CMSC733 Computer Processing of Pictorial Information

 General Information

Class Time

Tue., Thurs. 11:00-12:15

Room

CSI 3118

Course Info

See below

Text

Personnel

 Instructor TA Name David Jacobs Jin Sun Email djacobs at cs jinanderson at gmail Office AVW 4421 AVW 4420 Office hours Tues. 2:00-3:00 or by appt. Wed. 3:00-4:00 or by appt.

Announcements:

Description

This class will provide a general, graduate level introduction to computer vision. We will attempt to give students a broad understanding of all important topics in computer vision.

Prerequisites

Knowledge of and comfort with mathematics will be very helpful. At a minimum, students should know multivariable calculus and linear algebra. No knowledge of computer vision will be assumed, but we will go quickly, so some background in computer vision will certainly be helpful.

Text

There is no single required text. Required readings for the class will be listed below, and will be available online. Three books that will be useful are:

·         A draft of Richard Szeliski's computer vision book is available online. There will be some required readings from this book.

·        Introductory Techniques for 3-D Computer Vision by Trucco and Verri. This is more of an undergraduate text, and a bit old, so many topics are not covered. However, the fundamentals are explained very clearly.

·        Computer Vision: A Modern Approach by Forsyth and Ponce (2nd edition). This is well worth buying if you are a graduate student in computer vision.

Requirements

Assigned work for the class will consist of problem sets (40% of grade), a take-home midterm (20%), and a final exam (40%).

Problem Sets

 Assigned Due Problem Set 1 9/17/13 10/1/13 Edge Detection. Implement 2D edge detection, paper and pencil problems about convolution.  Problem Set  You will also use the following Matlab files: test_smooth_image.m, test_image_gradient.m, test_gradient_magnitude_direction.m, interpolate_gradients.m, and the images: swanbw.jpg, swanedges.jpg, swanedges_h.jpg Problem Set 2 10/1/13 10/17/13 Normalized Cut and Texture Synthesis. Problem Set. To test your normalized cut code, you will use the routine: test_normalized_cut_points.m. My results with this routine are here and here. For texture synthesis, look at the Efros and Leung paper. You can use the brick image to test your code. Problem Set 3 10/17/13 10/31/13 E-M and Mosaicing. Problem Set. For the mosaicing problem, you will use these images: Image 1 Image 2 and this matlab code. Midterm 10/31/13 11/7/13 Problem Set 4 11/12/13 11/26/13 Stereo matching with graph cuts. Problem Set. First test image pair: I1L.jpg I1R.jpg Second test image pair: T3bw.jpg T4bw.jpg Tresult.jpg Problem Set 5 11/26/13 12/10/13 Bag of words classification. Problem Set. ps5.m Review for Final 12/3/13

Class Schedule

This schedule should be considered more of a guideline than a rigid plan.

Lectures

 Class Topic Required Reading Background Reading Problem Sets 1. 9/3 Introduction Slides 2. 9/5 Fourier 1 Fourier Transforms This material is covered in many standard techniques.  You might look at:A Wavelet Tour of Signal Processing , by Mallat for this and material on wavelets. Chapters 2 and 3 are on the Fourier Transform.   I also like the discussion in Elementary Functional Analysis by Shilov (This is part of the Dover Classics series, so there is a cheap paperback edition).   Some of this material is discussed in Forsyth and Ponce, Chapter 7. 3. 9/10 Fourier 2 Szeliski, 3.2 and 3.4 4. 9/12 Diffusion and smoothing Diffusion   Diffusion Phenomena, by Ghez, Sections 1.1-1.4 (available from instructor) 5. 9/17 Edge Detection Edge Detection Canny edge detector 6. 9/19 Non-linear Diffusion Non-linear Diffusion   "A review of nonlinear diffusion filtering," by Joachim Weickert.  In Scale-Space Theory in Computer Vision, Lecture Notes in Computer Science, Vol. 1252, Springer, Berlin, pp. 3-28, 1997. See also Weickert's book: Anisotropic Diffusion in Image Processing 7. 9/24 Bilateral Filtering and Normalized Cut Carlo Tomasi and Roberto Manduchi.  Bilateral Filtering for Gray and Color Images. ICCV 1998. Jianbo Shi and Jitendra Malik. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence , 22(8):888-905, August 2000. Michael Elad.  On the Origin of the Bilateral Filter and ways to improve it.  IEEE Trans. on Image Processing, 2002. Spectral graph cut from a filtering point of view, by Ye, Lin, Song, Chen and Jacobs. 8. 9/26 K-means, E-M and Background Subtraction E-M, Mean shift, Mixtures of Gaussians   Adaptive background mixture models for real-time tracking, by Stauffer and Grimson Forsyth and Ponce, Computer Vision A Modern Approach, Chapter 16 E-M tutorial by Yair Weiss Szeliski, Section 5.3 9. 10/1 Texture Texture Texture Synthesis by Non-parametric Sampling by Efros and Leung. Web page contains links to the paper and pseudocode. Texture synthesis 10. 10/3 Markov Processes and Markov Random Fields Notes 11. 10/8 Graph cuts for segmentation and MRFs Interactive Graph Cuts for Optimal Boundary & Region Segmentation of Objects in N-D images. Yuri Boykov and Marie-Pierre Jolly. In International Conference on Computer Vision, (ICCV), vol. I, pp. 105-112, 2001. 12. 10/10 Features: corners and blobs For corners, see section in notes on edge detection. 13. 10/15 Matching: SIFT and RANSAC David G. Lowe, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, 60, 2 (2004), pp. 91-110. The RANSAC song 14. 10/17 Geometric transformations and mosaicing Geometric Transformation Slides Mosaicing 15. 10/22 Tracking An introduction to the Kalman filter, by Welch and Bishop (Section 1) Tutorial: the Kalman filter (contains a more complete derivation) 16. 10/24 Biological Vision Slides 17. 10/29 Cameras, perspective projection, projective geometry 18. 10/31 Stereo geometry Slides, Szeliski, 11-11.1.1 Midterm 19. 11/5 Stereo matching: dynamic programming and graph cuts Szeliski, 11.3-11.5.1 Fast approximate energy minimization via graph cuts, by Boykov, Veksler, and Zabih 20. 11/7 Stereo, contd. 21. 11/12 Review of midterm Graph cut stereo 22. 11/14 Optical flow and flow fields 23. 11/19 Structure-from-motion with perspective, the Essential matrix Szeliski, 7.2 through 7.2.1 24. 11/21 Structure-from-motion with scaled orthographic projection, factorization Szeliski, 7.3 25. 11/26 Classification Slides (from Andrew Zisserman) Text (from Kristin Grauman) Bag of words classifier 26. 12/3 Lighting Lambertian Reflectance and Linear Subspaces by Basri and Jacobs 27. 12/5 Review for final 28. 12/10 Sliding window detection Rapid object detection using a boosted cascade of simple features by Viola and Jones 29. 12/12 Detection: deformable part models, fine-grained classification P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan Object Detection with Discriminatively Trained Part Based Models   Pictorial Structures for Object Recognition Pedro F. Felzenszwalb, Daniel P. Huttenlocher 12/16 8:00-10:00 FINAL