Computer Vision

• Instructor: Pinar Duygulu
  
• Office :  EA 433
• e-mail : duygulu[at]cs.bilkent.edu.tr
  
• Phone :  (312) 290 31 43
• Office hours:  Tuesdays 16:40-17:30 and by appointment..
• Textbook: Computer Vision - A modern Aproach  by David A. Forsyth & Jean Ponce, Prentice Hall, Ed. 1, 2002.  
• Related Material : http://www.cs.bilkent.edu.tr/~duygulu/CVlinks.html
• Also other complementary articles that will be made available
• List of all materials is at  http://www.cs.bilkent.edu.tr/~duygulu/Courses/CS554/Spring2004/ReadingList.html
• Course web page: http://www.cs.bilkent.edu.tr/~duygulu/Courses/CS554/Spring2004
• Time & Location: Tuesdays 13:40-16:30 and Fridays  9:40-12:30, EA 502
  
• Course Description:  Basic concepts in computational vision. Relation to human visual perception. The analysis and understanding of image and video data. Mathematical foundations, image formation and representation, segmentation, feature extraction, contour and region analysis, camera geometry and calibration, stereo, motion, 3-D reconstruction, object and scene recognition, object and people tracking, human activity recognition and inference.
• Prerequisites:Knowledge of linear algebra and calculus, probability and statistics
  
• Grading:
• Homeworks (Implementation or paper review) - %35
• Paper presentations -  %15
  
• Term project (Demonstration + Report + Presentation) : %50

• Term Project Reports are available

• Assignments
• Due: April 9
• Reading assignment
• Write a summary of Canny's edge detection paper which will be distributed in the class on Tuesday (April 6)
• Due: April 13
• Programming assignment
  
• Write a matlab program that produces a Gaussian pyramid from an image (image1.jpg)
• Reading Assigment
• Write a summary of David Lowe's paper on distinctive image features
• Due : April 30
• Programming Assignment 
• Find salient points in the image and compute features aroun them to detect objects in cluttered backgroud or with occlusion and for matching images (images)

• Reading Assignment
• Write a summary of either Tomasi and Kanade or Lucas Kanade paper
  
• Due : May 7
  
• Reading Assignment
• Write a summary of Normalized Cuts paper
• shorter version of the paper is available now
• Due : May 20
• Programming assignment
• Retrieve images from a database for a query image and give rankings
  
• Compare images using
  
• global color histogram extracted from the whole image, and
  
• RGB mean and standard deviation values from the regions.
  
• Due : May 28, 13:00
• Project poster presentation

• Syllabus
  
• [April 2] Introduction
• Topics
• Overview and objectives (slides)
• Basic linear algebra (slides from Octavia Camps, Penn State)
  
• Introduction to Matlab
  
• Readings:
• Matlab primer (.pdf) by Kermit Sigmon, University of Florida
• Basic math (.pdf),  figures (.pdf) by Martial Hebert, CMU
• Links
• Vision in the news - CNN article
• The Mathworks' "Getting started with Matlab"
• Linear Algebra by Jim Hefferon
  


• [April 6] Linear filters
• Topics
• Linear filters, convolution, smoothing, derivatives, Fourier transform, sampling and aliazing, gaussian pyramids (slides)
• Readings
• Chapter 7
• Computer vision for interactive computer graphics,W. T. Freeman, D. Anderson, P. Beardsley, C. Dodge, H. Kage, K. Kyuma, Y. Miyake, M. Roth, K. Tanaka, C. Weissman, W. Yerazunis,  in IEEE Computer Graphics and Applications, volume 18, number 3, May--June, pp. 42-53, 1998.
• Links
• Joy of Convolution, JHU
• Tutorial on Convolution, by David Young
• [April 9] Edge detection
• Topics
• Derivatives, Edge detection, Hough Transform (slides)
  
• Readings
• Chapter 8
• A Computational Approach to Edge Detection, J. Canny, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 8, No. 6, Nov 1986.
• Links
• Tutorial on Hough Transform, by David Young
• Hough line detection applet, Hough circle detection applet

• [April 13] Texture
  
• Topics
• Texture analysis and synthesis (slides)
  
• Readings
• Chapter 9
  
•  Pyramid based Texture Analysis/Synthesis, David Heeger and James Bergen, SIGGARPH 1995
  
• Links
• Wavelet Tutorial by Robi Polikar
• Alyosha Efros' texture synthesis page, graphcut textures, image analogies

