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Articoli di riviste sul tema "Non-local matching"

Autore: Grafiati

Pubblicato: 7 luglio 2024

Ultima modifica: 19 luglio 2025

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Huang, Xu, Yongjun Zhang, and Zhaoxi Yue. "IMAGE-GUIDED NON-LOCAL DENSE MATCHING WITH THREE-STEPS OPTIMIZATION." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-3 (June 3, 2016): 67–74. http://dx.doi.org/10.5194/isprsannals-iii-3-67-2016.

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This paper introduces a new image-guided non-local dense matching algorithm that focuses on how to solve the following problems: 1) mitigating the influence of vertical parallax to the cost computation in stereo pairs; 2) guaranteeing the performance of dense matching in homogeneous intensity regions with significant disparity changes; 3) limiting the inaccurate cost propagated from depth discontinuity regions; 4) guaranteeing that the path between two pixels in the same region is connected; and 5) defining the cost propagation function between the reliable pixel and the unreliable pixel during disparity interpolation. This paper combines the Census histogram and an improved histogram of oriented gradient (HOG) feature together as the cost metrics, which are then aggregated based on a new iterative non-local matching method and the semi-global matching method. Finally, new rules of cost propagation between the valid pixels and the invalid pixels are defined to improve the disparity interpolation results. The results of our experiments using the benchmarks and the Toronto aerial images from the International Society for Photogrammetry and Remote Sensing (ISPRS) show that the proposed new method can outperform most of the current state-of-the-art stereo dense matching methods.

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Cai, Weibo, Jintao Cheng, Juncan Deng, et al. "Line Segment Matching Fusing Local Gradient Order and Non-Local Structure Information." Applied Sciences 12, no. 1 (2021): 127. http://dx.doi.org/10.3390/app12010127.

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Line segment matching is essential for industrial applications such as scene reconstruction, pattern recognition, and VSLAM. To achieve good performance under the scene with illumination changes, we propose a line segment matching method fusing local gradient order and non-local structure information. This method begins with intensity histogram multiple averaging being utilized for adaptive partitioning. After that, the line support region is divided into several sub-regions, and the whole image is divided into a few intervals. Then the sub-regions are encoded by local gradient order, and the intervals are encoded by non-local structure information of the relationship between the sampled points and the anchor points. Finally, two histograms of the encoded vectors are, respectively, normalized and cascaded. The proposed method was tested on the public datasets and compared with previous methods, which are the line-junction-line (LJL), the mean-standard deviation line descriptor (MSLD) and the line-point invariant (LPI). Experiments show that our approach has better performance than the representative methods in various scenes. Therefore, a tentative conclusion can be drawn that this method is robust and suitable for various illumination changes scenes.

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ZHANG, Chao, Haitian SUN, and Takuya AKASHI. "Robust Non-Parametric Template Matching with Local Rigidity Constraints." IEICE Transactions on Information and Systems E99.D, no. 9 (2016): 2332–40. http://dx.doi.org/10.1587/transinf.2015edp7492.

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Amit, Mika, Rolf Backofen, Steffen Heyne, et al. "Local Exact Pattern Matching for Non-Fixed RNA Structures." IEEE/ACM Transactions on Computational Biology and Bioinformatics 11, no. 1 (2014): 219–30. http://dx.doi.org/10.1109/tcbb.2013.2297113.

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Zhong, Ying, Xue-zhi Yang, Yi-ming Tang, Can-jun Liu, and Feng Yue. "Non-local Means Denoising Derived from Structure-adapted Block Matching." Journal of Electronics & Information Technology 35, no. 12 (2014): 2908–15. http://dx.doi.org/10.3724/sp.j.1146.2013.00099.

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Ying Luo, 罗颖, 霍冠英 Guanying Huo, 许金鑫 Jinxin Xu, and 李庆武 Qingwu Li. "Non-Local Stereo Matching Algorithm Based on Edge Constraint Iteration." Laser & Optoelectronics Progress 56, no. 15 (2019): 151501. http://dx.doi.org/10.3788/lop56.151501.

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Zahari, Madiha, Rostam Affendi Hamzah, Nurulfajar Abd Manap, and Adi Irwan Herman. "Stereo matching algorithm for autonomous vehicle navigation using integrated matching cost and non-local aggregation." Bulletin of Electrical Engineering and Informatics 12, no. 1 (2023): 328–37. http://dx.doi.org/10.11591/eei.v12i1.4122.

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Stereo matching algorithm plays an important role in an autonomous vehicle navigation system to ensure accurate three-dimensional (3D) information is provided. The disparity map produced by the stereo matching algorithm directly impacts the quality of the 3D information provided to the navigation system. However, the accuracy of the matching algorithm is a challenging part to be solved since it is directly affected by the surrounding environment such as different brightness, less texture surface, and different image pair exposure. In this paper, a new framework of stereo matching algorithm that used the integration of census transform (CT) and sum of absolute difference (SAD) at the matching cost computation step, non-local cost aggregation at the second step, winner take all strategy at the third step, and a median filter at the final step to minimize disparity map error. The results show that the accuracy of the disparity map is improved using the proposed methods after some parameter adjustment. Based on the standard Middlebury and KITTI benchmarking dataset, it shows that the proposed framework produced accurate results compared with other established methods.

