- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Color and depth image registration algorithm based on multi-vector-fields constraints
摘要： Image registration, which aim to establish a reliable feature relationship between images, is a critical problem in the field of image processing. In order to enhance the accuracy of color and depth image registration, this paper proposes an novel image registration algorithm based on multi-vector-fields constraints. We first initialize the edge information features of color and depth images, and establish putative correspondences based on edge information. Consider the correlation between the images, establish the functional relationships of the multi-vector-fields constraints based on the relationships. In the reproducing nuclear Hilbert space (RKHS), this constraint is added to the probability model, and the model parameters are optimized using the EM algorithm. Finally, the probability of corresponding edge points of the image is obtained. In order to further improve registration accuracy, this paper will change the input from one pair to two pairs and let the feature transformation relationship between images be iteratively evaluated using the parameter model. Taking publicly available RGB-D images as experimental subjects, results show that for single object image registration, the algorithm image registration accuracy in this paper is improved by about 5% compared with SC, ICP, and CPD algorithms. In addition, artificial noise was used to test the proposed algorithm’s anti-noise ability, results show that the proposed algorithm has superior anti-noise ability relative to SC, ICP and CPD algorithms.
关键词： Multi-vector-fields constraints,Image registration,EM algorithm,Depth image
更新于2025-09-23 15:23:52
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Multi-source Remote Sensing Image Registration Based on Contourlet Transform and Multiple Feature Fusion
摘要： Image registration is an indispensable component in multi-source remote sensing image processing. In this paper, we put forward a remote sensing image registration method by including an improved multi-scale and multi-direction Harris algorithm and a novel compound feature. Multi-scale circle Gaussian combined invariant moments and multi-direction gray level co-occurrence matrix are extracted as features for image matching. The proposed algorithm is evaluated on numerous multi-source remote sensor images with noise and illumination changes. Extensive experimental studies prove that our proposed method is capable of receiving stable and even distribution of key points as well as obtaining robust and accurate correspondence matches. It is a promising scheme in multi-source remote sensing image registration.
关键词： contourlet transform,multi-source remote sensing image registration,multi-direction gray level co-occurrence matrix,multi-scale circle Gaussian combined invariant moment,Feature fusion
更新于2025-09-23 15:23:52
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Remote Sensing Image Registration based on Phase Congruency Feature Detection and Spatial Constraint Matching
摘要： In this paper, a novel remote sensing image registration method based on phase congruency (PC) and spatial constraint is proposed. PC can provide intrinsic and meaningful image features, even when there are complex intensity changes or noise. Image features will be well detected from the corresponding PC images by the SAR-SIFT operator. It means that the feature detection methods in the frequency domain (PC) and the spatial domain (SAR-SIFT operator) are combined. To further improve the result of registration, spatial constraints, including point and line constraint, are established by utilizing the position and orientation information. Then, one to more matches can be removed and the influence of adjacent point can be greatly eliminated. The experimental results demonstrate that our method can obtain a better registration performance with higher accuracy and more correct correspondences than the state-of-the-art methods, such as SIFT, SAR-SIFT, SURF, PSO-SIFT, RIFT, and GLPM.
关键词： remote sensing,spatial constraint,SAR-SIFT operator,image registration,Phase congruency
更新于2025-09-23 15:23:52
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: A Novel Similarity Measure for Matching Local Image Descriptors
摘要： mp-dissimilarity is a recently proposed data-dependence similarity measure. In the literature, how mp-dissimilarity is generally used for matching local image descriptors has been formalized, and three matching strategies have been proposed by incorporating (cid:96)p-norm distance and mp-dissimilarity. Each of these three matching strategies is essentially a two-round matching process that utilizes (cid:96)p-norm distance and mp-dissimilarity individually. This paper presents two novel similarity measures for matching local image descriptors. The first similarity measure normalizes and weights the similarities that are calculated using (cid:96)p-norm distance and mp-dissimilarity, respectively. The second similarity measure involves a novel calculation that takes into account both spatial distance and data distribution between descriptors. The proposed similarity measures are extensively evaluated on a few image registration benchmark data sets. Experimental results will demonstrate that the proposed similarity measures achieve higher matching accuracy and are able to attain better recall results when registering multi-modal images compared with the existing matching strategies that combine (cid:96)p-norm distance and mp-dissimilarity.
