Instructor: Pinar Duygulu
Office :  EA 433
e-mail : duygulu[at]cs.bilkent.edu.tr
Phone :  (312) 290 31 43
Office hours:  by appointment..
Course web page: http://www.cs.bilkent.edu.tr/~duygulu/Courses/CS554/
Textbook: Computer Vision - A modern Aproach  by David A. Forsyth & Jean Ponce, Prentice Hall, Ed. 1, 2002
Other textbooks:
Computer Vision by Dana Ballard and Chris Brown (available online)
Digital Image Processing by Rafael Gonzalez and Richard Woods
Computer Vision by Linda Shapiro and George Stockman
Related Material: http://www.cs.bilkent.edu.tr/~duygulu/CVlinks.html
Also other complementary articles that will be made available
Time & Location: Thursdays 10:40-13:30, 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
Topics:
Introduction,  Color and Light, Linear Filters, Texture,  Edge detection,  Interest Points, Cameras, Multi-view Geometry, Stereopsis,  Motion,  Segmentation,  Object recognition,  Face recognition,  Image and Vieo Databases
Grading:
Projects 55% 
Midterm 15%
Final 20%
Paper Presentations 10%

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Announcements:

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Lectures

  

Introduction (slides)	Topics What is computer vision? Why is it difficult? Which cues do humans use to perceive? Application areas Links Vision in the news - CNN article Optical Illusions The joy of visual perception
Basics (slides)	Topics Image Representation,Review of Linear Algebra,Geometrical Transformations, Introduction to Matlab,Handling Images in Matlab Readings: Matlab primer  by Kermit Sigmon, University of Florida Matlab tutorial  by Stefan Roth, Brown University Matlab Tutorial Files [Basic Operations | Programming | Working with Images ] by Martial Hebert, CMU Basic math (.pdf),  figures (.pdf) by Martial Hebert, CMU
Image Processing (slides)	Topics Image Formation, Point Processing, Blob Processing, Binary image analysis,Thresholding,Connected component analysis,Mathematical morphology,Region propoerties Readings: Chapter 3 from Shapiro&Stockman Links Morphological operators applet,
Linear Filters (slides1, slides2)	Topics Linear filters, convolution, smoothing, derivatives, Fourier transform, sampling and aliazing, gaussian pyramids Readings Chapter 7 from Forsyth&Ponce Correlation and convolution, by David Jacobs 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 Tutorial on Convolution, by David Young
Edge Detection (slides)	Topics Derivatives, Edge detection, Hough Transform  Readings Chapter 8 from Forsyth&Ponce A Computational Approach to Edge Detection, J. Canny, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 8, No. 6, Nov 1986. Chapter 4 from Olivier Faugeras' book: Three-Dimensional Computer Vision, MIT Press, 1993 Links Tutorial on Hough Transform, by David Young Hough line detection applet, Hough circle detection applet
Texture (slides)	Topics Texture analysis and synthesis  Readings Chapter 9 from Forsyth&Ponce Pyramid based Texture Analysis/Synthesis, David Heeger and James Bergen, SIGGARPH 1995 A Computational Model of Texture Segmentation, J. Malik and P. Perona, Proc. Computer Vision and Pattern Recognition, 1989 Eraly Vision and Texture Perception, J.R. Bergen and E.H. Adelson, Nature, 1988 W.Y. Ma and B.S. Manjunath, Texture features and learning similarity, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, pp. 425-430, June, 1996 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 Links Wavelet Tutorial by Robi Polikar
Interest Points (slides)	Topics Harris Detector, Local invariant points, SIFT descriptors 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
Radiometry (slides)	Topics Radiometry, measuring light Readings: Chapter 4 from Forsyth&Ponce
Color (slides)	Topics Color perception, color spaces Readings: Chapter 6 from Forsyth&Ponce The foundations of color measurement and color perception by Brian A. Wandell
Cameras (slides)	Topics Perspective projection, Pinhole camera model, Lenses Readings Chapter 1 from Forsyth&Ponce An Introduction to Projective Geometry by Stan Birchfield Links Brunelleschi and the Origin of Linear Perspective
Camera Calibration (slides)	Topics Camera geometry, camera calibration  Readings Chapters 2 and 3 from Forsyth&Ponce A Flexible New Technique for Camera Calibration, Zhengyou Zhang,  Technical Report MSR-TR-98-71,  Microsoft Research, Redmond, WA 98052 Links Links related to Camera Calibration
Multi view Geometry (slides)	Topics Epipolar geometry Readings Chapter 10 from Forsyth&Ponce Epipolar Geometry and the Fundamental Matrix,  in Multiple View Geometry in Computer Vision , Hartley & Zisserman, 2000 Links Tutorials on Multi-view Geometry by Zisserman, Hartley and Pollefeys
Stereopsis (slides)	Topics Stereopsis, Matching, Reconstruction  Readings Chapter 11 from Forsyth&Ponce Links Middlebury Stereo Vision page - good source for stereo dataset Stereo Images with Ground Truth Disparity and Occlusion, from University of Bonn A Matlab code  by Carlo Tomasi to generate random dot stereograms and to find correspondences with the Marr-Poggio algorithm. The art of Stereo Photography How to freeview Stereo Images  Modeling and Rendering Architecture from Photographs, by Debevec, Taylor and Malik
Motion (slides)	Topics Optical flow, structure from motion, Tracking Readings 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 Detection and Tracking of Point Features.Carlo Tomasi and Takeo Kanade.  Carnegie Mellon University Technical Report CMU-CS-91-132, April 1991.    Good Features to Track, Jianbo Shi and Carlo Tomasi, IEEE Conference on Computer Vision and Pattern Recognition, pages 593-600, 1994. 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) 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 Barber pole illusion Image sequences: Flower garden(.mov) A review on background estimation tecniques
Mosaics (slides)	Topics Homographies, Image Mosaics Readings R. Szeliski and H.-Y. Shum, Creating Full View Panoramic Image Mosaics and Environment Maps, Proc. ACM SIGGRAPH, 1997, longer version: Panoramic Image Mosaics, Technical report, MSR-TR-97-23, 1997 M. Brown and D. G. Lowe. Recognising Panoramas. In Proceedings of the 9th International Conference on Computer Vision (ICCV2003), pages 1218-1225, Nice, France, October 2003. Planar Scenes and Homography lecture notes by Serge Belongie Links Autostitch: Automatic Image Stiching
Segmentation (slides)	Topics Segmentation, Grouping, Fitting 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  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 Matlab Code for k-means and Expectation Maximization by Carlo Tomasi A tutorial on Clustering Algorithms with a K-means clustering applet
Recognition (slides)	Topics Model based and template matching based methods for recognition Readings , Object Detection using statistics of parts, Henry Schneiderman and Takeo Kanade, International Journal of Computer Vision Robust Real-time Object Detection, Paul Viola, Micheal Jones, International Journal on Computer Vision, 2001 Recognition by linear combinations of models, S. Ullman and R. Basri, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.13, No.10, October 1991 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. Object Class Recognition by Unsupervised Scale-Invariant Learning, Rob Fergus, Pietro Perona, and Andrew Zisserman, Conference on Computer Vision and Pattern Recognition, (2003). Face recognition using eigenfaces, M. Turk and A. Pentland, Proc. IEEE Conference on Computer Vision and Pattern Recognition, Maui, Hawaii, 1991 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 Links International Object Recognition Workshop, Sicily, 2004 The PASCAL Visual Object Classes Challenge
Image and Video Databases (slides)	Topics Retrieval, browsing and other novel applications on large datasests Readings The Earth Mover's Distanceas a Metric for Image Retrieval, Y. Rubner, C. Tomasi, L.J. Guibas, Technical Report STAN-CS-TN-98-86, Computer Science Department, Stanford University, September 1998 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, CVPR 2004 Links TRECVID - TREC Video Retrieval Evaluation Blobworld - Image retrieval using regions
Student Presentations

