- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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An Extension of Phase Correlation-Based Image Registration to Estimate Similarity Transform Using Multiple Polar Fourier Transform
摘要： Image registration is a core technology of many different image processing areas and is widely used in the remote sensing community. The accuracy of image registration largely determines the effect of subsequent applications. In recent years, phase correlation-based image registration has drawn much attention because of its high accuracy and ef?ciency as well as its robustness to gray difference and even slight changes in content. Many researchers have reported that the phase correlation method can acquire a sub-pixel accuracy of 1/10 or even 1/100. However, its performance is acquired only in the case of translation, which limits the scope of the application of the method. However, there are few reports on the estimation of scales and angles based on the phase correlation method. To take advantage of the high accuracy property and other merits of phase correlation-based image registration and extend it to estimate the similarity transform, we proposed a novel algorithm, the Multilayer Polar Fourier Transform (MPFT), which uses a fast and accurate polar Fourier transform with different scaling factors to calculate the log-polar Fourier transform. The structure of the polar grids of MPFT is more similar to the one of the log-polar grid. In particular, for rotation estimation only, the polar grid of MPFT is the calculation grid. To validate its effectiveness and high accuracy in estimating angles and scales, both qualitative and quantitative experiments were carried out. The quantitative experiments included a numerical simulation as well as synthetic and real data experiments. The experimental results showed that the proposed method, MPFT, performs better than the existing phase correlation-based similarity transform estimation methods, the Pseudo-polar Fourier Transform (PPFT) and the Multilayer Fractional Fourier Transform method (MLFFT), and the classical feature-based registration method, Scale-Invariant Feature Transform (SIFT), and its variant, ms-SIFT.
关键词： similarity transform,image registration,Polar Fourier Transform,phase correlation,Log-polar Fourier Transform
更新于2025-09-23 15:22:29
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[IEEE 2018 IEEE 3rd International Conference on Signal and Image Processing (ICSIP) - Shenzhen, China (2018.7.13-2018.7.15)] 2018 IEEE 3rd International Conference on Signal and Image Processing (ICSIP) - Review of Research on Registration of SAR and Optical Remote Sensing Image Based on Feature
摘要： Synthetic Aperture Radar(SAR) and optical remote sensing image registration is the prerequisite for image fusion and it is of important theoretical significance and practical value. The image registration methods are mainly divided into the methods based on feature, the methods based on Gray-scale and others. This article systematically sorts out feature-based optical and SAR remote sensing image registration techniques, summarizes all types of image registration, points out their advantages and disadvantages and predicts the prospects of their future.
关键词： synthetic aperture radar(SAR),image registration,remote sensing,feature-based
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - A New Registration Algorithm for Multimodal Remote Sensing Images
摘要： Automatic registration of remote sensing images is a challenging problem in the applications of remote sensing. The multimodal remote sensing images have significant nonlinear radiometric differences, which lead to the failure of area-based and feature-based registration methods. In this paper, to overcome significant nonlinear radiometric differences and large scale differences of multimodal remote sensing images, we propose a new registration algorithm, which can meet the need of initial registration of multimodal remote sensing images that conform to similarity transformation model. Our synthetic and real-data experimental results demonstrate the effectiveness and good performance of the proposed method in terms of visualization and registration accuracy.
关键词： multi-scale atlas,phase correlation,Log-Gabor filter,Multimodal remote sensing images,image registration
更新于2025-09-23 15:22:29
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[IEEE 2018 China International SAR Symposium (CISS) - Shanghai (2018.10.10-2018.10.12)] 2018 China International SAR Symposium (CISS) - A Novel Two-Step Registration Method for Multi-Aspect SAR Images
摘要： Synthetic aperture radar (SAR) images of metal targets are highly sensitive to the observation angle, which is unbeneficial for target recognition. To solve this problem, a novel two-step image registration method is proposed in this paper. Firstly, multiple-aspects SAR images are projected into a unified coordinate system to complete the coarse image registration by using GPS information of the flight. Secondly, a fine SAR image registration method named GI-SIFT (Geographic information-scale invariant feature transform) is presented, which combines geographic information and scale-invariant feature transform algorithm. After registration, multi-aspects SAR images are fused to obtain multi-aspect scattering features of the target, thus improve the performance of targets recognition. Furthermore, the experiment results using real data demonstrate the effectiveness and benefits of the proposed method.
关键词： Geographic information,scale invariant feature transform,Multi-aspect SAR,Image registration
更新于2025-09-23 15:22:29
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Metric Learning for Patch-Based 3-D Image Registration
摘要： Patch-based image registration is a challenging problem in visual geometry, the crucial component of which is the selection of an appropriate similarity measure. The similarity measure participates in the objective calculation of the pose optimization, which determines the optimization convergence performance. In this paper, we propose learning a similarity metric of patches from reference and target images such that the pairwise patches with a small projection error receive high similarity scores. To achieve this objective, we designed and trained the classification, regression, and rank networks separately based on self-collected data sets. The network can directly output the projection error according to the patches, which is sensitive to the deviation of the pose transformation. We also designed evaluation criteria and validated the superior performance of the network's outputs compared with the performance of traditional methods, such as the sum of absolute difference and the sum of squared differences.
