Shadow Segmentation and Shadow-Free Chromaticity via Markov Random Fields
Shadow Segmentation and Shadow-Free Chromaticity via Markov Random Fields
复制标题
通过马尔可夫随机场进行阴影分割和无阴影色度
DOI：
10.2352/cic.2005.13.1.art00024
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发表时间：
2005
期刊：
影响因子：
--
通讯作者：
M. S. Drew
中科院分区：
文献类型：
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作者：
Cheng Lu;M. S. Drew
我们设计了一种基于光照不变性理论的彩色图像阴影区域提取算法。阴影是由照明的颜色和强度的局部变化引起的。使用色度和强度线索，光源的不连续性的措施，可以被本地识别的阴影边缘。我们使用我们的新措施的马尔可夫随机场模型的问题，找到阴影。一个图切割优化方法，然后应用到MRF找到全局最优分割的阴影图像。在以前的工作中，2-D色度颜色不变的图像恢复从灰度1-D不变的图像，通过添加背光，以匹配明亮的像素的色度。在这里，由于我们分割阴影，我们可以采取一种完全不同的方法，保持非阴影像素不变，同时为阴影像素添加光线，以便与相邻的非阴影像素匹配。结果是更令人信服的无阴影图像，阴影分割是优秀的。
We design an algorithm based on illuminant invariance theory to find shadow regions in a colour image. Shadows are caused by a local change in both the colour and the intensity of illumination. Using both chromaticity and intensity cues, an illuminant discontinuity measure is derived by which shadow edges can be locally identified. We model the problem of finding shadows by a Markov Random Field using our new measure. A graph-cut optimization method is then applied to the MRF to find the globally optimal segmentation of shadows in an image. In previous work, a 2-d chromaticity colour invariant image was recovered from a greyscale 1-d invariant image by adding back light so as to match the chromaticity of bright pixels. Here, since we segment shadows, we can take a completely different approach and leave nonshadow pixels unchanged, while adding light to shadow pixels so as to match neighbouring nonshadow pixels. The results are much more convincing shadow-free images, and shadowsegmentation is excellent.