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General Information:

Instructor: Ali Borji

 Email: aborji at crcv dot ucf dot edu (Please put [CAP6412] in the subject line of your emails).

 Course venue: HEC 0117 

 Office phone: 407 823 0667

Prerequisites:

• Prerequisite courses: CAP 5415 or CAP 4453 or permission of instructor.

• Recommended preparation: basic probability, statistics, linear algebra, calculus, optimization, and some programming languages (Matlab, python, C/C++, Java, etc.).

Overview:

This is an advanced course in computer vision. We will examine some central topics and key techniques in computer vision, mainly through reading, writing reviews on, presenting, and discussing the most recent and/or classic papers published on top-notch computer vision conferences and journals. Candidate topics are appended below. 

The main goal of the course is to prepare students for graduate research in computer vision. Through the class, the students are expected to understand in-depth the state-of-the-art approaches to the topics to be selected jointly by the students and the instructor. In addition to better domain knowledge in computer vision by the end of this course, the students will also develop the skills that are vital to their graduate research, such as writing paper reviews, presenting technical papers, analyzing the strengths and weaknesses of the research papers, and potentially identifying open questions and directions for future research.

Syllabus.pdf

Grading policy

• Reports (writing paper reviews): 20% 

• Paper presentations: 20% 

• Discussion and attendance: 10% 

• Course project: 40% (Here is a list of ideas to choose from project ideas; See also the project page).

• Project presentation: 10% 

Late policy

- Each student will have 5 late days in total for all the reports and projects.

Resources

• • Major Vision Conferences:

  • Computer Vision and Pattern Recognition (CVPR)

  • International Conference in Computer Vision (ICCV)

  • European Conference in Computer Vision (ECCV)

  • Advances in Neural Information Processing Systems (NIPS)

  • International Conference in Learning Representations (ICLR)

  • Major Vision Journals:

  • IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI)

    • International Journal of Computer Vision (IJCV)

    • IEEE Transactions on Image Processing (TIP)

• MATLAB Resources:

  • Introduction to MATLAB

    • MATLAB Tutorial, by Stefan Roth

    • MATLAB Primer, by MathWorks

    • Code Vectorization Guide, by MathWorks

    • Writing Fast MATLAB code, by Pascal Getreuer

    • MATLAB array manipulation tips and tricks, by Peter J. Acklam

    • Matlab Tutorial by L. Lazebnik

    • A more comprehensive Matlab tutorial by David Griffiths

    • Further documentation on Matlab can be found here

  • Python Resources

  • Book for learning python 

  • Coursera

  • https://www.learnpython.org/

  • Linear Algebra:

    • A Geometric Review of Linear Algebra, by Eero Simoncelli

    • An Introduction to Linear Algebra in Parallel Distributed Processing, by M.I. Jordan

    • Resources for scientific writing and talks:

  • Other Stuff:

    • Notes on writing, by Fredo Durand

    • How to write a great research paper, by Simon Peyton Jones (video)

    • Small Guide To Giving Presentations, by Markus PŸschel

    • Giving an effective presentation: Using Powerpoint and structuring a scientific talk, by Susan McConnell (video)

    • Writing papers and giving talks, by Bill Freeman (notes)

    • How to do research, by Bill Freeman

Other vision resources:

My own vision course at UWM: http://pantherfile.uwm.edu/borji/www/UWM-CS790.html

Computer Vision Systems at UCF: https://sites.google.com/site/ucfcap6411/home

Communication:

The course webpage will be updated regularly throughout the semester with lecture notes, programming and reading assignments and important deadlines. 

Collaboration Policy:

Collaboration policy - Homeworks must be done individually. Each student must hand in their own answers. In addition, each student must write their own code in the programming part of the assignment. It is acceptable, however, for students to collaborate in figuring out answers and helping each other solve the problems. We will be assuming that, as participants in a graduate course, you will be taking the responsibility to make sure you personally understand the solution to any work arising from such a collaboration.

© 2017 Ali Borji, University of Central Florida