AI 中文总结
该研究提出一种基于奈曼卡方散度的在线迭代学习方法，用于解决通用成像几何下图像序列间的点对应问题，算法实时运行且性能优于现有方法。
AI 中文摘要
我们提出一种用于迭代学习图像序列间点对应关系的新方法。在3D空间表面上移动的点被投影到两幅图像中，给定任一视图中的一个点，需确定其在另一视图中的对应位置。投影的几何结构与畸变、表面形状均未知。给定若干组点集对且无法访问3D场景时，可通过过度全局优化或假设透视投影模型时通过基础矩阵求解对应映射。然而，针对具有通用成像几何的点集对序列，迭代求解方案更优。我们推导了这样一种方法，该方法基于表示估计位置与实际位置不确定性的密度之间的奈曼卡方散度来优化映射。这些密度由基函数方法计算得到的通道向量表示。每新增一对图像，就更新这些向量间的映射，从而实现快速收敛与高精度。所得算法可实时运行，在多项实验中，其收敛性与精度均优于现有最先进方法。
英文摘要
We propose a novel method for iterative learning of point correspondences between image sequences. Points moving on surfaces in 3D space are projected into two images. Given a point in either view, the considered problem is to determine the corresponding location in the other view. The geometry and distortions of the projections are unknown as is the shape of the surface. Given several pairs of point-sets but no access to the 3D scene, correspondence mappings can be found by excessive global optimization or by the fundamental matrix if a perspective projective model is assumed. However, an iterative solution on sequences of point-set pairs with general imaging geometry is preferable. We derive such a method that optimizes the mapping based on Neyman's chi-square divergence between the densities representing the uncertainties of the estimated and the actual locations. The densities are represented as channel vectors computed with a basis function approach. The mapping between these vectors is updated with each new pair of images such that fast convergence and high accuracy are achieved. The resulting algorithm runs in real-time and is superior to state-of-the-art methods in terms of convergence and accuracy in a number of experiments.
Journal refIEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, E-ISSN 1939-3539, Vol. 35, no 1, p. 118-129
