Mon/Wed 9:45am-11:00am @ Keller Hall 3-111

Information

Syllabus

Instructor: Hyun Soo Park (hspark at umn.edu)
Office hour: Mon/Wed 4:00pm-5:00pm (Shepherd Laboratory 261)

TA: Jingfan Guo (guo00109 at umn.edu)
Office hour: Tue/Thr 4:00pm-5:00pm (Shepherd Laboratory 234)

Textbook: Not required but the following books will be frequently referred:
+ "Computer Vision: Algorithms and Applications", Richard Szeliski
+ "Computer Vision: A Modern Approach", David A. Forsyth and Jean Ponce

The course materials are inspired by the slides of Jianbo Shi (UPenn), Kris Kitani (CMU), James Hays (GATECH), Kristen Grauman (UT Austin), Steve Seitz (UW), and Robert Collins (PSU).

Slide

Lecture	Reading
Introduction
Image filtering	Ch 3 (Szeliski) Ch 4 (Forsyth)
Convolution	Ch 3 (Szeliski) Ch 4 (Forsyth) wiki
Image gradient	Ch 3 (Szeliski)
Edge	Ch 4 (Szeliski) Ch 5 (Forsyth) Canny, "A computational approach to edge detection" TPAMI (1986)
HOG	Ch 4 (Szeliski) Ch 5 (Forsyth) Dalal and Triggs, "Histograms of oriented gradients for human detection" CVPR (2005) Felzenszwalb et al., "Object detection with discriminatively trained part based models", TPAMI (2010)
Image pyramid	Ch 4, 5 (Forsyth) wiki P.A. Bromiley, "Products and Convolutions of Gaussian Probability Density Functions" Bert and Adelson, The Laplacian Pyramid as a Compact Image Code", Trans. on Comm. (1983) E.H. Andelson et al., "Pyramid methods in image processing" (1984)
Scale space	Ch 4, 5 (Forsyth) wiki Bretzner and Lindeberg, "Feature Tracking with Automatic Selection of Spatial Scales", CVIU (1998)
SIFT	Ch 5 (Forsyth) Lowe, "Distinctive Image Features from Scale-Invariant Keypoints". IJCV (2004) VLfeat SIFT
Feature matching	Ch 4 (Szeliski)
Image warping	Ch 4, 6, 9 (Szeliski)
RANSAC	Ch 6 (Szeliski) Ch 15 (Forsyth) Fischler and Bolles, "Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography" Comm. ACM. (1981)
Optical flow	Ch 8 (Szeliski) Lucas and Kanade, "An iterative image registration technique with an application to stereo vision", 1981
Good feature to track / Harris corner	Ch 4, 8 (Szeliski) Shi and Tomasi, "Good Features to Track," CVPR, 1994 Harris and Stephens, "A Combined Corner and Edge Detector". Alvey Vision Conference, 1988)
Image Alignment (Lucas-Kanade)	Ch 8 (Szeliski) Baker and Matthews, "Lucas-Kanade 20 Years On: A Unifying Framework: Part 1", 2002
Inverse Compositional Image Alignment	Baker and Matthews, "Lucas-Kanade 20 Years On: A Unifying Framework: Part 1", 2002
Meanshift Tracking (nonparametric tracking)	Ch 5.3 (Szeliski) Comaniciu et al., "Kernel-Based Object Tracking", TPAMI, 2003 Cheng, "Mean Shift, Mode Seeking, and Clustering", TPAMI, 1995 Fukunaga et al., "The Estimation of the Gradient of a Density Function, with Applications in Pattern Recognition", IEEE Transactions on Information Theory, 1975
Dense Optical Flow	Ch 8 (Szeliski) Horn and Schunck, "Determining Optical Flow", Artificial Intelligence, 1981
Eigenfaces	Ch 14 (Szeliski) Turk and Pentland, "Face recognition using eigenfaces" CVPR 1991
Fisher's Linear Discrimant	Ch 14 (Szeliski) Belhumeur et al., "Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection" PAMI 1997
Face Detection	Ch 14 (Szeliski) Viola and Jones, "Robust Real-time Object Detection", IJCV 2001 Freund and Schapire, "A Short Introduction to Boosting", 1999
Bag-of-Words Image Classification	Lazebnik et al., "Beyond bags of features: spatial pyramid matching for recognizing natural scene categories", CVPR 2006 Fei Fei, Fergus, Torralba, "Recognizing and Learning Object Categories", Short course (link)
Neural Network	Forsyth Ch 22
Convolutional Neural Network	LeCunn, "Gradient-based learning applied to document recognition", 1998 Krizhevsky et al., "ImageNet Classification with Deep Convolutional Neural Networks", NIPS 2012 Zeiler and Fergus, "Visualizing and Understanding Convolutional Networks", ECCV 2014 Mahendran and Vidaldi, "Understanding Deep Image Representations by Inverting Them", CVPR 2014
Training Convolutional Neural Network	Stanford Visual Recognition Lecture Note by Andrej Karpathy
CNN Object Detection	Stanford Visual Recognition Lecture Note by Andrej Karpathy Girshick et al., "Rich feature hierarchies for accurate object detection and semantic segmentation" Girshick, "Fast R-CNN" Ren et al., "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" Redmon et al, "You Only Look Once: Unified, Real-Time Object Detection"
Camera Model	Szeliski Ch 2 Forsyth Ch 1
Camera Projection	Szeliski Ch 2 Forsyth Ch 1
Projective Line	Szeliski Ch 2
Epipolar Geometry	Szeliski Ch 7 Forsyth Ch 10 Longuet-Higgins "A computer algorithm for reconstructing a scene from two projections". Nature 1981
Triangulation	Szeliski Ch 7
Stereo Reconstruction	Szeliski Ch 7 Forsyth Ch 10
Final Exam Review

Homework

	HW #1: Histogram of Oriented Gradients (last update Feb 2)
	HW #2: Registration (Inverse Compositional Image Tracking)
	HW #3: Scene Recognition (BoW)
	HW #4: Digit Recognition (CNN)
	HW #5: Stereo Reconstruction

Scholastic misconduct

Scholastic misconduct is broadly defined as "any act that violates the right of another student in academic work or that involves misrepresentation of your own work. Scholastic dishonesty includes, (but is not necessarily limited to): cheating on assignments or examinations; plagiarizing, which means misrepresenting as your own work any part of work done by another; submitting the same paper, or substantially similar papers, to meet the requirements of more than one course without the approval and consent of all instructors concerned; depriving another student of necessary course materials; or interfering with another student's work."