Lecture slides and readings

Course introduction and overview

• Date: Sept 4
• Lecture 1
• Lecture slides: keynote, pdf
• Readings
  • Richard Szeliski book, Chapter 1 (RS 1)
  • The speed of processing in the human visual system, Thorpe et al., Letters to Nature, 1996

Radiometry

• Date: Sept 9, 11
• Lecture 2
• Lecture slides: keynote, pdf
  • Note the last part was covered in the next lecture.
• Readings
  • RS 2
  • Surface reflectance estimation and natural illumination statistics, R.O. Dror, E.H. Adelson, and A.S. Willsky, Workshop on Statistical and Computational Theories of Vision 2001
  • Chapter 3 of Forsyth and Ponce on shape from shading.
  • Wikipedia: Photometric stereo, BRDF

Light and color

• Date: Sept 11, 16, 18
• Lecture 3, 4, 5
• Lecture slides: keynote, pdf
• Readings and resources
  • RS 2
  • Color matching applet from Stanford
  • B. Berlin and P. Kay, Basic Color Terms: Their Universality and Evolution (1969)
  • D.A. Forsyth, A novel algorithm for color constancy
  • Wikipedia: Trichromacy, Color constancy
  • https://www.ted.com/talks/beau_lotto_optical_illusions_show_how_we_see?language=en
  • https://en.wikipedia.org/wiki/Sergey_Prokudin-Gorsky

Image formation

• Date: Sept 23, 25
• Lecture 6, 7
• Lecture slides: keynote, pdf
• Readings
  • RS 2, 3
  • https://en.wikipedia.org/wiki/Camera
  • https://en.wikipedia.org/wiki/History_of_the_camera
  • Light stages: http://gl.ict.usc.edu/LightStages

Optical flow

• Date: Sept 30, Oct 2
• Lecture 8, 9
• Lecture slides: keynote, pdf
• Readings and references
  • Lucas Kanade optical flow
  • Motion magnification:
    • http://people.csail.mit.edu/celiu/motionmag/motionmag.html (covered in class)
    • http://people.csail.mit.edu/mrub/vidmag/

Modeling natural images

• Date: Oct 7
• Lecture 10
• Lecture slides: keynote, pdf
• Readings
  • RS 3

Image processing: linear filtering, etc.

• Date: Oct 9, 15
• Lecture 11, 12
• Lecture slides: keynote, pdf
• Readings
  • Hybrid image gallery: http://cvcl.mit.edu/hybrid_gallery/gallery.html
  • RS 3

Image alignment

• Date: Oct 16
• Lecture 13
• Lecture slides: keynote, pdf
  • Combined slides for the next few lectures
• Readings
  • David Lowe's SIFT page: https://www.cs.ubc.ca/~lowe/keypoints
  • Lindberg's scale-space theory: https://www.tandfonline.com/doi/abs/10.1080/757582976
  • Other SIFT implementations: VLFeat, OpenCV
  • RS 4

Image alignment: continued

• Date: Oct 21, 23
• Lecture 14, 15
• Lecture slides: (see above)
• Readings
  • RS 6 and 9. Chapter 9 goes into much more detail on how images can be stitched together to form panoramas.
  • http://www.robots.ox.ac.uk/~vgg/research/affine/index.html

Guest lectures (instructor travel)

• Date: Oct 28, 32
• Lecture 16, 17

Applications of feature matching and alignment

• Date: Nov 4
• Lecture 18
• Lecture slides: keynote, pdf
• Readings and references:
  • Photo tourism
  • Photogrammetry
  • Oxford building search, video google
  • David Nister's vocabulary tree for instance search

Recognition overview: datasets and classical image representations

• Date: Nov 6, 13
• Lecture 19
• Lecture slides: keynote, pdf
• Readings and references:
  • https://en.wikipedia.org/wiki/Bag-of-words_model_in_computer_vision
  • https://en.wikipedia.org/wiki/Histogram_of_oriented_gradients

Linear models

• Date: Nov 13, 18
• Lecture: 20, 21
• Lecture slides: keynote, pdf
• Readings and references:
  • https://en.wikipedia.org/wiki/Perceptron
  • http://vision.stanford.edu/teaching/cs231n-demos/linear-classify/

Neural networks

• Date: Nov 18, 20
• Lecture 22, 23
• Lecture slides: keynote, pdf
  • https://cs.nyu.edu/~fergus/papers/zeilerECCV2014.pdf
  • http://yosinski.com/deepvis
  • http://vis-www.cs.umass.edu/texture/
  • https://www.robots.ox.ac.uk/~vedaldi/assets/pubs/mahendran16visualizing.pdf

Learning and transfer with neural networks

• Date: Dec 4, Dec 9
• Lecture 23, 24
• Lecture slides: keynote, pdf
  • http://vis-www.cs.umass.edu/bcnn/
  • https://people.cs.umass.edu/~smaji/papers/task2vec-iccv19.pdf
  • CNN Features off-the-shelf: an Astounding Baseline for Recognition

Graphics and vision

• Date: Dec 11
• Lecture 25
• Lecture slides: keynote, pdf
  • http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture12.pdf
  • http://www.robots.ox.ac.uk/~vedaldi//research/visualization/visualization.html
  • http://vis-www.cs.umass.edu/texture/
  • https://dmitryulyanov.github.io/deep_image_prior
  • Generative adversarial networks (Goodfellow et al. paper)
  • "Can computers create art?" by Aaron Hertzmann https://arxiv.org/abs/1801.04486

Adversarial attacks against ML systems

• Date: N/A
• Lecture: N/A (from last year's)
• Lecture slides: keynote, pdf
  • https://www.kaggle.com/c/nips-2017-defense-against-adversarial-attack