- 标题
- 摘要
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- 实验方案
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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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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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Accuracy and reliability evaluation of 3D-LS for the discontinuity orientation identification with different registration/georeferencing modes
摘要： With the aid of three-dimensional laser scanning (3D-LS), a lot of geometric properties of rock discontinuities can be derived from the point cloud data. Due to the complexity of registration and georeferencing of multi-station point data, geological engineers tend to simplify processing by using single-station point data and orienting coarsely. However, there is a lack of accuracy and reliability study in the identification of discontinuity orientations with 3D-LS using different registration/georeferencing modes. In this study, the single-station scanning without registration/georeferencing was applied first to examine the accuracy and reliability of the scanner’s built-in direction system. After that, two types of automated registration/georeferencing modes were examined for the accuracy in rock mass discontinuity analysis. The results show that the dip angle measured by the scanner’s built-in directional system is reliable, accurate and can meet engineering requirements, while the dip direction measured by the scanner’s built-in directional system is unreliable and inaccurate. The dip direction is consistent but inaccurate through the semi-automated registration using natural point features and georeferencing by the scanner’s built-in directional system. Only through real-time kinematic (RTK) registration/georeferencing can the dip direction be reliable and accurate. It is observed that orientations captured by 3D-LS can be more accurate with RTK registration/georeferencing than manual survey.
关键词： orientation,3D laser scanning,rock discontinuities,georeferencing,registration
更新于2025-09-23 15:23:52
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Practical optimal registration of terrestrial LiDAR scan pairs
摘要： Point cloud registration is a fundamental problem in 3D scanning. In this paper, we address the frequent special case of registering terrestrial LiDAR scans (or, more generally, levelled point clouds). Many current solutions still rely on the Iterative Closest Point (ICP) method or other heuristic procedures, which require good initializations to succeed and/or provide no guarantees of success. On the other hand, exact or optimal registration algorithms can compute the best possible solution without requiring initializations; however, they are currently too slow to be practical in realistic applications. Existing optimal approaches ignore the fact that in routine use the relative rotations between scans are constrained to the azimuth, via the built-in level compensation in LiDAR scanners. We propose a novel, optimal and computationally efficient registration method for this 4DOF scenario. Our approach operates on candidate 3D keypoint correspondences, and contains two main steps: (1) a deterministic selection scheme that significantly reduces the candidate correspondence set in a way that is guaranteed to preserve the optimal solution; and (2) a fast branch-and-bound (BnB) algorithm with a novel polynomial-time subroutine for 1D rotation search, that quickly finds the optimal alignment for the reduced set. We demonstrate the practicality of our method on realistic point clouds from multiple LiDAR surveys.
关键词： Branch-and-bound,Exact optimization,Point cloud registration
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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Nonrigid reconstruction of 3D breast surfaces with a low-cost RGBD camera for surgical planning and aesthetic evaluation
摘要： Accounting for 26% of all new cancer cases worldwide, breast cancer remains the most common form of cancer in women. Although early breast cancer has a favourable long-term prognosis, roughly a third of patients suffer from a suboptimal aesthetic outcome despite breast conserving cancer treatment. Clinical-quality 3D modelling of the breast surface therefore assumes an increasingly important role in advancing treatment planning, prediction and evaluation of breast cosmesis. Yet, existing 3D torso scanners are expensive and either infrastructure-heavy or subject to motion artefacts. In this paper we employ a single consumer-grade RGBD camera with an ICP-based registration approach to jointly align all points from a sequence of depth images non-rigidly. Subtle body deformation due to postural sway and respiration is successfully mitigated leading to a higher geometric accuracy through regularised locally affine transformations. We present results from 6 clinical cases where our method compares well with the gold standard and outperforms a previous approach. We show that our method produces better reconstructions qualitatively by visual assessment and quantitatively by consistently obtaining lower landmark error scores and yielding more accurate breast volume estimates.
关键词： Breast cancer treatment,Nonrigid registration,Depth camera,3D surface reconstruction,Aesthetic evaluation
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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[IEEE 2018 24th International Conference on Pattern Recognition (ICPR) - Beijing, China (2018.8.20-2018.8.24)] 2018 24th International Conference on Pattern Recognition (ICPR) - Accurate 3-D Reconstruction with RGB-D Cameras using Depth Map Fusion and Pose Refinement
摘要： Depth map fusion is an essential part in both stereo and RGB-D based 3-D reconstruction pipelines. Whether produced with a passive stereo reconstruction or using an active depth sensor, such as Microsoft Kinect, the depth maps have noise and may have poor initial registration. In this paper, we introduce a method which is capable of handling outliers, and especially, even significant registration errors. The proposed method first fuses a sequence of depth maps into a single non-redundant point cloud so that the redundant points are merged together by giving more weight to more certain measurements. Then, the original depth maps are re-registered to the fused point cloud to refine the original camera extrinsic parameters. The fusion is then performed again with the refined extrinsic parameters. This procedure is repeated until the result is satisfying or no significant changes happen between iterations. The method is robust to outliers and erroneous depth measurements as well as even significant depth map registration errors due to inaccurate initial camera poses.
关键词： point cloud,3-D reconstruction,RGB-D cameras,pose refinement,depth map fusion,registration errors
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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[ACM Press the 10th International Conference - Nanjing, China (2018.08.17-2018.08.19)] Proceedings of the 10th International Conference on Internet Multimedia Computing and Service - ICIMCS '18 - Joint segmentation and registration for 4D lung CT images based on Markov random field
摘要： The study of segmentation and registration of lung volume in medical images has been an active area with the emergence and development of 4D CT (Computed Tomography) medical imaging techniques. Precise image segmentation and registration methods are becoming more and more important in computer-aided diagnosis and treatment. Despite the significant progress which has been made in the medical image segmentation and registration, lung segmentation and registration is still a challenging task. In this paper, a joint segmentation and registration method for 4D lung CT images is proposed, which extends a general simultaneous segmentation and registration framework based on MRF (Markov Random Field) and utilizes the segmentation results of one frame as an atlas for the initialization step. Furthermore, a stochastic sampling approach is introduced for the computation of registration similarity measurement. The proposed method is evaluated on a public lung CT data set and the experimental results show its improved performance compared with the conventional methods.
关键词： Markov Random Field,Registration,4D Image Processing,Lung CT,Segmentation
更新于2025-09-23 15:23:52