CAS CS 585 Image and Video Computing - Fall 2012

Course Info - Course Objectives - Course Materials - Grading Policy
Collaboration and Academic Integrity - Help - Course Schedule - Assignments - Computer Vision Companies

Lectures: Tuesday, Thursday 11 am - 12:30 pm in MCS B33
Instructor: Prof. Margrit Betke
Teaching Assistant: Diane Theriault
Grader: Mike Breslav
Class web page:
Class mailing list:
Contact Information:

Staff Email Phone Office Hours / Homework Demo Hours Office
Margrit Betke betke @ 353-8919   Tuesdays 2:30-4:00 pm and Thursdays, 12:30-1 pm and 2-3 pm,
and by appointment  
MCS 286  
Diane Theriault deht @ Please email Mondays 12:30-2 pm and Tuesdays 12:30-2 pm,
and by appointment  
Undergrad CS Lab,
730 Commonwealth Ave  
Mike Breslav breslav @ Please email Thursday and Friday afternoon, time slots TBA in class   Undergrad CS Lab,
730 Commonwealth Ave  

Seeing Me in My Office:
Please feel free to stop by my office anytime. My office is in MCS 286 (111 Cummington Mall). I am generally around every day, but often in meetings, so the best time to reach me is during office hours. You can also make an appointment by email. I'm happy to talk with you about the course, computer vision research, your plans for the future, or anything else. Check out my personal web page to get to know me a little.

Teaching Fellow Responsibilities:
Diane is responsible for helping you out during her office hours. Mike is responsible for grading the homework. Please talk to Diane if you have questions about the course material and Mike if you have questions about your homework grades.

Course Objectives

Our goal is to build computer systems that analyze images automatically and determine what the computer "sees" or "recognizes." The course gives you a fundamental introduction to computer vision methods. Applications include human-computer interfaces, face detection, medical image processing, infrared image analysis of animals, and vision systems for intelligent vehicles.

Prerequisites: 1 year programming experience (e.g., C, C++ or Java at CS 112 level), linear algebra or geometric algorithms, and calculus.

Course Materials

Handouts: The updated course syllabus and most handouts are made available online. Check our course web page at least once a week for homework assignments and other information.

Textbook: I recommend Robot Vision by BKP Horn, MIT Press. It is not required. I will propose alternative reading material and I will place the book on reserve in the Science Library.

Computing Environment: You will use one of the Computer Science Department's servers or, to download code and submit programming solutions.

To get an account, go to the Computer Science Department's Undergraduate Lab located at 730 Commonwealth Ave. You can work on various platforms in the lab there, use the cameras, and have immediate access to the computing staff. You can also access the servers remotely using scp and ssh.

Grading Policy

Homework: The homework includes bi-weekly programming, reading assignments, and problem sets. The due dates are listed below. Programs and reports must be submitted electronically. Solutions to problem sets must be submitted in class. They do not need to be typed but should look professional, in particular, write legibly and leave a margin for grading comments.

Guidelines for submission are provided with each assignment. Late solutions will be levied a late penalty of 20% per day (up to three days). After three days, no credit will be given.

You must demo your bi-weekly programming projects and your final class project to the grader within one of the offered time slots in order to receive full credit for your project submission. A signup sheet with available demo times will be circulated during class before the due date.

Your electronic project submission should include detailed instructions for compiling and setting up your program. The grader will recompile your program and test it on your input videos.

Project: Please read the project guidelines. You can propose your own project topic or use one of my project suggestions. I will discuss your project's scope, design, and presentation with you in my office hours and provide guidance throughout the semester. You may work in a group. You will be asked to select a project topic by the middle of the semester and present the final project in class at the end of the semester. Here will be the project schedule.

Computer Vision Talks: Students are strongly encouraged to attend the Image and Video Computing talks (Thursdays 1-2 pm, MCS 148) and the CS Department Colloquia (typically Wednesdays 11-12 or 3-4 pm, MCS 148) on course related topics.

Class Participation: Come to class and participate regularly. Reading the textbook and listening in class will only give you a "passive understanding" of the material. I encourage discussions in class to help you acquire an "active understanding" of the material so that you can evaluate existing computer vision techniques critically and develop your own creative solutions. I may give a short (announced) quiz so that shy students have a chance to discuss a topic in written form.

Take-Home Exam: There will be one take-home exam on the material discussed in the class and practiced with homeworks. To prepare for the exam, come to class, participate in our discussions, and keep up with homework assignments. There will not be a final in-class exam.

Grading Policy: Your final grade will be determined roughly as follows:

Collaboration and Academic Integrity

You are encouraged to collaborate on the solution of the homework. If you do, you must acknowledge your collaborators. Each student must submit his or her own electronic version of the solutions. You can request an exception to this rule for your final project. If you use algorithms or code that are not your own original work and that were not provided in class or discussed in the textbook, you must give a detailed acknowledgment of your source .

You are not allowed to collaborate on the solution of the take-home exam. Sources must be acknowledged.

Cheating and plagiarism are not worthy of Boston University students. I expect you to abide by the rule stated above and the standards of academic honesty and computer ethics policy described in and


Image and Video Computing is an elective course that will introduce you to an exciting topic in computer science. It should be fun and not too much of a struggle for you. Make sure that you have had the prerequisites. Depending on your level of programming experience and/or mathematics background, the course may be challenging for you. If you do not understand the material, ask for help immediately. Ask questions in class. If one student is confused about something, then maybe others are also confused and grateful that someone asked. Come and see me or the TF for help or send us email. Our task is to help you learn a very interesting topic!