• Presentation - by Ozcan Oksuz
  
• Texture Synthesis by Non-parametric Sampling, Alexei A. Efros and Thomas K. Leung, IEEE International Conference on Computer Vision (ICCV'99), Corfu, Greece, September 1999
• Image Quilting for Texture Synthesis and Transfer, Alexei A. Efros and William T. Freeman, Proceedings of SIGGRAPH '01, Los Angeles, California, August, 2001

• [April 16]  Interest Points
• Topics
• Harris Detector, Local invariant points, SIFT (slides)
  
• Readings
• A combined corner and edge detector, Chris Harris and Mike Stephans, Proceedings of The Fourth Alvey Vision Conference, Manchester, pp 147-151. 1988
• Local Greyvalue Invariants for Image Retrieval,  C. Schmid and R. Mohr. In Pattern Analysis and Machine Intelligence, 1997.
• Indexing based on scale invariant interest points. K.Mikolajczyk and C.Schmid. In International Conference on Computer Vision, 525-531, 2001
• Distinctive Image Features from Scale-Invariant Keypoints, David Lowe, International Journal of Computer Vision, 2004.

• Links
• Tutorial on Recognition and Matching based on local invariant features, by Cordelia Schmid and David Lowe, at CVPR 2003
• Presentation
• by Evren Karaca, Robust Real-time Object Detection, Paul Viola, Micheal Jones, International Journal on Computer Vision, 2001.(slides)

• [April 20] Color
• Topics
• Light and color (slides)
  
• Readings
• Chapters  4 and 6
• Colour in nature, book chapter
  
• Presentation
• by Selim Ciraci, Object Detection using statistics of parts, Henry Schneiderman and Takeo Kanade, International Journal of Computer Vision (slides)
  
• Guest Lecture
  
• Goksel Dedeoglu, Robotics Institure, Carnegie Mellon University, High-Zoom Video Hallucination by Exploiting Spatio-Temporal Regularities

• [April 23] no class
  
• [April 27] Cameras
• Topics
• Image formation, Camera geometry, Camera calibration (slides)
• Readings
• Chapters 1, 2 and 3
• An Introduction to Projective Geometry by Stan Birchfield
• A Flexible New Technique for Camera Calibration, Zhengyou Zhang,  Technical Report
  MSR-TR-98-71,  Microsoft Research, Redmond, WA 98052

• Links
• tutorial on digital cameras
• Brunelleschi and the Origin of Linear Perspective
• http://www.vision.caltech.edu/bouguetj/calib_doc/htmls/links.html
• Tsai camera calibration software
  

• Presentation
• by Tayfun Kucukyilmaz Face recognition using eigenfaces, M. Turk and A. Pentland, Proc. IEEE Conference on Computer Vision and Pattern Recognition, Maui, Hawaii, 1991. (slides)
• by Nazli Ikizler Name-It: Naming and Detecting Faces in News Videos, Satoh, S., Nakamura, Y., Kanade, T., IEEE Multimedia, 6(1), January-March, 1999 (slides)
  

• [April 30] Multi-view geometry & Stereo
• Topics
• Stereopsis, Epipolar geometry, Matching, Reconstruction (slides)
• Readings
• Chapters 10 and 11
• Epipolar Geometry and the Fundamental Matrix,  in Multiple View Geometry in Computer Vision , Hartley & Zisserman, 2000
• An iterative image registration technique with an application to stereo vision, Bruce Lucas and Takeo Kanade, Proceedings of the 7th International Joint Conference on Artificial Intelligence (IJCAI), 1981

• Links
• Middlebury Stereo Vision page - good source for stereo dataset
• Micheal Black's lecture slides
• epipolar geometry applet, Digital Michelangelo Project, , Nomad robot,  the 3D Room

• Presentation   
  
• by Emre Sahin, Feature based methods for structure and motion estimation, Phil Torr and Andrew Zisserman, in Vision Algorithms: Theory and Practice, B. Triggs, A. Zisserman, R. Szeliski (Eds.), Springer (2000).  (slides)

• [May 4] Motion
  
• Topics
• Optical flow, structure from motion, Tracking (slides)
  
• Readings
• Shape and motion from image streams under orthography---a factorization method.C. Tomasi and T. Kanade.  Technical Report TR-92-1270, Cornell University, March 1992.
• Detection and Tracking of Point Features.Carlo Tomasi and Takeo Kanade.  Carnegie Mellon University Technical Report CMU-CS-91-132, April 1991.

