Model (DPM) paper, PASCAL VOC paper, Diagnosing Errors paper, ImageNet Challenge paper  
Thu, Oct 17 Segmentation (slide 32 updated 10/18) Ch. 5.2 – 5.4; Martin et al. segmentation paper;
Shi and Malik normalized cuts paper
Assignment 2 due
Tue, Oct 22 Texture Ch. 10.5; Efros & Leung paper;
Efros & Freeman paper; Image Quilting
Thu, Oct 24 Midterm    
Tue, Oct 31 No class - fall break
Thu, Nov 2 No class - fall break
Tue, Nov 5 Motion, optical flow, tracking (slide 19 updated following lecture) Ch. 8.4; Lucas-Kanade paper;
Ch. 8.1 – 8.3 (optional)
Thu, Nov 7 3D vision: stereo, camera geometry and calibration Ch. 11.1 – 11.4,11.6; Ch. 12 (optional); Stanford CS 231A course notes on epipolar geometry  
Tue, Nov 12 Introduction to Deep Learning  
Thu, Nov 14 Deep learning II: Backpropagation    
Tue, Nov 19 Deep learning III: CNNs and ImageNet CNN Beginner's Guide  
Thu, Nov 21 Deep Learning IV: Training   Assignment 3 due
Tue, Nov 26 Deep Learning V: Training and architectures    
Thu, Nov 28 No class - Thanksgiving
Tue, Dec 3 Deep Learning VI: Advanced recognition topics  
Thu, Dec 5 William Pierson Field Lecture: Dr. Juan Carlos Niebles (Stanford) on "Human Event Understanding: From Actions to Tasks"   Assignment 4 due
Tue, Dec 10 Advanced topics: Fairness in computer vision  
Thu, Dec 12 Guest Lecture: Dr. Andras Ferencz (Mobileye) on "Computer vision for autonomous driving"  
Fri, Dec 13   Project milestone due
TBA Final project presentations    
Tue, Jan 14     Project report due

Last update 2-Dec-2019 13:31:38