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Journal articles on the topic 'Correspondence image matching'
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1
LEE, CHUNG-MONG, TING-CHUEN PONG, and JAMES R. SLAGLE. "A KNOWLEDGE-BASED SYSTEM FOR THE IMAGE CORRESPONDENCE PROBLEM." International Journal of Pattern Recognition and Artificial Intelligence 04, no. 01 (March 1990): 45–55. http://dx.doi.org/10.1142/s0218001490000046.
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The image correspondence problem has generally been considered the most difficult step in both stereo and temporal vision. Most existing approaches match area features or linear features extracted from an image pair. The approach described in this paper is novel in that it uses an expert system shell to develop an image correspondence knowledge-based system for the general image correspondence problem. The knowledge it uses consists of both physical properties and spatial relationships of the edges and regions in images for every edge or region matching. A computation network is used to represent this knowledge. It allows the computation of the likelihood of matching two edges or regions with logical and heuristic operators. Heuristics for determining the correspondences between image features and the problem of handling missing information will be discussed. The values of the individual matching results are used to direct the traversal and pruning of the global matching process. The problem of parallelizing the entire process will be discussed. Experimental results on real-world images show that all matching edges and regions have been identified correctly.
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Zhao, Zeng-Shun, Xiang Feng, Sheng-Hua Teng, Yi-Bin Li, and Chang-Shui Zhang. "Multiscale Point Correspondence Using Feature Distribution and Frequency Domain Alignment." Mathematical Problems in Engineering 2012 (2012): 1–14. http://dx.doi.org/10.1155/2012/382369.
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In this paper, a hybrid scheme is proposed to find the reliable point-correspondences between two images, which combines the distribution of invariant spatial feature description and frequency domain alignment based on two-stage coarse to fine refinement strategy. Firstly, the source and the target images are both down-sampled by the image pyramid algorithm in a hierarchical multi-scale way. The Fourier-Mellin transform is applied to obtain the transformation parameters at the coarse level between the image pairs; then, the parameters can serve as the initial coarse guess, to guide the following feature matching step at the original scale, where the correspondences are restricted in a search window determined by the deformation between the reference image and the current image; Finally, a novel matching strategy is developed to reject the false matches by validating geometrical relationships between candidate matching points. By doing so, the alignment parameters are refined, which is more accurate and more flexible than a robust fitting technique. This in return can provide a more accurate result for feature correspondence. Experiments on real and synthetic image-pairs show that our approach provides satisfactory feature matching performance.
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Fang, Bin, Kun Yu, Jie Ma, and Pei An. "EMCM: A Novel Binary Edge-Feature-Based Maximum Clique Framework for Multispectral Image Matching." Remote Sensing 11, no. 24 (December 15, 2019): 3026. http://dx.doi.org/10.3390/rs11243026.
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Seeking reliable correspondence between multispectral images is a fundamental and important task in computer vision. To overcome the nonlinearity problem occurring in multispectral image matching, a novel, edge-feature-based maximum clique-matching frame (EMCM) is proposed, which contains three main parts: (1) a novel strong edge binary feature descriptor, (2) a new correspondence-ranking algorithm based on keypoint distinctiveness analysis algorithms in the feature space of the graph, and (3) a false match removal algorithm based on maximum clique searching in the correspondence space of the graph considering both position and angle consistency. Extensive experiments are conducted on two standard multispectral image datasets with respect to the three parts. The feature-matching experiments suggest that the proposed feature descriptor is of high descriptiveness, robustness, and efficiency. The correspondence-ranking experiments validate the superiority of our correspondences-ranking algorithm over the nearest neighbor algorithm, and the coarse registration experiments show the robustness of EMCM with varied interferences.
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Chen, Lin, Rong Sheng Lu, Yan Qiong Shi, and Jian Sheng Tian. "A Differential Evolution Stereo Matching Method in Digital Image Correlation." Key Engineering Materials 625 (August 2014): 297–304. http://dx.doi.org/10.4028/www.scientific.net/kem.625.297.
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Stereo matching is widely used in three-dimensional (3D) reconstruction, stereo machine vision and digital image correlation. The aim of stereo matching process is to solve the well-known correspondence problem, which tries to match points or features from one image with the same points or features in another image from the same 3D scene. There are two basic ways, correlation-based and feature-based, are used to find the correspondences between two images. The correlation-based way is to determine if one location in one image looks/seems like another in another image, and the feature-based way to find if a subset of features in one image is similar in the another image. In stereo matching, a simple algorithm is to compare small patches between two rectified images by correlation search. For the pair images acquired from two cameras inevitably exists some rotation transformation, the algorithm first runs a preprocessing step to rectify the images with the epipolar rectification to simplify the problem of finding matching points between images. The epipolar rectification is to determine a transformation of each image plane such that pairs of conjugate epipolar lines become collinear and parallel to one of the image axes. It will lead the loss of gray information of images. The effect is dependent on the amount of angle. When the angle is big enough, the correlation search may yield error results because of retrograded correlation effect. In order to solve the problem, the paper presents an improved stereo matching algorithm with differential evolution to solve the correspondence problem. Our method doesn’t need to runs the preprocessing step to rectify the images with the epipolar rectification. It uses a differential evolution algorithm to minimize the correlation function which contains the angle information after acquiring the epipoar geometry constraint of two image pairs. Then it utilizes a flood-fill algorithm to search correspondence sub-region in the area around the epipolar line. The flood-fill algorithm can overcome the problem of the traditional row-column scanning search method, which will encounter boundary barrier where exists concave polygons or cavities. The Experimental results show that the proposed method can be easily implemented in stereo matching without loss of information of image features with large rotation angle transformation. In the paper, we will introduce the stereo matching principle and its algorithms, including the differential evolution algorithm for finding the correspondences with large rotation transformation between stereo image pairs and the flood-fill traversal strategy for matching large area with complex concave polygons or cavities. In the end of the paper, some experimental results will be given to illustrate the method effectiveness. Keywords: digital image correlation, stereo matching algorithm, epipolar geometry, flood fill algorithm, differential evolution, rotation angle
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Hödel, M., T. Koch, L. Hoegner, and U. Stilla. "MONOCULAR-DEPTH ASSISTED SEMI-GLOBAL MATCHING." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W7 (September 16, 2019): 55–62. http://dx.doi.org/10.5194/isprs-annals-iv-2-w7-55-2019.
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<p><strong>Abstract.</strong> Reconstruction of dense photogrammetric point clouds is often based on depth estimation of rectified image pairs by means of pixel-wise matching. The main drawback lies in the high computational complexity compared to that of the relatively straightforward task of laser triangulation. Dense image matching needs oriented and rectified images and looks for point correspondences between them. The search for these correspondences is based on two assumptions: pixels and their local neighborhood show a similar radiometry and image scenes are mostly homogeneous, meaning that neighboring points in one image are most likely also neighbors in the second. These rules are violated, however, at depth changes in the scene. Optimization strategies tend to find the best depth estimation based on the resulting disparities in the two images. One new field in neural networks is the estimation of a depth image from a single input image through learning geometric relations in images. These networks are able to find homogeneous areas as well as depth changes, but result in a much lower geometric accuracy of the estimated depth compared to dense matching strategies. In this paper, a method is proposed extending the Semi-Global-Matching algorithm by utilizing a-priori knowledge from a monocular depth estimating neural network to improve the point correspondence search by predicting the disparity range from the single-image depth estimation (SIDE). The method also saves resources through path optimization and parallelization. The algorithm is benchmarked on Middlebury data and results are presented both quantitatively and qualitatively.</p>
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Chen, Peizhi, and Xin Li. "Effective Volumetric Feature Modeling and Coarse Correspondence via Improved 3DSIFT and Spectral Matching." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/378159.