关键词： local descriptors,accuracy,mp-dissimilarity,image registration,(cid:96)p-norm distance,Similarity measure
更新于2025-09-23 15:23:52
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Achieving high-resolution thermal imagery in low-contrast lake surface waters by aerial remote sensing and image registration
摘要： A two-platform measurement system for realizing airborne thermography of the Lake Surface Water Temperature (LSWT) with ~0.8 m pixel resolution (sub-pixel satellite scale) is presented. It consists of a tethered Balloon Launched Imaging and Monitoring Platform (BLIMP) that records LSWT images and an autonomously operating catamaran (called ZiviCat) that measures in situ surface/near surface temperatures within the image area, thus permitting simultaneous ground-truthing of the BLIMP data. The BLIMP was equipped with an uncooled InfraRed (IR) camera. The ZiviCat was designed to measure along predefined trajectories on a lake. Since LSWT spatial variability in each image is expected to be low, a poor estimation of the common spatial and temporal noise of the IR camera (nonuniformity and shutter-based drift, respectively) leads to errors in the thermal maps obtained. Nonuniformity was corrected by applying a pixelwise two-point linear correction method based on laboratory experiments. A Probability Density Function (PDF) matching in regions of overlap between sequential images was used for the drift correction. A feature matching-based algorithm, combining blob and region detectors, was implemented to create composite thermal images, and a mean value of the overlapped images at each location was considered as a representative value of that pixel in the final map. The results indicate that a high overlapping field of view (~95%) is essential for image fusion and noise reduction over such low-contrast scenes. The in situ temperatures measured by the ZiviCat were then used for the radiometric calibration. This resulted in the generation of LSWT maps at sub-pixel satellite scale resolution that revealed spatial LSWT variability, organized in narrow streaks hundreds of meters long and coherent patches of different size, with unprecedented detail.
关键词： Lake surface water temperature,Uncooled infrared camera,Image registration,Lake Geneva,Thermal imagery,Aerial remote sensing
更新于2025-09-23 15:23:52
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[IEEE 2018 Digital Image Computing: Techniques and Applications (DICTA) - Canberra, Australia (2018.12.10-2018.12.13)] 2018 Digital Image Computing: Techniques and Applications (DICTA) - Inter-Subject Image Registration of Clinical Neck MRI Volumes using Discrete Periodic Spline Wavelet and Free form Deformation
摘要： This paper presents a framework for inter-patient image registration which uses a multi-thresholds, multi-similarity measures and multi-transformations based on compactly supported spline and discrete periodic spline wavelets (DPSWs) using the Gauss-Newton gradient descent (GNGD) and gradient descent (GD) optimization methods. Our primary intellectual contribution is incorporating DPSWs in the transformation while another includes fusing out-of-range concept in a surface matching technique which is implemented by a multi-transformations and multi-similarity measures. In particular, as a true deformation cannot be achieved by single combination of transformation, similarity measure (SM) and optimization of a registration process, a moving image is required to be brought within the range of a registration. On the other hand, the surface matching technique involves an edge position difference (EPD) SM in which coarse to fine surfaces are matched using multiple thresholds with a spline-based free from deformation (FFD) method. The registration experiments were performed on 3D clinical neck magnetic resonance (MR) images, with the results showing that our proposed method provides good accuracy and robustness.
