EEL806: Computer Vision

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Instructors: Sumeet Agarwal and Hiranmay Ghosh
4 credits (3-0-2)
Pre-requisites: EEL205 & EC120
Overlaps with: CSL840
I Semester 2013–14
M Th 17–18:20, II-247

How can machines or computers be equipped with the ability to process and understand the visual information in the world around us? This course will look at algorithms and models for vision. Our approach will be to think of real-world vision as a learning and inference problem from noisy data. In particular, we will focus on employing a statistical machine learning framework for the task of building classifiers for recognising various visual phenomena.

Evaluation components

• Minors: 20% (best of 2) [paper I] [paper II]
• Major: 20% [paper]
• Minor project: 15% [Suggested topics]
• Major project: 35% [Suggested topics]
• Class participation: 10%

Planned outline

• Primary reference: Richard Szeliski, Computer Vision: Algorithms and Applications (CVAA). Springer, 2010. [pdf]
• Secondary reference: Simon J. D. Prince, Computer Vision: Models, Learning, and Inference. Cambridge University Press, 2012. [pdf]
• Additional reference: Course materials from James Hays' CS143 course at Brown University.

Week(s)	Topics	Slides	CVAA Chapter(s)	Other References
1	Introduction, History of Computer Vision	Hays' intro; Optical illusions	1
1–2	Image formation: cameras, optics, geometry		2	Hartley, Minimizing Algebraic Error in Geometric Estimation Problems
3	Machine learning overview: clustering, classification	Very quick introduction; Hays' ML overview; Prince Chapter 6	—	Prince Chapter 6; Tutorial
4–5	Image filtering: Fourier transforms, pyramids, wavelets		3	Filtered image examples; Wavelet Tutorial; Viola-Jones paper
5–7	Segmentation as unsupervised learning: Gaussian Mixture Models, Markov Random Fields	GMMs and EM [Hays]; MRFs and graph cuts [Hays]	5	GMM tutorial; MRF tutorial; GrabCut paper; Practice Sheet I
7	Minor project presentations	On Piazza	—
7–8	Recognition: image processing, feature extraction, bag-of-words approaches		4, 14	Papers
8	Guest lecture 1: Dr. Sunil Kopparapu	Readable Image for the Visually Impaired	—	Readable Image for the Visually Impaired
8–9	Feature detection: edges, lines		4	Papers
9	Object/face/instance recognition	Recognition overview [Hays]; Local features [Hays]	14	SIFT paper [Lowe '99]
10	Context and scene understanding	Context and spatial layout [Hays]	14
10–11	Image indexing		4	LSH tutorial; Similarity Search
11–12	Deep learning for feature discovery and image classification	Google Brain	—	Andrew Ng's lecture notes; Google Brain paper; Deep learning tutorial
12	Multimedia semantics	Part I, Part II	—	Papers
13	Guest lecture 2: Prof. Amitabha Mukerjee	Hierarchies in representations	—
13	Journal club: Aesthetics in Vision		—	Papers
14	Major project presentations	On Piazza	—

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