Guided Locality Preserving Feature Matching for Remote Sensing Image Registration

Ma, Jiayi; Jiang, Junjun; Zhou, Huabing; Zhao, Ji; Guo, Xiaojie

doi:10.1109/tgrs.2018.2820040

Cited by 266 publications

(106 citation statements)

References 57 publications

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“…Even the best performing image pair shows an inlier ratio of only 18% (see Figure 7(a)), and for 27 out of 42 images (like the one shown in Figure 7(a)) no single correct match can be detected. Typical inlier ratios reported in literature with the same parameter settings are in the order of 75% [25]. The high number of mismatches is attributed to the vast structural differences in image content, and not to a small overlap between images, which is typically a major cause of matching errors [56].…”

Section: Performance Of Standard Local Feature Matchingmentioning

confidence: 90%

Feature-based groupwise registration of historical aerial images to present-day ortho-photo maps

Zambanini

2019

Pattern Recognition

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In this paper, we address the registration of historical WWII images to presentday ortho-photo maps for the purpose of geolocalization. Due to the challenging nature of this problem, we propose to register the images jointly as a group rather than in a step-by-step manner. To this end, we exploit Hough Voting spaces as pairwise registration estimators and show how they can be integrated into a probabilistic groupwise registration framework that can be efficiently optimized. The feature-based nature of our registration framework allows to register images with a-priori unknown translational and rotational relations, and is also able to handle scale changes of up to 30% in our test data due to a final geometrically guided matching step. The superiority of the proposed method over existing pairwise and groupwise registration methods is demonstrated on eight highly challenging sets of historical images with corresponding ortho-photo maps.

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Section: Performance Of Standard Local Feature Matchingmentioning

confidence: 90%

Feature-based groupwise registration of historical aerial images to present-day ortho-photo maps

Zambanini

2019

Pattern Recognition

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“…All face images are aligned in the FEI database. In practical, we can apply the automatic alignment methods and feature points matching methods [61], [62], [63] to preprocess face images. Similar to [27], [64], [31], we obtain the LR images by a filter (4×4 average smoothing) and 4× down-sampling.…”

Section: A Experimental Settingsmentioning

confidence: 99%

Context-Patch Face Hallucination Based on Thresholding Locality-Constrained Representation and Reproducing Learning

Jiang

Tang

et al. 2020

IEEE Trans. Cybern.

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Face hallucination is a technique that reconstruct high-resolution (HR) faces from low-resolution (LR) faces, by using the prior knowledge learned from HR/LR face pairs. Most state-of-the-arts leverage position-patch prior knowledge of human face to estimate the optimal representation coefficients for each image patch. However, they focus only the position information and usually ignore the context information of image patch. In addition, when they are confronted with misalignment or the Small Sample Size (SSS) problem, the hallucination performance is very poor. To this end, this study incorporates the contextual information of image patch and proposes a powerful and efficient context-patch based face hallucination approach, namely Thresholding Locality-constrained Representation and Reproducing learning (TLcR-RL). Under the context-patch based framework, we advance a thresholding based representation method to enhance the reconstruction accuracy and reduce the computational complexity. To further improve the performance of the proposed algorithm, we propose a promotion strategy called reproducing learning. By adding the estimated HR face to the training set, which can simulates the case that the HR version of the input LR face is present in the training set, thus iteratively enhancing the final hallucination result. Experiments demonstrate that the proposed TLcR-RL method achieves a substantial increase in the hallucinated results, both subjectively and objectively. Additionally, the proposed framework is more robust to face misalignment and the SSS problem, and its hallucinated HR face is still very good when the LR test face is from the real-world. The MATLAB source code is available at https://github.com/junjun-jiang/TLcR-RL.

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“…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]. In the literature, there exist various types of local image descriptors [14][15][16][17][18][19] among which SIFT-based local descriptors are particularly popular and have been extensively studied [8,[20][21][22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

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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Guided Locality Preserving Feature Matching for Remote Sensing Image Registration

Cited by 266 publications

References 57 publications

Feature-based groupwise registration of historical aerial images to present-day ortho-photo maps

Feature-based groupwise registration of historical aerial images to present-day ortho-photo maps

Context-Patch Face Hallucination Based on Thresholding Locality-Constrained Representation and Reproducing Learning

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

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