Journal articles: 'Local and global matching'

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Zhao, Jie, Shuchun Yu, and Hegao Cai. "Local‐global stereo matching algorithm." Aircraft Engineering and Aerospace Technology 78, no. 4 (July 2006): 289–92. http://dx.doi.org/10.1108/17488840610675564.

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Ainslie, George. "How do people choose between local and global bookkeeping?" Behavioral and Brain Sciences 19, no. 4 (December 1996): 574–75. http://dx.doi.org/10.1017/s0140525x00043004.

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AbstractThe matching law accounts for both addictive behavior and the usefulness of a person's evaluating choices in overall categories. To explain why overall bookkeeping, once learned, does not easily win out over local bookkeeping, another implication of matching is needed: intertemporal bargaining. The role of melioration, though probably important for new addiction is separate.

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Damjanović, Sanja, Ferdinand van der Heijden, and Luuk J. Spreeuwers. "Local Stereo Matching Using Adaptive Local Segmentation." ISRN Machine Vision 2012 (August 23, 2012): 1–11. http://dx.doi.org/10.5402/2012/163285.

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We propose a new dense local stereo matching framework for gray-level images based on an adaptive local segmentation using a dynamic threshold. We define a new validity domain of the frontoparallel assumption based on the local intensity variations in the 4 neighborhoods of the matching pixel. The preprocessing step smoothes low-textured areas and sharpens texture edges, whereas the postprocessing step detects and recovers occluded and unreliable disparities. The algorithm achieves high stereo reconstruction quality in regions with uniform intensities as well as in textured regions. The algorithm is robust against local radiometrical differences and successfully recovers disparities around the objects edges, disparities of thin objects, and the disparities of the occluded region. Moreover, our algorithm intrinsically prevents errors caused by occlusion to propagate into nonoccluded regions. It has only a small number of parameters. The performance of our algorithm is evaluated on the Middlebury test bed stereo images. It ranks highly on the evaluation list outperforming many local and global stereo algorithms using color images. Among the local algorithms relying on the frontoparallel assumption, our algorithm is the best-ranked algorithm. We also demonstrate that our algorithm is working well on practical examples as for disparity estimation of a tomato seedling and a 3D reconstruction of a face.

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Wang, Junqiu, and Yasushi Yagi. "Efficient Topological Localization Using Global and Local Feature Matching." International Journal of Advanced Robotic Systems 10, no. 3 (January 2013): 153. http://dx.doi.org/10.5772/55630.

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Kisel, Andrej, Alexej Kochetkov, and Justas Kranauskas. "Fingerprint Minutiae Matching without Global Alignment Using Local Structures." Informatica 19, no. 1 (January 1, 2008): 31–44. http://dx.doi.org/10.15388/informatica.2008.200.

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Norman, Larry. "Making bancassurance work: matching global power to local knowledge." International Journal of Bank Marketing 25, no. 2 (March 6, 2007): 117–19. http://dx.doi.org/10.1108/02652320710728447.

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Ma, Xin, Zhicheng Zhang, Danfeng Wang, Yu Luo, and Hui Yuan. "Adaptive Deconvolution-Based Stereo Matching Net for Local Stereo Matching." Applied Sciences 12, no. 4 (February 17, 2022): 2086. http://dx.doi.org/10.3390/app12042086.

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In deep learning-based local stereo matching methods, larger image patches usually bring better stereo matching accuracy. However, it is unrealistic to increase the size of the image patch size without restriction. Arbitrarily extending the patch size will change the local stereo matching method into the global stereo matching method, and the matching accuracy will be saturated. We simplified the existing Siamese convolutional network by reducing the number of network parameters and propose an efficient CNN based structure, namely adaptive deconvolution-based disparity matching net (ADSM net) by adding deconvolution layers to learn how to enlarge the size of input feature map for the following convolution layers. Experimental results on the KITTI2012 and 2015 datasets demonstrate that the proposed method can achieve a good trade-off between accuracy and complexity.

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Hou, Jing, Jin Xiang Pian, Ying Zhang, and Ming Yue Wang. "A Region-Based Image Matching Combining Global and Local Features." Applied Mechanics and Materials 182-183 (June 2012): 1868–72. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.1868.

