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Journal articles on the topic 'Matching points'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Ábrego, Bernardo M., Esther M. Arkin, Silvia Fernández-Merchant, Ferran Hurtado, Mikio Kano, Joseph S. B. Mitchell, and Jorge Urrutia. "Matching Points with Squares." Discrete & Computational Geometry 41, no. 1 (July 3, 2008): 77–95. http://dx.doi.org/10.1007/s00454-008-9099-1.

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2

Vincent, Etienne, and Robert Laganière. "Detecting and matching feature points." Journal of Visual Communication and Image Representation 16, no. 1 (February 2005): 38–54. http://dx.doi.org/10.1016/j.jvcir.2004.05.001.

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3

Caraballo, L. E., C. Ochoa, P. Pérez-Lantero, and J. Rojas-Ledesma. "Matching colored points with rectangles." Journal of Combinatorial Optimization 33, no. 2 (October 27, 2015): 403–21. http://dx.doi.org/10.1007/s10878-015-9971-x.

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4

Yuan, Wei, Shiyu Chen, Yong Zhang, Jianya Gong, and Ryosuke Shibasaki. "AN AERIAL-IMAGE DENSE MATCHING APPROACH BASED ON OPTICAL FLOW FIELD." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 543–48. http://dx.doi.org/10.5194/isprsarchives-xli-b3-543-2016.

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Dense matching plays an important role in many fields, such as DEM (digital evaluation model) producing, robot navigation and 3D environment reconstruction. Traditional approaches may meet the demand of accuracy. But the calculation time and out puts density is hardly be accepted. Focus on the matching efficiency and complex terrain surface matching feasibility an aerial image dense matching method based on optical flow field is proposed in this paper. First, some high accurate and uniformed control points are extracted by using the feature based matching method. Then the optical flow is calculated by using these control points, so as to determine the similar region between two images. Second, the optical flow field is interpolated by using the multi-level B-spline interpolation in the similar region and accomplished the pixel by pixel coarse matching. Final, the results related to the coarse matching refinement based on the combined constraint, which recognizes the same points between images. The experimental results have shown that our method can achieve per-pixel dense matching points, the matching accuracy achieves sub-pixel level, and fully meet the three-dimensional reconstruction and automatic generation of DSM-intensive matching’s requirements. The comparison experiments demonstrated that our approach’s matching efficiency is higher than semi-global matching (SGM) and Patch-based multi-view stereo matching (PMVS) which verifies the feasibility and effectiveness of the algorithm.

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Yuan, Wei, Shiyu Chen, Yong Zhang, Jianya Gong, and Ryosuke Shibasaki. "AN AERIAL-IMAGE DENSE MATCHING APPROACH BASED ON OPTICAL FLOW FIELD." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 543–48. http://dx.doi.org/10.5194/isprs-archives-xli-b3-543-2016.

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Dense matching plays an important role in many fields, such as DEM (digital evaluation model) producing, robot navigation and 3D environment reconstruction. Traditional approaches may meet the demand of accuracy. But the calculation time and out puts density is hardly be accepted. Focus on the matching efficiency and complex terrain surface matching feasibility an aerial image dense matching method based on optical flow field is proposed in this paper. First, some high accurate and uniformed control points are extracted by using the feature based matching method. Then the optical flow is calculated by using these control points, so as to determine the similar region between two images. Second, the optical flow field is interpolated by using the multi-level B-spline interpolation in the similar region and accomplished the pixel by pixel coarse matching. Final, the results related to the coarse matching refinement based on the combined constraint, which recognizes the same points between images. The experimental results have shown that our method can achieve per-pixel dense matching points, the matching accuracy achieves sub-pixel level, and fully meet the three-dimensional reconstruction and automatic generation of DSM-intensive matching’s requirements. The comparison experiments demonstrated that our approach’s matching efficiency is higher than semi-global matching (SGM) and Patch-based multi-view stereo matching (PMVS) which verifies the feasibility and effectiveness of the algorithm.

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6

Cui, Haihua, Wenhe Liao, Xiaosheng Cheng, Ning Dai, and Changye Guo. "Flexible point cloud matching method based on three-dimensional image feature points." Advances in Mechanical Engineering 10, no. 9 (September 2018): 168781401879503. http://dx.doi.org/10.1177/1687814018795032.

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Flexible and robust point cloud matching is important for three-dimensional surface measurement. This article proposes a new matching method based on three-dimensional image feature points. First, an intrinsic shape signature algorithm is used to detect the key shape feature points, using a weighted three-dimensional occupational histogram of the data points within the angular space, which is a view-independent representation of the three-dimensional shape. Then, the point feature histogram is used to represent the underlying surface model properties at a point whose computation is based on the combination of certain geometrical relations between the point’s nearest k-neighbors. The two-view point clouds are robustly matched using the proposed double neighborhood constraint of minimizing the sum of the Euclidean distances between the local neighbors of the point and feature point. The proposed optimization method is immune to noise, reduces the search range for matching points, and improves the correct feature point matching rate for a weak surface texture. The matching accuracy and stability of the proposed method are verified using experiments. This method can be used for a flat surface with weak features and in other applications. The method has a larger application range than the traditional methods.