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This paper presents a nonrigid coarse correspondence computation algorithm for volumetric images. Our matching algorithm first extracts then correlates image features based on a revised and improved 3DSIFT (I3DSIFT) algorithm. With a scale-related keypoint reorientation and descriptor construction, this feature correlation is less sensitive to image rotation and scaling. Then, we present an improved spectral matching (ISM) algorithm on correlated features to obtain a one-to-one mapping between corresponded features. One can effectively extend this feature correspondence to dense correspondence between volume images. Our algorithm can benefit nonrigid volumetric image registration in many tasks such as motion modeling in medical image analysis and processing.
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KAMEYAMA, KEISUKE, KAZUO TORAICHI, and YUKIO KOSUGI. "CONSTRUCTIVE RELAXATION MATCHING INVOLVING DYNAMICAL MODEL SWITCHING AND ITS APPLICATION TO SHAPE MATCHING." International Journal of Image and Graphics 02, no. 04 (October 2002): 655–67. http://dx.doi.org/10.1142/s0219467802000822.
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This paper introduces a novel approach for contour-based shape matching named as Constructive Relaxation Matching (CRM). Relaxation Matching (RM) introduced by Rosenfeld et al. is one of the standard method for quasi-optimally solving the local correspondences of the template and input images. RM relies on an energy-minimizing nature of the dynamical system to update the label assignment to the input objects. In image matching relying on a particular modeling method, apparently similar images can be judged as being quite distant, according to the nature of the modeling process and its outcome. In the proposed CRM, the modeling stage of a novel input image contour, conventionally done in the same procedure used for modeling the templates, will be included in the procedure of iterative relaxation matching. The model of the input will be dynamically constructed during relaxation, by unifying the pairs of objects having similar template label assignment probabilities. After describing the CRM procedures, the method is applied to simple shape matching problems demonstrating the ability to adaptively model the input image during relaxation. It is shown that the proposed CRM improves the object-label correspondence for evaluation of the image similarities in the following stages of shape matching applications.
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KUMAR, S. SRINIVAS, and B. N. CHATTERJI. "STEREO MATCHING ALGORITHMS BASED ON FUZZY APPROACH." International Journal of Pattern Recognition and Artificial Intelligence 16, no. 07 (November 2002): 883–99. http://dx.doi.org/10.1142/s0218001402002040.
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Stereo matching is the central problem of stereovision paradigm. Area-based techniques provide the dense disparity maps and hence they are preferred for stereo correspondence. Normalized cross correlation (NCC), sum of squared differences (SSD) and sum of absolute differences (SAD) are the linear correlation measures generally used in the area-based techniques for stereo matching. In this paper, similarity measure for stereo matching based on fuzzy relations is used to establish the correspondence in the presence of intensity variations in stereo images. The strength of relationship of fuzzified data of two windows in the left image and the right image of stereo image pair is determined by considering the appropriate fuzzy aggregation operators. However, these measures fail to establish correspondence of the pixels in the stereo images in the presence of occluded pixels in the corresponding windows. Another stereo matching algorithm based on fuzzy relations of fuzzy data is used for stereo matching in such regions of images. This algorithm is based on weighted normalized cross correlation (WNCC) of the intensity data in the left and the right windows of stereo image pair. The properties of the similarity measures used in these algorithms are also discussed. Experiments with various real stereo images prove the superiority of these algorithms over normalized cross correlation (NCC) under nonideal conditions.
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Phillips, P. J., J. Huang, and S. M. Dunn. "Computational Micrograph Registration with Sieve Processes." Proceedings, annual meeting, Electron Microscopy Society of America 54 (August 11, 1996): 440–41. http://dx.doi.org/10.1017/s0424820100164660.
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In this paper we present an efficient algorithm for automatically finding the correspondence between pairs of stereo micrographs, the key step in forming a stereo image. The computation burden in this problem is solving for the optimal mapping and transformation between the two micrographs. In this paper, we present a sieve algorithm for efficiently estimating the transformation and correspondence.In a sieve algorithm, a sequence of stages gradually reduce the number of transformations and correspondences that need to be examined, i.e., the analogy of sieving through the set of mappings with gradually finer meshes until the answer is found. The set of sieves is derived from an image model, here a planar graph that encodes the spatial organization of the features. In the sieve algorithm, the graph represents the spatial arrangement of objects in the image. The algorithm for finding the correspondence restricts its attention to the graph, with the correspondence being found by a combination of graph matchings, point set matching and geometric invariants.
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Hu, Zhi Ping, and Yuan Jun He. "A New Method on Matching Correspondence Features in Image Warping." Applied Mechanics and Materials 20-23 (January 2010): 1353–58. http://dx.doi.org/10.4028/www.scientific.net/amm.20-23.1353.
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This paper introduces a robust method for automatically matching features in images corresponding to the same physical point on an object captured from two arbitrary viewpoints. Starting from detected line segments in two or more images using phase congruency, the pairs of line segments are characterized by indices that encode the relative positions and orientations of those segments. The encoding indices are invariant with respect to viewpoint changes and the corresponding line segments between the images can be found by them. The feature matching is optimized for image warping where we wish to ignore unreliable matches at the expense of reducing the number of feature matches. This approach can be applied to the image warping to realize its automatism. Results are presented on real images.
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Du, Wen-Liang, Xiao-Yi Li, Ben Ye, and Xiao-Lin Tian. "A Fast Dense Feature-Matching Model for Cross-Track Pushbroom Satellite Imagery." Sensors 18, no. 12 (November 29, 2018): 4182. http://dx.doi.org/10.3390/s18124182.
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Feature-based matching can provide high robust correspondences and it is usually invariant to image scale and rotation. Nevertheless, in remote sensing, the robust feature-matching algorithms often require costly computations for matching dense features extracted from high-resolution satellite images due to that the computational complexity of conventional feature-matching model is O ( N 2 ) . For replacing the conventional feature-matching model, a fast dense (FD) feature-matching model is proposed in this paper. The FD model reduces the computational complexity to linear by splitting the global one-to-one matching into a set of local matchings based on a classic frame-based rectification method. To investigate the possibility of applying the classic frame-based method on cross-track pushbroom images, a feasibility study is given by testing the frame-based method on 2.1 million independent experiments provided by a pushbroom based feature-correspondences simulation platform. Moreover, to improve the stability of the frame-based method, a correspondence-direction-constraint algorithm is proposed for providing the most favourable seed-matches/control-points. The performances of the FD and the conventional models are evaluated on both an automatic feature-matching evaluation platform and real satellite images. The evaluation results show that, for the feature-matching algorithms which have high computational complexity, their running time for matching dense features reduces from hours level to minutes level when they are operated on the FD model. Meanwhile, based the FD method, feature-matching algorithms can achieve comparable matching results as they achieved based on the conventional model.
