Robust feature matching via progressive smoothness consensus

Xia, Yifan; Jiang, Jie; Lu, Yifan; Liu, Wei; Ma, Jiayi

doi:10.1016/j.isprsjprs.2023.01.016

Cited by 8 publications

(3 citation statements)

References 55 publications

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“…Homography matrix estimation tasks are conducted on humanoid-eye binocular images. For these tasks, following Xia’s work [ 35 ], homography error is considered, which adopts RANSAC as the estimator to derive the geometric model. In this case, a corresponding estimated homography is identified as accurate when its homography error is less than three pixels, and the accuracy (Acc.)…”

Section: Experiments and Discussionmentioning

confidence: 99%

Feature Extraction and Matching of Humanoid-Eye Binocular Images Based on SUSAN-SIFT Algorithm

et al. 2023

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Imitating the visual characteristics of human eyes is one of the important tasks of digital image processing and computer vision. Feature correspondence of humanoid-eye binocular images is a prerequisite for obtaining the fused image. Human eyes are more sensitive to edge, because it contains much information. However, existing matching methods usually fail in producing enough edge corresponding pairs for humanoid-eye images because of viewpoint and view direction differences. To this end, we propose a novel and effective feature matching algorithm based on edge points. The proposed method consists of four steps. First, the SUSAN operator is employed to detect features, for its outstanding edge feature extraction capability. Second, the input image is constructed into a multi-scale structure based on image pyramid theory, which is then used to compute simplified SIFT descriptors for all feature points. Third, a novel multi-scale descriptor is constructed, by stitching the simplified SIFT descriptor of each layer. Finally, the similarity of multi-scale descriptors is measured by bidirectional matching, and the obtained preliminary matches are refined by subsequent procedures, to achieve accurate matching results. We respectively conduct qualitative and quantitative experiments, which demonstrate that our method can robustly match feature points in humanoid-eye binocular image pairs, and achieve favorable performance under illumination changes compared to the state-of-the-art.

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Section: Experiments and Discussionmentioning

confidence: 99%

Feature Extraction and Matching of Humanoid-Eye Binocular Images Based on SUSAN-SIFT Algorithm

et al. 2023

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“…For real data, SARBuD (SAR Building Dataset) dataset [43] is tested, and we also collected several satellite images with different deformations, e.g., multiview changes, multiple modes and outliers. Several typical methods are selected to make comparisons, i.e., RANSAC [44], CPD [24], RPM-L2E [45], PR-GLS [28], GCPD [46], DFM [47], LAF [48], and PSC [49]. All the experiments are implemented in MATLAB and Python running on a platform with Intel Core i7 CPU and 16 GB RAM.…”

Section: Methodsmentioning

confidence: 99%

AA-LMM: Robust Accuracy-Aware Linear Mixture Model for Remote Sensing Image Registration

Yang,

Li,

2023

Remote Sensing

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Remote sensing image registration has been widely applied in military and civilian fields, such as target recognition, visual navigation and change detection. The dynamic changes in the sensing environment and sensors bring differences to feature point detection in amount and quality, which is still a common and intractable challenge for feature-based registration approaches. With such multiple perturbations, the extracted feature points representing the same physical location in space may have different location accuracy. Most existing matching methods focus on recovering the optimal feature correspondences while they ignore the diversities of different points in position, which easily brings the model into a bad local extrema, especially when existing with the outliers and noises. In this paper, we present a novel accuracy-aware registration model for remote sensing. A soft weighting is designed for each sample to preferentially select more reliable sample points. To better estimate the transformation between input images, an optimal sparse approximation is applied to approach the transformation by multiple iterations, which effectively reduces the computation complexity and also improves the accuracy of approximation. Experimental results show that the proposed method outperforms the state-of-the-art approaches in both matching accuracy and correct matches.

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“…On the other hand, local transformations provide a more generalized approach and can describe complex geometric relationships between image pairs. These complex relationships can arise due to various factors, including nonrigid transformations [18][19][20][21][22][23][24][25][26], occlusion and repetitive patterns [27][28][29][30][31], and sensor distortions [32][33][34][35][36][37]. Both global and local transformations offer unique advantages in handling image geometry.…”

Section: Introductionmentioning

confidence: 99%

A Survey on the Deep Learning-Based Mismatch Removal: Principles and Methods

Chen,

Deng,

Zhang

et al. 2023

IEEE Access

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Due to the inherent limitations of matching algorithms and the complexities associated with image contents, mismatches are inevitable and can have detrimental effects on downstream tasks in computer vision and remote sensing. Researchers have published numerous reviews on mismatch removal, which may suffer from two primary deficiencies. Firstly, these reviews are often embedded within studies that primarily focus on image matching, thereby limiting the detailed and comprehensive analysis of mismatch removal methods. Secondly, reviews of deep learning (DL)-based methods, despite their numerous existence and interconnection, tend to be fragmentary and lack a systematic approach. To address these two shortcomings, this paper presents a comprehensive survey of DL-based mismatch removal principles and methods. We provide a summary of network architectures, techniques for extracting geometrical information, and various training modes. Specifically, we highlight the importance of permutation invariance in mining operations, enumerate a majority of existing mining methods, and provide an explanation of their permutation invariant properties. Furthermore, we present both the intuitive motivation and mathematical analysis of commonly used methods, elucidating their underlying principles and efficacy. In the conclusion, we predict upcoming trends based on the findings of our review, aiming to provide valuable insights into mismatch removal techniques and guide their practical applications.

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Robust feature matching via progressive smoothness consensus

Cited by 8 publications

References 55 publications

Feature Extraction and Matching of Humanoid-Eye Binocular Images Based on SUSAN-SIFT Algorithm

Feature Extraction and Matching of Humanoid-Eye Binocular Images Based on SUSAN-SIFT Algorithm

AA-LMM: Robust Accuracy-Aware Linear Mixture Model for Remote Sensing Image Registration

A Survey on the Deep Learning-Based Mismatch Removal: Principles and Methods

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