CS 583: Introduction to Computer Vision

Spring 2017

[Announcements] [Syllabus] [Lectures] [Assignments]

Monday 6:00-8:50PM@University Crossings 149
Ko Nishino
e-mail: kondrexel.edu
office: University Crossing 100G
phone: (215) 895-2678
office hours: Monday 1:00-2:00pm or by e-mail appointment
Paras Wadekar
office: Cyber Learning Center
office hours: Please check CLC schedule


If you are enrolled in the online section CS583-900 and you do not know where the online presentations are, send the instructor an email. All course materials including lecture slides and notes, and assignments will be posted on BlackboardLearn.



The goal of computer vision is to enable computers see the world. By using a camera as the eye of a computer, studies in computer vision seek to develop better means to capture and extract useful visual information from images and videos and to use such information to automatically interpret the beautiful world surrounding us.

This course provides an introduction to computer vision. The first half of this course will focus on fundamental models and algorithms in computer vision, including such topics as image formation, image sensing, image filtering, edge extraction, brightness and reflectance. In the second half, we will mainly focus on computer vision applications, including various algorithms for reconstructing 3D shape (shape-from-X, stereo, photometric stereo), and recognizing objects in images.


This course aims for students to (1) understand and apply fundamental mathematical and computational techniques in computer vision and (2) implement basic computer vision applications.


Basic (undergraduate-level) understanding of Linear Algebra and Calculus will be necessary. For the assignments, one will need to program in Python (example skeleton codes will be prepared). Assignments will require access to a digital camera. Students are encouraged to purchase one (an inexpensive one will suffice) if he/she does not own one.


The following is a list of topics that will be covered in this course. The timeline is preliminary and will most likely change.
Week 1 4/3 Introduction, Image Formation, Image Sensing
Week 2 4/10 Camera Models, Projective Geometry (Project 1 assigned)
Week 3 4/17 Image Filtering
Week 4 4/24 Image Filtering, Edge Detection (Project 1 due)
Week 5 5/1 Motion, Mosaicing (Project 2 assigned)
Week 6 5/8 Lightness, Radiometry and Reflectance
Week 7 5/15 Photometric Stereo, Shape-from-Shading (Project 2 due)
Week 8 5/22 Stereo, Structure from Motion (Project 3 assigned)
Week 9 5/29 Memorial Day No Class
Week 10 6/5 Recognition (Project 3 due)
Week 11 6/12 Final Exam


Robot Vision, by B.K.P. Horn, MIT Press, 1986. (ISBN: 0262081598)
Most of the lectures will follow this book. Although it is not required, it is highly recommended. You can order this textbook from Drexel Bookstore.
Supplemental readings will be posted in Lectures.

The following is a list of general computer vision text books recommended (but not required) for supplemental reading.

Computer Vision: Algorithms and Applications, by R. Szeliski, Springer, 2011. (ISBN: 9781848829343)
Computer Vision: A Modern Approach, by D.A. Forsyth and J. Ponce, Prentice Hall, 2002. (ISBN: 0130851981)
A Guided Tour of Computer Vision, by V.S. Nalwa, Addison-Wesley, 1993. (ISBN: 0201548534).
Computer Vision: Three-Dimensional Data from Images, by R. Klette, K.Schluns, and A. Koschan, Springer Singapore, 1998. (ISBN: 9813083719)


Students will be assigned 3 multi-week individual projects. These projects will bring all aspects of the learned material at each stage. In each project, graduate students will be required to do additional implementation. The first two of these projects will also be competitions; students will vote for the top 3 artifacts in class. Those who produced the top 3 artifacts will receive extra credits according to their ranks. See Assignments for details.

You must be the sole original author of all assignments and examination solutions in their entirety. As the university's policy explains, penalties up to and including a failing grade for the course with no opportunity to withdraw, will be given for plagiarism, fabrication, or cheating*.
*The standards for originality in a program are similar to those of other written works. Programs by different authors show clear and substantial differences as judged by most criteria, including but not limited to: choice of variable and procedure names, line spacing and indentation, choice of program structure, choice of algorithms, ordering of modules, module design, and ordering and choice of instructions. The original author of an assignment can explain each detail and how they came to create it on their own.


