EECS 442: Computer Vision (Winter 2022)
S is Computer Vision: Algorithms and Applications by Richard Szeliski, which can be found
here. The chapters refer to the first edition. This will be more accessible.
H&Z is Multiple View Geometry in Computer Vision by Richard Hartley and Andrew Zisserman, which
can be obtained via the library in electronic form (scroll past the physical copies). I'd recommend
this only if you're feeling adventerous.
ESL is Elements of Statistical Learning by Hastie, Tibshirani, and Friedman, which can be
found here (PDF). This is relatively accessible.
Kolter is Zico Kolter's linear algebra review and reference note here (PDF). For
the purpose of 442, feel free to skip ‘‘Determinants’’, ‘‘Quadratic Forms and Positive Semidefinite Matrices’’ (although this is good to know),
‘‘The Hessian’’, and ‘‘Gradients and Hessians of Quadratic and Linear Functions’’ (until we hit deep learning), and ‘‘Gradients of the Determinant’’.
Date  Topic  Materials  Wednesday January 4  Introduction + Cameras 1 Overview, Logistics, Pinhole Camera Model, Homogeneous Coordinates
 Slides (PDF) Slides (PPTX) Reading: S2.1, H&Z 2, 6 Homogeneous Coordinates Dolly Zoom on a Cube  Monday January 9  Cameras 2 Intrinsics & Extrinsic Matrices, Lenses
 Slides (PDF) Slides (PPTX) Reading: S2.1, H&Z 2, 6  Wednesday January 11  Math Recap Floating point numbers, Vector & Matrices, Eigenvectors and values, Singular Values, Derivatives
 Slides (PDF) Slides (PPTX) Reading: Kolter Things Don't Add Up Using a Byte Distance 3 Ways  Monday January 16  No Class  Martin Luther King Day
  Wednesday January 18  Light & Shading Human Vision, Color Vision, Reflection
 Slides (PDF) Slides (PPTX) Reading: S2.2, S2.3  Monday January 23  Filtering Linear Filters, Blurring, Separable Filters, Gradients
 Slides (PDF) Slides (PPTX) Convolving Gracefully  Wednesday January 25  Homework 1 Due
  Wednesday January 25  Detectors & Descriptors 1 Edge Detection, Gaussian Derivatives, Harris Corners
 Slides (PDF) Slides (PPTX) Multiscale Harris Corner Detection  Monday January 30  Detectors & Descriptors 2 ScaleSpace, Laplacian Blob Detection, SIFT
 Slides (PDF) Slides (PPTX)  Wednesday February 1  Transforms 1 Linear Regression, Total Least Squares, RANSAC, Hough Transform
 Slides (PDF) Slides (PPTX) Reading: S2.1, S6  Monday February 6  Transforms 2 Affine and Perspective Transforms, Fitting Transformations
 Slides (PDF) Slides (PPTX) Reading: S2.1, S6 Grace in the Middle  Wednesday February 8  Homework 2 Due
  Wednesday February 8  Machine Learning Supervised Learning, Train/Val/Test Splits, Linear Regression, Regularization
 Slides (PDF) Slides (PPTX) Reading: ESL 3.1, 3.2 (skim)  Monday February 13  Optimization SGD, SGD+Momentum
 Slides (PDF) Slides (PPTX)  Wednesday February 15  Neural Networks Backprop, Fully Connected Neural Networks
 Slides (PDF) Slides (PPTX)  Monday February 20  Convolutional Networks 1 Convolution, Pooling
 Slides (PDF) Slides (PPTX)  Wednesday February 22  Homework 3 Due; Nope! March 6
  Wednesday February 22  Convolutional Networks 2 BatchNorm, CNN Architectures, Initialization, Augmentation, Transfer Learning
 Slides (PDF) Slides (PPTX)  Monday February 27  Spring Break
  Wednesday March 1  Spring Break
  Monday March 6  Segmentation Semantic/Instance Segmentation
 Slides (PDF) Slides (PPTX)  Wednesday March 8  Detection & Other Topics Detection, Other Stuff
 Slides (PDF) Slides (PPTX)  Monday March 13  Image Synthesis
 Slides (PDF) Slides (PPTX)  Wednesday March 15  Midterm
  Monday March 20  Project Proposal Due
  Monday March 20  Transformers & Other Models Transformers, Other Models
 Slides (PDF) Slides (PPTX)  Wednesday March 22  Camera Calibration Intro to 3D, Camera Calibration
 Slides (PDF) Slides (PPTX) Reading: S6.3  Monday March 27  Epipolar Geometry Epipolar Geometry, The Fundamental & Essential Matrices
 Reading: S11  Wednesday March 29  Stereo Twoview Stereo, Multiview Stereo
 Reading: S11  Friday March 31  Homework 4 Due
  Monday April 3  Structure from Motion Incremental/batch Structure from Motion
 Reading: S7  Wednesday April 5  SingleView 3D Perspective Invariants, Measuring Things
  Monday April 10  Learning 3D LearningBased 3D
  Wednesday April 12  Homework 5 Due
  Wednesday April 12  Ethics & Fairness Fairness, Ethics
  Monday April 17  TBD TBD
 
Reuse policy:
I am extremely grateful to the many researchers who have made their slides and
course materials available. Please feel to reuse any of my materials while
crediting appropriately and making sure original attributions to these generous
researchers is preserved. Please consider making your own course materials public.