You may also ask help from graduate students who are tutors in the undergraduate laboratory. Many of them have expertise in image and video computing. The names of tutors and their hours are listed on the Tutoring Schedule.

Course Schedule

Date Topics Readings Assignments
9/4 Course Introduction: Why study IVC? Image Formation, Image and Video Formats, Color. Lecture 1 links and Wiki Intro or Horn Ch. 1.  
9/6 Representations of object location, image projections, template matching, the flood fill algorithm, background differencing. Skin-based face detection. Image Pyramids. Handout of slides. Wiki on template matching A1 out
9/11-13 Similarity Functions (SSD, NCC), Binary Image Analysis: Moments, Orientation, circularity measure. Motion: Template-based Tracking. Wiki on Normalized correlation, Freeman et al. 9/11: A1 due. 9/14: A1 graded.
See results.
9/18-20 Binary Image Analysis: Circularity measures, distance measures. ROC analysis. Tumor Detection in Computed Tomography Images. Neighborhoods, Multiple Component Labeling. Human Computer Interfaces for People with Disabilities. Inspection, Virtual Colonoscopy
Horn Ch. 3, Fawcett (ROC analysis), Hu Moments, Hausdorff distance, HCI Lecture, Betke et al. 2002. Handouts on Multiple Object Labeling (Horn Ch. 4). A2 out.
9/25-27 Morphology. Thinning and Swelling, Circuit Board Inspection, Object Skeletons. Segmentation: Thresholding techniques, Region Merging, Splitting, and Growing, Region Representations. Horn Ch. 4, Morphology, Erosion, Skeleton, Segmentation. Optional: Wang et al. 2005. 9/27: A2 due. A3 out.
10/2-4 Medical Image Databases. Image Smoothing, Edge Detection, Active Contours Petrakis and Faloutsos, 1997, Wiki on Edge Detection and the Canny Edge Detector. Williams and Shah, 1992, 10/2 A2 graded. See results
10/11 Tracking Methods and Applications: Tracking Groups of Animals
No class on Tuesday (Monday schedule).
Last day to drop class (without a 'W' grade), Tuesday, 10/11/2011.
Censusing Millions of Bats. 10/11: A3 due. Project proposals (P1) out.
10/16-18 Optical-flow-based Tracking, Horn-and-Schunk Algorithm, Structure from Motion Horn papers. 10/16: A3 graded. See results A4 out.
10/23-25 Facial Feature Tracking, Rotation, Chest Imaging: Computation Models 10/23: A4 due. 10/26: A4 graded. See results
10/30, 11/1 Absolute Orientation in 2D. Lung Nodule Registration. Quaternions, Absolute Orientation in 3D. Range Image Registration. Lung Surface Alignment. Ko and Betke, 2001. Betke, Hong et al. 10/30: Project proposals (P1) due in class, Project assignment (P2) out.
11/1: Project proposal feedback (P1).
11/6-8 Stereoscopy. Relative Orientation.
Friday, 11/11/2011: Last day to drop class (with a 'W' grade).
Epstein and Betke, 2009. Work on your projects.
11/13-15, 11/20 Interactive Graphics. Object Recognition, Kernel-based Detection, Feature-based Detection, Nonlinear Optimization, Simulated Annealing. Image formation: Perspective Projection Horn papers, handouts of Yacoob's work, facial action units, Shugrina et al. Wiki on SIFT, Betke and Makris, 2001, Betke and Makris, 1995, 11/13: Exam out. 11/20: Exam due.
11/21-25 Thanksgiving Recess    
11/27-29, 12/4 Lenses, Shading, Lambert's Law, Phong's Model, Photometric Stereo, Shape from Shading. Wiki on Lambert's law, Lambertian reflectance, Phong's model. Horn papers (or Chs. 10, 11, 17). Work on your projects. 11/29: Exam graded. A5 out.
12/6, 12/11 Student Projects: Guidelines, Topics, Schedule   12/6: Projects (P2) due, 12/11: A5


The assignments will have some programming components and some paper-and-pencil exercises. The links will became active when assignment is announced.

There will be two assignments that relate to your projects (P1 and P2). For project ideas, check here.

(Potential) Topics:


The take-home exam is due on Tuesday, November 20, in class. It is not available electronically. Make sure to come to class on Tuesday, November 13, to receive a copy.

Computer Vision Links

Check out if you need additional ideas for your class project, if you are looking for a job in computer vision (list of companies), or if you are interested in computer vision research. You will find a list of links to computer vision conferences, journals, research groups, and companies.

Calculus Background

I do not expect you to have a background multivariate calculus. I will introduce the tools we will need. You may find the first few chapters of these notes by Cain and Herod useful, in particular, partial derivatives, Taylor polynomial, Multivariate Taylor polynomial.

Margrit Betke, Professor
Computer Science Department
Boston University
111 Cummington Mall (campus map)
Boston, MA 02215
Email: betke @
Phone: 617-353-8919
Fax: 617-353-6457

Last updated: August 23, 2012