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7

Piech, Mateusz, Aleksander Smywinski-Pohl, Robert Marcjan, and Leszek Siwik. "Towards Automatic Points of Interest Matching." ISPRS International Journal of Geo-Information 9, no. 5 (May 1, 2020): 291. http://dx.doi.org/10.3390/ijgi9050291.

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Complementing information about particular points, places, or institutions, i.e., so-called Points of Interest (POIs) can be achieved by matching data from the growing number of geospatial databases; these include Foursquare, OpenStreetMap, Yelp, and Facebook Places. Doing this potentially allows for the acquisition of more accurate and more complete information about POIs than would be possible by merely extracting the information from each of the systems alone. Problem: The task of Points of Interest matching, and the development of an algorithm to perform this automatically, are quite challenging problems due to the prevalence of different data structures, data incompleteness, conflicting information, naming differences, data inaccuracy, and cultural and language differences; in short, the difficulties experienced in the process of obtaining (complementary) information about the POI from different sources are due, in part, to the lack of standardization among Points of Interest descriptions; a further difficulty stems from the vast and rapidly growing amount of data to be assessed on each occasion. Research design and contributions: To propose an efficient algorithm for automatic Points of Interest matching, we: (1) analyzed available data sources—their structures, models, attributes, number of objects, the quality of data (number of missing attributes), etc.—and defined a unified POI model; (2) prepared a fairly large experimental dataset consisting of 50,000 matching and 50,000 non-matching points, taken from different geographical, cultural, and language areas; (3) comprehensively reviewed metrics that can be used for assessing the similarity between Points of Interest; (4) proposed and verified different strategies for dealing with missing or incomplete attributes; (5) reviewed and analyzed six different classifiers for Points of Interest matching, conducting experiments and follow-up comparisons to determine the most effective combination of similarity metric, strategy for dealing with missing data, and POIs matching classifier; and (6) presented an algorithm for automatic Points of Interest matching, detailing its accuracy and carrying out a complexity analysis. Results and conclusions: The main results of the research are: (1) comprehensive experimental verification and numerical comparisons of the crucial Points of Interest matching components (similarity metrics, approaches for dealing with missing data, and classifiers), indicating that the best Points of Interest matching classifier is a combination of random forest algorithm coupled with marking of missing data and mixing different similarity metrics for different POI attributes; and (2) an efficient greedy algorithm for automatic POI matching. At a cost of just 3.5% in terms of accuracy, it allows for reducing POI matching time complexity by two orders of magnitude in comparison to the exact algorithm.

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8

Bereg, Sergey, Nikolaus Mutsanas, and Alexander Wolff. "Matching points with rectangles and squares." Computational Geometry 42, no. 2 (February 2009): 93–108. http://dx.doi.org/10.1016/j.comgeo.2008.05.001.

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9

Yuan, Jian Ying, Xian Yong Liu, and Zhi Qiang Qiu. "A Robust Feature Points Matching Algorithm in 3D Optical Measuring System." Advanced Materials Research 383-390 (November 2011): 5193–99. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.5193.

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In optical measuring system with a handheld digital camera, image points matching is very important for 3-dimensional(3D) reconstruction. The traditional matching algorithms are usually based on epipolar geometry or multi-base lines. Mistaken matching points can not be eliminated by epipolar geometry and many matching points will be lost by multi-base lines. In this paper, a robust algorithm is presented to eliminate mistaken matching feature points in the process of 3D reconstruction from multiple images. The algorithm include three steps: (1) pre-matching the feature points using constraints of epipolar geometry and image topological structure firstly; (2) eliminating the mistaken matching points by the principle of triangulation in multi-images; (3) refining camera external parameters by bundle adjustment. After the external parameters of every image refined, repeat step (1) to step (3) until all the feature points been matched. Comparative experiments with real image data have shown that mistaken matching feature points can be effectively eliminated, and nearly no matching points have been lost, which have a better performance than traditonal matching algorithms do.

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10

Shan, X. J., and P. Tang. "A Robust False Matching Points Detection Method for Remote Sensing Image Registration." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W3 (April 29, 2015): 699–702. http://dx.doi.org/10.5194/isprsarchives-xl-7-w3-699-2015.

