Evidence map›Paper›PMID 33915845›Full record

ArticleSensors (Basel, Switzerland)2021

An Improved Method for Stable Feature Points Selection in Structure-from-Motion Considering Image Semantic and Structural Characteristics.

Fei Wang, Zhendong Liu, Hongchun Zhu, Pengda Wu, Chengming Li

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Fei WangCollege of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China.
Zhendong LiuNational Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, Xi'an 710072, China.
Hongchun ZhuCollege of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China.
Pengda WuNational Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, Xi'an 710072, China.
Chengming LiCollege of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China.ORCID 0000-0002-9438-4871

Funding

Basal Research Fund of CASM AR 2008/2009National Key Research and Development Program of China 2018YFB2100702National Natural Science Foundation of China 41871375
6 · The paper itself

Abstract

Feature matching plays a crucial role in the process of 3D reconstruction based on the structure from motion (SfM) technique. For a large collection of oblique images, feature matching is one of the most time-consuming steps, and the matching result directly affects the accuracy of subsequent tasks. Therefore, how to extract the reasonable feature points robustly and efficiently to improve the matching speed and quality has received extensive attention from scholars worldwide. Most studies perform quantitative feature point selection based on image Difference-of-Gaussian (DoG) pyramids in practice. However, the stability and spatial distribution of feature points are not considered enough, resulting in selected feature points that may not adequately reflect the scene structures and cannot guarantee the matching rate and the aerial triangulation accuracy. To address these issues, an improved method for stable feature point selection in SfM considering image semantic and structural characteristics is proposed. First, the visible-band difference vegetation index is used to identify the vegetation areas from oblique images, and the line feature in the image is extracted by the optimized line segment detector algorithm. Second, the feature point two-tuple classification model is established, in which the vegetation area recognition result is used as the semantic constraint, the line feature extraction result is used as the structural constraint, and the feature points are divided into three types. Finally, a progressive selection algorithm for feature points is proposed, in which feature points in the DoG pyramid are selected by classes and levels until the number of feature points is satisfied. Oblique images of a 40-km

Indexed as

3D reconstructionfeature point selectionimage semantic and structural characteristicsoblique imagestwo-tuple classification model

Identifiers

PMID33915845
PMCPMC8036694

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