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A new approach is presented to match two images in presenting large scale changes. The novelty of our algorithm is a hierarchical matching strategy for global region features and local descriptors, which combines the descriptive power of global features and the discriminative power of local descriptors. To predict the likely location and scale of an object, global features extracted from the segmentation regions is used in the first stage for an efficient region matching. This initial matching can be ambiguous due to the instability and unreliability of global region feature, and therefore in the later stage local descriptors are matched within each region pair to discard false positives and the final matches are filtered by RANSAC. Experiments show the effectiveness and superiority of the proposed method in comparing to other approaches.

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Wenzhen Zhou, and Wenzhao Zhang. "Fingerprint matching based on global orientation field and local features." International Journal of Digital Content Technology and its Applications 6, no. 19 (October 31, 2012): 279–87. http://dx.doi.org/10.4156/jdcta.vol6.issue19.35.

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Ma, Lin, Wenhao Jiang, Zequn Jie, and Xu Wang. "Bidirectional image-sentence retrieval by local and global deep matching." Neurocomputing 345 (June 2019): 36–44. http://dx.doi.org/10.1016/j.neucom.2018.11.089.

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Ellingson, Leif, and Jinfeng Zhang. "Protein Surface Matching by Combining Local and Global Geometric Information." PLoS ONE 7, no. 7 (July 17, 2012): e40540. http://dx.doi.org/10.1371/journal.pone.0040540.

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Dudek, Roman, Simone Croci, Aljosa Smolic, and Sebastian Knorr. "Robust global and local color matching in stereoscopic omnidirectional content." Signal Processing: Image Communication 74 (May 2019): 231–41. http://dx.doi.org/10.1016/j.image.2019.02.013.

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Rhee, S., and T. Kim. "DENSE 3D POINT CLOUD GENERATION FROM UAV IMAGES FROM IMAGE MATCHING AND GLOBAL OPTIMAZATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 6, 2016): 1005–9. http://dx.doi.org/10.5194/isprsarchives-xli-b1-1005-2016.

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3D spatial information from unmanned aerial vehicles (UAV) images is usually provided in the form of 3D point clouds. For various UAV applications, it is important to generate dense 3D point clouds automatically from over the entire extent of UAV images. In this paper, we aim to apply image matching for generation of local point clouds over a pair or group of images and global optimization to combine local point clouds over the whole region of interest. We tried to apply two types of image matching, an object space-based matching technique and an image space-based matching technique, and to compare the performance of the two techniques. The object space-based matching used here sets a list of candidate height values for a fixed horizontal position in the object space. For each height, its corresponding image point is calculated and similarity is measured by grey-level correlation. The image space-based matching used here is a modified relaxation matching. We devised a global optimization scheme for finding optimal pairs (or groups) to apply image matching, defining local match region in image- or object- space, and merging local point clouds into a global one. For optimal pair selection, tiepoints among images were extracted and stereo coverage network was defined by forming a maximum spanning tree using the tiepoints. From experiments, we confirmed that through image matching and global optimization, 3D point clouds were generated successfully. However, results also revealed some limitations. In case of image-based matching results, we observed some blanks in 3D point clouds. In case of object space-based matching results, we observed more blunders than image-based matching ones and noisy local height variations. We suspect these might be due to inaccurate orientation parameters. The work in this paper is still ongoing. We will further test our approach with more precise orientation parameters.

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Rhee, S., and T. Kim. "DENSE 3D POINT CLOUD GENERATION FROM UAV IMAGES FROM IMAGE MATCHING AND GLOBAL OPTIMAZATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 6, 2016): 1005–9. http://dx.doi.org/10.5194/isprs-archives-xli-b1-1005-2016.

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3D spatial information from unmanned aerial vehicles (UAV) images is usually provided in the form of 3D point clouds. For various UAV applications, it is important to generate dense 3D point clouds automatically from over the entire extent of UAV images. In this paper, we aim to apply image matching for generation of local point clouds over a pair or group of images and global optimization to combine local point clouds over the whole region of interest. We tried to apply two types of image matching, an object space-based matching technique and an image space-based matching technique, and to compare the performance of the two techniques. The object space-based matching used here sets a list of candidate height values for a fixed horizontal position in the object space. For each height, its corresponding image point is calculated and similarity is measured by grey-level correlation. The image space-based matching used here is a modified relaxation matching. We devised a global optimization scheme for finding optimal pairs (or groups) to apply image matching, defining local match region in image- or object- space, and merging local point clouds into a global one. For optimal pair selection, tiepoints among images were extracted and stereo coverage network was defined by forming a maximum spanning tree using the tiepoints. From experiments, we confirmed that through image matching and global optimization, 3D point clouds were generated successfully. However, results also revealed some limitations. In case of image-based matching results, we observed some blanks in 3D point clouds. In case of object space-based matching results, we observed more blunders than image-based matching ones and noisy local height variations. We suspect these might be due to inaccurate orientation parameters. The work in this paper is still ongoing. We will further test our approach with more precise orientation parameters.

