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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >SCM: Spatially Coherent Matching With Gaussian Field Learning for Nonrigid Point Set Registration
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SCM: Spatially Coherent Matching With Gaussian Field Learning for Nonrigid Point Set Registration

机译：SCM：与高斯野外学习的空间相干匹配非防护点设置注册

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摘要

While point set registration has been studied in many areas of computer vision for decades, registering points encountering different degradations remains a challenging problem. In this article, we introduce a robust point pattern matching method, termed spatially coherent matching (SCM). The SCM algorithm consists of recovering correspondences and learning nonrigid transformations between the given model and scene point sets while preserving the local neighborhood structure. Precisely, the proposed SCM starts with the initial matches that are contaminated by degradations (e.g., deformation, noise, occlusion, rotation, multiview, and outliers), and the main task is to recover the underlying correspondences and learn the nonrigid transformation alternately. Based on unsupervised manifold learning, the challenging problem of point set registration can be formulated by the Gaussian fields criterion under a local preserving constraint, where the neighborhood structure could be preserved in each transforming. Moreover, the nonrigid transformation is modeled in a reproducing kernel Hilbert space, and we use a kernel approximation strategy to boost efficiency. Experimental results demonstrate that the proposed approach robustly rejecting mismatches and registers complex point set pairs containing large degradations.
机译：在几十年的计算机视觉的许多领域研究了点设置注册，虽然遇到不同的降级的登记点仍然是一个具有挑战性的问题。在本文中，我们介绍了一种强大的点模式匹配方法，称为空间相干匹配（SCM）。 SCM算法包括在保留本地邻域结构的同时恢复给定模型和场景点集之间的对应关系和学习非重力转换。精确地，所提出的SCM以劣化污染的初始匹配开始（例如，变形，噪声，遮挡，旋转，多视图和异常值），并且主要任务是恢复底层的对应关系，并交替地学习非rigid转换。基于无监督的歧管学习，可以通过在局部保留约束下的高斯字段标准来配制点设置登记的具有挑战性的问题，其中可以在每个变换中保留邻域结构。此外，非脂肪变换在再现内核希尔伯特空间中建模，并且我们使用内核近似策略来提高效率。实验结果表明，所提出的方法强大地拒绝不匹配和寄存器复杂点设置对包含大的降级。

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