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Illumination Estimation from a Single Natural Image

CSE 591 - Recognizing People, Objects and Actions                                                                                                                                                          Home Back to Courses

Team: Sagnik Dhar, Debaleena Chattopadhay



Abstract:
'Illumination’ as an independent parameter, sets the context of an image as much as any other parameter would. As pointed out by [1], computer vision researchers have mostly been inclined to designing systems which are illumination invariant. The publication, “Estimating natural illumination from a single outdoor scene”, on the other hand has pointed out that one could use illumination as an important tool to better analyze images. We based our work on this publication and tried to estimate the illumination context of a natural scene from a single image. The paper uses the three most evident cues in a natural scene i.e. the sky, shadows on the ground and the varied intensities of the vertical surfaces to estimate the direction of light. They have pointed out that each of the three cues are not so strong themselves, but the combination of these weak cues give us a reliable estimate of the direction of the sun and the illumination context of the image. We have gone ahead and analyzed their work and tried to implement the idea on our own dataset. During the course of implementation, we have done a few things differently to see how the alternate approaches perform. To validate our work, we devised a simple experiment where we let people label the direction of the sun in the images in our dataset and compared the results to what our algorithm generates. Not only did we get a good overview about the strengths and weaknesses of the approach taken by authors, we also realised that they were trying to solve a problem which is tough even for the human visual system.

(The complete project report)
Mail me if you want to take a look at the code or the dataset we used.

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Sagnik Dhar,
Jan 8, 2010, 9:55 PM
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