Space object recognition plays an important role in spatial exploitation and surveillance, followed by two main problems： lacking of data and drastic changes in viewpoints. In this article, firstly, we build a three-dimensional （3D） satellites dataset named BUAA Satellite Image Dataset （BUAA-SID 1.0） to supply data for 3D space object research. Then, based on the dataset, we propose to recognize full-viewpoint 3D space objects based on kernel locality preserving projections （KLPP）. To obtain more accurate and separable description of the objects, firstly, we build feature vectors employing moment invariants, Fourier descriptors, region covariance and histogram of oriented gradients. Then, we map the features into kernel space followed by dimensionality reduction using KLPP to obtain the submanifold of the features. At last, k-nearest neighbor （kNN） is used to accomplish the classification. Experimental results show that the proposed approach is more appropriate for space object recognition mainly considering changes of viewpoints. Encouraging recognition rate could be obtained based on images in BUAA-SID 1.0, and the highest recognition result could achieve 95.87%.
提出了一种基于轮廓线统计量的前景分割Markov随机场(Markov random field,MRF)模型,和Grabcut等以往模型不同,本文模型通过在分割标签的编码中加入对轮廓线方向的考虑,将Gestalt知觉组织的原则加入分割约束中去,从而使分割边界更为平滑,作为前景分割和Gestalt知觉组织原则研究的基本框架,本文模型的系统结构分为前景分割、注意力选择和信息整合三个子模块,与相关神经生理研究的结论相一致,最后,分别给出了基于本文模型的自动和半自动前景分割实现,结果好于Grabcut等相关算法的结果。
