The Robotics Institute

Carnegie Mellon Robotics Institute

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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.

Notes

Associated Center(s) / Consortia: Vision and Autonomous Systems Center

Associated Lab(s) / Group(s): Computational Symmetry

Associated Project(s): Near Regular Texture -- Analysis, Synthesis and Manipulation, Texture Replacement in Real Images, A Computational Model for Repeated Pattern Perception using Crystallographic Groups, Image De-fencing

Note: (to appear)

Text Reference

Yanxi Liu, Tamara Belkina, James H. Hays, and Roberto Lublinerman, "Image De-fencing," Proceedings of CVPR 2008, June, 2008.

BibTeX Reference

@inproceedings{Liu_2008_6043,
   author = "Yanxi Liu and Tamara Belkina and James H. Hays and Roberto Lublinerman",
   title = "Image De-fencing",
   booktitle = "Proceedings of CVPR 2008",
   month = "June",
   year = "2008",
   Notes = "(to appear)"
}

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