关键词： Image registration,edge position difference,multi-resolution,inter-subject registration,optimization,discrete periodic spline wavelet
更新于2025-09-23 15:22:29
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[IEEE 2018 10th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC) - Hangzhou (2018.8.25-2018.8.26)] 2018 10th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC) - SAR and Optical Image Registration Method Based on Quantum Particle Swarm Optimization
摘要： Abstract: In the most important step of GIS and optical image fusion, in order to improve the registration accuracy and efficiency of the strategic target, a new image registration method based on quantum particle swarm optimization (QPSO) with independent optical selection is presented. The proposed method consists of three steps: first, it decomposes the optical image into different frequency components with the wavelet transform; second, it extracts the feature corner points with the Harris corner detector; and finally, it constructs the similarity measure by combining the mutual information and the spatial distance, and uses the QPSO algorithm to search the optimal transformation parameters. The experimental results show that the proposed method is effective and feasible, it can achieve high accuracy and robustness for the low-frequency component of the optical image.
关键词： GIS Image,Image Registration,Independent Optical Selection,Quantum Particle Swarm Optimization,Optical Image
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 - Efficient Registration for InSAR Large-Scale Image Using Quadtree Segmentation
摘要： In this paper, an efficient registration algorithm for InSAR large scale image via discrete Fourier transform (DFT) model of the maximum correlation and image quadtree segmentation is proposed. In the scheme, a DFT-based sub-pixel registration model of InSAR complex images is constructed. Then, efficient sub-pixel registration for InSAR large-scale image is achieved by joint quadtree segmentation and DFT-based interpolation registration. Simulation and experimental results are presented to confirm the effectiveness of the proposed algorithm. The results demonstrate that the algorithm not only can achieve sub-pixel registration of InSAR large-scale image, but also has higher computational efficiency compared with the traditional maximum correlation registration method.
关键词： quadtree segmentation,maximum correlation,InSAR,large-scale image,image registration
更新于2025-09-23 15:22:29
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[IEEE 2018 IEEE 6th Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE) - Vilnius, Lithuania (2018.11.8-2018.11.10)] 2018 IEEE 6th Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE) - Deep Neural Network-based Feature Descriptor for Retinal Image Registration
摘要： Feature description is an important step in image registration workflow. Discriminative power of feature descriptors affects feature matching performance and overall results of image registration. Deep Neural Network-based (DNN) feature descriptors are emerging trend in image registration tasks, often performing equally or better than hand-crafted ones. However, there are no learned local feature descriptors, specifically trained for human retinal image registration. In this paper we propose DNN-based feature descriptor that was trained on retinal image patches and compare it to well-known hand-crafted feature descriptors. Training dataset of image patches was compiled from nine online datasets of eye fundus images. Learned feature descriptor was compared to other descriptors using Fundus Image Registration dataset (FIRE), measuring amount of correctly matched ground truth points (Rank-1 metric) after feature description. We compare the performance of various feature descriptors applied for retinal image feature matching.
关键词： artificial neural networks,biomedical imaging,machine learning,image registration,retinal images,feature descriptors
更新于2025-09-23 15:22:29
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A Review of Point Feature Based Medical Image Registration
摘要： Point features, as the basis of lines, surfaces, and bodies, are commonly used in medical image registration. To obtain an elegant spatial transformation of extracted feature points, many point set matching algorithms (PMs) have been developed to match two point sets by optimizing multifarious distance functions. There are ample reviews related to medical image registration and PMs which summarize their basic principles and main algorithms separately. However, to data, detailed summary of PMs used in medical image registration in different clinical environments has not been published. In this paper, we provide a comprehensive review of the existing key techniques of the PMs applied to medical image registration according to the basic principles and clinical applications. As the core technique of the PMs, geometric transformation models are elaborated in this paper, demonstrating the mechanism of point set registration. We also focus on the clinical applications of the PMs and propose a practical classification method according to their applications in different clinical surgeries. The aim of this paper is to provide a summary of point-feature-based methods used in medical image registration and to guide doctors or researchers interested in this field to choose appropriate techniques in their research.
关键词： Assessment,Application,Point set matching,Medical image registration,Optimization
更新于2025-09-23 15:22:29