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Teng, Sheng Hua, and Ning Yang. "A Hybrid Dense Matching Method for Satellite Images." Advanced Materials Research 726-731 (August 2013): 4547–51. http://dx.doi.org/10.4028/www.scientific.net/amr.726-731.4547.
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To solve the dense matching problem for stereoscopic satellite images, a hybrid matching scheme integrating multiple methods is proposed. This scheme utilizes two types of matching element including grid points and edge points. First the geometrically constrained cross-correlation (GC3) method is used to extract matching grids. While in less textured regions using GC3 cannot get sufficient matching grids, so the local affine transformation is used to establish the region correspondence, and more matching grids can be generated. Edges are extracted by Canny operator and approximated with a series of straight edge segments using a polygon approximation. Based on these approximated edges, edge correspondences between image pairs are established using GC3. This scheme fuses the region based and the feature based matching methods. It has been tested with real satellite images and the results demonstrate its accuracy and efficiency.
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Hu, H., and B. Wu. "PRECISION 3D SURFACE RECONSTRUCTION FROM LRO NAC IMAGES USING SEMI-GLOBAL MATCHING WITH COUPLED EPIPOLAR RECTIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W1 (July 25, 2017): 55–61. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w1-55-2017.
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The Narrow-Angle Camera (NAC) on board the Lunar Reconnaissance Orbiter (LRO) comprises of a pair of closely attached high-resolution push-broom sensors, in order to improve the swath coverage. However, the two image sensors do not share the same lenses and cannot be modelled geometrically using a single physical model. Thus, previous works on dense matching of stereo pairs of NAC images would generally create two to four stereo models, each with an irregular and overlapping region of varying size. Semi-Global Matching (SGM) is a well-known dense matching method and has been widely used for image-based 3D surface reconstruction. SGM is a global matching algorithm relying on global inference in a larger context rather than individual pixels to establish stable correspondences. The stereo configuration of LRO NAC images causes severe problem for image matching methods such as SGM, which emphasizes global matching strategy. Aiming at using SGM for image matching of LRO NAC stereo pairs for precision 3D surface reconstruction, this paper presents a coupled epipolar rectification methods for LRO NAC stereo images, which merges the image pair in the disparity space and in this way, only one stereo model will be estimated. For a stereo pair (four) of NAC images, the method starts with the boresight calibration by finding correspondence in the small overlapping stripe between each pair of NAC images and bundle adjustment of the stereo pair, in order to clean the vertical disparities. Then, the dominate direction of the images are estimated by project the center of the coverage area to the reference image and back-projected to the bounding box plane determined by the image orientation parameters iteratively. The dominate direction will determine an affine model, by which the pair of NAC images are warped onto the object space with a given ground resolution and in the meantime, a mask is produced indicating the owner of each pixel. SGM is then used to generate a disparity map for the stereo pair and each correspondence is transformed back to the owner and 3D points are derived through photogrammetric space intersection. Experimental results reveal that the proposed method is able to reduce gaps and inconsistencies caused by the inaccurate boresight offsets between the two NAC cameras and the irregular overlapping regions, and finally generate precise and consistent 3D surface models from the NAC stereo images automatically.
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Elashry, A., B. Sluis, and C. Toth. "IMPROVING RANSAC FEATURE MATCHING BASED ON GEOMETRIC RELATION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2021 (June 28, 2021): 321–27. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2021-321-2021.
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Abstract. Feature Matching between images is an essential task for many computer vision and photogrammetry applications, such as Structure from Motion (SFM), Surface Extraction, Visual Simultaneous Localization and Mapping (VSLAM), and vision-based localization and navigation. Among the matched point pairs, there are typically false positive matches. Therefore, outlier detection and rejection are important steps in any vision application. RANSAC has been a well-established approach for outlier detection. The outlier ratio and the number of required correspondences used in RANSAC determine the number of iterations needed, which ultimately, determines the computation time. We propose a simple algorithm (GR_RANSAC) based on the two-dimensional spatial relationships between points in the image domain. The assumption is that the distances and bearing angles between the 2D feature points should be similar in images with small disparity, such as the case for video image sequences. In the proposed approach, the distances and angles are measured from a reference point in the first image and its correspondence in the other image, and the points with any significant differences are considered as outliers. This process can pre-filter the matched points, and thus increase the inliers’ ratio. As a result, GR_RANSAC can converge to the correct hypothesis in fewer trial runs than ordinary RANSAC.
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Jian, Li, Feng Jiao Zheng, and Yu Rong Ma. "The Study of Laser Scanning Point Cloud Data Automatic and High-Precision Mosaics Technology." Advanced Materials Research 926-930 (May 2014): 3418–21. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.3418.
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This paper has proposed a laser scanning point cloud mosaic scheme on the basis of 2D images matching and 3D correspondence feature points refining. This scheme is aimed to solve existing problems in laser scanning point cloud mosaic technology, such as low efficiency,poor accuracy and low automation. Firstly, the 2D images were generated from the derivative information by an interpolation algorithm. Then 2D correspondence feature points were obtained through GPU acceleration SIFT image matching and eliminates gross errors. In order to improve the accuracy of mosaic,the correspondence feature points must be refined through further constraint. Secondly, the 3D correspondence feature points were acquired based on an inversion algorithm using the 2D correspondence feature points.
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Xia, G., and C. Hu. "AUTOMATIC MATCHING OF LARGE SCALE IMAGES AND TERRESTRIAL LIDAR BASED ON APP SYNERGY OF MOBILE PHONE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 1925–29. http://dx.doi.org/10.5194/isprs-archives-xlii-3-1925-2018.
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The digitalization of Cultural Heritage based on ground laser scanning technology has been widely applied. High-precision scanning and high-resolution photography of cultural relics are the main methods of data acquisition. The reconstruction with the complete point cloud and high-resolution image requires the matching of image and point cloud, the acquisition of the homonym feature points, the data registration, etc. However, the one-to-one correspondence between image and corresponding point cloud depends on inefficient manual search. The effective classify and management of a large number of image and the matching of large image and corresponding point cloud will be the focus of the research. In this paper, we propose automatic matching of large scale images and terrestrial LiDAR based on APP synergy of mobile phone. Firstly, we develop an APP based on Android, take pictures and record related information of classification. Secondly, all the images are automatically grouped with the recorded information. Thirdly, the matching algorithm is used to match the global and local image. According to the one-to-one correspondence between the global image and the point cloud reflection intensity image, the automatic matching of the image and its corresponding laser radar point cloud is realized. Finally, the mapping relationship between global image, local image and intensity image is established according to homonym feature point. So we can establish the data structure of the global image, the local image in the global image, the local image corresponding point cloud, and carry on the visualization management and query of image.
