Texture
Replacement In Real Images
Yanghai Tsin, Yanxi Liu
and Visvanathan Ramesh
Appears In IEEE Computer
Vision and Pattern Recognition (CVPR)
2001, Kauai, Hawaii
The paper
is available at the RI publication page.
|
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. |
Example 1
(Click for full size image.
Same for the following)
The Original Image
Texture Replaced Image.
Texture Pattern In the Original
Image
Example 2
The Original Image
Texture Replaced Image.
Texture Pattern In the Original
Image