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Li, Dongrui, Xiaofeng Huang, and Ying Yang. "Research on image denoising algorithm based on non-local block matching." International Journal of Information and Communication Technology 16, no. 3 (2020): 245. http://dx.doi.org/10.1504/ijict.2020.10027484.

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Yang, Ying, Dongrui Li, and Xiaofeng Huang. "Research on image denoising algorithm based on non-local block matching." International Journal of Information and Communication Technology 16, no. 3 (2020): 245. http://dx.doi.org/10.1504/ijict.2020.106317.

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Ma Qingqing, 马晴晴, and 王彩芳 Wang Caifang. "Non-Local Stereo Matching Algorithm Based on Color and Edge Information." Laser & Optoelectronics Progress 57, no. 10 (2020): 101020. http://dx.doi.org/10.3788/lop57.101020.

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Mikhlin, Yu V. "Matching of local expansions in the theory of non-linear vibrations." Journal of Sound and Vibration 182, no. 4 (1995): 577–88. http://dx.doi.org/10.1006/jsvi.1995.0218.

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Ji, Jing. "Non-local matching of adjectival modifiers in Mandarin stacked relative clauses." Proceedings of the Linguistic Society of America 8, no. 1 (2023): 5489. http://dx.doi.org/10.3765/plsa.v8i1.5489.

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Bhatt (2002) argues for a head-raising analysis (HRA) of relative clauses based on the interpretation of certain adjectival modifiers on the head. This paper evaluates Bhatt’s argument in the configurations of stacked relative clauses (SRCs) in Mandarin and argues that the internal interpretation of adjectival modifiers on the head is not a sufficient argument for HRA. We show that adjectival modifiers on the external head of SRCs can receive an internal interpretation when reconstruction is not possible. We propose that the internal reading can instead be derived by non-local matching between the adjectival modifier and its internal representation.

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Liu, Y. J., Q. Sun, and X. L. Fan. "A non-intrusive global/local algorithm with non-matching interface: Derivation and numerical validation." Computer Methods in Applied Mechanics and Engineering 277 (August 2014): 81–103. http://dx.doi.org/10.1016/j.cma.2014.04.012.

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Bu, Penghui, Hang Wang, Yihua Dou, and Hong Zhao. "Linear Time Non-Local Cost Aggregation on Complementary Spatial Tree Structures." Applied Sciences 13, no. 21 (2023): 11892. http://dx.doi.org/10.3390/app132111892.

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Studies on many dense correspondence tasks in the field of computer vision attempt to find spatially smooth results. A typical way to solve these problems is by smoothing the matching costs using edge-preserving filters. However, local filters generate locally optimal results, in that they only take the costs over a small support window into account, and non-local filters based on a minimum spanning tree (MST) tend to overuse the piece-wise constant assumption. In this paper, we propose a linear time non-local cost aggregation method based on two complementary spatial tree structures. The geodesic distances in both the spatial and intensity spaces along the tree structures are used to evaluate the similarity of pixels, and the final aggregated cost is the sum of the outputs from these two trees. The filtering output of a pixel on each tree can be obtained by recursively aggregating the costs along eight sub-trees with linear time complexity. The only difference between the filtering procedures on these two spatial tree structures is the order of the filtering. Experimental results in optical flow estimation and stereo matching on the Middlebury and KITTI datasets demonstrate the effectiveness and efficiency of our method. It turns out that our method outperforms typical non-local filters based on the MST in cost aggregation. Moreover, a comparison of handcrafted features and deep features learned by convolutional neural networks (CNNs) in calculating the matching cost is also provided. The code will be available soon.

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Wei, Xiaopeng, Xiaoyong Fang, Qiang Zhang, and Dongsheng Zhou. "3D point pattern matching based on spatial geometric flexibility." Computer Science and Information Systems 7, no. 1 (2010): 231–46. http://dx.doi.org/10.2298/csis1001231w.

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We propose a new method for matching two 3D point sets of identical cardinality with global similarity but local non-rigid deformations and distribution errors. This problem arises from marker based optical motion capture (Mocap) systems for facial Mocap data. To establish one-to-one identifications, we introduce a forward 3D point pattern matching (PPM) method based on spatial geometric flexibility, which considers a non-rigid deformation between the two point-sets. First, a model normalization algorithm based on simple rules is presented to normalize the two point-sets into a fixed space. Second, a facial topological structure model is constructed, which is used to preserve spatial information for each FP. Finally, we introduce a Local Deformation Matrix (LDM) to rectify local searching vector to meet the local deformation. Experimental results confirm that this method is applicable for robust 3D point pattern matching of sparse point sets with underlying non-rigid deformation and similar distribution.

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Liu, Pengfei, Shuaichen Chang, Xuanjing Huang, Jian Tang, and Jackie Chi Kit Cheung. "Contextualized Non-Local Neural Networks for Sequence Learning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 6762–69. http://dx.doi.org/10.1609/aaai.v33i01.33016762.

