IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
复制标题
DOI：
10.1109/tc.1978.1674981
复制
发表时间：
2015
期刊：
影响因子：
--
通讯作者：
中科院分区：
文献类型：
--
作者：
基于调和映射的曲面对应关系自动计算是计算机视觉、计算机图形学和计算几何领域的一个研究热点。它可以帮助记录和理解物理和生物现象，并在生物识别，医学成像和运动捕捉感应中具有广泛的应用。虽然调和映射的研究已经有了很多成果，但在一般拓扑曲面上具有界标约束的纯调和映射的计算方面进展有限。本文通过将目标曲面上的黎曼度量变为双曲度量，保证了调和映射在界标约束下是一个双同态，从而克服了这一问题。计算算法基于Ricci流和非线性热扩散方法。该方法具有通用性和鲁棒性。我们采用我们的算法来研究约束表面配准问题，适用于计算机视觉和医学成像应用。实验结果表明，通过改变黎曼度量，配准始终是同构的，并取得了相对较高的性能时，与一些流行的表面配准评价标准。
Automatic computation of surface correspondence via harmonic map is an active research field in computer vision, computer graphics and computational geometry. It may help document and understand physical and biological phenomena and also has broad applications in biometrics, medical imaging and motion capture inducstries. Although numerous studies have been devoted to harmonic map research, limited progress has been made to compute a diffeomorphic harmonic map on general topology surfaces with landmark constraints. This work conquers this problem by changing the Riemannian metric on the target surface to a hyperbolic metric so that the harmonic mapping is guaranteed to be a diffeomorphism under landmark constraints. The computational algorithms are based on Ricci flow and nonlinear heat diffusion methods. The approach is general and robust. We employ our algorithm to study the constrained surface registration problem which applies to both computer vision and medical imaging applications. Experimental results demonstrate that, by changing the Riemannian metric, the registrations are always diffeomorphic and achieve relatively high performance when evaluated with some popular surface registration evaluation standards.