CS4670/5670 - Introduction to Computer Vision

Picture credit: Magritte and some computer vision researchers

Quick info

Instructor: Bharath Hariharan
Lecture time: Mon. / Wed. / Fri. 1:25pm - 2:10pm
Lecture venue: Phillips Hall 101
Piazza
TAs:

Jimmy Briggs
Athena Huang
Zhiqiu Lin
Yiwei Ni
Karun Singh
Alvin Zhu

Office Hours:

Bharath: M/W/Thur 10-11 am (at 311 Gates Hall)
Jimmy Briggs: Tu 10-11 am (Gates G13) and Fri 10-11 am (Gates G11)
Athena Huang: Tu 7-9pm (Gates G11)
Zhiqiu Lin:
Yiwei Ni: W 5-7 pm (Gates G15)
Karun Singh: Fri 2:30-4:30 pm (Rhodes 405)
Alvin Zhu: W 3:30-5:30 (Gates 406)

Overview:

This course will serve as a detailed introduction to computer vision. The emphasis will be on covering the fundamentals which underly both computer vision research and applications. A tentative list of topics is below:

Geometry / Physics of image formation
Properties of images and basic image processing
3D reconstruction
Grouping (of image pixels into objects)
Machine learning in computer vision: basics, hand-designed feature vectors, convolutional networks
Detecting and localizing objects

We will have a combination of about 4 programming assignments and 2 written homeworks. This is subject to change.

Office Hour Calendar

Lectures / Notes:

Reference: Rick Szeliski's book. This is not a textbook, in that it covers a lot more material in a lot more detail, but can be used for additional reading.
Course plan: Below is the (tentative) list of classes, with possible additional readings. These may change as the semester progresses.

Date	Topic (with linked notes / slides)	Additional reading	Assignments etc
Jan 24	Introduction [ppt \| pdf]	Szeliski 1	-
Jan 26	The visual world [ppt \| pdf]	Szeliski 2	-
Jan 29	Image filtering [ppt \| pdf]	Szeliski 3.1-3.2	-
Jan 31	Image filtering and Fourier transforms [ppt \| pdf]	Szeliski 3.4	-
Feb 2	Fourier transforms and resizing and resampling [ppt \| pdf]	Szeliski 3.4, 2.3.1	-
Feb 5	Resizing, resampling and pyramids [ppt \| pdf]	Szeliski 3.5, 2.3.1	-
Feb 7	Grouping I - Edge detection [ppt \| pdf]	Szeliski 3.5, 4.2	-
Feb 9	Numpy / scipy tutorial [ppt \| pdf]	-	-
Feb 12	Grouping II - Edge detection and k-means [ppt \| pdf]	Szeliski 4.1, 5.3	-
Feb 14	Grouping III - Images as graphs[ppt \| pdf]	Szeliski 5.3	PA1 due
Feb 16	Grouping IV \| The correspondence problem [ppt \| pdf]	-	-
Feb 19	February break	-	-
Feb 21	Feature detection [ppt \| pdf]	Szeliski 4.1	-
Feb 23	Harris corner detector [ppt \| pdf]	Szeliski 4.1	-
Feb 26	Feature descriptors and matching - I [ppt \| pdf]	Szeliski 4.1	-
Feb 28	Feature descriptors and matching - II [ppt \| pdf]	Szeliski 4.1	-
Mar 2	--Snow day--	Szeliski 2.1	-
Mar 5	Feature descriptors and matching - III \| Geometry of image formation - I [ppt \| pdf]	Szeliski 2.1	-
Mar 7	Geometry of image formation - II [ppt \| pdf]	Szeliski 2.1	-
Mar 9	Homogenous coordinates \| Camera calibration - I [ppt \| pdf]	Szeliski 2.1, 6.1, 6.2	-
Mar 12	Prelim review	-	-
Mar 14	Camera calibration - II \| Triangulation [ppt \| pdf]	-	-
Mar 16	Homographies \| RANSAC [ppt \| pdf]	Szeliski 6.1	-
Mar 19	RANSAC and Hough transforms [ppt \| pdf]	Szeliski 6.1	PA3 out
Mar 21	Prelim Discussion \| Two-view stereo - I [ppt \| pdf]	Szeliski 7.2	-
Mar 23	Two-view stereo [ppt \| pdf]	Szeliski 7.2	-
Mar 26	Epipolar geometry [ppt \| pdf]	Szeliski 7.1-7.4	-
Mar 28	Epipolar geometry - II [ppt \| pdf]	Szeliski 7.1-7.4	-
Mar 30	Radiometry [ppt \| pdf]	Szeliski 2.2	PA3 due
Apr 2	Spring break	-	-
Apr 4	Spring break	-	-
Apr 6	Spring break	-	-
Apr 9	Photometric stereo - I [ppt \| pdf]	Szeliski 12.1.1	-
Apr 11	Photometric stereo - II [ppt \| pdf]	Szeliski 12.1.1 Practice questions for stereo	-
Apr 13	Photometric stereo - III \| Intro to recognition [ppt \| pdf]	-	-
Apr 16	Intro to machine learning - optimization \| the ERM principle [ppt \| pdf]	-	-
Apr 18	Machine learning and optimization \| the ERM principle [ppt \| pdf]	Answers for stereo practice questions Practice questions for photometric stereo	-
Apr 20	Regularization \| Linear classifiers and HOG / SIFT Bag-of-words [ppt \| pdf]	-	-
Apr 23	Non-linear classifiers [ppt \| pdf]	-	-
Apr 25	Convolutional networks \| Backpropagation [ppt\|pdf]	Answers for photometric stereo practice questions Practice questions for learning / Bag-of-words	-
Apr 27	Backpropagation [ppt\|pdf]	-	-
Apr 30	Image classification \| transfer learning [ppt\|pdf]	-	-
May 2	Transfer learning \| Object detection - I [ppt\|pdf]	-	-
May 4	Object detection - II [ppt \| pdf]	-	-
May 7	Object detection - III \| Semantic segmentation [ppt \| pdf]	-	-
May 9	Conclusion	Practice questions for learning / Bag-of-words More practice questions Answers to more practice questions	-