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Recently, a large number of neural mechanisms and models have been proposed for sequence learning, of which selfattention, as exemplified by the Transformer model, and graph neural networks (GNNs) have attracted much attention. In this paper, we propose an approach that combines and draws on the complementary strengths of these two methods. Specifically, we propose contextualized non-local neural networks (CN3), which can both dynamically construct a task-specific structure of a sentence and leverage rich local dependencies within a particular neighbourhood.Experimental results on ten NLP tasks in text classification, semantic matching, and sequence labelling show that our proposed model outperforms competitive baselines and discovers task-specific dependency structures, thus providing better interpretability to users.

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Liu, Hedong, Xiaobo Li, Zhenzhou Cheng, Tiegen Liu, Jingsheng Zhai, and Haofeng Hu. "Polarimetric image denoising via non-local based cube matching convolutional neural network." Optics and Lasers in Engineering 184 (January 2025): 108684. http://dx.doi.org/10.1016/j.optlaseng.2024.108684.

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Bajardi, Francesco, and Salvatore Capozziello. "Non-Local Cosmology: From Theory to Observations." Symmetry 16, no. 5 (2024): 579. http://dx.doi.org/10.3390/sym16050579.

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We examine the key aspects of gravitational theories that incorporate non-local terms, particularly in the context of cosmology and spherical symmetry. We thus explore various extensions of General Relativity, including non-local effects in the action through the function F(R,□−1R), where R denotes the Ricci curvature scalar and the operator □−1 introduces non-locality. By selecting the functional forms using Noether Symmetries, we identify exact solutions within a cosmological framework. We can thus reduce the dynamics of these chosen models and obtain analytical solutions for the equations of motion. Therefore, we study the capability of the selected models in matching cosmological observations by evaluating the phase space and the fixed points; this allows one to further constrain the non-local model selected by symmetry considerations. Furthermore, we also investigate gravitational non-local effects on astrophysical scales. In this context, we seek symmetries within the framework of f(R,□−1R) gravity and place constraints on the free parameters. Specifically, we analyze the impact of non-locality on the orbits of the S2 star orbiting SgrA*.

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Piao, Daqing (Daching), and Leopold Katz. "On the Band-Limited Information Throughput of Free-Selective and Free-Responsive Spatially Non-Local Perception." Journal of Scientific Exploration 37, no. 3 (2023): 490–516. http://dx.doi.org/10.31275/20232951.

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A single-blind experiment was conducted on free-responsive spatially non-local perception of free-selective simple photographic targets. One author (the tasker) chose a photographic target not subjected to a priori compiling, and the other author (the perceiver) attempted to unconventionally perceive the target. Feedback was expected prior to a new target being selected. A hundred trials were completed over 11 months. Thirteen judges offered gradings that collectively projected an apparent information requisition yield (AIRY). The AIRY refers to two aspects of the matching of a taskette (a target’s counterpart) with respect to the target based on fuzzy scales from 0% to 100%, graded with an increment of 10%. The type-I or “tangibility” aspect of matching assessed the extent of the resemblance of a taskette to the target in terms of the shape or structural details of the primary physical entity/event. The type-II or “intangibility” aspect of the matching assessed the degree of the reconstruction of a taskette to the target in terms of the feature or functionality of the predominant physical entity/event. A total of 6 taskettes received >50% grades in both type-I and type-II aspects of matching with their respective targets. This outcome may correspond to an effect size of 1.47 if the chance-expectation may give a benchmark proportion of 3 out of 100 trials producing >50% grades in both type-I and type-II aspects of matching. Patterns indicating target interference associated with two trials entail further investigation. The approaches offer new insights to quantifying the band-limited information throughputs of spatially non-local perception.

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Chen, Xianming, Chaoyang Zhang, and Zhaoxian Zhou. "Boosting non-graph matching feature-based face recognition with a multi-stage matching strategy." International Journal of Wavelets, Multiresolution and Information Processing 15, no. 02 (2017): 1750017. http://dx.doi.org/10.1142/s0219691317500175.

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In this paper, a multi-stage matching strategy is proposed to boost the performance of a non-graph matching feature-based face recognition. As the gallery size increases, the problem of recognition degradation gradually arises, due to the fact that the correct matching of feature points becomes more and more difficult. Other than only one round of matching in traditional methods, the multi-stage matching strategy determines the recognition result step by step. Instead of finding the best one matching, each step picks out a small portion of the training candidates and removes the others. The behavior of picking and removing repeats until the number of remaining candidates is small enough to produce the final result. Two multi-stage matching algorithms, n-ary elimination and divide and conquer, are introduced into the non-graph matching feature-based method from the perspectives of global and local, respectively. The experimental result shows that with the multi-stage matching strategy, the recognition accuracy of the non-graph matching feature-based method is evidently boosted. Moreover, the improvement level also increases with the gallery size.

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Sandeep Mishra, Akanksha Tiwari, and Ved Trivedi. "Volunteer and NGO Matching Platform." International Research Journal on Advanced Engineering Hub (IRJAEH) 2, no. 04 (2024): 742–50. http://dx.doi.org/10.47392/irjaeh.2024.0104.