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Chen, Ai Hua, Cheng Hui Gao, and Bing Wei He. "Image Stereo Correspondence Method for Stereo Vision." Applied Mechanics and Materials 475-476 (December 2013): 337–41. http://dx.doi.org/10.4028/www.scientific.net/amm.475-476.337.
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Image stereo correspondence is the core technology of stereo vision. It has been widely studied and applied in the fields such as 3D reconstruction, vision measurement and target recognition. According to characteristics and application of stereo matching technology, the image stereo correspondence methods can be classified into three categories: local stereo correspondence, global stereo correspondence and semi-global stereo correspondence. Some image stereo correspondence solutions and problems are emphatically analyzed. Finally some future research issues on image stereo correspondence are highlighted.
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Deng, Kexin, Jie Tian, Jian Zheng, Xing Zhang, Xiaoqian Dai, and Min Xu. "Retinal Fundus Image Registration via Vascular Structure Graph Matching." International Journal of Biomedical Imaging 2010 (2010): 1–13. http://dx.doi.org/10.1155/2010/906067.
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Motivated by the observation that a retinal fundus image may contain some unique geometric structures within its vascular trees which can be utilized for feature matching, in this paper, we proposed a graph-based registration framework called GM-ICP to align pairwise retinal images. First, the retinal vessels are automatically detected and represented as vascular structure graphs. A graph matching is then performed to find global correspondences between vascular bifurcations. Finally, a revised ICP algorithm incorporating with quadratic transformation model is used at fine level to register vessel shape models. In order to eliminate the incorrect matches from global correspondence set obtained via graph matching, we proposed a structure-based sample consensus (STRUCT-SAC) algorithm. The advantages of our approach are threefold: (1) global optimum solution can be achieved with graph matching; (2) our method is invariant to linear geometric transformations; and (3) heavy local feature descriptors are not required. The effectiveness of our method is demonstrated by the experiments with 48 pairs retinal images collected from clinical patients.
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Yuan, Yuan, Xu, Gong, and Shibasaki. "Dense Image-Matching via Optical Flow Field Estimation and Fast-Guided Filter Refinement." Remote Sensing 11, no. 20 (October 17, 2019): 2410. http://dx.doi.org/10.3390/rs11202410.
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The development of an efficient and robust method for dense image-matching has been a technical challenge due to high variations in illumination and ground features of aerial images of large areas. In this paper, we propose a method for the dense matching of aerial images using an optical flow field and a fast-guided filter. The proposed method utilizes a coarse-to-fine matching strategy for a pixel-wise correspondence search across stereo image pairs. The pyramid Lucas–Kanade (L–K) method is first used to generate a sparse optical flow field within the stereo image pairs, and an adjusted control lattice is then used to derive the multi-level B-spline interpolating function for estimating the dense optical flow field. The dense correspondence is subsequently refined through a combination of a novel cross-region-based voting process and fast guided filtering. The performance of the proposed method was evaluated on three bases, namely, the matching accuracy, the matching success rate, and the matching efficiency. The evaluative experiments were performed using sets of unmanned aerial vehicle (UAV) images and aerial digital mapping camera (DMC) images. The results showed that the proposed method afforded the root mean square error (RMSE) of the reprojection errors better than ±0.5 pixels in image, and a height accuracy within ±2.5 GSD (ground sampling distance) from the ground. The method was further compared with the state-of-the-art commercial software SURE and confirmed to deliver more complete matches for images with poor-texture areas, the matching success rate of the proposed method is higher than 97% while SURE is 96%, and there is 47% higher matching efficiency. This demonstrates the superior applicability of the proposed method to aerial image-based dense matching with poor texture regions.
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Cui, Beibei, and Jean-Charles Créput. "NCC Based Correspondence Problem for First- and Second-Order Graph Matching." Sensors 20, no. 18 (September 8, 2020): 5117. http://dx.doi.org/10.3390/s20185117.
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Automatically finding correspondences between object features in images is of main interest for several applications, as object detection and tracking, identification, registration, and many derived tasks. In this paper, we address feature correspondence within the general framework of graph matching optimization and with the principal aim to contribute. We proposed two optimized algorithms: first-order and second-order for graph matching. On the one hand, a first-order normalized cross-correlation (NCC) based graph matching algorithm using entropy and response through Marr wavelets within the scale-interaction method is proposed. First, we proposed a new automatic feature detection processing by using Marr wavelets within the scale-interaction method. Second, feature extraction is executed under the mesh division strategy and entropy algorithm, accompanied by the assessment of the distribution criterion. Image matching is achieved by the nearest neighbor search with normalized cross-correlation similarity measurement to perform coarse matching on feature points set. As to the matching points filtering part, the Random Sample Consensus Algorithm (RANSAC) removes outliers correspondences. One the other hand, a second-order NCC based graph matching algorithm is presented. This algorithm is an integer quadratic programming (IQP) graph matching problem, which is implemented in Matlab. It allows developing and comparing many algorithms based on a common evaluation platform, sharing input data, and a customizable affinity matrix and matching list of candidate solution pairs as input data. Experimental results demonstrate the improvements of these algorithms concerning matching recall and accuracy compared with other algorithms.
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Bian, Jia-Wang, Wen-Yan Lin, Yun Liu, Le Zhang, Sai-Kit Yeung, Ming-Ming Cheng, and Ian Reid. "GMS: Grid-Based Motion Statistics for Fast, Ultra-robust Feature Correspondence." International Journal of Computer Vision 128, no. 6 (December 17, 2019): 1580–93. http://dx.doi.org/10.1007/s11263-019-01280-3.
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AbstractFeature matching aims at generating correspondences across images, which is widely used in many computer vision tasks. Although considerable progress has been made on feature descriptors and fast matching for initial correspondence hypotheses, selecting good ones from them is still challenging and critical to the overall performance. More importantly, existing methods often take a long computational time, limiting their use in real-time applications. This paper attempts to separate true correspondences from false ones at high speed. We term the proposed method (GMS) grid-based motion Statistics, which incorporates the smoothness constraint into a statistic framework for separation and uses a grid-based implementation for fast calculation. GMS is robust to various challenging image changes, involving in viewpoint, scale, and rotation. It is also fast, e.g., take only 1 or 2 ms in a single CPU thread, even when 50K correspondences are processed. This has important implications for real-time applications. What’s more, we show that incorporating GMS into the classic feature matching and epipolar geometry estimation pipeline can significantly boost the overall performance. Finally, we integrate GMS into the well-known ORB-SLAM system for monocular initialization, resulting in a significant improvement.
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Lu, Nai Guang, Peng Sun, Wen Yi Deng, Lian Qing Zhu, and Xiao Ping Lou. "A Novel Point Matching Method for Stereovision Measurement Using RANSAC Affine Transformation." Key Engineering Materials 437 (May 2010): 184–88. http://dx.doi.org/10.4028/www.scientific.net/kem.437.184.
