Dept. of Electr. and Comput. Eng., California Univ., Irvine, CA.
IEEE Trans Image Process. 1997;6(7):1002-13. doi: 10.1109/83.597275.
Spatial filters provide a useful and efficient means of analyzing an input color image into components that capture different spatial properties. Representations based on spatial filtering have restricted usefulness for recognition, however, because the output of a spatial filter across an image depends on the scene illumination conditions. We use a physically accurate linear model for spectral reflectance to derive invariants of distributions in spatially filtered color images that do not depend on the scene illumination. These invariants can be used for the illumination-invariant recognition of regions following an arbitrary linear filtering operation. We describe a method for illumination correction based on color distributions and introduce an illumination change consistency constraint that is useful for verifying matches obtained using the invariants. We show, using a set of classification experiments, that the filtered distribution invariants can significantly improve the capability of a recognition system in environments where illumination cannot be controlled.
空间滤波器为分析输入彩色图像提供了一种有用且高效的方法，可以将其分解为捕获不同空间属性的分量。然而，基于空间滤波的表示对于识别的用处有限，因为空间滤波器在图像上的输出取决于场景照明条件。我们使用光谱反射率的精确线性模型来推导出空间滤波彩色图像中分布的不变量，这些不变量不依赖于场景照明。这些不变量可用于对任意线性滤波操作后的区域进行照明不变识别。我们描述了一种基于颜色分布的照明校正方法，并引入了照明变化一致性约束，该约束对于验证使用不变量获得的匹配很有用。我们使用一组分类实验表明，在无法控制照明的环境中，滤波分布不变量可以显著提高识别系统的能力。
