| Lectures: | Tuesday, Thursday 11 am - 12:30 pm in MCS B33 |
| Instructor: | Prof. Margrit Betke |
| Teaching Fellow: | Danna Gurari | Class web page: |
http://www.cs.bu.edu/faculty/betke/cs585 |
| Class mailing list: |
cascs585a1-l@bu.edu | Contact Information: |
| Staff | Phone | Office Hours | Office | |
| Margrit Betke | betke @ cs.bu.edu | 353-8919 |
Tuesdays 12:30-2:30, Wednesdays 3:30-4:30,
and by appointment |
MCS 286 |
| Danna Gurari | dgurari @ cs.bu.edu | Please email |
Wednesdays 4:30-6:30 and Fridays 3:30-4:30,
and by appointment |
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 St). 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:
Danna is responsible for
helping you out during her office hours and grading the homework. Please
email them if you have questions about your homework grades.
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.
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 csa2.bu.edu or csa3.bu.edu, 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.
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 (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.
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 is the project schedule.
Computer Vision Talks: Students are strongly encouraged to attend the Image and Video Computing talks (Wednesdays 1:30-2:30 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 http://www.bu.edu/computing/ethics/ and http://www.bu.edu/cas/students/undergrad-resources/code
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 |
| 9/6 | Course Introduction: Why study IVC? Image Formation, Image and Video Formats, Color. | Lecture 1 links and Wiki Intro or Horn Ch. 1. | |
| 9/8 | Face Detection. Similarity Functions, Image Pyramids. Motion: Template-based Tracking (Traffic Applications, Camera Mouse). | HCI Lectures, Wiki on template matching and normalized correlation, Betke et al. 2002. | A1 out |
| 9/13-15 | Human Computer Interfaces for People with Disabilities. Binary Image Analysis: Moments, Orientation, circularity measure. Tumor Detection in Computed Tomography Images. | HCI Lectures, Handout or Horn Ch. 3, Ko and Betke, 2001. | 9/15: A1 due. A2 out |
| 9/20-22 | Binary Image Analysis: Circularity measures, distance
measures. ROC analysis. Neighborhoods, Multiple Component Labeling,
Morphology.
Inspection, Virtual Colonoscopy | Lecture Notes, Hu Moments, Hausdorff distance, Fawcett (ROC analysis), Morphology, Erosion, Handouts on Multiple Object Labeling (Horn Ch. 4). | 9/22: A1 graded. See everybody's results. |
| 9/27-29 | Thinning, Swelling, Circuit Board Inspection, Object Skeletons. Segmentation: Thresholding techniques, Region Merging, Splitting, Growing, Region Representations, Medical Image Databases. Image formation: Perspective Projection. | Skeleton, Wang et al. 2005, Petrakis and Faloutsos, 1997, | 9/29: A2 due, A3 out |
| 10/4-6 | Image Smoothing, Edge Detection, Object Recognition, Kernel-based Detection, Feature-based Detection, Nonlinear Optimization, Simulated Annealing. | Wiki on Edge Detection, Betke and Makris, 2001, Betke and Makris, 1995, | 10/6: A2 graded |
| 10/11-13 | Tracking Methods and Applications:
Interactive Graphics.
Last day to drop class (without a 'W' grade), Tuesday, 10/11/2011. |
Freeman et al., Horn papers, handouts of Yacoob's work, facial action units, Shugrina et al. Wiki on SIFT, Censusing Millions of Bats. | 10/13: A3 due. A4 out. Project proposals (P1) out. |
| 10/18-20 | Optical-flow-based Tracking, Horn-and-Schunk Algorithm, Structure from Motion | Horn papers. | A3 graded. |
| 10/25-27 | Facial Feature Tracking, Animal Tracking, Rotation, | Chest Imaging: Computation Models | 10/27: Project proposals (P1) due in class, Project assignment (P2) out. |
| 11/1-3 | Absolute Orientation in 2D. Lung Nodule Registration. | Betke, Hong et al. | A4 due. Project proposal feedback (P1). |
| 11/8-10 |
Stereoscopy. Relative Orientation.
Friday, 11/11/2011: Last day to drop class (with a 'W' grade). |
Epstein and Betke, 2009. | Work on your projects. |
| 11/15-22 | Active Contours | Horn paper, Williams and Shah, 1992 | 11/15: Exam out. 11/22: Exam due. |
| 11/23-27 | Thanksgiving Recess | ||
| 11/29-12/1 | 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. 12/1: Exam graded, A5 canceled. |
| 12/6-8 | Student Projects: Guidelines, Topics, Schedule | 12/6: Projects (P2) due, 12/8: A5 canceled |
Assignments
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:
Exam
The take-home exam is due on Tuesday, November 22, in class. It is not available electronically. Make sure to come to class on Tuesday, November 15, to receive a copy.
Check out http://www.cs.bu.edu/faculty/betke/links.html 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.
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