Computer Vision
CS 554


Department of Computer Engineering, Bilkent University

Lectures

Assignments

Policies

Instructor: Pinar Duygulu
Office :  EA 420
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: Algorithms and Applications by Richard Szeliski (available online)

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 Links:

CVOnline
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 70%  (5-8 individual projects)
Quizzes 30% (includes one pop-up presentation)

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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,
Filters (slides1, slides2, slides3)	Topics Linear filters, convolution, smoothing, derivatives, Fourier transform, sampling and aliazing, pyramids, template matching 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 and Texture (slides)	Topics Derivatives, Edge detection, Texture analysis  Readings Chapter 8 and 9 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 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 Links Wavelet Tutorial by Robi Polikar
Local Features (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
Window Based Detectors (slides)
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
Object Recognition (slides)	Links o Recognizing and Learning Object Categories, by Li Fei-Fei, Rob Fergus, Antonio Torralba International Object Recognition Workshop, Sicily, 2004 The PASCAL Visual Object Classes Challenge
Classification (slides)
Scene Classification (slides)
Context (slides)
Challenges in Large Scale	Attributes, by David Forsyth Big Visual Data, by Alyosha Efros
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
Tracking and understanding human activities	Readings David A. Forsyth, Okan Arikan, Leslie Ikemoto, James O' Brien, Deva Ramanan, Computational Studies of Human Motion: Part 1, Tracking and Motion Synthesis, Foundations and Trends¨ in Computer Graphics and Vision Volume 1 Issue 2/3 (255pp), 2006  Links o Tutorial on Human Activity Analysis, by J. K. Aggarwal, Michael S. Ryoo, Kris M. Kitani, CVPR 2011 Lecture on Activity Recognition, by James Hays Looking at people, David Forsyth
Detection and Recognition of faces
Student Presentations

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Assignments:

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Policies

Important notes about evaluation:

    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

    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.

    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