Texture replacement in real images
Texture replacement in real images
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DOI：
10.1109/cvpr.2001.991009
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发表时间：
2001-12
期刊：
影响因子：
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通讯作者：
Yanghai Tsin;Yanxi Liu;Visvanathan Ramesh
中科院分区：
文献类型：
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作者：
Yanghai Tsin;Yanxi Liu;Visvanathan Ramesh
真实图像中的纹理替换有很多应用，例如室内设计、数字电影制作和计算机图形学。目标是替换图像中的某些指定纹理图案，同时保留光照效果、阴影和遮挡。为了获得令人信服的替换结果，我们必须检测纹理图案并估计给定图像的光照图。本文考虑了接近规则的平面纹理图案。给定一个样本纹理补丁，计算出一个标准图块。候选纹理区域由标准图块和每个图像块之间的互信息确定。具有高互信息分数的区域用于估计允许的照明分布，其由缓存的统计数据表示。空间照明变化约束由马尔可夫随机场模型表示。纹理分割和光照图的最大后验估计以随机退火方式求解，即马尔可夫链蒙特卡罗方法。使用这种统计采样模型可以获得视觉上令人满意的结果。
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 the 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.