通过格拉斯曼二次赋值实现鲁棒仿射点匹配

IF 3.9 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pattern Recognition Letters Pub Date : 2024-10-01 DOI:10.1016/j.patrec.2024.09.016

Alexander Kolpakov , Michael Werman

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引用次数: 0

摘要

用格拉斯曼进行鲁棒仿射匹配（RoAM）是一种对点云进行仿射配准的新算法。该算法基于最小化格拉斯曼两个元素之间的弗罗贝尼斯距离。为此，该算法使用了二次赋值问题（QAP）的不定期松弛，并对几种仿射特征匹配方法进行了研究和比较。实验证明，与之前的方法相比，RoAM 对噪声和点差异的鲁棒性更强。

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Robust affine point matching via quadratic assignment on Grassmannians

Robust Affine Matching with Grassmannians (RoAM) is a new algorithm to perform affine registration of point clouds. The algorithm is based on minimizing the Frobenius distance between two elements of the Grassmannian. For this purpose, an indefinite relaxation of the Quadratic Assignment Problem (QAP) is used, and several approaches to affine feature matching are studied and compared. Experiments demonstrate that RoAM is more robust to noise and point discrepancy than previous methods.

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来源期刊

Pattern Recognition Letters 工程技术-计算机：人工智能

CiteScore

12.40

自引率

5.90%

发文量

287

审稿时长

9.1 months

期刊介绍： Pattern Recognition Letters aims at rapid publication of concise articles of a broad interest in pattern recognition. Subject areas include all the current fields of interest represented by the Technical Committees of the International Association of Pattern Recognition, and other developing themes involving learning and recognition.