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Dall'Asta, E., and R. Roncella. "A comparison of semiglobal and local dense matching algorithms for surface reconstruction." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-5 (June 6, 2014): 187–94. http://dx.doi.org/10.5194/isprsarchives-xl-5-187-2014.

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Encouraged by the growing interest in automatic 3D image-based reconstruction, the development and improvement of robust stereo matching techniques is one of the most investigated research topic of the last years in photogrammetry and computer vision. The paper is focused on the comparison of some stereo matching algorithms (local and global) which are very popular both in photogrammetry and computer vision. In particular, the Semi-Global Matching (SGM), which realizes a pixel-wise matching and relies on the application of consistency constraints during the matching cost aggregation, will be discussed. The results of some tests performed on real and simulated stereo image datasets, evaluating in particular the accuracy of the obtained digital surface models, will be presented. Several algorithms and different implementation are considered in the comparison, using freeware software codes like MICMAC and OpenCV, commercial software (e.g. Agisoft PhotoScan) and proprietary codes implementing Least Square e Semi-Global Matching algorithms. The comparisons will also consider the completeness and the level of detail within fine structures, and the reliability and repeatability of the obtainable data.

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Li, Wenwen. "Biometric Recognition of Finger Knuckle Print Based on the Fusion of Global Features and Local Features." Journal of Healthcare Engineering 2022 (January 7, 2022): 1–11. http://dx.doi.org/10.1155/2022/6041828.

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Compared with the most traditional fingerprint identification, knuckle print and hand shape are more stable, not easy to abrase, forge, and pilfer; in aspect of image acquisition, the requirement of acquisition equipment and environment are not high; and the noncontact acquisition method also greatly improves the users’ satisfaction; therefore, finger knuckle print and hand shape of single-mode identification system have attracted extensive attention both at home and abroad. A large number of studies show that multibiometric fusion can greatly improve the recognition rate, antiattack, and robustness of the biometric recognition system. A method combining global features and local features was designed for the recognition of finger knuckle print images. On the one hand, principal component analysis (PCA) was used as the global feature for rapid recognition. On the other hand, the local binary pattern (LBP) operator was taken as the local feature in order to extract the texture features that can reflect details. A two-layer serial fusion strategy is proposed in the combination of global and local features. Firstly, the sample library scope was narrowed according to the global matching result. Secondly, the matching result was further determined by fine matching. By combining the fast speed of global coarse matching and the high accuracy of local refined matching, the designed method can improve the recognition rate and the recognition speed.

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Wang, Jinghui, Ke Gong, Timo Balz, Norbert Haala, Uwe Soergel, Lu Zhang, and Mingsheng Liao. "Radargrammetric DSM Generation by Semi-Global Matching and Evaluation of Penalty Functions." Remote Sensing 14, no. 8 (April 7, 2022): 1778. http://dx.doi.org/10.3390/rs14081778.

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Radargrammetry is a useful approach to generate Digital Surface Models (DSMs) and an alternative to InSAR techniques that are subject to temporal or atmospheric decorrelation. Stereo image matching in radargrammetry refers to the process of determining homologous points in two images. The performance of image matching influences the final quality of DSM used for spatial-temporal analysis of landscapes and terrain. In SAR image matching, local matching methods are commonly used but usually produce sparse and inaccurate homologous points adding ambiguity to final products; global or semi-global matching methods are seldom applied even though more accurate and dense homologous points can be yielded. To fill this gap, we propose a hierarchical semi-global matching (SGM) pipeline to reconstruct DSMs in forested and mountainous regions using stereo TerraSAR-X images. In addition, three penalty functions were implemented in the pipeline and evaluated for effectiveness. To make accuracy and efficiency comparisons between our SGM dense matching method and the local matching method, the normalized cross-correlation (NCC) local matching method was also applied to generate DSMs using the same test data. The accuracy of radargrammetric DSMs was validated against an airborne photogrammetric reference DSM and compared with the accuracy of NASA’s 30 m SRTM DEM. The results show the SGM pipeline produces DSMs with height accuracy and computing efficiency that exceeds the SRTM DEM and NCC-derived DSMs. The penalty function adopting the Canny edge detector yields a higher vertical precision than the other two evaluated penalty functions. SGM is a powerful and efficient tool to produce high-quality DSMs using stereo Spaceborne SAR images.

