Region-based image registration for remote sensing imagery

Okorie, Azubuike; Makrogiannis, Sokratis

doi:10.1016/j.cviu.2019.102825

Cited by 13 publications

(7 citation statements)

References 26 publications

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“…Furthermore, these detectors still find or identify the local regions; even they have been transformed by viewpoint, illumination, scale, blur, and compression. Traditional methods are designed according to the prior mathematical theory, which is called a handcrafted operator [16,25,33,44,45] and classified into corner, binary corner, and blob. Corner points, defined as the intersecting location of two edge lines, are implemented by gradient computation (Harris) or comparison of pixels within the template [25,26,28,30].…”

Section: Feature Detectionmentioning

confidence: 99%

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A Review of Keypoints’ Detection and Feature Description in Image Registration

Liu

Wang

2021

Scientific Programming

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For image registration, feature detection and description are critical steps that identify the keypoints and describe them for the subsequent matching to estimate the geometric transformation parameters between two images. Recently, there has been a large increase in the research methods of detection operators and description operators, from traditional methods to deep learning methods. To solve the problem, that is, which operator is suitable for specific application problems under different imaging conditions, the paper systematically reviewed commonly used descriptors and detectors from artificial methods to deep learning methods, and the corresponding principle, analysis, and comparative experiments are given as well. We introduce the handcrafted detectors including FAST, BRISK, ORB, SURF, SIFT, and KAZE and the handcrafted descriptors including BRISK, FREAK, BRIEF, SURF, ORB, SIFT, KAZE. At the same time, we review detectors based on deep learning technology including DetNet, TILDE, LIFT, multiscale detector, SuperPoint, and descriptors based on deep learning including pretrained descriptor, Siamese descriptor, LIFT, triplet network, and SuperPoint. Two group of comparison experiments are compared comprehensively and objectively on representative datasets. Finally, we concluded with insightful discussions and conclusions of descriptor and detector selection for specific application problem and hope this survey can be a reference for researchers and engineers in image registration and related fields.

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Section: Feature Detectionmentioning

confidence: 99%

“…e local features used in image registration, include points, lines, contours, and polygons, etc. [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. It is difficult to describe and locate line, contour, and polygon structure, so the keypoint is used as the primary feature in image registration.…”

Section: Introductionmentioning

confidence: 99%

A Review of Keypoints’ Detection and Feature Description in Image Registration

Liu

Wang

2021

Scientific Programming

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“…Keypoint-based registration is a popular research direction in feature-based registration methods [45,46]. Generally, the Harris algorithm, scale invariant feature transform (SIFT) algorithm, and the speeded-up robust features (SURF) algorithm are used to extract keypoints, and most contemporary studies focus on these algorithms for improvement [47][48][49]. Registration methods based on keypoints primarily include keypoint acquisition, keypoint matching, transformation parameter solving, and transformation model optimization [50].…”

Section: A Image Registration Based On Keypointsmentioning

confidence: 99%

Anatomical Point-of-Interest Detection in Head MRI Using Multipoint Feature Descriptor

Chen

et al. 2020

IEEE Access

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Automatic detection of specific anatomical points of interest (POIs) in head magnetic resonance imaging (MRI) is a technical bottleneck in medical image registration and big data analysis for head diseases. A technique for automatically retrieving POIs in head MRI scans is explored in this study. A Haar-like feature of the image is introduced to generate the feature description vector of the POI, and the most appropriate standard vector is selected from multiple marked images. A fixed five-point joint feature description is proposed for solid POI detection, and an adaptive three-point joint feature description is proposed for cavity POI detection. A total of 516 head MRI volumes were used for anatomical point detection. A solid point detection experiment was conducted using the POIs of the right/left internal acoustic pore (RIA/LIA). The POIs of the right/left ascending segment of the internal carotid artery in the posterior cavernous sinus (RAS/LAS) were used in a cavity point detection experiment. The experimental results show that the accuracies of the solid point detection for LIA and RIA are 81.8% and 84.7%, respectively. Those of cavity point detection for LAS and RAS are 66.7% and 76.2%. The performance of the proposed method is better than those of BRIEF and SIFT algorithm. The proposed method can facilitate the marking of anatomical points for doctors, thus providing technical support for head image automatic registration and big data analysis for head diseases.

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“…These methods determine spatial transformation parameters according to the correspondence features. Point features [ 26 ], edge features [ 27 ], and morphological region features [ 28 ] are three dominant features. Since points are easier to extract and describe with a simplified form than the other two features, the point feature becomes the commonly used feature [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

An Infrared-Visible Image Registration Method Based on the Constrained Point Feature

Han

Liu

et al. 2021

Sensors

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It is difficult to find correct correspondences for infrared and visible image registration because of different imaging principles. Traditional registration methods based on the point feature require designing the complicated feature descriptor and eliminate mismatched points, which results in unsatisfactory precision and much calculation time. To tackle these problems, this paper presents an artful method based on constrained point features to align infrared and visible images. The proposed method principally contains three steps. First, constrained point features are extracted by employing an object detection algorithm, which avoids constructing the complex feature descriptor and introduces the senior semantic information to improve the registration accuracy. Then, the left value rule (LV-rule) is designed to match constrained points strictly without the deletion of mismatched and redundant points. Finally, the affine transformation matrix is calculated according to matched point pairs. Moreover, this paper presents an evaluation method to automatically estimate registration accuracy. The proposed method is tested on a public dataset. Among all tested infrared-visible image pairs, registration results demonstrate that the proposed framework outperforms five state-of-the-art registration algorithms in terms of accuracy, speed, and robustness.

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Region-based image registration for remote sensing imagery

Cited by 13 publications

References 26 publications

A Review of Keypoints’ Detection and Feature Description in Image Registration

A Review of Keypoints’ Detection and Feature Description in Image Registration

Anatomical Point-of-Interest Detection in Head MRI Using Multipoint Feature Descriptor

An Infrared-Visible Image Registration Method Based on the Constrained Point Feature

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