When dealing with pattern recognition problems one encounters different types of prior knowledge. It is important to incorporate such knowledge into classification method at hand. A very common type of prior knowledge is many data sets are on some kinds of manifolds. Distance based classification methods can make use of this by a modified distance measure called geodesic distance.We introduce a new kind of kernels for support vector machines which incorporate geodesic distance and therefore are applicable in cases such transformation invariance is known. Experiments results show that the performance of our method is comparable to that of other state-of-the-art method.
提出了两种图像生成的方法:(1)由图像分割、三维重建和投影生成构成.该方法考虑了图像中各部分表面发射模型的差异,通过将图像各区域划分成朗伯表面和镜面反射,分别进行三维重建,然后融合两结果,改变光照方向和强度,投影生成新的图像;(2)将Shape from shading和Shape from texture技术融合起来.采用Gabor滤波器将图像中的纹理成分和阴影成分区分开来,再用两种方法各自生成三维立体图像,依据它们的特性在频域融合两个三维图像,然后再改变视角和光照的强度、方向,生成仿真的二维图像.实验表明,由该两种方法恢复出的形状优于传统的估计方法,生成的图像真实感强.