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Assignments:

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Policies

Important notes about evaluation:

    Assignments:
        There will be three reading assignments and three programming assignments      
        Late homeworks are not accepted     
        All programming assignments are due midnight and will be sent by e-mail
            In your e-mail use the following format in the title
            CS554 - Programming assignment #
            Your programming assignmenments should be sent as a tar ball in the following format
                <name_surname_PA_#>.tar
        All reading assignments are due before the lecture hours and will be given to the instructor as printed out
            Reading assignments will be summary of the given paper. It should be about one page,
            and you should explain the main contributions and the important points of the proposed
            methods in your own words. Do not include any figures or formulas. Do not write the values
            of parameters or thresholds unless they are very important. Assume that you are submitting
            one or two pages summary of your paper to a conference.

    Projects 
    The project may be
        An original implementation of a new or published idea,
        A detailed emprical evaluation and comparison of the existing implementations of two or more methods,     
    You will work in groups of two or three
    You are required to write a proposal, progress report, final report, and do a demonstration and a presentation 
        Project proposal will be a short description of the problem you would like to tackle, objective of the study,
         proposed algorithms, hardware/software tools and data that you plan to utilize, and evaluation strategies
        that you plan to use. Also provide a short list of related references.
        Progress report will describe your progress in the project and your plans for the rest of the semester
        Final report will be a well-written report which provides proper motivation for the task, proper citation
        and discussion of related literature, proper explanation of the details of the approach and implementation
        strategies, proper performance evaluation, and detailed discussion of the results. You should highlight
        your contributions and conclusions.
            Final report guidelines:
            Follow IEEE two-column format as shown in the example and the format definition table and glossary.
            The page limit is 6 pages.
            The report should not have any page numbers, headers or footers.
            You can use IEEE's LaTeX template or Word template. (LaTeX users: Be sure to use the template's conference mode.)
            PDF submission is recommended.
        Presentation will be in the form of poster session and each team will show their contributions on a poster
        which will fit to a board of approximately 1m X 1m
       
    Presentations:
    Your presentations will be evaluated according to the following criteria. Please, consider them in preparing your presentations:
        Understanding of the topic - how confident are you with the paper that you present
        Review of the related work - not just mentioning but by reading some of them to understand and relate to your paper
        Giving an overview of the paper -  the main contributions of the paper, and an overview of the approach
        Explaining the details - understanding and explaining the formulas and methods given in the paper
        Presentation - in general how well you are prepared to give the talk
        Use of visual material when available