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)

 

 



Lectures

  



Introduction
(slides)



Basics
(slides)

  • Topics
    • Image Representation,Review of Linear Algebra,Geometrical Transformations, Introduction to Matlab,Handling Images in Matlab
  • Readings:


Image Processing
(slides)

  • Topics
    • Image Formation, Point Processing, Blob Processing, Binary image analysis,Thresholding,Connected component analysis,Mathematical morphology,Region propoerties
  • Readings:
  • Links



Filters
(slides1, slides2, slides3)

  • Topics
    • Linear filters, convolution, smoothing, derivatives, Fourier transform, sampling and aliazing, pyramids, template matching
  • Readings


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

 


Local Features

(slides)

  • Topics
    • Harris Detector, Local invariant points, SIFT descriptors

Window Based Detectors

(slides)






Segmentation
(slides)

  • Topics
    • Segmentation, Grouping, Fitting





Object Recognition

(slides)

  • Links

o    Recognizing and Learning Object Categories, by Li Fei-Fei, Rob Fergus, Antonio Torralba


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)

 

Cameras
(slides)


Camera Calibration
(slides)



Multi view Geometry
(slides)

  • Topics
    • Epipolar geometry




Stereopsis
(slides)

  • Topics
    • Stereopsis, Matching, Reconstruction 
  • Readings
    • Chapter 11 from Forsyth&Ponce







Motion
(slides)

  • Topics
    • Optical flow, structure from motion, Tracking
  • Readings


Mosaics
(slides)




Tracking and understanding human activities

o    Tutorial on Human Activity Analysis, by J. K. Aggarwal, Michael S. Ryoo, Kris M. Kitani, CVPR 2011

Detection and Recognition of faces

 

Student Presentations

 


Assignments:



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