April 13th, 2012 @ 2:30pm Jon Barron (Berkeley)

Shape, Albedo, and Illumination from Shading

Traditional methods for recovering scene properties such as shape, albedo, or illumination rely on multiple observations of the same scene to over-constrain the problem (structure from motion, photometric stereo, etc). Recovering these scene qualities given just a single image seems almost impossible in comparison — the space of albedos, shapes, and illuminations that exactly reproduce a single image is vast. However, certain worlds are clearly more likely than others: shapes tend to be smooth, albedos tend to be uniform, and illumination tends to be natural.

We therefore pose this problem as one of statistical inference, and define an optimization problem that searches for the most likely explanation of a single image. To this end we present priors on shape and albedo inspired by the models applied to natural images for denoising or deblurring. Our resulting technique can be viewed as a superset of several classic computer vision problems such as shape-from-shading, “intrinsic images”, illumination estimation, and color constancy. Our one unified technique appears to outperform all previous algorithms for solving these constituent tasks, given only a single image.

Bio:

Jon Barron is 4th year PhD candidate at UC Berkeley, supervised by Jitendra Malik. He is currently a visiting student with MIT's vision group, working with Bill Freeman and Ted Adelson. His research concerns intrinsic images, shape reconstruction, and biomedical imaging.

seminars/seminaritems/2012-04-13.txt · Last modified: 2012/03/26 00:50 by silberman