.png){kind=icon}
返回
Geometry consistency aware confidence evaluation for feature matching
.png){kind=icon}
PRE
AI
DOI：10.1016/j.imavis.2020.103984.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Most existing approaches prunewrong matches via estimating an image transformation or solving a graph-based global matching optimization problem, which usually suffers fromvarying local transformations and outliers. Inspired by the insight that neighboring truematches usually hold consistent local topological structures across images, in this paper we propose a new approach to evaluate the confidence of each putative match based on how well its two keypoints can predict each other by exploring the geometric constraintwith its neighboringmatches. With the evaluation, a two- stage approach combining recursively false match pruning and correct match incrementing is presented to obtain the reliable matches. Experiments on various image pairs demonstrate that our approach can conduct robust feature matching in challenging conditions and outperform state-of-theart approaches. (c) 2020 Elsevier B.V. All rights reserved.
Keyword:
Feature matching
Topological structure
Matching confidence
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.2
论文数:
4.1K
被引数:
6.7K
机构
引用论文
.png){kind=icon}
分享收藏Oral Idarubicin as a Single Agent Therapy in Patients with Relapsed or Resistant Multiple Myeloma
PREAI
分享收藏RANDOM SAMPLE CONSENSUS - A PARADIGM FOR MODEL-FITTING WITH APPLICATIONS TO IMAGE-ANALYSIS AND AUTOMATED CARTOGRAPHY随机样本共识-模型拟合的范例，可应用于图像分析和自动制图
OAAI
OAAI分享收藏
查看更多内容