Texbooks

S is Computer Vision: Algorithms and Applications by Richard Szeliski, which can be found here.

H&Z is Multiple View Geometry in Computer Vision by Richard Hartley and Andrew Zisserman, which is available from the UM Library (Login required).

ESL is Elements of Statistical Learning by Hastie, Tibshirani, and Friedman, which can be found here

Event Date Description Course Materials
Lecture 1 Tuesday
January 19		 Introduction
Computer vision overview
Course logistics
Pinhole camera
Homogenous coordinates 		[Intro slides (pdf)]
[Intro slides (pptx)]
[Camera slides (pdf)]
[Camera slides (pptx)]
[video]
Lecture 2 Thursday
January 21		 Cameras II
Intrinsic / Extrinsic matrices
Thin lens model
Depth of field 		[slides (pdf)]
[slides (pptx)]
[video]
[Reading: S 2.1]
[Reading: H&Z 2, 6]
[Homogeneous coordinates notebook]
[Rendering a cube/dolly zoom notebook]
Lecture 3 Tuesday
January 26		 Light and Shading
Human vision
Color vision
Color spaces
Diffuse and Specular Reflection 		[slides (pdf)]
[slides (pptx)]
[video]
[Reading: S2.2, S2.3]
Lecture 4 Thursday
January 28		 Math Review
Floating point numbers
Vectors, matrices
Eigenvalues, Eigenvectors
Least Squares
Singular Values
Derivatives 		[slides (pdf)]
[slides (pptx)]
[video]
Lecture 5 Tuesday
February 2		 Filtering
Linear filters
Blur and sharpening filters
Separable filters
Image gradients 		[slides (pdf)]
[slides (pptx)]
[video]
[Convolving Gracefully notebook]
HW0 Due Wednesday
February 3		 Homework 0 Due [HW0]
Lecture 6 Thursday
February 4		 Detectors and Descriptors I
Edge detection
Gaussian Derivative filter
Harris Corner detector 		[slides (pdf)]
[slides (pptx)]
[video]
Lecture 7 Tuesday
February 9		 Detectors and Descriptors II
Scale-space pyramid
Laplacian blob detectors
SIFT descriptors 		[slides (pdf)]
[slides (pptx)]
[video]
[Multiscale Harris Detector notebook]
HW1 Due Wednesday
February 10		 Homework 1 Due [HW1]
Lecture 8 Thursday
February 11		 Transformations I
Linear Regression
Total Linear Regression
RANSAC
Hough Transform 		[slides (pdf)]
[slides (pptx)]
[video]
[Reading: S2.1, S6]
Lecture 9 Tuesday
February 16		 Transformations II
Affine, Projective transforms
Fitting transforms 		[slides (pdf)]
[slides (pptx)]
[video]
[Reading: S2.1, S6]
Lecture 10 Thursday
February 18		 Machine Learning
Image warping / blending
Supervised vs Unsupervised learning
Train / Test splits
Linear Regression
Regularization 		[slides (pdf)]
[slides (pptx)]
[video]
Lecture 11 Tuesday
February 23		 Linear Models
Cross-Validation
K-Nearest Neighbors
SVM loss
Cross-Entropy loss 		[slides (pdf)]
[slides (pptx)]
[video]
[CS231n Linear Classification]
Well-Being Day Thursday
February 25		 No Class: Well-Being Day
HW2 Due Friday
February 26		 Homework 2 Due [HW2]
Lecture 12 Tuesday
March 2		 Optimization
Stochastic Gradient Descent
SGD + Momentum 		[slides (pdf)]
[slides (pptx)]
[video]
[CS231n Optimization]
Lecture 13 Thursday
March 4		 Neural Networks
Overfitting / Underfitting
Bias / Variance tradeoff
Fully-connected neural networks
Biological neurons 		[slides (pdf)]
[slides (pptx)]
[video]
[CS231n Neural Networks]
Lecture 14 Tuesday
March 9		 Backpropagation
Computational Graphs
Backpropagation
Matrix multiplication example 		[slides (pdf)]
[slides (pptx)]
[video]
[231n Backpropagation]
[Backprop for Matrix Multiply]
[Olah on Backprop]
[Nielsen on Backprop]
HW3 Due Wednesday
March 10		 Homework 3 Due [HW3]
Lecture 15 Thursday
March 11		 Convolutional Networks I
Convolution
Pooling 		[slides (pdf)]
[slides (pptx)]
[video]
Lecture 16 Tuesday
March 16		 Convolutional Networks II
Batch Normalization
CNN Architectures
Weight initialization
Data augmentation
Transfer learning 		[slides (pdf)]
[slides (pptx)]
[video]
Lecture 17 Thursday
March 18		 Segmentation and Detection
Semantic Segmentation
Instance Segmentation
Two-Stage Detectors
Single-Stage Detectors 		[slides (pdf)]
[slides (pptx)]
[video]
Tuesday
March 23		 No Class: Well-Being Day
Lecture 18 Thursday
March 25		 Fairness in AI [slides (pdf)]
[slides (pptx)]
[video]
HW4 Due Monday
March 29		 Homework 4 Due [HW4]
Lecture 19 Tuesday
March 30		 Optical Flow [slides (pdf)]
[slides (pptx)]
[video]
Lecture 20 Thursday
April 1		 3D Vision + Calibration [slides (pdf)]
[slides (pptx)]
[video]
Proposal Due Monday
April 5		 Project Proposal due [Proposal Details]
Lecture 21 Tuesday
April 6		 Single-View Geometry [slides (pdf)]
[slides (pptx)]
[video]
Lecture 22 Thursday
April 8		 Epipolar Geometry [slides (pdf)]
[slides (pptx)]
[video]
HW5 Due Friday
April 9		 Homework 5 Due [HW5]
Lecture 23 Tuesday
April 13		 Stereo Vision [slides (pdf)]
[slides (pptx)]
[video]
Lecture 24 Thursday
April 15		 Structure from Motion [slides (pdf)]
[slides (pptx)]
[video]
Lecture 25 Tuesday
April 20		 Applications: Learning-Based 3D [video]
HW6 Not Due Tuesday
April 20		 Homework 6 Due [HW6]
Tuesday
April 27		 Final Project Due (Showcase + Report) [Info (pdf)]