• Links
• KLT : Implementation of Kanade-Lucas-Tomasi Feature Tracker
• Motion Flow in Computer Vision, by Jana Kosecka and Shankar Sastry
  
• Lecture on motion, by Robyn Owens
• Layered Image Representation demo by J. Wang
• rotating cube illusion, barber pole illusion, drop shadow illusion

• [May 7]  Segmentation
• Topics
• Segmentation and Grouping, Line fitting (slides)
  
• Readings
  
• Normalized cuts and image segmentation, Jianbo Shi and Jitendra Malik, IEEE Transactions on Pattern Analysis and Machine Intelligence(PAMI), 2000
• a shorter version  published in IEEE Conf. Computer Vision and Pattern Recognition(CVPR), June 1997, Puerto Rico
•  Laws of Organization in Perceptual Forms, Max Wertheimer, first published as Untersuchungen zur Lehre von der Gestalt II, in Psycologische Forschung, 4, 301-350. Translation published in Ellis, W. (1938). A source book of Gestalt psychology (pp. 71-88). London: Routledge & Kegan Paul.
• Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues, D. Martin, C. Fowlkes, and J. Malik.   to appear in PAMI 
  

• Presentation
• by Behcet Ugur Toreyin Mean Shift: A Robust Approach Toward Feature Space Analysis, Dorin Comaniciu and Peter Meer, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, no.5 , May 2002. (slides)
  
• by Ayisigi Basak Sevdik, Body Plans, Forsyth, D.A. and Fleck, M.M., Proc. in Proceedings of Computer Vision and Pattern Recognition (CVPR), 1997. (slides)
  

• Links
• Computational Models of Perceptual Organization, talk slides by Stella Yu
• Laws of Organization in Perceptual Forms, Max Wertheimer
• The Berkeley Segmentation Dataset and Benchmark
• K-means clustering applet, morphological operators applet,  normalized-cuts mass-spring  movie

• [May 11]  Recognition
• Topics
• Model based and template matching based methods for recognition (slides)
  
• Presentation
• by Rabia Nuray Finding and tracking people from the bottom up, Deva Ramanan and David Forsyth,  in Proceedings of Computer Vision and Pattern Recognition (CVPR), Madison, Wisconsin, June 2003. (slides)
  
• by Tayfun Aytac Object Class Recognition by Unsupervised Scale-Invariant Learning, Rob Fergus, Pietro Perona, and Andrew Zisserman, Conference on Computer Vision and Pattern Recognition, (2003). (slides)
  
• by Aykut Unal,  Shape Matching and Object Recognition Using Shape Contexts , Serge Belongie, Jitendra Malik and Jan Puzicha, IEEE Transactions on Pattern Analysis and Machine Intelligence(PAMI), 24(4):509-522, April 2002. (slides)
  
• [May 14] Applications - Image and Video databases
• Topics
• Retrieval, browsing and other novel applications on large datasests (slides)
  
• Links
• TRECVID - TREC Video Retrieval Evaluation
• Presentation
• by Tagmac Topal, The Earth Mover's Distance as a Metric for Image Retrieval, Yossi Rubner, Carlo Tomasi, and Leonidas J. Guibas, Technical Report STAN-CS-TN-98-86, Computer Science Department, Stanford University, September 1998 (slides)
  
• by Erhan Okuyan, Faces and Names in the News Tamara Miller, Alexander C. Berg, Jaety Edwards, Michael Maire, Ryan White, Yee Whye Teh, Eric Learned-Miller, David A. Forsyth, To Appear CVPR 2004 (slides)
• by Ozlem Subakan, Silhouette-based Human Identification from Body Shape and Gait, R.Collins, R.Gross and J.Shi, Intl' Conference on Face and Gesture, October 2002, pp.351-356. (slides)
  
• [May 18]  
• Presentation
• by Serkan Bayraktar, The Optimal Distance Measure for Object Detection S. Mahamud and M. Hebert,IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Wisconsin, June 2003. (slides)
  
• by Asli Ayaz, Recovering Human Body Configurations: Combining Segmentation and Recognition, G. Mori, X. Ren, A. Efros, and J. Malik, , Computer Vision and Pattern Recognition (to appear), 2004.(slides)
• by Umit Tezcan, Man-Made Structure Detection in Natural Images using a Causal Multiscale Random Field, S. Kumar and M. Hebert,  in proc. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR),  Vol. 1, pp. 119-126,  2003 (slides)
  
• by Tarkan Sevilmis, Face Recognition Across Pose and Illumination R. Gross, S. Baker, I. Matthews, and T. Kanade, Handbook of Face Recognition, Stan Z. Li and Anil K. Jain, ed., Springer-Verlag, 2005 (slides)