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Given the influences of illumination, imaging angle, and geometric distortion, among others, false matching points still occur in all image registration algorithms. Therefore, false matching points detection is an important step in remote sensing image registration. Random Sample Consensus (RANSAC) is typically used to detect false matching points. However, RANSAC method cannot detect all false matching points in some remote sensing images. Therefore, a robust false matching points detection method based on Knearest- neighbour (K-NN) graph (KGD) is proposed in this method to obtain robust and high accuracy result. The KGD method starts with the construction of the K-NN graph in one image. K-NN graph can be first generated for each matching points and its K nearest matching points. Local transformation model for each matching point is then obtained by using its K nearest matching points. The error of each matching point is computed by using its transformation model. Last, L matching points with largest error are identified false matching points and removed. This process is iterative until all errors are smaller than the given threshold. In addition, KGD method can be used in combination with other methods, such as RANSAC. Several remote sensing images with different resolutions and terrains are used in the experiment. We evaluate the performance of KGD method, RANSAC + KGD method, RANSAC, and Graph Transformation Matching (GTM). The experimental results demonstrate the superior performance of the KGD and RANSAC + KGD methods.

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11

Zhu, Hong Bo, Xue Jun Xu, Xue Song Chen, and Shao Hua Jiang. "A Rapid Matching Algorithm Based on Filtering Feature Points." Applied Mechanics and Materials 66-68 (July 2011): 1954–59. http://dx.doi.org/10.4028/www.scientific.net/amm.66-68.1954.

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Matching feature points is an important step in image registration. For high- dimensional feature vector, the process of matching is very time-consuming, especially matching the vast amount of points. In the premise of ensuring the registration, filtering the candidate vectors to reduce the number of feature vectors, can effectively reduce the time matching the vectors. This paper presents a matching algorithm based on filtering the feature points on their characteristics of the corner feature. The matching method can effectively improve the matching speed, and can guarantee registration accuracy as well.

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12

Yang, Yao, Jinkang Wei, Ximing Zhan, and Xikui Miao. "A novel method for SIFT features matching based on feature dimension matching degree." MATEC Web of Conferences 277 (2019): 02027. http://dx.doi.org/10.1051/matecconf/201927702027.

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We proposes a method for fast matching SIFT feature points based on SIFT feature descriptor vector element matching. First, we discretize each dimensional feature element into an array address based on a fixed threshold value and store the corresponding feature point labels in an address. If the same dimensional feature element of the descriptor vector has the same discrete value, their feature point labels may fall into the same address. Secondly, we search the mapping address of the feature descriptor vector element to obtain the matching state of the corresponding dimensions of the feature descriptor vector, thus obtaining the number of dimensions matching between feature points and feature dimension matching degree. Then we use the feature dimension matching degree to obtain the suspect matching feature points. Finally we use the Euclidean distance to eliminate the mismatching feature points to obtain accurate matching feature point pairs. The method is essentially a high-dimensional feature vector matching method based on local feature vector element matching. Experimental results show that the new algorithm can guarantee the number of matching SIFT feature points and their matching accuracy and that its running time is similar to that of HKMT, RKDT and LSH algorithms

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13

Castillo-Rosado, Katy, and José Hernández-Palancar. "Latent Fingerprint Matching Using Distinctive Ridge Points." Informatica 30, no. 3 (January 1, 2019): 431–54. http://dx.doi.org/10.15388/informatica.2019.213.

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14

Zhang, Changlong, Miaomiao Wang, Yong Li, and Xuesong Yang. "Nearest Neighbour Corner Points Matching Detection Algorithm." MATEC Web of Conferences 22 (2015): 01035. http://dx.doi.org/10.1051/matecconf/20152201035.

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15

Babatunde, Iwasokun Gabriel. "Fingerprint Matching Using Minutiae-Singular Points Network." International Journal of Signal Processing, Image Processing and Pattern Recognition 8, no. 2 (February 28, 2015): 375–88. http://dx.doi.org/10.14257/ijsip.2015.8.2.35.

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16

Loho, Georg, and Ben Smith. "Matching fields and lattice points of simplices." Advances in Mathematics 370 (August 2020): 107232. http://dx.doi.org/10.1016/j.aim.2020.107232.

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17

Biniaz, Ahmad, Anil Maheshwari, and Michiel Smid. "Strong matching of points with geometric shapes." Computational Geometry 68 (March 2018): 186–205. http://dx.doi.org/10.1016/j.comgeo.2017.06.009.

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18

Aichholzer, Oswin, Helmut Alt, and Günter Rote. "Matching Shapes with a Reference Point." International Journal of Computational Geometry & Applications 07, no. 04 (August 1997): 349–63. http://dx.doi.org/10.1142/s0218195997000211.