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According to a poll conducted in 2021, during the epidemic, the number of donors in India increased by 43%. During the pandemic, Indian non-governmental organizations (NGOs) volunteered their services in every manner conceivable, and they still do. However, over the years, the proportion of volunteers has remained consistent. This demonstrates a disconnect between the general population and nonprofits as well as volunteerism. Establishing a platform that connects these volunteers with other charitable organizations would be crucial. The "Volunteer Matching Platform" project may be able to bridge this gap. This web-based platform is intended to facilitate volunteer connections between non-governmental organizations and local groups. NGOs will use this platform to promote their events to the nation's citizens. On the other hand, volunteers will be able to browse through the available opportunities, filtering based on their interests, location, and availability.

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Chen, Siyuan, Siyuan Cao, Haokun Wang, Yaoguang Sun, and Yankai Xu. "Post-stack seismic data interpolation using a fast non-local similarity matching algorithm." Studia Geophysica et Geodaetica 65, no. 1 (2021): 59–69. http://dx.doi.org/10.1007/s11200-020-0133-y.

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Leduc, Roxane, Hernan Carrillo, and Nicolas Papadakis. "Non-local Matching of Superpixel-based Deep Features for Color Transfer and Colorization." Image Processing On Line 14 (October 29, 2024): 232–49. http://dx.doi.org/10.5201/ipol.2024.522.

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Yang, Yang, Shaoyi Du, and Zhuo Chen. "A Method for Non-Rigid Face Alignment via Combining Local and Holistic Matching." PLOS ONE 11, no. 8 (2016): e0159376. http://dx.doi.org/10.1371/journal.pone.0159376.

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Liang, Meiyu, Junping Du, Linghui Li, and Shouxin Cao. "Super-resolution reconstruction based on multisource bidirectional similarity and non-local similarity matching." IET Image Processing 9, no. 11 (2015): 931–42. http://dx.doi.org/10.1049/iet-ipr.2014.0658.

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Pham, Cuong Cao, Vinh Quang Dinh, and Jae Wook Jeon. "Robust non-local stereo matching for outdoor driving images using segment-simple-tree." Signal Processing: Image Communication 39 (November 2015): 173–84. http://dx.doi.org/10.1016/j.image.2015.09.012.

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Zhang, Xiaohu, and Qin Sun. "Study on Parallel Numerical Calculation of Structural Domain Decomposition with Non-Matching Meshes." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 37, no. 4 (2019): 650–55. http://dx.doi.org/10.1051/jnwpu/20193740650.

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For the parallel solution of structural finite element problem with non-matching multi-domains, a nonmatched finite element tearing and interconnectiong (FETI) parallel algorithm is proposed based on L-FETI method by introducing the frame node force and modifying the frame displacement compatibility condition and the load balance condition. Based on the radial basis functions (RBF) interpolation technique, the data transfer of internal force and displacement between the neighboring subdomains has a unified matrix format and is easy to program by introducing a local coordinate system into the non-matching interfaces. Taking the finite element model for plate bending problem with four subdomains as an example, two kinds of multi-domains models, matching and non-matching meshes, are constructed respectively. The numerical results show that the out-of-plane deflection of the same nodes on the domain decomposition frame is in a good agreement, which shows that the present method is reasonable and effective for solving the parallel non-matching multi-domains model.

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Zhu, Hao, Ke Zou, Yongfu Li, Ming Cen, and Lyudmila Mihaylova. "Robust Non-Rigid Feature Matching for Image Registration Using Geometry Preserving." Sensors 19, no. 12 (2019): 2729. http://dx.doi.org/10.3390/s19122729.

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In this paper, a robust non-rigid feature matching approach for image registration with geometry constraints is proposed. The non-rigid feature matching approach is formulated as a maximum likelihood (ML) estimation problem. The feature points of one image are represented by Gaussian mixture model (GMM) centroids, and are fitted to the feature points of the other image by moving coherently to encode the global structure. To preserve the local geometry of these feature points, two local structure descriptors of the connectivity matrix and Laplacian coordinate are constructed. The expectation maximization (EM) algorithm is applied to solve this ML problem. Experimental results demonstrate that the proposed approach has better performance than current state-of-the-art methods.

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Yang, Houye, Jindong Yu, Zhuo Li, and Ze Yu. "Non-Local SAR Image Despeckling Based on Sparse Representation." Remote Sensing 15, no. 18 (2023): 4485. http://dx.doi.org/10.3390/rs15184485.

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Speckle noise is an inherent problem of synthetic aperture radar (SAR) images, which not only seriously affects the acquisition of SAR image information, but also greatly reduces the efficiency of image segmentation and feature classification. Therefore, research on how to effectively suppress speckle noise while preserving SAR image content information as much as possible has received increasing attention. Based on the non-local idea of SAR image block-matching three-dimensional (SAR-BM3D) algorithm and the concept of sparse representation, a novel SAR image despeckling algorithm is proposed. The new algorithm uses K-means singular value decomposition (K-SVD) to learn the dictionary to distinguish valid information and speckle noise and constructs a block filter based on K-SVD for despeckling, so as to avoid strong point diffusion problem in SAR-BM3D and achieve better speckle noise suppression with stronger adaptability. The experimental results on real SAR images show that the proposed algorithm achieves better comprehensive effect of speckle noise suppression in terms of evaluation indicators and information preservation of SAR images compared with several existing algorithms.