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Sekuler, Allison B., Stephen E. Palmer, and Carol Flynn. "Local and Global Processes in Visual Completion." Psychological Science 5, no. 5 (September 1994): 260–67. http://dx.doi.org/10.1111/j.1467-9280.1994.tb00623.x.

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In the natural environment, objects are frequently occluded, and people continuously complete partly occluded objects Do local processes or global processes control the completion of partly occluded objects? To answer this question, most previous studies simply asked subjects to draw the completions they “saw” Such drawing tasks are highly subjective, and they provide equivocal results Our studies are the first to use an objective, implicit paradigm (primed matching) to determine the extent to which local or global processes underlie the visual completion of partly occluded objects Our results suggest that global processes dominate perceptual completion, whereas local processes do not play a large role Therefore, local theories of completion, or theories in which local processes dominate, cannot be entirely correct

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Li, Liangping, Sanjay Srinivasan, Haiyan Zhou, and J. Jaime Gómez-Hernández. "A local–global pattern matching method for subsurface stochastic inverse modeling." Environmental Modelling & Software 70 (August 2015): 55–64. http://dx.doi.org/10.1016/j.envsoft.2015.04.008.

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Fine, Justin M., Aaron D. Likens, Eric L. Amazeen, and Polemnia G. Amazeen. "Emergent complexity matching in interpersonal coordination: Local dynamics and global variability." Journal of Experimental Psychology: Human Perception and Performance 41, no. 3 (2015): 723–37. http://dx.doi.org/10.1037/xhp0000046.

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Kumar, N. B. Mahesh, and K. Premalatha. "Palmprint Authentication System Based on Local and Global Feature Fusion Using DOST." Journal of Applied Mathematics 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/918376.

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Palmprint is the region between wrist and fingers. In this paper, a palmprint personal identification system is proposed based on the local and global information fusion. The local and global information is critical for the image observation based on the results of the relationship between physical stimuli and perceptions. The local features of the enhanced palmprint are extracted using discrete orthonormal Stockwell transform. The global feature is obtained by reducing the scale of discrete orthonormal Stockwell transform to infinity. The local and global matching distances of the two palmprint images are fused to get the final matching distance of the proposed scheme. The results show that the fusion of local and global features outperforms the existing works on the available three datasets.

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Hödel, M., T. Koch, L. Hoegner, and U. Stilla. "MONOCULAR-DEPTH ASSISTED SEMI-GLOBAL MATCHING." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W7 (September 16, 2019): 55–62. http://dx.doi.org/10.5194/isprs-annals-iv-2-w7-55-2019.

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Abstract. Reconstruction of dense photogrammetric point clouds is often based on depth estimation of rectified image pairs by means of pixel-wise matching. The main drawback lies in the high computational complexity compared to that of the relatively straightforward task of laser triangulation. Dense image matching needs oriented and rectified images and looks for point correspondences between them. The search for these correspondences is based on two assumptions: pixels and their local neighborhood show a similar radiometry and image scenes are mostly homogeneous, meaning that neighboring points in one image are most likely also neighbors in the second. These rules are violated, however, at depth changes in the scene. Optimization strategies tend to find the best depth estimation based on the resulting disparities in the two images. One new field in neural networks is the estimation of a depth image from a single input image through learning geometric relations in images. These networks are able to find homogeneous areas as well as depth changes, but result in a much lower geometric accuracy of the estimated depth compared to dense matching strategies. In this paper, a method is proposed extending the Semi-Global-Matching algorithm by utilizing a-priori knowledge from a monocular depth estimating neural network to improve the point correspondence search by predicting the disparity range from the single-image depth estimation (SIDE). The method also saves resources through path optimization and parallelization. The algorithm is benchmarked on Middlebury data and results are presented both quantitatively and qualitatively.

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van Lier, Rob J., Emanuel L. J. Leeuwenberg, and Peter A. van der Helm. "Multiple Completions Primed by Occlusion Patterns." Perception 24, no. 7 (July 1995): 727–40. http://dx.doi.org/10.1068/p240727.

