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6.801/6.866 Machine Vision (U)

Seeing the Future
(3.4 4.4 9.2)

Lecturer: : B. Horn
Lecturer's Rating: 5.9/7.0
Prerequisites: 18.01,18.02,18,03,18.06,6.003,Comfortable with math
Response rate: 20 out of
Difficulty: 5.0/7.0
Overall Rating: 5.8/7.0
Term Evaluated: Fall 2004

Lecturer's Comments:

Understanding the physics of image formation is a prerequisite for developing methods for recovering information about the three-dimensional world from mere two-dimensional projections. Inevitably the physics leads to a mathematical formulation that can then be exploited. The course explores the "physics based" and "inverse graphics" approaches to optical flow, motion vision, photogrammetry, binocular stereo, shape from shading, shape representation, recognition and pose determination. Also covered are methods of applicability in industrial machine vision such as binary image processing, pattern matching, and edge and line finding. Some students find it exciting to apply familiar techniques of applied mathematics to particular practical problems in machine interpretation of sensory data.


An introduction to the field of machine vision, taught by one of the prolific contributors in the field. The class concentrates more on the mathematical foundations and fundamental concepts of low level vision processing and less on implementation of these ideas. The idea is that these fundamental ideas that machine vision is based on will not change, even as technology changes.


  • Prof. Horn and Horn’s lectures
  • Problem sets
  • Take home exams
  • Humorous lectures


  • Grungy Math
  • To much theory
  • Lack of course notes

The majority of students took this class because either they were interested in the subject, needed it for their research, or it fulfilled a requirement. The students felt that it could give them strong background in the area and a building block in such areas as medical imaging, pattern recognition and other advanced topics. Students recommended that anyone interested in machine vision for research should take this class, but said that it is not for those interested in learning about applications of machine vision because it is so theoretical.

Lecturer B. Horn (5.9/7.0, 20 responses) Most students felt that Prof. Horn had a very good presentation style and was very knowledgeable in the area. Many students complained that there was too much to write down in class and that made it hard to understand the lecture. Most of the students agreed that this could be fixed if course notes had been handed out. Students also felt that Prof. Horn has a good teaching style, used the blackboard well and had great humor, the jokes through the lecture really added to the enjoyment. Some students felt that the lectures were to0 quick and that Prof. Horn went into to much details in math proofs instead of giving the big picture.

TA Y. Fang (5.9/7.0, 14 responses) All together students were very happy with Fang. She did not teach a recitation, but had office hours, answered questions about the problem sets and graded the problem sets. They said she was very helpful in office hours and always willing to stay late. Students also said that she replayed to email very quickly and was available when ever they need her. In addition, the students said that she was a fast homework grader and was very generous when giving extensions. Some students felt that she was sometimes unclear when answering emails and didn’t always listen to students question.

On average students spent 11 hours on each problem set. There were five problem sets through out the term. All together students felt that the problem sets were very useful, well designed and helped understand the material. A lot of students did feel that the problem set were too math intensive and difficult. One student even said that “Some problems were too involved in grungy math that you forget what the point is.” In addition, some felt that the problem sets were not clear at time.

There was no lab for this class. Graduate students taking 6.866 did have to complete a final project. The projects were done individualistically and the students could freely choose in what area they wanted to do their final project. Most students felt that projects were fun and that through the projects they got to apply what they learned in class. A few students did feel that the project expectations were too vague.

The readings consisted of the course text book which was written by the professor. Most students found the text relevant and helpful. But many did complain that the text book was a little outdated and tended to be too concise. Most students said that class notes would have been useful.

The grading policy was as follows: 6.801 - Undergraduate section - 50% Problem sets, 50% Take home exams 6.866 - Graduate section – 33% Problem sets, 33% Take home exams, 33% Final Project The two exams were in a take home exam format. Most students liked the take home exam format. They said that the take home exams were slightly longer problem set, but similar in difficulty and very fair. Some students did feel that the exams were difficult and too long.

Very Theoretical, not applied Be ready for alot of math. Go to lecture, essentail.

"Learn all the cool stuff you can do with low level processing"
"Horn really only teaches his contributions to the field and no one elses. The flip side to that is that he's made so many contributions that is is not a bad way to learn"
"The notion that

Dated: March 06, 2005
Eta Kappa Nu, MIT