A Novel Correspondence Selection Technique for Affine Rigid Image Registration

Lv, Guohua

doi:10.1109/access.2018.2847399

Cited by 11 publications

(13 citation statements)

References 35 publications

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“…The ground-truths of test image pairs are all known or provided. A maximum of four pixels error is considered when deciding whether a match is correct or not, which is consistent with existing literature [5,8,40].…”

Section: Evaluation Metricssupporting

confidence: 58%

“…Local image features [1] are of vital importance in the field of image processing and have been widely studied in various applications such as object recognition [2], image retrieval [3] and image registration [4][5][6][7][8][9][10][11]. A local image feature [12,13] such as a keypoint or corner is encoded into a local descriptor by representing image information within a local region such as color, gradient and shape [14].…”

Section: Introductionmentioning

confidence: 99%

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Exploring the Utilization of Gradient Information in SIFT Based Local Image Descriptors

Dong¹,

Han²,

Lv³

et al. 2019

Symmetry

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The utilization of gradient information is a key issue in building Scale Invariant Feature Transform (SIFT)-like descriptors. In the literature, two types of gradient information, i.e., Gradient Magnitude (GM) and Gradient Occurrence (GO), are used for building descriptors. However, both of these two types of gradient information have limitations in building and matching local image descriptors. In our prior work, a strategy of combining these two types of gradient information was proposed to intersect the keypoint matches which are obtained by using gradient magnitude and gradient occurrence individually. Different from this combination strategy, this paper explores novel strategies of weighting these two types of gradient information to build new descriptors with high discriminative power. These proposed weighting strategies are extensively evaluated against gradient magnitude and gradient occurrence as well as the combination strategy on a few image registration datasets. From the perspective of building new descriptors, experimental results will show that each of the proposed strategies achieve higher matching accuracy as compared to both GM-based and GO-based descriptors. In terms of recall results, one of the proposed strategies outperforms both GM-based and GO-based descriptors.

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Section: Evaluation Metricssupporting

confidence: 58%

Section: Introductionmentioning

confidence: 99%

Exploring the Utilization of Gradient Information in SIFT Based Local Image Descriptors

Dong¹,

Han²,

Lv³

et al. 2019

Symmetry

Self Cite

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show abstract

“…To align two scans captured in arbitrary positions with partial overlaps, the first thing to do with 3D registration is to estimate the correspondences between the two scans [5], [6], [16]. The correspondences can be estimated based on various…”

Section: Introductionmentioning

confidence: 99%

Global Adaptive 4-Points Congruent Sets Registration for 3D Indoor Scenes With Robust Estimation

Sun

Zhang

et al. 2020

IEEE Access

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As a powerful global registration method for point clouds, the 4-points congruent sets (4PCS) algorithm has been wildly used in the 3D scene reconstruction field. In this paper, we propose an adaptive 4PCS (A4PCS), which aims to provide a robustness rigid transformation for two or more overlapping laser scans. The proposed method only incorporates the distance information of the stereoscopic base set and a fast mechanism for congruent base extraction into 4PCS. To ensure the registration accuracy when dealing with restrictive situations, such as point clouds with small overlaps or scenes with symmetrical structures, a non-coplanar 4-points base set is adopted without extra time consumption. Besides, the adaptive set finetuning is introduced to the point pair searching process to accelerate the convergence of the algorithm. In addition, we replace the binary cost function of the original 4PCS with a modified estimator to strengthen the robustness of the proposed method. Experiments on thirteen pairs of point clouds for 3D indoor scenes, including ten regular size models and three scenes of one large-scale model, can demonstrate the accuracy and efficiency of the proposed method. INDEX TERMS Global registration, 4-points congruent sets (4PCS), rigid transformation, point clouds.

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“…is widely adopted during the second stage, e.g., [9]- [12], to eliminate the outliers from the constructed putative set. The main goal of the correspondence selection is to seek as many true matches (i.e., inliers) as possible from a given putative set while minimizing false matches.…”

Section: Introductionmentioning

confidence: 99%

A GMS-Guided Approach for 2D Feature Correspondence Selection

Qunfang

Jia

et al. 2020

IEEE Access

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Feature correspondence selection, which aims to seek as many true matches (i.e., inliers) as possible from a given putative set while minimizing false matches (i.e., outliers), is crucial to many feature-matching based tasks in computer vision. It remains a challenging problem how to deal with putative sets with low inlier ratios. To address this problem, in this paper, we propose a novel correspondence selection strategy, which is guided by Grid-based Motion Statistics (GMS). We first adopt the GMS to generate a small correspondence set with a high inlier ratio. Then, an accurate geometric model is built using the above correspondence set. Finally, the built geometric model is used to filter the given putative correspondence set to obtain true correspondences. The experimental results on benchmark datasets demonstrate that our proposed approach outperforms the state-of-the-art approaches for putative sets with various inlier ratios, especially for cases with low inlier ratios. INDEX TERMS Correspondence selection, grid-based motion statistics (GMS), geometric model, outlier.

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A Novel Correspondence Selection Technique for Affine Rigid Image Registration

Cited by 11 publications

References 35 publications

Exploring the Utilization of Gradient Information in SIFT Based Local Image Descriptors

Exploring the Utilization of Gradient Information in SIFT Based Local Image Descriptors

Global Adaptive 4-Points Congruent Sets Registration for 3D Indoor Scenes With Robust Estimation

A GMS-Guided Approach for 2D Feature Correspondence Selection

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