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There is a strong tendency to complete a partly occluded shape. Two types of pattern completion, global and local, are frequently reported. By means of the primed-matching paradigm, it has previously been shown that global completions are prevalent for stimuli in which regularity is abundantly present. In our study the primed-matching paradigm is applied to such stimuli in order to find out whether the rival local completion is generated as well. Therefore anomalous completions are added to the experimental design. Priming effects both on global and on local completions are compared with priming effects on those anomalous completions. The data indeed suggest that the occlusion patterns evoked not only a global but also a local completion.

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Zhou, Zhili, Kunde Lin, Yi Cao, Ching-Nung Yang, and Yuling Liu. "Near-Duplicate Image Detection System Using Coarse-to-Fine Matching Scheme Based on Global and Local CNN Features." Mathematics 8, no. 4 (April 22, 2020): 644. http://dx.doi.org/10.3390/math8040644.

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Due to the great success of convolutional neural networks (CNNs) in the area of computer vision, the existing methods tend to match the global or local CNN features between images for near-duplicate image detection. However, global CNN features are not robust enough to combat background clutter and partial occlusion, while local CNN features lead to high computational complexity in the step of feature matching. To achieve high efficiency while maintaining good accuracy, we propose a coarse-to-fine feature matching scheme using both global and local CNN features for real-time near-duplicate image detection. In the coarse matching stage, we implement the sum-pooling operation on convolutional feature maps (CFMs) to generate the global CNN features, and match these global CNN features between a given query image and database images to efficiently filter most of irrelevant images of the query. In the fine matching stage, the local CNN features are extracted by using maximum values of the CFMs and the saliency map generated by the graph-based visual saliency detection (GBVS) algorithm. These local CNN features are then matched between images to detect the near-duplicate versions of the query. Experimental results demonstrate that our proposed method not only achieves a real-time detection, but also provides higher accuracy than the state-of-the-art methods.

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Tan, Hao-Ru, Chuang Wang, Si-Tong Wu, Tie-Qiang Wang, Xu-Yao Zhang, and Cheng-Lin Liu. "Proxy Graph Matching with Proximal Matching Networks." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 11 (May 18, 2021): 9808–15. http://dx.doi.org/10.1609/aaai.v35i11.17179.

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Estimating feature point correspondence is a common technique in computer vision. A line of recent data-driven approaches utilizing the graph neural networks improved the matching accuracy by a large margin. However, these learning-based methods require a lot of labeled training data, which are expensive to collect. Moreover, we find most methods are sensitive to global transforms, for example, a random rotation. On the contrary, classical geometric approaches are immune to rotational transformation though their performance is generally inferior. To tackle these issues, we propose a new learning-based matching framework, which is designed to be rotationally invariant. The model only takes geometric information as input. It consists of three parts: a graph neural network to generate a high-level local feature, an attention-based module to normalize the rotational transform, and a global feature matching module based on proximal optimization. To justify our approach, we provide a convergence guarantee for the proximal method for graph matching. The overall performance is validated by numerical experiments. In particular, our approach is trained on the synthetic random graphs and then applied to several real-world datasets. The experimental results demonstrate that our method is robust to rotational transform and highlights its strong performance of matching accuracy.

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Liu, Sheng, Haiqiang Jin, Xiaojun Mao, Binbin Zhai, Ye Zhan, and Xiaofei Feng. "Selective Segmentation for Global Optimization of Depth Estimation in Complex Scenes." Scientific World Journal 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/868674.

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This paper proposes a segmentation-based global optimization method for depth estimation. Firstly, for obtaining accurate matching cost, the original local stereo matching approach based on self-adapting matching window is integrated with two matching cost optimization strategies aiming at handling both borders and occlusion regions. Secondly, we employ a comprehensive smooth term to satisfy diverse smoothness request in real scene. Thirdly, a selective segmentation term is used for enforcing the plane trend constraints selectively on the corresponding segments to further improve the accuracy of depth results from object level. Experiments on the Middlebury image pairs show that the proposed global optimization approach is considerably competitive with other state-of-the-art matching approaches.

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Xia, G., and C. Hu. "AUTOMATIC MATCHING OF LARGE SCALE IMAGES AND TERRESTRIAL LIDAR BASED ON APP SYNERGY OF MOBILE PHONE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 1925–29. http://dx.doi.org/10.5194/isprs-archives-xlii-3-1925-2018.

