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Speaker |
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Date |
Nov 1, 2007 |
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Time |
2:30PM to 3:30PM |
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Place |
GRAIL (CSE 291) |
This talk will be a short and informal discussion on ongoing, half-baked
research (with Dan Goldman, and Sujit Kuthirummal and Shree Nayar from
Columbia) that we hope to get feedback on.
A specific camera model will have many particularities that cause it to deviate from the standard pinhole camera model
used in computer vision; these include a non-linear response function, vignetting, radial distortion, chromatic aberration, and
dead pixels. There are a multitude of techniques for calibrating cameras to measure
these phenomena, but they are typically laborious and involve capturing many
"special" images. We are exploring a different approach, that takes
advantage of the fact that there are millions of images online in a database
such as flickr.com that are labeled with their camera model and other details
in their EXIF tags. Given such a dataset, can we learn the particularities of a
camera by examining the statistics of thousands of images taken by it? And if
so, can we calibrate the world's cameras by simply crawling
the web, without requiring any access to the cameras themselves, any user
effort, or any special calibration images? In this discussion I'll report on
our initial efforts to measure camera vignetting, and
mention a so-far un-tested approach to measuring camera response functions.