Hurlbert A C, Poggio T A
Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge 02139.
Science. 1988 Jan 29;239(4839):482-5. doi: 10.1126/science.3340834.
A lightness algorithm that separates surface reflectance from illumination in a Mondrian world is synthesized automatically from a set of examples, which consist of pairs of input (intensity signal) and desired output (surface reflectance) images. The algorithm, which resembles a new lightness algorithm recently proposed by Land, is approximately equivalent to filtering the image through a center-surround receptive field in individual chromatic channels. The synthesizing technique, optimal linear estimation, requires only one assumption, that the operator that transforms input into output is linear. This assumption is true for a certain class of early vision algorithms that may therefore be synthesized in a similar way from examples. Other methods of synthesizing algorithms from examples, or "learning," such as back-propagation, do not yield a significantly better lightness algorithm.
一种在蒙德里安世界中将表面反射率与光照分离的明度算法是根据一组示例自动合成的，这些示例由输入（强度信号）和期望输出（表面反射率）图像对组成。该算法类似于兰德最近提出的一种新的明度算法，近似于通过各个颜色通道中的中心-周边感受野对图像进行滤波。合成技术，即最优线性估计，只需要一个假设，即将输入转换为输出的算子是线性的。对于某一类早期视觉算法来说，这个假设是成立的，因此这类算法可能会以类似的方式从示例中合成。其他从示例中合成算法或“学习”的方法，如反向传播，并没有产生明显更好的明度算法。