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The digitalization of Cultural Heritage based on ground laser scanning technology has been widely applied. High-precision scanning and high-resolution photography of cultural relics are the main methods of data acquisition. The reconstruction with the complete point cloud and high-resolution image requires the matching of image and point cloud, the acquisition of the homonym feature points, the data registration, etc. However, the one-to-one correspondence between image and corresponding point cloud depends on inefficient manual search. The effective classify and management of a large number of image and the matching of large image and corresponding point cloud will be the focus of the research. In this paper, we propose automatic matching of large scale images and terrestrial LiDAR based on APP synergy of mobile phone. Firstly, we develop an APP based on Android, take pictures and record related information of classification. Secondly, all the images are automatically grouped with the recorded information. Thirdly, the matching algorithm is used to match the global and local image. According to the one-to-one correspondence between the global image and the point cloud reflection intensity image, the automatic matching of the image and its corresponding laser radar point cloud is realized. Finally, the mapping relationship between global image, local image and intensity image is established according to homonym feature point. So we can establish the data structure of the global image, the local image in the global image, the local image corresponding point cloud, and carry on the visualization management and query of image.

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Oyedele, Adesegun, and Fuat Firat. "Institutions, small local firms’ strategies, and global alliances in sub-Saharan Africa emerging markets." International Marketing Review 37, no. 1 (December 16, 2019): 156–82. http://dx.doi.org/10.1108/imr-01-2019-0022.

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Purpose The purpose of this paper is to respond to the call of international marketing professionals for more studies on strategies that firms use in response to the complexities of interacting with other institutions in the emerging markets (EMs) of sub-Saharan Africa. The key research question investigated by employing the exploratory qualitative data gathered is: What strategies and global alliances do small local firms (SLFs) in Nigeria adopt to succeed under complex market conditions? Design/methodology/approach The methodology employed is exploratory qualitative research. The authors conducted extended interviews to generate rich case study data from the top management of the selected SLFs in Nigeria. The interview data were assessed using open, axial and selective coding to uncover macro-narratives that guide SLFs’ strategies and global alliances. Findings The macro-narratives derived from the qualitative case analysis reveal a theoretical framework centered on three major elements of competitive strategies in Nigeria: build global capacity and strategic alliances from the get-go; develop local strategic alliances; master matching alliance partners’ needs to create innovative payment plans and, when necessary, shift the transaction cost burden to alliance partners. Matching theory rather than traditional network theories is better at explicating SLFs’ alliances in Nigeria. Implementation of these strategies requires flexible strategic initiatives. Originality/value The study adapts institutional interaction theory, network theory, matching alliance perspective, trade credit theories and the literature on small firms’ strategies in EMs to explicate successful small local firm strategies and global alliances under complex market conditions in Nigeria. The recognition that SLFs regularly migrate and shift the burden of transactions’ cost to multiple stakeholders in the supply network by matching customers and supplier needs is important. The discovery of matching theory in explicating SLFs’ global alliances in Nigeria is unique to this study.

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WU, En-sheng, and Min-chen ZHU. "Corner matching method of constraints of distance combining local and global information." Journal of Computer Applications 30, no. 1 (March 9, 2010): 68–70. http://dx.doi.org/10.3724/sp.j.1087.2010.00068.

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SOUNDAR, K. RUBA, and K. MURUGESAN. "CORRELATION BASED FACE MATCHING IN COMBINED GLOBAL AND LOCAL PRESERVING FEATURE SPACE." International Journal of Pattern Recognition and Artificial Intelligence 24, no. 08 (December 2010): 1281–94. http://dx.doi.org/10.1142/s021800141000838x.

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Face recognition plays a vital role in authentication, monitoring, indexing, access control and other surveillance applications. Much research on face recognition with various feature based approaches using global or local features employing a number of similarity measurement techniques have been done earlier. Feature based approaches using global features can effectively preserve only the Euclidean structure of face space, that suffer from lack of local features which may play a major role in some applications. On the other hand, wtih local features only the face subspace that best detects the essential face manifold structure is obtained and it also suffers loss in global features which may also be important in some other applications. Measuring similarity or distance between two feature vectors is also a key step for any pattern matching application. In this work, a new combined approach for recognizing faces that integrates the advantages of the global feature extraction technique by Linear Discriminant Analysis (LDA) and the local feature extraction technique by Locality Preserving Projections (LPP) with correlation based similarity measurement technique has been discussed. This has been validated by performing various experiments and by making a fair comparison with conventional methods.

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Zhan, Yaru, Xiuyang Zhao, Xue Lin, Junkai Liu, Mingjun Liu, and Dongmei Niu. "Graph matching based on local and global information of the graph nodes." Multimedia Tools and Applications 79, no. 17-18 (January 7, 2020): 11567–90. http://dx.doi.org/10.1007/s11042-019-08516-x.