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A binocular point matching method using affine transformation is presented in this paper to deal with matching ambiguities. The epipolar geometry is used to determine all the possible matching pairs to form an initial correspondence data set. Then, an affine registration model with four parameters that is invariant to scaling, rotation and translation is built using the Random Sample Consensus (RANSAC) method to describe the coordinate transformation between the two members of correspondences. Finally, correspondences are picked out using minimal nearest neighbor distances based on the geometric similarity between the right image points and the transformed left ones. The proposed method is applied to measure the profile of a 3.5m parabolic reflector of an inflatable antenna and proved to be able to handle the extra or missing point matching problems coursed by occlusion and sheltering. Satisfactory results are obtained with high correct rate for matching most image pairs despite the significantly different viewpoints, which indicates its validity and application feasibility in automatic stereovision measurement field.
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Kawamoto, Kazuhiko, Atsushi Imiya, and Kaoru Hirota. "Voting-Based Approach to Nullspace Search for Correspondence Matching and Shape Recovery." Journal of Advanced Computational Intelligence and Intelligent Informatics 10, no. 1 (January 20, 2006): 11–16. http://dx.doi.org/10.20965/jaciii.2006.p0011.
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A simultaneous search, callednullspace search, for matching correspondences among images and recovering 3-D objects is proposed by using a voting-based method to circumvent erroneous recovery of 3-D objects arising from wrong matched correspondences among images. It is able to avoid occlusion problems and cope with remarkable changes in visibility in a long image sequence. An experiment is done with synthetic and real image sequences, consisted of 30 images of a sphere and 10 images of a toy house, under the condition that 3-D points are occluded at most 50% of the sequence and the camera moves with rotational as well as translational motions. The proposed method gives a basis for organizing multiple dynamic images where occlusion occurs frequently.
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Lejhter, S. V., and S. N. Chukanov. "MATCHING OF IMAGES BASED ON THEIR DIFFEOMORPHIC MAPPING." Computer Optics 42, no. 1 (March 30, 2018): 96–104. http://dx.doi.org/10.18287/2412-6179-2018-42-1-96-104.
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We discuss a problem of comparison of two diffeomorphic images, namely, the source and target images of a diffeomorphism, which are given by two sets of points. The problem is solved on the basis of a method of constructing a minimized functional characterizing the evolution of the diffeomorphic source-to-target image transformation and a penalty for the image path deviation from the required trajectory. An algorithm for solving the diffeomorphic transformation equation is developed on the basis of the gradient descent method. The considered problem of comparison of two images can be used for constructing an optimal metamorphism of images, when there is no exact correspondence between the source and target images of the diffeomorphism.
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Ai, Dan, Jing Li Shi, Jun Jun Cao, and Hong Yan Zhong. "Multi-Modal Medical Image Registration of by Combining Improved Mean Shift and RPM." Applied Mechanics and Materials 239-240 (December 2012): 1472–75. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.1472.
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Landmark correspondence plays a decisive role in the landmark-based multi-modality image registration. We combine RPM (Robust Point Matching) and improved Mean Shift to estimate the correspondence of landmarks in images. We improve the target mode and bandwidth used in Mean Shift, and we also perform RPM to estimate the initial landmark correspondence. Next, we use improved Mean Shift to adjust corresponding relations between points. Our method is benefit to make corresponding relations between points more accurate and impels the convergence process of RPM to be related to the image content. Experimental results show that our method can achieve accurate registration of the multi-modal images.
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Shen, Dinggang, Wai-him Wong, and Horace H. S. Ip. "Affine-invariant image retrieval by correspondence matching of shapes." Image and Vision Computing 17, no. 7 (May 1999): 489–99. http://dx.doi.org/10.1016/s0262-8856(98)00141-3.
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Wang, Xin, and Jin Wang. "Model Based Targets Matching from Image Frames." Advanced Materials Research 341-342 (September 2011): 748–52. http://dx.doi.org/10.4028/www.scientific.net/amr.341-342.748.
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We present a new algorithm to locate targets by matching image frames taken from a moving platform. We have noticed that an image point is environment sensitive, but those energy changes of grouped points have their own statistical similarities in two image frames within limited time interval. This approach analyzes correspondence of energy points around every feature points between inter-frames in image sequence in order to decide those feature points. Successful results are given for a vide frames.
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Lin, Yimin, Naiguang Lu, Xiaoping Lou, Fang Zou, Yanbin Yao, and Zhaocai Du. "Matching Cost Filtering for Dense Stereo Correspondence." Mathematical Problems in Engineering 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/654139.
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Dense stereo correspondence enabling reconstruction of depth information in a scene is of great importance in the field of computer vision. Recently, some local solutions based on matching cost filtering with an edge-preserving filter have been proved to be capable of achieving more accuracy than global approaches. Unfortunately, the computational complexity of these algorithms is quadratically related to the window size used to aggregate the matching costs. The recent trend has been to pursue higher accuracy with greater efficiency in execution. Therefore, this paper proposes a new cost-aggregation module to compute the matching responses for all the image pixels at a set of sampling points generated by a hierarchical clustering algorithm. The complexity of this implementation is linear both in the number of image pixels and the number of clusters. Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art local methods in terms of both accuracy and speed. Moreover, performance tests indicate that parameters such as the height of the hierarchical binary tree and the spatial and range standard deviations have a significant influence on time consumption and the accuracy of disparity maps.
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Fu, Y., Y. Ye, G. Liu, B. Zhang, and R. Zhang. "ROBUST MULTIMODAL IMAGE MATCHING BASED ON MAIN STRUCTURE FEATURE REPRESENTATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 583–89. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-583-2020.
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Abstract. Image matching is a crucial procedure for multimodal remote sensing image processing. However, the performance of conventional methods is often degraded in matching multimodal images due to significant nonlinear intensity differences. To address this problem, this letter proposes a novel image feature representation named Main Structure with Histogram of Orientated Phase Congruency (M-HOPC). M-HOPC is able to precisely capture similar structure properties between multimodal images by reinforcing the main structure information for the construction of the phase congruency feature description. Specifically, each pixel of an image is assigned an independent weight for feature descriptor according to the main structure such as large contours and edges. Then M-HOPC is integrated as the similarity measure for correspondence detection by a template matching scheme. Three pairs of multimodal images including optical, LiDAR, and SAR data have been used to evaluate the proposed method. The results show that M-HOPC is robust to nonlinear intensity differences and achieves the superior matching performance compared with other state-of-the-art methods.
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RÉGIS, SÉBASTIEN, ANDREI DONCESCU, GUILLAUME MOREAU, and PHILIPPE FUCHS. "DENSE STEREO CORRESPONDENCE USING QUARTERS OF WAVELET TRANSFORM." International Journal of Pattern Recognition and Artificial Intelligence 19, no. 01 (February 2005): 21–43. http://dx.doi.org/10.1142/s0218001405003892.
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In stereo research the construction of a dense disparity map is a complicated task when the scene contains a lot of occlusions. In this case in the neighborhood of occlusions, we could consider that the images have a non-stationary behavior. In this paper we propose a new method for computing a dense disparity map using the decomposition of the 2D wavelet transform in four quarters allowing us to find a corresponding pixel in the case of occlusion as well. Our algorithm constructs in each pixel of two images four estimators corresponding to each quarter. The matching of the four wavelet coefficient estimators in the right image with the other four in the left image allow us to construct a dense map disparity map in each pixel of an image.
