Event Type  Date  Description  Course Materials 

Lecture 1  Tuesday September 25 
Course introduction Computer vision overview Course logistics 
Slides
[pptx]
[pdf]
Logistics slides [pptx] [pdf] 
Lecture 2  Thursday September 27 
Linear algrebra Transformation matrixes Eigenvalues and eigenvectors Matrix calculus and hessian 
Slides
[pptx]
[pdf]
Color Slides [pptx] [pdf] 
HW0 Due  Monday October 1, 11:59pm 
Homework #0 due Basics 
[Homework #0] 
Lecture 3  Tuesday October 2 
Linear algebra continued & Pixels Pixels and image representation 
Slides
[pptx]
[pdf]

Lecture 4  Thursday October 4 
Filters Linear systems Convolutions and crosscorrelations 
Slides
[pptx]
[pdf]

Lecture 5  Tuesday October 9 
Edge detection Derivative of gaussians Sobel filters Canny edge detector 
Slides
[pptx]
[pdf]

Lecture 6  Thursday October 11 
Features and fitting RANSAC Local features Harris corner detection 
Slides
[pptx]
[pdf]

HW1 Due  Friday October 12, 11:59pm 
Homework #1 due Filters  Instagram 
[Homework #1] 
Lecture 7  Tuesday October 16 
Feature descriptors Difference of gaussians Scale invariant feature transform Image stitching 
Slides
[pptx]
[pdf]

Lecture 8  Thursday October 18 
Resizing Energy function Seam carving 
Slides
[pptx]
[pdf]

HW2 Due  Friday October 19, 11:59pm 
Homework #2 due Edges  Smart car lane detection 
[Homework #2] 
Lecture 9  Tuesday October 23 
Semantic segmentation Gestalt theory of perceptual grouping Aggomerative clustering Superpixels and oversegmentation 
Slides
[pptx]
[pdf]

Lecture 10  Thursday October 25 
Clustering Kmeans Mean shift 
Slides
[pptx]
[pdf]

HW3 Due  Friday October 26, 11:59pm 
Homework #3 due Panorama  Image stitching 
[Homework #3] 
Lecture 11  Tuesday October 30 
Object recognition Nearest neighbors Classification pipeline 
Slides
[pptx]
[pdf]

Lecture 12  Thursday November 1 
Dimensionality reduction Singular value decomposition Principal component analysis 
Slides
[pptx]
[pdf]

HW4 Due  Friday November 2, 11:59pm 
Homework #4 due Resizing  Seam carving 
[Homework #4] 
Lecture 13  Tuesday November 6 
Face identification Eigenfaces and fisherfaces Linear Discriminant Analysis 
Slides
[pptx]
[pdf]

Lecture 14  Thursday November 8 
Visual Bag of Words Texture features Visual bag of words Image pyramids 
Slides
[pptx]
[pdf]

HW5 Due  Friday November 9, 11:59pm 
Homework #5 due Segmentation  Clustering 
[Homework #5] 
Lecture 15  Tuesday November 13 
Detecting objects by parts Deformable parts model Object detection 
Slides
[pptx]
[pdf]

Lecture 16  Thursday November 15 
Visual ontologies Imagenet Semantic hierarchy Fine grained classes 
Slides
[pptx]
[pdf]

HW6 Due  Friday November 16, 11:59pm 
Homework #6 due Recognition  Classification 
[Homework #6] 
Lecture 17  Tuesday November 27 
Motion Optical Flow LucasKanade method HornSchunk Method Pyramids for large motion Common Fate 
Slides
[pptx]
[pdf]

Lecture 18  Thursday November 29 
Tracking Feature Tracking Lucas Kanade Tomasi (KLT) tracker 
Slides
[pptx]
[pdf]

HW7 Due  Friday November 30, 11:59pm 
Homework #7 due Object detection  constellation models 
[Homework #7] 
Lecture 19  Tuesday December 4 
Deep learning Convolutional neural networks Backpropagation 
Slides
[pptx]
[pdf]

Lecture 20  Thursday December 7 
Deep Learning continued + Final Review Convolutions  revisited 
Slides [pptx] [pdf] 
HW8 Due  Friday December 8, 11:59pm 
Homework #8 due Tracking  Optical flow 
[Homework #8] 
Final  Monday  December 10, 12:15 to 3:15pm Location: 420040 
Practice exam available on Piazza. 