CS6777: Optimization Methods for Computer Vision Applications

July - November, 2017

Course Contents

  • Objectives:
    • In the recent past, algorithms of solving many ill-posed problems in the field of Computer Vision are derived from modern Optimization methods. Allied areas of Machine learning, Pattern recognition and video processing have also seen a rise in the use of such methods. This course will provide an overview of the theories and hands-on practice, required by students and scholars who intend to specialize in this field, to solve complex problems in computer vision and associated fields of study.
  • References

    Textbooks
    Marco Alexander Treiber, Optimization for Computer Vision: An Introduction to Core Concepts and Methods, Springer 2013.
    Andreas Antoniou and Wu-Sheng Lu, Practical Optimization: Algorithms and Engineering Applications, Springer 2007.

    References
    Richard Szeliski, Computer Vision: Algorithms and Applications, Springer-Verlag London Limited 2011.
    Alan C. Bovik, Handbook of Image and Video Processing, ELSEVIER, ACADEMIC PRESS 2005.
    Stephen Boyd and Lieven Vandenberghe, Convex Optimization, Cambridge University Press 2004.
    Daphne Koller and Nir Friedman, Probabilistic Graphical Models - Principles and Techniques, The MIT Press. 2009.
    Bernhard Scholkopf and Alexander J. Smola, Learning with Kernels - Support Vector Machines, Regularization, Optimization and Beyond, MIT Press 2002.
    Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep Learning, MIT Press 2016.

    Lecture Slides

    1 Linear Regression  Download Slide
    2 Polar Parameters Estimation for LSQ Download Slide
    3 Optimization Methods Download Slide
    4 Super-Resolution Edge Reconstruction + Restoration Download Slide
    5 Filtering Download Slide
    6 ICCV Tutorial on Graph Cuts Download Slide
    7 Mathematical Image Processing Download Slide
    8 Scene Modelling, Recognition and Tracking Download Slide
    9 RANSAC Download Slide
    10 Simulated Annealing Download Slide

    Assignment

    Assignment 1 Download Slide

    Link to Download R-software

    Announcement

    Tentative schedule for the July-November 2017 Semester

    Marks Distribution

    Tutorial Dates

    Seminar Dates

    Software Assignment Deadlines

    Exams