Wednesday, January 28, 2009

SUNS 2009: Scene Understanding Symposium at MIT

This Friday (January 30, 2009) I will be attending SUNS 2009, otherwise known as the Scene Understanding Symposium, held at MIT and organized by Aude Oliva, Thomas Serre, and Antonio Torralba. It is free, so grad students in the area should definitely go!

Quoting the SUNS 2009 homepage, "SUnS 09 features 16 speakers and about 20 poster presenters from a variety of disciplines (neurophysiology, cognitive neuroscience, visual cognition, computational neuroscience and computer vision) who will address a range of topics related to scene and natural image understanding, attention, eye movements, visual search, and navigation."

I'm looking forward to the talks by researchers such as Aude Oliva, David Forsyth, Alan Yuille, and Ted Adelson. I will try to blog about some cool stuff while I'm there.

Tuesday, January 13, 2009

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 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?