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For two given point sets, we present a very simple (almost trivial) algorithm to translate one set so that the Hausdorff distance between the two sets is not larger than a constant factor times the minimum Hausdorff distance which can be achieved in this way. The algorithm just matches the so-called Steiner points of the two sets. The focus of our paper is the general study of reference points (like the Steiner point) and their properties with respect to shape matching. For more general transformations than just translations, our method eliminates several degrees of freedom from the problem and thus yields good matchings with improved time bounds.

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19

Eppstein, David, Marc van Kreveld, Bettina Speckmann, and Frank Staals. "Improved Grid Map Layout by Point Set Matching." International Journal of Computational Geometry & Applications 25, no. 02 (June 2015): 101–22. http://dx.doi.org/10.1142/s0218195915500077.

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Associating the regions of a geographic subdivision with the cells of a grid is a basic operation that is used in various types of maps, like spatially ordered treemaps and Origin-Destination maps (OD maps). In these cases the regular shapes of the grid cells allow easy representation of extra information about the regions. The main challenge is to find an association that allows a user to find a region in the grid quickly. We call the representation of a set of regions as a grid a grid map. We introduce a new approach to solve the association problem for grid maps by formulating it as a point set matching problem: Given two sets [Formula: see text] (the centroids of the regions) and [Formula: see text] (the grid centres) of [Formula: see text] points in the plane, compute an optimal one-to-one matching between [Formula: see text] and [Formula: see text]. We identify three optimisation criteria that are important for grid map layout: maximise the number of adjacencies in the grid that are also adjacencies of the regions, minimise the sum of the distances between matched points, and maximise the number of pairs of points in [Formula: see text] for which the matching preserves the directional relation (SW, NW, etc.). We consider matchings that minimise the [Formula: see text]-distance (Manhattan-distance), the ranked [Formula: see text]-distance, and the [Formula: see text]-distance, since one can expect that minimising distances implicitly helps to fulfill the other criteria. We present algorithms to compute such matchings and perform an experimental comparison that also includes a previous method to compute a grid map. The experiments show that our more global, matching-based algorithm outperforms previous, more local approaches with respect to all three optimisation criteria.

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20

Kerner, S., I. Kaufman, and Y. Raizman. "ROLE OF TIE-POINTS DISTRIBUTION IN AERIAL PHOTOGRAPHY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3/W4 (March 17, 2016): 41–44. http://dx.doi.org/10.5194/isprsarchives-xl-3-w4-41-2016.

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Automatic image matching algorithms, and especially feature-based methods, profoundly changed our understanding and requirements of tie points. The number of tie points has increased by orders of magnitude, yet the notions of accuracy and reliability of tie points remain equally important. The spatial distribution of tie points is less predictable, and is subject only to limited control. Feature-based methods also highlighted a conceptual division of the matching process into two separate stages – feature extraction and feature matching. <br><br> In this paper we discuss whether spatial distribution requirements, such as Von Gruber positions, are still relevant to modern matching methods. We argue that forcing such patterns might no longer be required in the feature extraction stage. However, we claim spatial distribution is important in the feature matching stage. <br><br> We will focus on terrains that are notorious for difficult matching, such as water bodies, with real data obtained by users of VisionMap’s A3 Edge camera and LightSpeed photogrammetric suite.

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Kerner, S., I. Kaufman, and Y. Raizman. "ROLE OF TIE-POINTS DISTRIBUTION IN AERIAL PHOTOGRAPHY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3/W4 (March 17, 2016): 41–44. http://dx.doi.org/10.5194/isprs-archives-xl-3-w4-41-2016.

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Automatic image matching algorithms, and especially feature-based methods, profoundly changed our understanding and requirements of tie points. The number of tie points has increased by orders of magnitude, yet the notions of accuracy and reliability of tie points remain equally important. The spatial distribution of tie points is less predictable, and is subject only to limited control. Feature-based methods also highlighted a conceptual division of the matching process into two separate stages – feature extraction and feature matching. <br><br> In this paper we discuss whether spatial distribution requirements, such as Von Gruber positions, are still relevant to modern matching methods. We argue that forcing such patterns might no longer be required in the feature extraction stage. However, we claim spatial distribution is important in the feature matching stage. <br><br> We will focus on terrains that are notorious for difficult matching, such as water bodies, with real data obtained by users of VisionMap’s A3 Edge camera and LightSpeed photogrammetric suite.

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22

Gao, C., and G. Xue. "A MATCHING METHOD TO REDUCE THE INFLUENCE OF SAR GEOMETRIC DEFORMATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 355–58. http://dx.doi.org/10.5194/isprs-archives-xlii-3-355-2018.