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Rambousky, R., J. Nitsch, and H. Garbe. "Matching the termination of radiating non-uniform transmission-lines." Advances in Radio Science 11 (July 4, 2013): 259–64. http://dx.doi.org/10.5194/ars-11-259-2013.

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Abstract. In this contribution a concept of matching the termination of radiating non-uniform transmission-lines is proposed. Using Transmission-Line Super Theory, position and frequency dependent line parameters can be obtained. Therefore, a characteristic impedance can be determined which is also position and frequency dependent. For a single wire transmission-line it could be shown that the maximum value of that characteristic impedance is an optimal termination in the sense of minimizing the variation of the current on the line. This indicates that matching is not a local effect at the position of the concentrated load but a cooperative process including the whole non-uniform transmission-line. In addition this choice of termination minimizes the variation of the radiated power over frequency.

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Kayali, Nour. "“Sounds like home”: The effect of listener on matching guise in regional accent on syntactic acceptability." Journal of the Acoustical Society of America 152, no. 4 (2022): A176. http://dx.doi.org/10.1121/10.0015945.

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This study unites sociophonetic speech perception and syntax research by matching or mismatching social expectations of participants during a syntactic grammaticality judgement task. I hypothesize that regional syntactic structures are more acceptable when heard in their corresponding local, regional accent compared to a nonlocal accent. The experiment focuses on Southern American English as spoken in western Kentucky as the local accent and personal datives and double modals as matching, regional grammatical structures, along with a set of nonlocal filler sentences. 81 University of Kentucky participants completed a between-subjects matched guise survey. Regional structures are consistently rated more acceptable in the local accent than the nonlocal. Self-identified Kentuckian Participants rated local structures higher than non-Kentuckian participants, regardless of which accent they heard. This is prominent in ratings of the nonlocal audio; Kentuckian participants rated both local structures in the nonlocal accent approximately fifteen points higher than non-Kentuckians. In contrast, non-Kentuckian participants mark filler sentences in the local audio lower than the nonlocal, a variation absent from Kentuckian participants’ ratings. These results suggest that grammaticality judgements result from an interplay of sociocultural expectations with the phonological and syntactic structure of an utterance. Judgement of structural grammaticality is not independent of social expectation.

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Zhang, Han, Lin Lei, Weiping Ni, et al. "Registration of Large Optical and SAR Images with Non-Flat Terrain by Investigating Reliable Sparse Correspondences." Remote Sensing 15, no. 18 (2023): 4458. http://dx.doi.org/10.3390/rs15184458.

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Optical and SAR image registration is the primary procedure to exploit the complementary information from the two different image modal types. Although extensive research has been conducted to narrow down the vast radiometric and geometric gaps so as to extract homogeneous characters for feature point matching, few works have considered the registration issue for non-flat terrains, which will bring in more difficulties for not only sparse feature point matching but also outlier removal and geometric relationship estimation. This article addresses these issues with a novel and effective optical-SAR image registration framework. Firstly, sparse feature points are detected based on the phase congruency moment map of the textureless SAR image (SAR-PC-Moment), which helps to identify salient local regions. Then a template matching process using very large local image patches is conducted, which increases the matching accuracy by a significant margin. Secondly, a mutual verification-based initial outlier removal method is proposed, which takes advantage of the different mechanisms of sparse and dense matching and requires no geometric consistency assumption within the inliers. These two procedures will produce a putative correspondence feature point (CP) set with a low outlier ratio and high reliability. In the third step, the putative CPs are used to segment the large input image of non-flat terrain into dozens of locally flat areas using a recursive random sample consensus (RANSAC) method, with each locally flat area co-registered using an affine transformation. As for the mountainous areas with sharp elevation variations, anchor CPs are first identified, and then optical flow-based pixelwise dense matching is conducted. In the experimental section, ablation studies using four precisely co-registered optical-SAR image pairs of flat terrain quantitatively verify the effectiveness of the proposed SAR-PC-Moment-based feature point detector, big template matching strategy, and mutual verification-based outlier removal method. Registration results on four 1 m-resolution non-flat image pairs prove that the proposed framework is able to produce robust and quite accurate registration results.

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Diao, Haiwen, Ying Zhang, Lin Ma, and Huchuan Lu. "Similarity Reasoning and Filtration for Image-Text Matching." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (2021): 1218–26. http://dx.doi.org/10.1609/aaai.v35i2.16209.

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Image-text matching plays a critical role in bridging the vision and language, and great progress has been made by exploiting the global alignment between image and sentence, or local alignments between regions and words. However, how to make the most of these alignments to infer more accurate matching scores is still underexplored. In this paper, we propose a novel Similarity Graph Reasoning and Attention Filtration (SGRAF) network for image-text matching. Specifically, the vector-based similarity representations are firstly learned to characterize the local and global alignments in a more comprehensive manner, and then the Similarity Graph Reasoning (SGR) module relying on one graph convolutional neural network is introduced to infer relation-aware similarities with both the local and global alignments. The Similarity Attention Filtration (SAF) module is further developed to integrate these alignments effectively by selectively attending on the significant and representative alignments and meanwhile casting aside the interferences of non-meaningful alignments. We demonstrate the superiority of the proposed method with achieving state-of-the-art performances on the Flickr30K and MSCOCO datasets, and the good interpretability of SGR and SAF with extensive qualitative experiments and analyses.

