Lin W C, Liao F Y, Tsao C K, Lingutla T
Dept. of Electr. Eng. and Comput. Sci., Northwestern Univ., Evanston, IL.
IEEE Trans Neural Netw. 1991;2(1):84-92. doi: 10.1109/72.80293.
A hierarchical approach is proposed for solving the surface and vertex correspondence problems in multiple-view-based 3D object-recognition systems. The proposed scheme is a coarse-to-fine search process, and a Hopfield network is used at each stage. Compared with conventional object-matching schemes, the proposed technique provides a more general and compact formulation of the problem and a solution more suitable for parallel implementation. At the coarse search stage, the surface matching scores between the input image and each object model in the database are computed through a Hopfield network and are used to select the candidates for further consideration. At the fine search stage, the object models selected from the previous stage are fed into another Hopfield network for vertex matching. The object model that has the best surface and vertex correspondences with the input image is finally singled out as the best matched model. Experimental results are reported using both synthetic and real range images to corroborate the proposed theory.
提出了一种分层方法来解决基于多视图的3D物体识别系统中的表面和顶点对应问题。所提出的方案是一个从粗到精的搜索过程，并且在每个阶段都使用霍普菲尔德网络。与传统的物体匹配方案相比，所提出的技术为该问题提供了更通用和紧凑的公式化表述以及更适合并行实现的解决方案。在粗搜索阶段，通过霍普菲尔德网络计算输入图像与数据库中每个物体模型之间的表面匹配分数，并用于选择进一步考虑的候选模型。在精搜索阶段，将从上一阶段选择的物体模型输入到另一个霍普菲尔德网络进行顶点匹配。最终，与输入图像具有最佳表面和顶点对应的物体模型被挑选出来作为最佳匹配模型。报告了使用合成和真实距离图像的实验结果，以证实所提出的理论。
