Image De-fencing - Robotics Institute Carnegie Mellon University

Image De-fencing

Yanxi Liu, Tamara Belkina, James H. Hays, and Roberto Lublinerman
Conference Paper, Proceedings of (CVPR) Computer Vision and Pattern Recognition, June, 2008

Abstract

We introduce a novel image segmentation algorithm that uses translational symmetry as the primary foreground/background separation cue. We investigate the process of identifying and analyzing image regions that present approximate translational symmetry for the purpose of image fourground/background separation. In conjunction with texture-based inpainting, understanding the different see-through layers allows us to perform powerful image manipulations such as recovering a mesh-occluded background (as much as 53% occluded area) to achieve the effect of image and photo de-fencing. Our algorithm consists of three distinct phases - (1) automatically finding the skeleton structure of a potential frontal layer (fence) in the form of a deformed lattice, (2) separating foreground/background layers using appearance regularity, and (3) occluded foreground inpainting to reveal a complete, non-occluded image. Each of these three tasks presents its own special computational challenges that are not encountered in previous, general image de-layering or texture inpainting applications.

BibTeX

@conference{Liu-2008-9996,
author = {Yanxi Liu and Tamara Belkina and James H. Hays and Roberto Lublinerman},
title = {Image De-fencing},
booktitle = {Proceedings of (CVPR) Computer Vision and Pattern Recognition},
year = {2008},
month = {June},
}