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Liamwiset, Chalida, and Vatanawood Wiwat. "Detection of Design Patterns in Software Design Model Using Graph." Applied Mechanics and Materials 411-414 (September 2013): 559–62. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.559.

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Detection of design patterns in software design phase possibly ensures the non-functional requirements, regarding performance features, before investing the implementation. We formalize the structural UML class diagram using graph. By applying graph matching technique, we propose an alternative of subgraph matching algorithm to extract the local properties of the UML class diagrams and perform the detecting of subgraph of possible design patterns found in the target software design model.

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Schwartzman-Nowik, Zohar, and Benjamin J. Brown. "Generalizing the matching decoder for the Chamon code." Quantum 9 (June 18, 2025): 1775. https://doi.org/10.22331/q-2025-06-18-1775.

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Different choices of quantum error-correcting codes can reduce the demands on the physical hardware needed to build a quantum computer. To achieve the full potential of a code, we must develop practical decoding algorithms that can correct errors that have occurred with high likelihood. Matching decoders are very good at correcting local errors while also demonstrating fast run times that can keep pace with physical quantum devices. We implement variations of a matching decoder for a three-dimensional, non-CSS, low-density parity check code known as the Chamon code, which has a non-trivial structure that does not lend itself readily to this type of decoding. The non-trivial structure of the syndrome of this code means that we can supplement the decoder with additional steps to improve the threshold error rate, below which the logical failure rate decreases with increasing code distance. We find that a generalized matching decoder that is augmented by a belief-propagation step prior to matching gives a threshold of 10.5% for depolarizing noise.

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Lv, Rui Peng, Hai Gang Sui, Ji Hui Tu, Xiao Yu Cai, and Liang Dong. "Object Tracking across Non-Overlapping Cameras Based on Improved TLD and Multi-Feathers Object Matching." Applied Mechanics and Materials 602-605 (August 2014): 1713–17. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.1713.

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Object tracking across non-overlapping views is a hot and important research topic in compute vision. In this paper, a novel method to track the interested object continuously across non-overlapping cameras is presented. This challenging task is taken as two sub-problems: single camera object tracking and object matching across disjoint cameras. An object tracking algorithm which improves Tracking-Learning-Detection (TLD) algorithm by adding background extraction and Kalman filter is presented to deal with the first problem. A new object matching algorithm based on the fusion of global features and local features at the assistance of 3D GIS is also introduced for object matching across disjoint cameras. The proposed approach does not need a training phase and inter-camera calibration. Experiments are carried out on real world videos to validate the proposed approach.

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Cheon, Bong-Won, and Nam-Ho Kim. "AWGN Removal Using Modified Steering Kernel and Image Matching." Applied Sciences 12, no. 22 (2022): 11588. http://dx.doi.org/10.3390/app122211588.

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Image noise occurs during acquisition and transmission and adversely affects processes, such as image segmentation and object recognition and classification. Various techniques are being studied for noise removal, and with the recent development of hardware and image processing algorithms, noise removal techniques that combine non-local techniques are attracting attention. However, one disadvantage of this method is that blurring occurs in the edges and boundary line of the resulting image. In this study, we proposed a modified local steering kernel based on image matching to improve these shortcomings. The proposed technique uses image matching to differentiate the weight obtained by the steering kernel according to the local characteristics of the image and calculates the weight of the filter based on the similarity between the center window and the matching window. The resulting images were quantitatively evaluation and enlargement of images were used and compared with the existing noise removal algorithms. The proposed algorithm showed clearer contrast in the enlarged images and better results than the conventional image restoration techniques in the quantitative evaluation using peak signal-to-noise ratio and structural similarity index.

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LIU, Liu, Xuezhi YANG, Fang ZHOU, and Wenhui LANG. "Non-local filtering for polarimetric SAR data based on three dimensional patch matching wavelet transform." National Remote Sensing Bulletin 21, no. 2 (2017): 218–27. http://dx.doi.org/10.11834/jrs.20176257.

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Chen, Yingpin, Yuan Liao, Yuxi He, et al. "Non-local similar block matching and hybrid low-rank tensor network for colour image inpainting." Digital Signal Processing 162 (July 2025): 105169. https://doi.org/10.1016/j.dsp.2025.105169.

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Delgado, Francisco, and Carlos Cardoso-Isidoro. "Non-Local Parallel Processing and Database Settlement Using Multiple Teleportation Followed by Grover Post-Selection." Entropy 25, no. 2 (2023): 376. http://dx.doi.org/10.3390/e25020376.

