EECS 442: Computer Vision (Winter 2019)

This is the schedule.

Date Topic Suggested Reading Slides Assignments/Things Out/Things Due
Thu Jan 10 Introduction None PDF PPTX
Tue Jan 15 Cameras I S2.1 (for reference H&Z 2, 6) PDF PPTX
Thu Jan 17 Cameras II Same as above (same as above) HW 1 Out
Tue Jan 22 Light And Shading S 2.2, S2.3 PDF PPTX
Thu Jan 24 Linear and Non-linear Filtering S3.2 PDF PPTX
Tue Jan 29 Detectors and Descriptors I S4.1 PDF PPTX
Thu Jan 31 Polar vortex! HW1 Due on Friday Feb 1, HW2 Out
Tue Feb 5 Detectors and Descriptors II S4.1 PDF PPTX
Thu Feb 7 Transformations I S2.1, S6 PDF PPTX
Tue Feb 12 Transformations II S2.1, S6 PDF PPTX
Thu Feb 14 Linear Classifiers I S14 (skim) for context OR ESL 3.1, 3.2 (skim) and ESL 12.3.2PDF PPTX HW2 Due, HW 3 Out
Tue Feb 19 Linear Classifiers II As above PDF PPTX Earliest Project Proposal Can Be Submitted
Thu Feb 21 Backpropagation CS231n Backprop ExamplesPDF PPTX
Tue Feb 26 Convolutional Neural Nets Part I CS231n Convnets PDF PPTX
Thu Feb 28 Midterm
Tue Mar 5 Spring Break
Thu Mar 7 Spring Break
Tue Mar 12 Convolutional Neural Nets Part II CS231n ConvnetsPDF PPTX
Thu Mar 14 Pixel Labeling Deconvolution artifacts PDF PPTX HW3 Due, HW4 Out
Tue Mar 19 Detection PDF PPTX Latest Project Proposal Can Be Submitted
Thu Mar 21 Optical Flow S8.4 PDF PPTX
Tue Mar 26 Tracking and Video Problems S4.1.4 simple tracking with code PDF PPTX
Thu Mar 28 Calibration S6.3 calibration with opencv PDF PPTX
Tue Apr 2 Single view geometry PDF PPTX
Thu Apr 4 Epipolar geometry S11.1 PDF PPTX HW4 Due, HW5 Out
Tue Apr 9 Stereo S11 PDF PPTX
Thu Apr 11 Structure from Motion S7 PDF PPTX
Tue Apr 16 Advanced Applications: Learning + Geometry PDF Progress Report Due
Thu Apr 18 Advanced Applications: Embodiment HW5 Due
Tue Apr 23 Advanced Applications: Language PDF Final Projects Due during April 28

Re-use policy: I am extremely grateful to the many researchers who have made their slides and course materials available. Please feel to re-use any of my materials while crediting appropriately and making sure original attributions to these generous researchers is preserved.

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 can be obtained via the library in electronic form (scroll past the physical copies).

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