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There are large geometrical deformations in SAR image, including foreshortening, layover, shade，which leads to SAR Image matching with low accuracy. Especially in complex terrain area, the control points are difficult to obtain, and the matching is difficult to achieve. Considering the impact of geometric distortions in SAR image pairs, a matching algorithm with a combination of speeded up robust features (SURF) and summed of normalize cross correlation (SNCC) was proposed, which can avoid the influence of SAR geometric deformation. Firstly, SURF algorithm was utilized to predict the search area. Then the matching point pairs was selected based on summed of normalized cross correlation. Finally, false match points were eliminated by the bidirectional consistency. SURF algorithm can control the range of matching points, and the matching points extracted from the deformation area are eliminated, and the matching points with stable and even distribution are obtained. The experimental results demonstrated that the proposed algorithm had high precision, and can effectively avoid the effect of geometric distortion on SAR image matching. Meet accuracy requirements of the block adjustment with sparse control points.

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23

Liu, Minshi, Ling Zhang, Junlian Ge, Yi Long, and Weitao Che. "Map Matching for Urban High-Sampling-Frequency GPS Trajectories." ISPRS International Journal of Geo-Information 9, no. 1 (January 5, 2020): 31. http://dx.doi.org/10.3390/ijgi9010031.

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As a fundamental component of trajectory processing and analysis, trajectory map-matching can be used for urban traffic management and tourism route planning, among other applications. While there are many trajectory map-matching methods, urban high-sampling-frequency GPS trajectory data still depend on simple geometric matching methods, which can lead to mismatches when there are multiple trajectory points near one intersection. Therefore, this study proposed a novel segmented trajectory matching method in which trajectory points were separated into intersection and non-intersection trajectory points. Matching rules and processing methods dedicated to intersection trajectory points were developed, while a classic “Look-Ahead” matching method was applied to non-intersection trajectory points, thereby implementing map matching of the whole trajectory. Then, a comparative analysis between the proposed method and two other new related methods was conducted on trajectories with multiple sampling frequencies. The results indicate that the proposed method is not only competent for intersection matching with high-frequency trajectory data but also superior to two other methods in both matching efficiency and accuracy.

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Cai, Guo Yong, Rui Lv, Li Yuan Wang, and Hao Wu. "A Novel Map Matching Algorithm." Applied Mechanics and Materials 556-562 (May 2014): 4139–45. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4139.

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Presently, most of the researches on Map Matching focus on high sampling rate and accurate GPS points. This paper discusses a challenging problem with low GPS sampling rate and some continuous points with large deviation. To solve the problem efficiently, a novel matching method named F&PT is proposed. Firstly we employ a new method to generate sets of candidate roads. The GPS error analysis based on points is translated into based on roads. Secondly, a local strategy based on potential real trajectories is applied to solve the core problem of selecting an optimal road from the sets which contain only two candidate roads. In this approach, we consider not only the spatial location information and orientation angle information but also the influence of local points on the matching point from a new sight. We evaluate our method based on a real trajectory dataset. The experiments show that the proposed method has a higher accuracy compared with the related methods, e.g. ST-matching algorithm and IVMM algorithm, when the sampling interval is less than 210s.

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Lv, Jing Guo, Wei Zhe Kong, and Dong Yue Li. "Fast 3-D Feature Point Detector Based on Harris." Applied Mechanics and Materials 325-326 (June 2013): 1567–70. http://dx.doi.org/10.4028/www.scientific.net/amm.325-326.1567.

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Image matching is a key a key technology to be solved in the fields of digital photogrammetry and computer vision. Matching based on points matching is most widely used now. How to locate the right feature points is vital. Only accurate feature points can make right matching results. The paper introduces a method of 3-D Harris detector. The two steps matching have been done in our works. The results of experiments show that the 3-D Harris detector is accuracy and efficient.

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Tang, De Jun, Wei Shi Zhang, Lian Fu Li, and Yan Si. "Image Fast Matching Basing on Local Information." Advanced Materials Research 268-270 (July 2011): 1376–81. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.1376.

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The image matching technology is very important technology in computer vision. It is a wide range of application areas, such as aerial image analysis, industrial inspection, and stereo vision, medical, meteorological, and intelligent robots. The article introduces several important image matching technology, and some common fast image matching usage. Propose the image fast matching method basing on local information, mainly use template matching basing on local image features to achieve, by extraction of the selected feature points (including the obvious point, corner points, edge points, edge line, etc.) extracted, and through the calculation of similarity, and by using fast matching algorithm to achieve fast and accurate image matching requirements.

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Denton, Jason A., and J. Ross Beveridge. "An algorithm for projective point matching in the presence of spurious points." Pattern Recognition 40, no. 2 (February 2007): 586–95. http://dx.doi.org/10.1016/j.patcog.2006.04.031.

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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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29

Zhao, Chunhui, Bin Fan, Jinwen Hu, Zhiyuan Zhang, and Quan Pan. "Matching Algorithm of Statistical Optimization Feature Based on Grid Method." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 37, no. 2 (April 2019): 249–57. http://dx.doi.org/10.1051/jnwpu/20193720249.

