Computer Vision Courses, Measurement, and Perception

The new semester began at CMU and I'm happy to announce that I'm TAing my advisor's 16-721 Learning Based Methods in Vision this semester. I'm also auditing Martial Hebert's Geometry Based Methods in Vision.

This semester we're trying to encourage students of 16-721 LBMV09 to discuss papers using a course discussion blog. Quicktopic has been used in the past, but this semester we're using Google's Blogger.com for the discussion!

In the first lecture of LBMV, we discussed the problem of Measurement versus Perception in a Computer Vision context. The idea is that while we could build vision systems to measure the external world, it is percepts such as "there is a car on the bottom of the image" and not measurements such as "the bottom of the image is gray" that we are ultimately interested in. However, the line between measurement and perception is somewhat blurry. Consider the following gedanken experiment: place a human in a box and feed him an image and the question "is there a car on the bottom of the image?". Is it legitimate to call this apparatus as a measurement device? If so, then isn't perception a type of measurement? We would still have the problem of building a second version of this measurement device -- different people have different notions of cars and when we start feeding two apparatuses examples of objects that are very close to trucks/buses/vans/cars then would would loss measurement repeatability.

This whole notion of measurement versus perception in computer vision is awfully similar to the theory and observation problem in philosophy of science. Thomas Kuhn would say that the window through which we peer (our scientific paradigm) circumscribes the world we see and thus it is not possible to make theory-independent observations. For a long time I have been a proponent of this post modern view of the world. The big question that remains is: for computer vision to be successful how much consensus must there be between human perception and machine perception? If according to Kuhn Aristotelian and Galilean physicists would have different "observations" of an experiment, then should we expect intelligent machines to see the same world that we see?

6.870 Object Recognition and Scene Understanding

This semester at MIT, Antonio Torralba is teaching 6.870 Object Recognition and Scene Understanding. Topics include object recognition, multiclass categorization, objects in context, internet vision, 3D object models, scene-level analysis, as well as "What happens if we solve object recognition?"

Why should anybody care what courses new faculty are offering, especially if they are being taught at another academic institution? The answer is simple. The new rising stars (a.k.a the new faculty) teach graduate-level courses that reflect ideas which these professors are truly passionate about. Besides the few initial semesters, when new faculty sometimes have to teach introductory level courses, these special topic courses (most often they are grad-level courses) reflect what has been going on in their heads for the past 10 years. Such courses reflect the past decade of research interests (pursued by the new professor) and the material is often presented in such a way that the students will get inspired and have the best opportunity to one day surpass the professor. I'm a big advocate of letting faculty teach their own courses -- of course introductory level undergraduate courses still have to be taught somehow...

A new professor's publication list is a depiction of what kind of research was actually pursued; however, the material comprising a special topic course presents a theme -- a conceptual layout -- which is sometimes a better predictor of where a professor's ideas (and inadvertently the community's) are actually going long-term. If you want to see where Computer Vision is going, just see what new faculty are teaching.

On the course homepage, Antonio Professor Torralba mentions other courses taught at other universities by the new breed of Professors such as Computer Vision by Rob Fergus and Internet Vision by Tamara Berg. Note: I have only listed Antonio's, Rob's, and Tamara's courses since they are Fall2008 offerings -- many other courses exist but are from Fall07 or other semesters.

Thanks to my advisor, Alyosha Efros, for pointing out this new course.

On another note, I'm back at CMU from a Google summer internship where I was working with Thomas Leung on computer vision related problems.