Graphics enhanced version of this site
Texture Replacement in Real Images
Head: Yanxi Liu
Contact: Yanxi Liu (yanxi@cs.cmu.edu)
Mailing address:
Carnegie Mellon University
Robotics Institute
5000 Forbes Avenue
Pittsburgh, PA 15213
Associated center: VASC
Associated lab/group: Computational Symmetry
For more information, see this project's homepage.
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. 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.
Note: This list may not be comprehensive. It contains only those publications in the RI publications database. Entries are listed in reverse chronological order.