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The matching algorithm based on image feature points is widely used in image retrieval, target detection, identification and other image processing fields. Aiming at the problem that the feature matching algorithm has low recall rate, a statistical optimization feature based on grid of the normalized cross correlation function is proposed. The matching main direction difference and scale ratio are introduced to feature matching process, for comprehensively utilizing SIFT feature points' information, such as the main direction, scale and position constrains, to accelerate the solution of matching position constraint under the grid framework, which optimizes the feature matching results and improves the recall rate and comprehensive match performance. Firstly, the nearest neighbor matching feature points of each feature point in the original image are found in the target image, and the initial matching results are obtained. Secondly, the matching main direction difference is used to eliminate most mismatches of the initial matching. Thirdly, the matching images are meshed based on the matching scale ratio information, and the position information of the matching feature points distributed among the grids is gathered statistics. Finally, the normalized cross correlation function of each small grid in the original image is calculated to determine whether the matching in the small grid is correct, and the optimized feature matching results are obtained. The experimental results show that the matching accuracy of the new algorithm is similar to that of classical feature matching algorithms, meanwhile the matching recall rate is increased by more than 10%, and a better comprehensive matching performance is obtained.

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30

LIU, Yang. "Realtime feature points matching method in game engine." Journal of Computer Applications 28, no. 3 (July 10, 2008): 799–800. http://dx.doi.org/10.3724/sp.j.1087.2008.00799.

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31

Huang, Jingjin, and Guoqing Zhou. "On-Board Detection and Matching of Feature Points." Remote Sensing 9, no. 6 (June 13, 2017): 601. http://dx.doi.org/10.3390/rs9060601.

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32

Xu, Shuo Bo, Di Shi Xu, and Hua Fang. "Stereo Matching Algorithm Based on Detecting Feature Points." Advanced Materials Research 433-440 (January 2012): 6190–94. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.6190.

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A new method for solving the stereo matching problem in the presence of large occlusion is presented. This method for stereo matching and occlusion detection is based on searching disparity point. In this paper, we suppose that a pair of epipolar-line images is a projection of a group of piece-wise straight lines on the left and right images respective. Therefore the disparity curve corresponding to a pair of epipolar-line images may be approximated by a group of piece-wise straight lines. Then the key of solving disparity curve is how to get the “characteristic points” on the group of piece-wise straight lines. Based on this view, we fetched out the conception “disparity point”, and three kinds of special disparity points are correctly corresponding to the “characteristic point”. By analyzing intensity property of a disparity point and its neighbor points, an approach which combines stepwise hypothesis-verification strategy with three constraint conditions is devised to extract the candidate disparity points from the epipolar images.

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Goh, Kam Meng, Syed Abd Rahman Abu-Bakar, Musa Mohd Mokji, and Usman U. Sheikh. "Enhanced Rotational Feature Points Matching using Orientation Correction." ELCVIA Electronic Letters on Computer Vision and Image Analysis 13, no. 1 (July 24, 2014): 68. http://dx.doi.org/10.5565/rev/elcvia.554.

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Zhao, Yue, and Jianbo Su. "Local sharpness distribution–based feature points matching algorithm." Journal of Electronic Imaging 23, no. 1 (January 29, 2014): 013011. http://dx.doi.org/10.1117/1.jei.23.1.013011.

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Bastos, Luísa F., and João Manuel R. S. Tavares. "Matching of objects nodal points improvement using optimization." Inverse Problems in Science and Engineering 14, no. 5 (July 2006): 529–41. http://dx.doi.org/10.1080/17415970600573767.

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Gu, Shan, Jianjiang Feng, Jiwen Lu, and Jie Zhou. "Latent Fingerprint Registration via Matching Densely Sampled Points." IEEE Transactions on Information Forensics and Security 16 (2021): 1231–44. http://dx.doi.org/10.1109/tifs.2020.3032041.

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Marcotte, Odile, and Subhash Suri. "Fast Matching Algorithms for Points on a Polygon." SIAM Journal on Computing 20, no. 3 (June 1991): 405–22. http://dx.doi.org/10.1137/0220026.

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Luo, Nan, Quansen Sun, Qiang Chen, Zexuan Ji, and Deshen Xia. "A Novel Tracking Algorithm via Feature Points Matching." PLOS ONE 10, no. 1 (January 24, 2015): e0116315. http://dx.doi.org/10.1371/journal.pone.0116315.

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Huemer, Clemens, Pablo Pérez-Lantero, Carlos Seara, and Rodrigo I. Silveira. "Matching points with disks with a common intersection." Discrete Mathematics 342, no. 7 (July 2019): 1885–93. http://dx.doi.org/10.1016/j.disc.2019.03.003.