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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 (May 18, 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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Fan, Dong Jin, and Li Dong Wang. "A Novel Fingerprint Matching Algorithm Based on Compatible Multi-Area Alignment." Applied Mechanics and Materials 347-350 (August 2013): 3104–8. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3104.

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Most minutiae-based matching algorithms consist of two phases, local match and global match. In the local phase, some corresponding pairs are obtained by comparing the affine-invariable features of minutiae. And then two images are aligned based on the candidate pairs. However, some spurious candidate pairs and the large nonlinear deformation in images lead to the failure in global match. In this paper, we proposed a novel minutiae-based matching scheme which insert a filtering step after the local match to discard the incompatible pairs and renovate the global match by dividing the whole image into small areas according to the location of the candidate pairs. Results on databases of FVC2004 validate our algorithm.

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Shi, Buhai, Qingming Zhang, and Haibo Xu. "A Geometrical-Information-Assisted Approach for Local Feature Matching." Mathematical Problems in Engineering 2019 (February 24, 2019): 1–15. http://dx.doi.org/10.1155/2019/1409672.

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This paper presents a geometrical-information-assisted approach for matching local features. With the aid of Bayes’ theorem, it is found that the posterior confidence of matched features can be improved by introducing global geometrical information given by distances between feature points. Based on this result, we work out an approach to obtain the geometrical information and apply it to assist matching features. The pivotal techniques in this paper include (1) exploiting elliptic parameters of feature descriptors to estimate transformations that map feature points in images to points in an assumed plane; (2) projecting feature points to the assumed plane and finding a reliable referential point in it; (3) computing differences of the distances between the projected points and the referential point. Our new approach employs these differences to assist matching features, reaching better performance than the nearest neighbor-based approach in precision versus the number of matched features.

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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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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/isprs-annals-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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Shu, Ran, Ho-Gun Ha, Dae-Chul Kim, and Yeong-Ho Ha. "Integrated Color Matching in Stereoscopic Image by Combining Local and Global Color Compensation." Journal of the Institute of Electronics Engineers of Korea 50, no. 12 (December 25, 2013): 168–75. http://dx.doi.org/10.5573/ieek.2013.50.12.168.

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Yin, Jichao, Han-Young Park, Akhil Datta-Gupta, Michael J. King, and Manoj K. Choudhary. "A hierarchical streamline-assisted history matching approach with global and local parameter updates." Journal of Petroleum Science and Engineering 80, no. 1 (December 2011): 116–30. http://dx.doi.org/10.1016/j.petrol.2011.10.014.

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Huang, Kai. "On-line signature verification based on dynamic segmentation and global and local matching." Optical Engineering 34, no. 12 (December 1, 1995): 3480. http://dx.doi.org/10.1117/12.215474.

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Chen, Shengyong, Zhongjie Wang, Hanyang Tong, Sheng Liu, and Beiwei Zhang. "Optimal Feature Matching for 3D Reconstruction by Combination of Global and Local Information." Intelligent Automation & Soft Computing 17, no. 7 (January 2011): 957–68. http://dx.doi.org/10.1080/10798587.2011.10643202.

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Li, Haojie, Xiaohui Wang, Jinhui Tang, and Chunxia Zhao. "Combining global and local matching of multiple features for precise item image retrieval." Multimedia Systems 19, no. 1 (May 31, 2012): 37–49. http://dx.doi.org/10.1007/s00530-012-0265-1.

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Zhu, Sa, Weixuan Ma, and Jian Yao. "Global and local geometric constrained feature matching for high resolution remote sensing images." Computers and Electrical Engineering 103 (October 2022): 108337. http://dx.doi.org/10.1016/j.compeleceng.2022.108337.

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Aoki, Terumasa, and Van Nguyen. "Global Distribution Adjustment and Nonlinear Feature Transformation for Automatic Colorization." Advances in Multimedia 2018 (2018): 1–15. http://dx.doi.org/10.1155/2018/1504691.

