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Carnegie Mellon Robotics Institute

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Abstract

Texture replacement in real images has many applications, such as interior design, digital movie making and computer graphics. The goal is to replace some specified texture patterns in an image while preserving lighting effects, shadows and occlusions. To achieve convincing replacement results we have to detect texture patterns and estimate lighting map from a given image. Near regular planar texture patterns are considered in this paper. Given a sample texture patch, a standard tile is computed. Candidate texture regions are determined by mutual information between the standard tile and each image patch. Regions with high mutual information scores are used to estimate the admissible lighting distributions, which is represented by cached statistics. Spatial lighting change constraints are represented by a Markov random field model. Maximum a posteriori estimation of the texture segmentation and lighting map is solved in a stochastic annealing fashion, namely, the Markov Chain Monte Carlo method. Visually satisfactory result is achieved using this statistical sampling model.

Keywords

texture replacement, texture segmentation, color constancy, cached statistics, mutual information, Markov Random Field, Markov Chain Monte Carlo method

Notes

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

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

Associated Project(s): Texture Replacement in Real Images and A Computational Model for Repeated Pattern Perception using Crystallographic Groups

Number of pages: 6

Text Reference

Yanghai Tsin, Yanxi Liu, and Visvanathan Ramesh, "Texture Replacement in Real Images," IEEE Conference on Computer Vision and Pattern Recognition, December, 2001, pp. 539 = 544.

BibTeX Reference

@inproceedings{Tsin_2001_3814,
   author = "Yanghai Tsin and Yanxi Liu and Visvanathan Ramesh",
   title = "Texture Replacement in Real Images",
   booktitle = "IEEE Conference on Computer Vision and Pattern Recognition",
   pages = "539 = 544",
   month = "December",
   year = "2001",
   volume = "2",
}

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