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Li, Jing, and Tao Yang. "Efficient and Robust Feature Matching via Local Descriptor Generalized Hough Transform." Applied Mechanics and Materials 373-375 (August 2013): 536–40. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.536.
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Robust and efficient indistinctive feature matching and outliers removal is an essential problem in many computer vision applications. In this paper we present a simple and fast algorithm named as LDGTH (Local Descriptor Generalized Hough Transform) to handle this problem. The main characteristics of the proposed method include: (1) A novel local descriptor generalized hough transform framework is presented in which the local geometric characteristics of invariant feature descriptors are fused together as a global constraint for feature correspondence verification. (2) Different from standard generalized hough transform, our approach greatly reduces the computational and storage requirements of parameter space through taking advantage of the invariant feature correspondences. (3) The proposed algorithm can be seamlessly embedded into the existing image matching framework, and significantly improve the image matching performance both in speed and robustness in challenge conditions. In the experiment we use both synthetic image data and real world data with high outliers ratio and severe changes in view point, scale, illumination, image blur, compression and noises to evaluate the proposed method, and the results demonstrate that our approach achieves achieves faster and better matching performance compared to the traditional algorithms.
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Lu, Gang, J. P. Kang, and Z. N. Zhai. "A Image Registration Algorithm Based on Wavelet Pyramid for CCD Image." Applied Mechanics and Materials 644-650 (September 2014): 4273–77. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.4273.
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Image registration is the key process in analyzing images and data from satellites. Feature-based methods find correspondence pixels which point to the same place between two images. In this paper, a wavelet pyramid hierarchical image registration algorithm is presented. First mismatching exclusion policy on the top of pyramid is used. Other hand search strategy which gets the scope of the search layer on the bottom of the pyramid is adopted. Both of them rely on pair of matching-right points. Experimental results show that the algorithm can significantly improve the search efficiency, and obtain a good match accuracy and reliability.
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Khan, Sheheryar, Mehmood Nawaz, Xu Guoxia, and Hong Yan. "Image Correspondence With CUR Decomposition-Based Graph Completion and Matching." IEEE Transactions on Circuits and Systems for Video Technology 30, no. 9 (September 2020): 3054–67. http://dx.doi.org/10.1109/tcsvt.2019.2935838.
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Li, Xin Yu, and Dong Yi Chen. "Invariant Features Tracking Based on Transiently Chaotic Neural Network for Markerless Augmented Reality." Advanced Materials Research 181-182 (January 2011): 37–42. http://dx.doi.org/10.4028/www.scientific.net/amr.181-182.37.
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Tracking and registration of camera and object is one of the most important issues in Augmented Reality (AR) systems. Markerless visual tracking technologies with image feature are used in many AR applications. Feature point based neural network image matching method has attracted considerable attention in recent years. This paper proposes an approach to feature point correspondence of image sequence based on transient chaotic neural networks. Rotation and scale invariant features are extracted from images firstly, and then transient chaotic neural network is used to perform global feature matching and perform the initialization phase of the tracking. Experimental results demonstrate the efficiency and the effectiveness of the proposed method.
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YAO, JIANHUA, and RUSSELL TAYLOR. "NON-RIGID REGISTRATION AND CORRESPONDENCE FINDING IN MEDICAL IMAGE ANALYSIS USING MULTIPLE-LAYER FLEXIBLE MESH TEMPLATE MATCHING." International Journal of Pattern Recognition and Artificial Intelligence 17, no. 07 (November 2003): 1145–65. http://dx.doi.org/10.1142/s0218001403002873.
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In this paper we present a novel technique for non-rigid medical image registration and correspondence finding based on a multiple-layer flexible mesh template matching technique. A statistical anatomical model is built in the form of a tetrahedral mesh, which incorporates both shape and density properties of the anatomical structure. After the affine transformation and global deformation of the model are computed by optimizing an energy function, a multiple-layer flexible mesh template matching is applied to find the vertex correspondence and achieve local deformation. The multiple-layer structure of the template can be used to describe different scale of anatomical features; furthermore, the template matching is flexible which makes the correspondence finding robust. A leave-one-out validation has been conducted to demonstrate the effectiveness and accuracy of our method.
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Chukanov, Sergey N. "The Determination of Distances between Images by de Rham Currents Method." Modeling and Analysis of Information Systems 27, no. 1 (March 23, 2020): 96–107. http://dx.doi.org/10.18255/1818-1015-2020-1-96-107.
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The goal of the paper is to develop an algorithm for matching the shapes of images of objects based on the geometric method of de Rham currents and preliminary affine transformation of the source image shape. In the formation of the matching algorithm, the problems of ensuring invariance to geometric image transformations and ensuring the absence of a bijective correspondence requirement between images segments were solved. The algorithm of shapes matching based on the current method is resistant to changes of the topology of object shapes and reparametrization. When analyzing the data structures of an object, not only the geometric form is important, but also the signals associated with this form by functional dependence. To take these signals into account, it is proposed to expand de Rham currents with an additional component corresponding to the signal structure. To improve the accuracy of shapes matching of the source and terminal images we determine the functional on the basis of the formation of a squared distance between the shapes of the source and terminal images modeled by de Rham currents. The original image is subjected to preliminary affine transformation to minimize the squared distance between the deformed and terminal images.
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Robitaille, Nicolas, Abderazzak Mouiha, Burt Crépeault, Fernando Valdivia, and Simon Duchesne. "Tissue-Based MRI Intensity Standardization: Application to Multicentric Datasets." International Journal of Biomedical Imaging 2012 (2012): 1–11. http://dx.doi.org/10.1155/2012/347120.
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Intensity standardization in MRI aims at correcting scanner-dependent intensity variations. Existing simple and robust techniques aim at matching the input image histogram onto a standard, while we think that standardization should aim at matching spatially corresponding tissue intensities. In this study, we present a novel automatic technique, called STI forSTandardization of Intensities, which not only shares the simplicity and robustness of histogram-matching techniques, but also incorporates tissue spatial intensity information. STI uses joint intensity histograms to determine intensity correspondence in each tissue between the input and standard images. We compared STI to an existing histogram-matching technique on two multicentric datasets, Pilot E-ADNI and ADNI, by measuring the intensity error with respect to the standard image after performing nonlinear registration. The Pilot E-ADNI dataset consisted in 3 subjects each scanned in 7 different sites. The ADNI dataset consisted in 795 subjects scanned in more than 50 different sites. STI was superior to the histogram-matching technique, showing significantly better intensity matching for the brain white matter with respect to the standard image.
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Zhang, Zhi Gang, Hong Yu Bian, Hui Xu, and Zi Qi Song. "Method of Image Matching for Seabed Sonar Images Based on Regions of Interest." Applied Mechanics and Materials 532 (February 2014): 126–29. http://dx.doi.org/10.4028/www.scientific.net/amm.532.126.
