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.
|
AbstractTexture 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