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Quantum information applications emerged decades ago, initially introducing a parallel development that mimicked the approach and development of classical computer science. However, in the current decade, novel computer-science concepts were rapidly extended to the fields of quantum processing, computation, and communication. Thus, areas such as artificial intelligence, machine learning, and neural networks have their quantum versions; furthermore, the quantum brain properties of learning, analyzing, and gaining knowledge are discussed. Quantum properties of matter conglomerates have been superficially explored in such terrain; however, the settlement of organized quantum systems able to perform processing can open a new pathway in the aforementioned domains. In fact, quantum processing involves certain requisites as the settlement of copies of input information to perform differentiated processing developed far away or in situ to diversify the information stored there. Both tasks at the end provide a database of outcomes with which to perform either information matching or final global processing with at least a subset of those outcomes. When the number of processing operations and input information copies is large, parallel processing (a natural feature in quantum computation due to the superposition) becomes the most convenient approach to accelerate the database settlement of outcomes, thus affording a time advantage. In the current study, we explored certain quantum features to realize a speed-up model for the entire task of processing based on a common information input to be processed, diversified, and finally summarized to gain knowledge, either in pattern matching or global information availability. By using superposition and non-local properties, the most valuable features of quantum systems, we realized parallel local processing to set a large database of outcomes and subsequently used post-selection to perform an ending global processing or a matching of information incoming from outside. We finally analyzed the details of the entire procedure, including its affordability and performance. The quantum circuit implementation, along with tentative applications, were also discussed. Such a model could be operated between large processing technological systems using communication procedures and also on a moderately controlled quantum matter conglomerate. Certain interesting technical aspects involving the non-local control of processing via entanglement were also analyzed in detail as an associated but notable premise.

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Kadmin, A. F., R. A. Hamzah, M. N. Abd Manap, M. S. Hamid, and T. F. Tg Wook. "Local stereo matching algorithm using modified dynamic cost computation." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 3 (2021): 1312. http://dx.doi.org/10.11591/ijeecs.v22.i3.pp1312-1319.

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Stereo matching is an essential subject in stereo vision architecture. Traditional framework composition consists of several constraints in stereo correspondences such as illumination variations in images and inadequate or non-uniform light due to uncontrollable environments. This work improves the local method stereo matching algorithm based on the dynamic cost computation method for depth measurement. This approach utilised modified dynamic cost computation in the matching cost. A modified census transform with dynamic histogram is used to provide the cost in the cost computation. The algorithm applied the fixed-window strategy with bilateral filtering to retain image depth information and edge in the cost aggregation stage. A winner takes all (WTA) optimisation and left-right check with adaptive bilateral median filtering are employed for disparity refinement. Based on the Middlebury benchmark dataset, the algorithm developed in this work has better accuracy and outperformed several other state-of-the-art algorithms.

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Kadmin, A. F., R. A. Hamzah, M. N. Abd Manap, M. S. Hamid, and T. F. Tg. Wook. "Local stereo matching algorithm using modified dynamic cost computation." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 3 (2021): 1312–19. https://doi.org/10.11591/ijeecs.v22.i3.pp1312-1319.

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Wang, Jingjing, Fangyan Dong, Yutaka Hatakeyama, Hajime Nobuhara, and Kaoru Hirota. "Local Character Tensors for 3D Registration Method on Free-View Datasets." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 7 (2007): 848–57. http://dx.doi.org/10.20965/jaciii.2007.p0848.

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A local character tensor is proposed for the automatic three-dimensional (3D) pair-wise registration based on free-view 3D datasets. In the proposed method, there are two characters, i.e., the optimal segmentation to realize the automatic processing and local character tensor to improve the matching probability. It is applied for solving the mismatching problem and large-scale 3D datasets, using non-structured datasets are tested in a PC with Intel Pentium M 1.50 GHz and 1.0 GB memory. Pair-wised experimental results show the proposed method increases average 12.6% matching probability and decreases average 18.9 seconds computational time compared to the conventional local character based registration method. This registration method can be further applied to 3D reconstruction from navigation, model based object recognition to accurate 3D geometric object model application.

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Ma, Shaokang, Xiuhong Li, Kangwei Liu, Tianchi Qiu, and Yulong Liu. "Research on Image Stitching Algorithm Based on Point-Line Consistency and Local Edge Feature Constraints." Entropy 26, no. 1 (2024): 61. http://dx.doi.org/10.3390/e26010061.

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Image stitching aims to synthesize a wider and more informative whole image, which has been widely used in various fields. This study focuses on improving the accuracy of image mosaic and proposes an image mosaic method based on local edge contour matching constraints. Because the accuracy and quantity of feature matching have a direct influence on the stitching result, it often leads to wrong image warpage model estimation when feature points are difficult to detect and match errors are easy to occur. To address this issue, the geometric invariance is used to expand the number of feature matching points, thus enriching the matching information. Based on Canny edge detection, significant local edge contour features are constructed through operations such as structure separation and edge contour merging to improve the image registration effect. The method also introduces the spatial variation warping method to ensure the local alignment of the overlapping area, maintains the line structure in the image without bending by the constraints of short and long lines, and eliminates the distortion of the non-overlapping area by the global line-guided warping method. The method proposed in this paper is compared with other research through experimental comparisons on multiple datasets, and excellent stitching results are obtained.