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One of the most effective instruments for target detection in turbid waters is imaging sonar. However, the aspect angle of imaging sonar is usually small and that is a sacrifice for high detection precision. To make imaging sonar practical in large scale target detection with wide aspect angle, investigating image matching methods for continuous sonar frames is of great importance. A novel image matching method using local features of SIFT is described in this paper, which mainly focuses on the problem of weak echo signals and the following sonar images mismatch. The correspondence between objects and cast shadow regions is employed to extract regions of interest. Besides, status parameters of underwater vehicle are used to approximate the image transformation. Image segmentation methods are involved to decrease the size of the feature extracting regions and reduce the impact of non-target seabed areas, which improves the stability of this sonar image matching method significantly.
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Hung, C. H., W. C. Chang, and L. C. Chen. "ORIENTATION MODELING FOR AMATEUR CAMERAS BY MATCHING IMAGE LINE FEATURES AND BUILDING VECTOR DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 39–42. http://dx.doi.org/10.5194/isprsarchives-xli-b3-39-2016.
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With the popularity of geospatial applications, database updating is getting important due to the environmental changes over time. Imagery provides a lower cost and efficient way to update the database. Three dimensional objects can be measured by space intersection using conjugate image points and orientation parameters of cameras. However, precise orientation parameters of light amateur cameras are not always available due to their costliness and heaviness of precision GPS and IMU. To automatize data updating, the correspondence of object vector data and image may be built to improve the accuracy of direct georeferencing. This study contains four major parts, (1) back-projection of object vector data, (2) extraction of image feature lines, (3) object-image feature line matching, and (4) line-based orientation modeling. In order to construct the correspondence of features between an image and a building model, the building vector features were back-projected onto the image using the initial camera orientation from GPS and IMU. Image line features were extracted from the imagery. Afterwards, the matching procedure was done by assessing the similarity between the extracted image features and the back-projected ones. Then, the fourth part utilized line features in orientation modeling. The line-based orientation modeling was performed by the integration of line parametric equations into collinearity condition equations. The experiment data included images with 0.06 m resolution acquired by Canon EOS Mark 5D II camera on a Microdrones MD4-1000 UAV. Experimental results indicate that 2.1 pixel accuracy may be reached, which is equivalent to 0.12 m in the object space.
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Hung, C. H., W. C. Chang, and L. C. Chen. "ORIENTATION MODELING FOR AMATEUR CAMERAS BY MATCHING IMAGE LINE FEATURES AND BUILDING VECTOR DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 39–42. http://dx.doi.org/10.5194/isprs-archives-xli-b3-39-2016.
Full textAbstract:
With the popularity of geospatial applications, database updating is getting important due to the environmental changes over time. Imagery provides a lower cost and efficient way to update the database. Three dimensional objects can be measured by space intersection using conjugate image points and orientation parameters of cameras. However, precise orientation parameters of light amateur cameras are not always available due to their costliness and heaviness of precision GPS and IMU. To automatize data updating, the correspondence of object vector data and image may be built to improve the accuracy of direct georeferencing. This study contains four major parts, (1) back-projection of object vector data, (2) extraction of image feature lines, (3) object-image feature line matching, and (4) line-based orientation modeling. In order to construct the correspondence of features between an image and a building model, the building vector features were back-projected onto the image using the initial camera orientation from GPS and IMU. Image line features were extracted from the imagery. Afterwards, the matching procedure was done by assessing the similarity between the extracted image features and the back-projected ones. Then, the fourth part utilized line features in orientation modeling. The line-based orientation modeling was performed by the integration of line parametric equations into collinearity condition equations. The experiment data included images with 0.06 m resolution acquired by Canon EOS Mark 5D II camera on a Microdrones MD4-1000 UAV. Experimental results indicate that 2.1 pixel accuracy may be reached, which is equivalent to 0.12 m in the object space.
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Ren, Chaofeng, Junfeng Xie, Xiaodong Zhi, Yun Yang, and Shuai Yang. "Laser Spot Center Location Method for Chinese Spaceborne GF-7 Footprint Camera." Sensors 20, no. 8 (April 18, 2020): 2319. http://dx.doi.org/10.3390/s20082319.
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The Gaofen-7 (GF-7) satellite is equipped with two area array sensor footprint cameras to capture the laser altimeter spot. In order to establish a direct correspondence between the laser data and the stereo image data, a new method is proposed to fit the center of the spot using the brightness difference between the spot image and the footprint image. First, the geometric registration between the spot image and the footprint image is completed based on feature matching or template matching. Then, the brightness values between the two images are extracted from the corresponding image position to form a measurement, and the least squares adjustment method is used to calculate the parameters of the brightness conversion model between the spot image and the footprint image. Finally, according to the registration relationship, the center of the identified spots is respectively positioned in the footprint images, so that the laser spots are accurately identified in the along-track stereo footprint images. The experimental results show that the spot error of this method is less than 0.7 pixel, which has higher reliability and stability, and can be used for a GF-7 satellite footprint camera.
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Carrasco, Miguel, Domingo Mery, Andrés Concha, Ramiro Velázquez, Roberto De Fazio, and Paolo Visconti. "An Efficient Point-Matching Method Based on Multiple Geometrical Hypotheses." Electronics 10, no. 3 (January 22, 2021): 246. http://dx.doi.org/10.3390/electronics10030246.
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Point matching in multiple images is an open problem in computer vision because of the numerous geometric transformations and photometric conditions that a pixel or point might exhibit in the set of images. Over the last two decades, different techniques have been proposed to address this problem. The most relevant are those that explore the analysis of invariant features. Nonetheless, their main limitation is that invariant analysis all alone cannot reduce false alarms. This paper introduces an efficient point-matching method for two and three views, based on the combined use of two techniques: (1) the correspondence analysis extracted from the similarity of invariant features and (2) the integration of multiple partial solutions obtained from 2D and 3D geometry. The main strength and novelty of this method is the determination of the point-to-point geometric correspondence through the intersection of multiple geometrical hypotheses weighted by the maximum likelihood estimation sample consensus (MLESAC) algorithm. The proposal not only extends the methods based on invariant descriptors but also generalizes the correspondence problem to a perspective projection model in multiple views. The developed method has been evaluated on three types of image sequences: outdoor, indoor, and industrial. Our developed strategy discards most of the wrong matches and achieves remarkable F-scores of 97%, 87%, and 97% for the outdoor, indoor, and industrial sequences, respectively.
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Atoui, Hussein, Serge Miguet, and David Sarrut. "A FAST MORPHING-BASED INTERPOLATION FOR MEDICAL IMAGES: APPLICATION TO CONFORMAL RADIOTHERAPY." Image Analysis & Stereology 25, no. 2 (May 3, 2011): 95. http://dx.doi.org/10.5566/ias.v25.p95-103.
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A method is presented for fast interpolation between medical images. The method is intended for both slice and projective interpolation. It allows offline interpolation between neighboring slices in tomographic data. Spatial correspondence between adjacent images is established using a block matching algorithm. Interpolation of image intensities is then carried out by morphing between the images. The morphing-based method is compared to standard linear interpolation, block-matching-based interpolation and registrationbased interpolation in 3D tomographic data sets. Results show that the proposed method scored similar performance in comparison to registration-based interpolation, and significantly outperforms both linear and block-matching-based interpolation. This method is applied in the context of conformal radiotherapy for online projective interpolation between Digitally Reconstructed Radiographs (DRRs).