关键词： neural network,Image registration,pose optimization,metric learning
更新于2025-09-23 15:22:29
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Automatic Registration of INSAT-3D Daily Images Using Mutual Information and Stochastic Optimization Technique
摘要： An automatic image registration approach is presented here can be used to register daily images of Indian geostationary satellite system INSAT-3D acquired every 30 min’ interval without use of any ground control points (GCPs). There is always a pressing need to register meteorological images that are acquired over earth from geostationary platforms every 15–30 min, covering almost one-third of the earth. Weather forecast activities include derivation of atmospheric motion vectors, which demand immediate processing of such images to a reasonable accuracy in terms of its relative location accuracy. Generally followed approaches make use of image navigation models and GCPs drawn from known landmarks in land ocean boundaries and correlate image features before estimating a transform to warp the current acquisition to a known geometry. However, the hierarchical (coarse to ?ne) approach explained here makes use of intensity based Mutual Information as a similarity measure from a population of pixels selected randomly and uses stochastic gradient descent optimizer to estimate an af?ne transform between registering image pair, delivers satisfactory results.
关键词： Mutual information,Stochastic optimization,INSAT-3D,Image registration
更新于2025-09-23 15:21:21
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New Evolutionary-Based Techniques for Image Registration
摘要： The work reported in this paper aims at the development of evolutionary algorithms to register images for signature recognition purposes. We propose and develop several registration methods in order to obtain accurate and fast algorithms. First, we introduce two variants of the firefly method that proved to have excellent accuracy and fair run times. In order to speed up the computation, we propose two variants of Accelerated Particle Swarm Optimization (APSO) method. The resulted algorithms are significantly faster than the firefly-based ones, but the recognition rates are a little bit lower. In order to find a trade-off between the recognition rate and the computational complexity of the algorithms, we developed a hybrid method that combines the ability of auto-adaptive Evolution Strategies (ES) search to discover a global optimum solution with the strong quick convergence ability of APSO. The accuracy and the efficiency of the resulted algorithms have been experimentally proved by conducting a long series of tests on various pairs of signature images. The comparative analysis concerning the quality of the proposed methods together with conclusions and suggestions for further developments are provided in the final part of the paper.
关键词： hybrid techniques,image recognition,image registration,firefly technique,evolutionary computing,affine perturbation,evolution strategies,mutual information
更新于2025-09-19 17:15:36
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Multi-Modal Medical Image Registration with Full or Partial Data: A Manifold Learning Approach
摘要： Multi-modal image registration is the primary step in integrating information stored in two or more images, which are captured using multiple imaging modalities. In addition to intensity variations and structural differences between images, they may have partial or full overlap, which adds an extra hurdle to the success of registration process. In this contribution, we propose a multi-modal to mono-modal transformation method that facilitates direct application of well-founded mono-modal registration methods in order to obtain accurate alignment of multi-modal images in both cases, with complete (full) and incomplete (partial) overlap. The proposed transformation facilitates recovering strong scales, rotations, and translations. We explain the method thoroughly and discuss the choice of parameters. For evaluation purposes, the effectiveness of the proposed method is examined and compared with widely used information theory-based techniques using simulated and clinical human brain images with full data. Using RIRE dataset, mean absolute error of 1.37, 1.00, and 1.41 mm are obtained for registering CT images with PD-, T1-, and T2-MRIs, respectively. In the end, we empirically investigate the efficacy of the proposed transformation in registering multi-modal partially overlapped images.
关键词： partially overlapped images,multi-modality,manifold learning,medical image registration
更新于2025-09-19 17:15:36
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[IEEE 2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) - Auckland, New Zealand (2019.5.20-2019.5.23)] 2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) - A Method for Three-Dimensional Measurements Using Widely Angled Stereoscopic Cameras
摘要： Computer vision technologies have become popular tools for performing non-contact measurements. Stereoscopic systems have been used in several applications for length and geometry measurements. Three-dimensional (3D) reconstruction is an essential part of performing 3D measurements. A variety of methods have been developed for 3D reconstruction in stereoscopic systems. Block matching methods are considered as the most suitable option for 3D measurements, but they require the views to be similar for the cameras of a stereoscopic system. To satisfy this need, the cameras of a stereoscopic system should have small angles between their optical axes on the object’s surface. However, it is not always feasible nor desirable to arrange cameras in this way for some applications. We have proposed a new method to address this restriction. Our method uses an initial transform between the images from two cameras to make the views similar. Points on the transformed images are used as initial estimates of matched points in the two camera views. The points are then matched between the two images using an accurate subpixel image registration algorithm. The new method was tested using an object with known dimensions. The maximum measurement error achieved was 0.05 mm with a standard deviation of 0.09 mm for 10 measurements of a 12 mm length. The high accuracy of this method makes it a suitable option for applications that require reliable 3D measurements.
关键词： subpixel,3D reconstruction,block matching,stereoscopic measurements,wide base line cameras,image registration
更新于2025-09-16 10:30:52
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[IEEE 2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) - Beijing (2018.8.19-2018.8.20)] 2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) - Multi-Modal Remote Sensing Image Registration Based on Multi-Scale Phase Congruency
摘要： Automatic matching of multi-modal remote sensing images remains a challenging task in remote sensing image analysis due to significant non-linear radiometric differences between the phase congruency model with illumination and contrast invariance for image matching, and extends the model to a novel image registration method, named as multi-scale phase consistency (MS-PC). The Euclidean distance between MS-PC descriptors is used as similarity metric to achieve correspondences. The proposed method is evaluated with four pairs of multi-model remote sensing images. The experimental results show that MS-PC is more robust to the radiation differences between images, and performs better than two popular method (i.e. SIFT and SAR-SIFT) in both registration accuracy and tie points number.
关键词： Phase Congruency,Image Registration,Multi-modal Remote Sensing Image,Multi-scale
更新于2025-09-10 09:29:36