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Lindeberg, Tony. "Image Matching Using Generalized Scale-Space Interest Points." Journal of Mathematical Imaging and Vision 52, no. 1 (October 24, 2014): 3–36. http://dx.doi.org/10.1007/s10851-014-0541-0.

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Nibouche, O., and J. Jiang. "Palmprint matching using feature points and SVD factorisation." Digital Signal Processing 23, no. 4 (July 2013): 1154–62. http://dx.doi.org/10.1016/j.dsp.2013.02.011.

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Carlsson, John Gunnar, Benjamin Armbruster, Saladi Rahul, and Haritha Bellam. "A Bottleneck Matching Problem with Edge-Crossing Constraints." International Journal of Computational Geometry & Applications 25, no. 04 (December 2015): 245–61. http://dx.doi.org/10.1142/s0218195915500144.

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Motivated by a crane assignment problem, we consider a Euclidean bipartite matching problem with edge-crossing constraints. Specifically, given [Formula: see text] red points and [Formula: see text] blue points in the plane, we want to construct a perfect matching between red and blue points that minimizes the length of the longest edge, while imposing a constraint that no two edges may cross each other. We show that the problem cannot be approximately solved within a factor less than 1:277 in polynomial time unless [Formula: see text]. We give simple dynamic programming algorithms that solve our problem in two special cases, namely (1) the case where the red and blue points form the vertices of a convex polygon and (2) the case where the red points are collinear and the blue points lie to one side of the line through the red points.

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Wang, Jingxue, Ning Zhang, Xiangqian Wu, and Weixi Wang. "Hierarchical Point Matching Method Based on Triangulation Constraint and Propagation." ISPRS International Journal of Geo-Information 9, no. 6 (May 26, 2020): 347. http://dx.doi.org/10.3390/ijgi9060347.

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Reliable image matching is the basis of image-based three-dimensional (3D) reconstruction. This study presents a quasi-dense matching method based on triangulation constraint and propagation as applied to different types of close-range image matching, such as illumination change, large viewpoint, and scale change. The method begins from a set of sparse matched points that are used to construct an initial Delaunay triangulation. Edge-to-edge matching propagation is then conducted for the point matching. Two types of matching primitives from the edges of triangles with areas larger than a given threshold in the reference image, that is, the midpoints of edges and the intersections between the edges and extracted line segments, are used for the matching. A hierarchical matching strategy is adopted for the above-mentioned primitive matching. The points that cannot be matched in the first stage, specifically those that failed in a gradient orientation descriptor similarity constraint, are further matched in the second stage. The second stage combines the descriptor and the Mahalanobis distance constraints, and the optimal matching subpixel is determined according to an overall similarity score defined for the multiple constraints with different weights. Subsequently, the triangulation is updated using the newly matched points, and the aforementioned matching is repeated iteratively until no new matching points are generated. Twelve sets of close-range images are considered for the experiment. Results reveal that the proposed method has high robustness for different images and can obtain reliable matching results.

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ROTBARD, OREN, H. B. MITCHELL, and D. D. ESTRAKH. "POINT MATCHING WITH A "SOFT" RELAXATION ALGORITHM." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 08, no. 04 (August 2000): 481–93. http://dx.doi.org/10.1142/s0218488500000320.

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Point-matching involves the matching of pairs of points from two sets of partially correlated points. It is an important task which is used in many different areas of signal processing. Although it is possible to perform point-matching using a brute-force algorithm, the high computational complexity makes it unfeasible even for a moderate number of points. In these circumstances an iterative relaxation algorithm is widely used. The traditional relaxation algorithm works well as long as the number of points in one set which do not have a corresponding pair in the second set is small and the positions of all the points are accurately known. When these conditions do not hold, the performance of the relaxation algorithm is substantially reduced. In this paper we formulate a "soft" relaxation algorithm using the concept of fuzzy linguistic quantifiers. The performance of the new relaxation algorithm is found to consistently exceed that of the traditional relaxation algorithm.

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Xu, Ai Hua, An Tao, Jia Jing Zhuo, and Lan Qiong Sun. "A New Matching Algorithm Based on Characteristic Points of Epipolar Line." Applied Mechanics and Materials 522-524 (February 2014): 1166–70. http://dx.doi.org/10.4028/www.scientific.net/amm.522-524.1166.

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The characteristic points matching is the hot point in the aerial images processing. The matching algorithm of characteristic points based on epipolar line was put forward according to the characteristics of aerial images. This algorithm overcomes some problems in the process of image sampling, solves the matching problem of untextured regions better, and deletes the dissimilarity points, shortens the calculation time greatly as well.

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Huang, Yea Shuan, Zhi Hong Ou, Hung Hsiu Yu, and Hsiang Wen Hsieh. "Non-Uniform SURF Feature Point Detection and Matching." Applied Mechanics and Materials 284-287 (January 2013): 3184–88. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.3184.

