USING A COLOR REFLECTION MODEL TO SEPARATE HIGHLIGHTS FROM OBJECT COLOR
USING A COLOR REFLECTION MODEL TO SEPARATE HIGHLIGHTS FROM OBJECT COLOR
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DOI：
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发表时间：
1987
影响因子：
2.1
通讯作者：
G. Klinker;S. Shafer;T. Kanade;Schenley Park
中科院分区：
文献类型：
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作者：
G. Klinker;S. Shafer;T. Kanade;Schenley Park
当前的图像分割方法被诸如高光之类的人为因素所混淆，因为它们不是基于这些现象的任何物理模型。在本文中，我们提出了一种彩色图像理解方法，该方法考虑了由于高光和阴影引起的颜色变化。基于介质材料（如塑料）反射的物理特性，我们证明了物体的每个像素的颜色可以被描述为物体颜色和高光颜色的线性组合。根据该模型，来自一个物体的所有颜色像素在颜色空间中形成一个平面簇，其形状由物体和高光颜色以及物体形状和照明几何形状决定。我们提出了一种利用聚类形状中物体颜色和高光颜色之间的色差，将每个像素的颜色分离为哑光分量和高光分量的方法。这将生成两个固有图像，一个显示没有高光的场景，另一个只显示高光。固有图像可能是计算机视觉中各种不能检测或分析高光的算法的有用工具，例如立体视觉、运动分析、阴影形状和高光形状。我们已经将这种方法应用于实验室环境中的真实图像，我们展示了这些结果，并讨论了精密彩色成像特有的一些实用问题。
Current methods for image segmentation are confused by artifacts such as highlights, because they are not based on any physical model of these phenomena. In this paper, we present an approach to color image understanding that accounts for color variations due to highlights and shading. Based on the physics of reflection by dielectric materials, such as plastic, we show that the color of every pixel from an object can be described as a linear combination of the object color and the highlight color. According to this model, all color pixels from one object form a planar cluster in the color space whose shape is determined by the object and highlight colors and by the object shape and illumination geometry. We present a method which exploits the color difference between object color and highlight color, as exhibited in the cluster shape, to separate the color of every pixel into a matte component and a highlight component. This generates two intrinsic images, one showing the scene without highlights, and the other one showing only the highlights. The intrinsic images may be a useful tool for a variety of algorithms in computer vision that cannot detect or analyze highlights, such as stereo vision, motion analysis, shape from shading, and shape from highlights. We have applied this method to real images in a laboratory environment, and we show these results and discuss some of the pragmatic issues endemic to precision color imaging.