COS 429 - Computer Vision

Fall 2016

Course home Outline and Lecture Notes Assignments


Readings

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 http://szeliski.org/Book/. In addition, we will be reading a few recent vision papers.


Schedule

Date Lecture (click for notes) Readings Assignments
Thu, Sep 15 Introduction to computer vision Ch. 1.1  
Tue, Sep 20 Image formation and capture Ch. 2.2.3 – 2.3  
Thu, Sep 22 Convolution and filtering Ch. 3.2, 3.4, 4.2  
Tue, Sep 27  Feature detectors and descriptors Ch. 4, Trucco & Verri, Ch. 4.1 – 4.3,
SIFT paper (optional)
 
Thu, Sep 29 Fitting, Hough transforms, RANSAC    
Tue, Oct 4 Image alignment and stitching Ch. 6.1.1 – 6.1.4; Ch. 9 (optional);
Multires blending paper
 
Thu, Oct 6 Intro to recognition Ch. 14 Assignment 1 due
Tue, Oct 11 Classification Dalal & Triggs paper  
Thu, Oct 13 Part-based models    
Tue, Oct 18 Texture Ch. 10.5; Efros & Leung paper;
Efros & Freeman paper; Image Quilting
 
Thu, Oct 20 Segmentation and clustering Ch. 5.2 – 5.4; Martin et al. segmentation paper;
Shi and Malik normalized cuts paper
 
Tue, Oct 25 Motion, Optical Flow, Tracking I Ch. 8.4; Lucas-Kanade paper;
Ch. 8.1 – 8.3 (optional)
 
Thu, Oct 27 Tracking II   Assignment 2 due
Tue, Nov 1 No class - fall break
Thu, Nov 3 No class - fall break
Tue, Nov 8 Intro to 3D vision; stereo Ch. 11.1 – 11.4; Ch. 12 (optional)  
Thu, Nov 10 Multiview reconstruction Ch. 11.6; Voxel coloring paper  
Tue, Nov 15 Camera geometry and calibration Ch. 2.1; Ch. 6.3 (optional)  
Thu, Nov 17 3D features and shape matching    
Tue, Nov 22 Active 3D scanning methods    
Thu, Nov 24 No class - Thanksgiving
Tue, Nov 29 Introduction to Deep Learning    
Thu, Dec 1 Deep Learning II: Layer types and training    
Tue, Dec 6 Deep Learning III: Initialization, Architectures, Applications    
Thu, Dec 8 Deep Learning IV: Object Detection and Segmentation    
Tue, Dec 13 Deep Learning V: Segmentation   Assignment 3 due
Thu, Dec 15 Deep Learning VI    
Sun, Dec 18   Assignment 4 due
Thu, Jan 12 Final project presentations    
Fri, Jan 13 Final project presentations    
Tue, Jan 17     Project reports due



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