Course Objectives

This is an introductory course on computer vision and computational photography. This course will explore image formation, image features, image segmentation, image stitching, image recognition, motion estimation, 3D point clouds and will touch upon basics of augmented reality.

Pre-requisites

Basics of Linear algebra and basics of MATLAB programming. Knowledge of basic signal processing and other programming langauges like python and C/C++ is a bonus.

Reference Books

• Computer Vision: Algorithms and Applications, Springer, Richard Szeliski, 2010.
• Computer vision: A Modern Approach, Prentice Hall, David Forsyth, and Jean Ponce, 2002.
• Digital Image Processing, Prentice Hall, Rafael Gonzalez, and Richard Woods, 2008.
• Feature Extraction & Image Processing for Computer Vision, Academic Press, Mark Nixon, and Alberto Aguado, 2012.
• Multiple view geometry in computer vision, Cambridge university press, Richard Hartley, and Andre Zisserman, 2003.

Other Good Computer Vision Courses and Tutorials

• CIS 581: Computer Vision and Computational Photography at University of Pennsylvania by Prof. Jianbo Shi.
• CS 143: Introduction to Computer Vision at Brown University by Prof. James Hays
• CS 131 Computer Vision: Foundations and Applications at Stanford University by Profs. Fei Fei Li and Juan Carlos Niebles.
• Recognizing and Learning Object Categoriesby Profs. Fei Fei Li from Stanford University, Rob Fergus from New York University and Antonio Torralba from Massachusetts Institute of Technology.
• CAP 5415: Computer Vision at University of Central Florida by Prof. Mubarak Shah.

Course Work Load

This is an advanced undergraduate course inspired by the graduate level and PhD level courses from other universities. This course competes in content with advanced graduate courses. Hence, expect to put in a lot more work into this course as compared to other 400 level courses.

Course Structure

This course is designed to be very hands on and hence will have five projects. The projects have different weight-age due to their difficulty and work-load levels.

All the projects combined will account to 83% of the grade. Project 1 accounts for 10% of the project grade, Projects 2 through 4 are each 20% of the project grade, and project 5 (the final project) is 30% of the project grade. There will be extra credit in all projects.

Homework assignments account for 10% of the grade. They are designed to teach basic concepts not explicitly covered in the projects (but covered in class). There will be between three and five homework assignments. We are currently determining exactly how many, and will update this page shortly.

Class participation is the remaining 7% of the grade. This is determined by active participation on the course Piazza.

Late Policy

There are 3 late days in total for all submissions except the final project. A student can submit an assignment (project/homework) late (after the due date) using the late day without any penalty. Using a partial day for a late day counts as the full day. For example, if the due date is today at 11:59:59 PM, and you submit tomorrow at 7AM or 7PM you have used one of your late days. After the exhaustion of late days each student will have a flat penalty of 20% for the first day and 10% per day after the first day. We have designed the late penalty so that all students keep in pace with the course. If you are using a late day, mention it in the title of your submission as ``USING x NUMBER OF LATE DAY(S)''.

Software

We will be evaluating most of the assignments using an auto-grader. Hence, your code should run on a linux system with the same basic MATLAB packages available to you for this course. If for some reason if you have to use a different toolbox or programming language contact the TAs before doing so.

Collaboration

You are allowed to and encouraged to discuss the concepts regarding first four projects with up-to 2 other students. You MUST write the names of the people with whom you discussed in your report. No collaboration is allowed on the homeworks and the final project. Any discussions on the homeworks must be cited. If you are caught on a collaboration in a homework without appropraite citation such instances will be dealt with very harshly and typically result in a failing course grade.

Academic Accommodations

Any student eligible for and requesting academic accommodations due to a disability is requested to provide, to the instructor in office hours, a letter of accommodation from the Office of Accessibility and Disability Services (ADS) within the first TWO weeks of the semester.

Honor Code

Collaboration is encouraged, but one should know the difference between collaboration and cheating. Cheating is prohibited and will carry serious consequences. Cheating may be defined as using or attempting to use unauthorized assistance, material, or study aids in academic work or examinations. Some examples of cheating are: collaborating on a take-home exam or homework unless explicitly allowed; copying homework; handing in someone else's work as your own; and plagiarism.

You are allowed to and encouraged to discuss the concepts regarding projects with up-to 2 other students. You MUST write the names of the people with whom you discussed in your report. When it comes to formulating or writing solutions you must work alone. No collaboration is allowed on the homeworks and the final project. Any discussions on the homeworks must be cited. You may use free and publicly available sources, such as books, journal and conference publications, and web pages, as research material for your answers. (You will not lose points for using external sources.)

You may not use any service that involves payment, and you must clearly and explicitly cite all outside sources and materials that you made use of. We consider the use of uncited external sources as portraying someone else's work as your own, and as such it is a violation of the University's policies on academic dishonesty. Instances will be dealt with harshly and typically result in a failing course grade.

Unless otherwise specified, you should assume that that the UMD Code of Academic Integrity applies.