Projects: 84% (28% x 3)
Exams: 16% (final ; closed book)
Assignments turned in up to one day late incur a 50% penalty; assignments turned in more than one day late cannot be accepted and receive a score of 0. Missed exams also receive a score of 0. Make-up exams will only be allowed in extreme circumstances.


The instructor will disseminate important announcements, lecture slides and notes through DrexelLearn. Please makes sure to check your official drexel email.


Attendance for lectures and exams is expected. In the case of a school closing on an exam day, the exam will be given in the next class period. The Drexel snow emergency information number is (215) 895-6358.

Students requesting accommodations due to a disability at Drexel University need to present a current Accommodation Verification Letter (AVL) to faculty before accommodations can be made. AVL are issued by the Office of Disability Resources (ODR). For additional information, visit the ODR website at http://www.drexel.edu/oed/disabilityResources, or contact the Office for more information: 215-895-1401 (V), or disability@drexel.edu.

Academic honesty is essential. Cheating, academic misconduct, plagiarism, and fabrication of any submitted material, including both code and prose, are serious breaches of academic integrity and will be dealt with accordingly. Violations will result minimally in a grade of zero for the exam/assignment in question, an additional reduction of one letter grade in the overall course grade, and a report of the violation to the Drexel administration; further penalties may apply to more serious and/or repeat violations. Please refer to Drexel's official Academic Honesty Policy for more information.


All slides are from previous offering and contents for this offering is subject to change. The descriptions about projects are outdated. The slides are only provided as a guidelines of the planned material covered in each week and to help study ahead of time.
See Drexel Learn for slides from this term. They will be posted the next day of the lecture.
Project assignment dates may also change depending on the progress.

Online students: Class recordings will be posted to the course catalog link sent to you in the beginning of the term as they become available (IRT uploads usually within a few days from the class).
Week Topic Slides Reading Assignment
1 (9/25) Introduction
Image Formation
[ PDF ] Robot Vision Ch. 1 and 2 Self Introduction
2 (10/2) Camera Models
Projective Geometry
[ PDF ] Mundy and Zisserman's Book Appendix (Read 23.1-23.5 and 23.10)
Simoncelli's Linear Algebra Notes Geometric Review and Least Squares
3 (10/9) Image Filtering [ PDF ] Robot Vision Ch. 6 and 7 Project 1 assigned (due 10/22)
4 (10/16) Edge Detection
[ PDF ] Robot Vision Ch. 8 and 12
5 (10/23) Motion cnt'd
[ PDF ] Szeliski's tutorial (especially, sections 2 and 3) Project 2 assigned (due 11/12, cylindrical image due 10/29)
6 (10/30) Mosaicing
Robot Vision Ch. 9  
7 (11/6) Lightness
[ PDF ] Robot Vision Ch. 9  
8 (11/13) Photometric Stereo
[ PDF ] Robot Vision Ch. 10 Project 3 assigned (due 12/3)
9 (11/20) Stereo
[ PDF ] Robot Vision Ch. 11 (and 13)  
10 (11/27) No Class  
11 (12/4) Recognition [ PDF ]    

Acknowledgments: Part of this course is similar to the courses offered at Columbia (Shree Nayar), Univ. of Washington (Steve Seitz) and CMU (Alyosha Efros). The instructor thanks the instructors of these courses for the materials (slides, content) used in this course. In addition, several photographs and illustrations are borrowed from Internet sources. The instructor thanks them all.
Permission to use/modify materials: The instructor gladly gives permission to use and modify any of the slides for academic and research purposes. Since a lot of the material is borrowed from other sources, please acknowledge the original sources too. Finally, since this is a continuously evolving course, all suggestions and corrections (major, minor) are welcome!