Auto-Rectification of User Photos
Venue
Proceedings of International Conference on Image Processing, ICIP, IEEE (2014), pp. 3479-3483
Publication Year
2014
Authors
Krishnendu Chaudhury (aka Krish Chaudhury), Stephen DiVerdi, Sergey Ioffe
BibTeX
Abstract
The image auto rectification project at Google aims to create a pleasanter version
of user photos by correcting the small, involuntary camera rotations (roll / pitch/
yaw) that often occur in non-professional photographs. Our system takes the image
closer to the fronto-parallel view by performing an affine rectification on the
image that restores parallelism of lines that are parallel in the fronto-parallel
image view. This partially corrects perspective distortions, but falls short of
full metric rectification which also restores angles between lines. On the other
hand the 2D homography for our rectification can be computed from only two (as
opposed to three) estimated vanishing points, allowing us to fire upon many more
images. A new RANSAC based approach to vanishing point estimation has been
developed. The main strength of our vanishing point detector is that it is
line-less, thereby avoiding the hard, binary (line/no-line) upstream decisions that
cause traditional algorithm to ignore much supporting evidence and/or admit noisy
evidence for vanishing points. A robust RANSAC based technique for detecting
horizon lines in an image is also proposed for analyzing correctness of the
estimated rectification. We post-multiply our affine rectification homography with
a 2D rotation which aligns the closer vanishing point with the image Y axis.
