Image De-fencing

Members: Yanxi Liu, James Hays, Lublinerman Roberto, Tamara Belkina

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 foreground/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.