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Li, Yongfei, Shicheng Wang, Hao He, Deyu Meng, and Dongfang Yang. "Fast Aerial Image Geolocalization Using the Projective-Invariant Contour Feature." Remote Sensing 13, no. 3 (January 30, 2021): 490. http://dx.doi.org/10.3390/rs13030490.
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We address the problem of aerial image geolocalization over an area as large as a whole city through road network matching, which is modeled as a 2D point set registration problem under the 2D projective transformation and solved in a two-stage manner. In the first stage, all the potential transformations aligning the query road point set to the reference road point set are found by local point feature matching. A local geometric feature, called the Projective-Invariant Contour Feature (PICF), which consists of a road intersection and the closest points to it in each direction, is specifically designed. We prove that the proposed PICF is equivariant under the 2D projective transformation group. We then encode the PICF with a projective-invariant descriptor to enable the fast search of potential correspondences. The bad correspondences are then removed by a geometric consistency check with the graph-cut algorithm effectively. In the second stage, a flexible strategy is developed to recover the homography transformation with all the PICF correspondences with the Random Sample Consensus (RANSAC) method or to recover the transformation with only one correspondence and then refine it with the local-to-global Iterative Closest Point (ICP) algorithm when only a few correspondences exist. The strategy makes our method efficient to deal with both scenes where roads are sparse and scenes where roads are dense. The refined transformations are then verified with alignment accuracy to determine whether they are accepted as correct. Experimental results show that our method runs faster and greatly improves the recall compared with the state-of-the-art methods.
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Haraguchi, Ryo, Naozo Sugimoto, Shigeru Eiho, and Yoshio Ishida. "Three Dimensional Reconstruction of Coronary Arteries by Using Registration and Texture-Mapping onto Epicardial Surface on Nuclear 3D Image." International Journal of Image and Graphics 03, no. 04 (October 2003): 567–87. http://dx.doi.org/10.1142/s0219467803001196.
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This paper deals with a new method of three dimensional reconstruction of coronary arteries, by using the texture-mapping technique on a myocardial nuclear image. The bi-plane CAG images are texture-mapped onto a LV surface model which is pre-determined on a nuclear 3D image. By maximizing a matching degree between two mapped CAG images, registration between CAG and nuclear image is performed automatically. By taking only true images from the mapped CAG images, we can obtain 3D reconstructed coronary image on the LV surface model. This method has the great advantage that it is not necessary to extract the feature points, nor is there a need to identify the correspondence. The obtained images give us a clear understanding of the relation between the coronary artery and the function of the myocardium.
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Olague, Gustavo, Francisco Fernández, Cynthia B. Pérez, and Evelyne Lutton. "The Infection Algorithm: An Artificial Epidemic Approach for Dense Stereo Correspondence." Artificial Life 12, no. 4 (October 2006): 593–615. http://dx.doi.org/10.1162/artl.2006.12.4.593.
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We present a new bio-inspired approach applied to a problem of stereo image matching. This approach is based on an artificial epidemic process, which we call the infection algorithm. The problem at hand is a basic one in computer vision for 3D scene reconstruction. It has many complex aspects and is known as an extremely difficult one. The aim is to match the contents of two images in order to obtain 3D information that allows the generation of simulated projections from a viewpoint that is different from the ones of the initial photographs. This process is known as view synthesis. The algorithm we propose exploits the image contents in order to produce only the necessary 3D depth information, while saving computational time. It is based on a set of distributed rules, which propagate like an artificial epidemic over the images. Experiments on a pair of real images are presented, and realistic reprojected images have been generated.
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Lu, Nai Guang, Ming Li Dong, P. Sun, and J. W. Guo. "A Point Matching Method for Stereovision Measurement." Key Engineering Materials 381-382 (June 2008): 305–8. http://dx.doi.org/10.4028/www.scientific.net/kem.381-382.305.
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Many vision tasks such as 3D measurement, scene reconstruction, object recognition, etc., rely on feature correspondence among images. This paper presents a point matching method for 3D surface measurement. The procedure of the method is as follows: (1) rectification for stereo image pairs; (2) computation of epipolar lines; (3) sequential matching in vertical direction; (4) sequential matching in horizontal direction. The fourth step is performed to deal with the ambiguity in dense areas where points have closer vertical coordinates. In the fourth step a threshold limit of vertical coordinate difference is designed to determine those points potential to cause ambiguity. This method was applied to the 3D surface measurement for an inflatable parabolic reflector with validity of point matching up to 100%. Experiment results show that this method is feasible in application of sparse point matching for continuous surface measurements.
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Zheng, Yuanjie, Ebenezer Daniel, Allan A. Hunter, Rui Xiao, Jianbin Gao, Hongsheng Li, Maureen G. Maguire, David H. Brainard, and James C. Gee. "Landmark matching based retinal image alignment by enforcing sparsity in correspondence matrix." Medical Image Analysis 18, no. 6 (August 2014): 903–13. http://dx.doi.org/10.1016/j.media.2013.09.009.
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Fischer, Peter, Philipp Schuegraf, Nina Merkle, and Tobias Storch. "AN EVOLUTIONARY ALGORITHM FOR FAST INTENSITY BASED IMAGE MATCHING BETWEEN OPTICAL AND SAR SATELLITE IMAGERY." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-3 (April 23, 2018): 83–90. http://dx.doi.org/10.5194/isprs-annals-iv-3-83-2018.
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This paper presents a hybrid evolutionary algorithm for fast intensity based matching between satellite imagery from SAR and very high-resolution (VHR) optical sensor systems. The precise and accurate co-registration of image time series and images of different sensors is a key task in multi-sensor image processing scenarios. The necessary preprocessing step of image matching and tie-point detection is divided into a search problem and a similarity measurement. Within this paper we evaluate the use of an evolutionary search strategy for establishing the spatial correspondence between satellite imagery of optical and radar sensors. The aim of the proposed algorithm is to decrease the computational costs during the search process by formulating the search as an optimization problem. Based upon the canonical evolutionary algorithm, the proposed algorithm is adapted for SAR/optical imagery intensity based matching. Extensions are drawn using techniques like hybridization (e.g. local search) and others to lower the number of objective function calls and refine the result. The algorithm significantely decreases the computational costs whilst finding the optimal solution in a reliable way.
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Fan, Shu Kai S., Yu Chiang Chuang, and Jia Rong Wu. "A New Cross-Correlation Based Image Registration Method." Applied Mechanics and Materials 58-60 (June 2011): 1979–84. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.1979.
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Image registration is a fundamental task for combining two or more images taken from different viewpoints, different times, or different sensors. It is a process of determining the point by point correspondence between two images from the same scene. The proposed image registration method uses the area-based approach to process image registration and the objective is to find the maximum similarity through the cross-correlation measure. Most cross-correlation methods are developed based on image intensities for the direct matching purpose. However, it is extremely sensitive to the intensity changes. To counteract illumination effect, the proposed method replaces the intensity with the gradient information, and this concept comes originally from the Hough transform that points having the same parameters and should be on the same line. These two parameters are combined as the similarity between images for image registration. The experimental results obtained by means of several test images illustrate the effectiveness of the proposed image registration method.