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This paper presents a method for detecting feature points from an image and locating their matching correspondence points across images. The proposed method leverages a novel rapid LBP feature point detection to filter out texture-less SURF feature points. The detected feature points, also known as Non-Uniform SURF feature points, are used to match corresponding feature points from other frame images to reliably locate positions of moving objects. The proposed method consists of two processing modules: Feature Point Extraction (FPE) and Feature Point Mapping (FPM). First, FPE extracts salient feature points with Feature Transform and Feature Point Detection. FPM is then applied to generate motion vectors of each feature point with Feature Descriptor and Feature Point Matching. Experiments are conducted on both artificial template patterns and real scenes captured from moving camera at different speed settings. Experimental results show that the proposed method outperforms the commonly-used SURF feature point detection and matching approach.

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Fursov, V. A., Ye V. Goshin, and K. G. Pugachev. "Adaptive algorithm of conforming image matching." Information Technology and Nanotechnology, no. 2416 (2019): 26–33. http://dx.doi.org/10.18287/1613-0073-2019-2416-26-33.

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This paper presents an adaptive algorithm of conforming image matching based on the principle of conformity. The algorithm consists of several main stages. At the first stage, we find the corresponding points using a minimum value of conformity as the measure of points’ similarity. We define a conformity function as the sum of all possible combinations of squared differences of pixel intensity values on the fragments that are matched. Then, we perform an adaptive procedure of errors correction considering an intensity gradient distribution. An important feature of the algorithm is the finding of error points using a criterion of maximum value of samples’ conformity for every fragment of the disparity map. The results of experiments on the "Teddy" test images are shown.

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Wei, Ji Zhou, Shu Chun Yu, Wen Fei Dong, Chao Feng, and Bing Xie. "Stereo Matching Algorithm Based on Pyramid Double Dynamic Programming." Advanced Materials Research 981 (July 2014): 352–55. http://dx.doi.org/10.4028/www.scientific.net/amr.981.352.

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A stereo matching algorithm was proposed based on pyramid algorithm and dynamic programming. High and low resolution images was computed by pyramid algorithm, and then candidate control points were stroke on low-resolution image, and final control points were stroke on the high-resolution images. Finally, final control points were used in directing stereo matching based on dynamic programming. Since the striking of candidate control points on low-resolution image, the time is greatly reduced. Experiments show that the proposed method has a high matching precision.

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Li, Zhu Lin. "A Multi-Gradation Stereo Matching Method Based on Edge Feature Points." Advanced Materials Research 748 (August 2013): 624–28. http://dx.doi.org/10.4028/www.scientific.net/amr.748.624.

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A gradation stereo matching algorithm based on edge feature points was proposed. Its basic idea is: firstly edge feature points of image pair were extracted; then, gradient invariability and singular eigenvalue invariability were analyzed, two-grade stereo matching method was build, foundation matrix was solved further, and three-grade stereo matching algorithm was finished by foundation matrix guidance. The result indicates that the algorithm can improve matching precision, from 58.3% to 73.2%, it is simple and utility, and it is important for object recognition, tracking, and three-dimensional reconstruction.

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Chen, M., Q. Zhu, S. Yan, and Y. Zhao. "LGS: LOCAL GEOMETRICAL STRUCTURE-BASED INTEREST POINT MATCHING FOR WIDE-BASELINE IMAGERY IN URBAN AREAS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W5 (May 29, 2019): 13–20. http://dx.doi.org/10.5194/isprs-annals-iv-2-w5-13-2019.

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<p><strong>Abstract.</strong> Feature matching is a fundamental technical issue in many applications of photogrammetry and remote sensing. Although recently developed local feature detectors and descriptors have contributed to the advancement of point matching, challenges remain with regard to urban area images that are characterized by large discrepancies in viewing angles. In this paper, we define a concept of local geometrical structure (LGS) and propose a novel feature matching method by exploring the LGS of interest points to specifically address difficult situations in matching points on wide-baseline urban area images. In this study, we first detect interest points from images using a popular detector and compute the LGS of each interest point. Then, the interest points are classified into three categories on the basis of LGS. Thereafter, a hierarchical matching framework that is robust to image viewpoint change is proposed to compute correspondences, in which different feature region computation methods, description methods, and matching strategies are designed for various types of interest points according to their LGS properties. Finally, random sample consensus algorithm based on fundamental matrix is applied to eliminate outliers. The proposed method can generate similar feature descriptors for corresponding interest points under large viewpoint variation even in discontinuous areas that benefit from the LGS-based adaptive feature region construction. Experimental results demonstrate that the proposed method provides significant improvements in correct match number and matching precision compared with other traditional matching methods for urban area wide-baseline images.</p>

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