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Xiao, Min, and Yuhong Guo. "Semi-Supervised Kernel Matching for Domain Adaptation." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (2021): 1183–89. http://dx.doi.org/10.1609/aaai.v26i1.8292.

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In this paper, we propose a semi-supervised kernel matching method to address domain adaptation problems where the source distribution substantially differs from the target distribution. Specifically, we learn a prediction function on the labeled source data while mapping the target data points to similar source data points by matching the target kernel matrix to a submatrix of the source kernel matrix based on a Hilbert Schmidt Independence Criterion. We formulate this simultaneous learning and mapping process as a non-convex integer optimization problem and present a local minimization procedure for its relaxed continuous form. Our empirical results show the proposed kernel matching method significantly outperforms alternative methods on the task of across domain sentiment classification.

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Huang, X., R. Qin, and M. Chen. "DISPARITY REFINEMENT OF BUILDING EDGES USING ROBUSTLY MATCHED STRAIGHT LINES FOR STEREO MATCHING." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-1 (September 26, 2018): 77–84. http://dx.doi.org/10.5194/isprs-annals-iv-1-77-2018.

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<p><strong>Abstract.</strong> Stereo dense matching has already been one of the dominant tools in 3D reconstruction of urban regions, due to its low cost and high flexibility in generating 3D points. However, the image-derived 3D points are often inaccurate around building edges, which limit its use in several vision tasks (e.g. building modelling). To generate 3D point clouds or digital surface models (DSM) with sharp boundaries, this paper integrates robustly matched lines for improving dense matching, and proposes a non-local disparity refinement of building edges through an iterative least squares plane adjustment approach. In our method, we first extract and match straight lines in images using epipolar constraints, then detect building edges from these straight lines by comparing matching results on both sides of straight lines, and finally we develop a non-local disparity refinement method through an iterative least squares plane adjustment constrained by matched straight lines to yield sharper and more accurate edges. Experiments conducted on both satellite and aerial data demonstrate that our proposed method is able to generate more accurate DSM with sharper object boundaries.</p>

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Zhang, Yufeng, Jinghao Zhang, Zeyu Cui, Shu Wu, and Liang Wang. "A Graph-based Relevance Matching Model for Ad-hoc Retrieval." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (2021): 4688–96. http://dx.doi.org/10.1609/aaai.v35i5.16599.

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To retrieve more relevant, appropriate and useful documents given a query, finding clues about that query through the text is crucial. Recent deep learning models regard the task as a term-level matching problem, which seeks exact or similar query patterns in the document. However, we argue that they are inherently based on local interactions and do not generalise to ubiquitous, non-consecutive contextual relationships. In this work, we propose a novel relevance matching model based on graph neural networks to leverage the document-level word relationships for ad-hoc retrieval. In addition to the local interactions, we explicitly incorporate all contexts of a term through the graph-of-word text format. Matching patterns can be revealed accordingly to provide a more accurate relevance score. Our approach significantly outperforms strong baselines on two ad-hoc benchmarks. We also experimentally compare our model with BERT and show our advantages on long documents.

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Li, ManLong, and Kin Tak U. "An Enhanced Fractal Image Compression Algorithm Based on Adaptive Non-Uniform Rectangular Partition." Electronics 14, no. 13 (2025): 2550. https://doi.org/10.3390/electronics14132550.

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The Basic Fractal Image Compression (BFIC) method is widely known for its high computational complexity and long encoding time under a fixed block segmentation. To address these limitations, we propose an enhanced fractal image compression algorithm based on adaptive non-uniform rectangular partition (FICANRP). This novel approach adaptively partitions the image into variable-sized range blocks (R-blocks) and non-overlapping domain blocks (D-blocks) guided by local texture and feature. By converting the similarity-matching process for R-blocks into a localized search strategy based on block size and feature classification, the FICANRP method significantly reduces computational overhead. Moreover, employing a non-overlapping partition strategy for D-blocks drastically reduces the number of D-blocks and the associated spatial coordinate data while preserving high matching accuracy. This reduction, coupled with the block similarity matching algorithm that overcomes traditional fractal computation redundancy, significantly decreases algorithmic complexity and encoding time. Additionally, by adaptively segmenting R-blocks into varying sizes according to local texture, the proposed method minimizes redundancy in smooth regions while preserving fine details in complex areas. The experimental results show that compared with BFIC, FICANRP has a compression ratio (CR) improvement range of 0.84–2.29 times, a PSNR improvement range of 0.25–4.8 dB, and an acceleration encoding time efficiency improvement of 54.14×–1448.73×. Compared with QFIC, under the same PSNR, the FICANRP compression ratio (CR) improvement range is 0.87–19.12 times, and the accelerated encoding time (ET) efficiency is increased by 37.26×–114.83×.

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Sellitto, A., R. Borrelli, F. Caputo, A. Riccio, and F. Scaramuzzino. "Application to plate components of a kinematic global‐local approach for non‐matching finite element meshes." International Journal of Structural Integrity 3, no. 3 (2012): 260–73. http://dx.doi.org/10.1108/17579861211264370.

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