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Automatic colorization is generally classified into two groups: propagation-based methods and reference-based methods. In reference-based automatic colorization methods, color image(s) are used as reference(s) to reconstruct original color of a gray target image. The most important task here is to find the best matching pairs for all pixels between reference and target images in order to transfer color information from reference to target pixels. A lot of attractive local feature-based image matching methods have already been developed for the last two decades. Unfortunately, as far as we know, there are no optimal matching methods for automatic colorization because the requirements for pixel matching in automatic colorization are wholly different from those for traditional image matching. To design an efficient matching algorithm for automatic colorization, clustering pixel with low computational cost and generating descriptive feature vector are the most important challenges to be solved. In this paper, we present a novel method to address these two problems. In particular, our work concentrates on solving the second problem (designing a descriptive feature vector); namely, we will discuss how to learn a descriptive texture feature using scaled sparse texture feature combining with a nonlinear transformation to construct an optimal feature descriptor. Our experimental results show our proposed method outperforms the state-of-the-art methods in terms of robustness for color reconstruction for automatic colorization applications.

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Xue, Xingsi, Xiaojing Wu, Chao Jiang, Guojun Mao, and Hai Zhu. "Integrating Sensor Ontologies with Global and Local Alignment Extractions." Wireless Communications and Mobile Computing 2021 (February 5, 2021): 1–10. http://dx.doi.org/10.1155/2021/6625184.

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In order to enhance the communication between sensor networks in the Internet of things (IoT), it is indispensable to establish the semantic connections between sensor ontologies in this field. For this purpose, this paper proposes an up-and-coming sensor ontology integrating technique, which uses debate mechanism (DM) to extract the sensor ontology alignment from various alignments determined by different matchers. In particular, we use the correctness factor of each matcher to determine a correspondence’s global factor, and utilize the support strength and disprove strength in the debating process to calculate its local factor. Through comprehensively considering these two factors, the judgment factor of an entity mapping can be obtained, which is further applied in extracting the final sensor ontology alignment. This work makes use of the bibliographic track provided by the Ontology Alignment Evaluation Initiative (OAEI) and five real sensor ontologies in the experiment to assess the performance of our method. The comparing results with the most advanced ontology matching techniques show the robustness and effectiveness of our approach.

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Sun, Bingbing, and Tariq Alkhalifah. "A robust waveform inversion using a global comparison of modeled and observed data." Leading Edge 38, no. 3 (March 2019): 185–92. http://dx.doi.org/10.1190/tle38030185.1.

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A high-resolution model of the subsurface is the product of a successful full-waveform inversion (FWI) application. However, this optimization problem is highly nonlinear, and thus, we iteratively update the subsurface model by minimizing a misfit function that measures the difference between observed and modeled data. The L2-norm misfit function provides a simple, sample-by-sample comparison between the observed and modeled data. However, it is susceptible to local minima in the objective function if the low-wavenumber components of the initial model are not accurate enough. We review an alternative formulation of FWI based on a more global comparison. A combination of Radon transform and utilizing a matching filter allows for comparisons beyond sample to sample. We combine two recent developments to suggest the following algorithm for optimal inversion: (1) we compute the matching filter between the observed and modeled data in the Radon domain, which helps reduce the crosstalk introduced in the deconvolution step of computing the matching filter, and (2) we use Wasserstein distance to measure the distance between the resulting matching filter in the Radon domain and a representation of the Dirac delta function, which provides us with the optimal transport between the two distribution functions. We use a modified Marmousi model to show how this Radon-domain optimal-transport-based matching-filter approach can mitigate cycle skipping. Starting from a rather simplified v(z) media as the initial model, the proposed method can invert for the Marmousi model with considerable accuracy, while standard L2-norm formulation is trapped in a local minimum. Application of the proposed method to an offshore data set further demonstrates its robustness and effectiveness.

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Zhou, Zi Wei, Ge Li, Chang Le Li, and Ji Zhuang Fan. "A Improved Stereo Matching Fast Algorithm Based on Dynamic Programming." Key Engineering Materials 531-532 (December 2012): 657–61. http://dx.doi.org/10.4028/www.scientific.net/kem.531-532.657.

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Compared with the local algorithm in stereo matching, the high quality disparity space image is calculated by the global algorithm, which is difficult to use in practical application for its long computation time. The dynamic programming is one of the global algorithms with a fast matching speed, but it has strip blemish in matching result. In this paper, a new dynamic programming based method is proposed to accelerate the matching speed and improve the matching quality. Firstly, the color feature of two images are calculated using the Laplacians of Gaussian pyramid algorithm, and the color feature of the image pair obtained are matched. Secondly, the matching points are taken as the ground control points of the scan line, which is cut into several short line segments. Finally, all line segments are matched to obtain the disparity of the scan line. The experimental results show that the matching speed is accelerated greatly with improved disparity image quality

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Mantica, S., A. Cominelli, and G. Mantica. "Combining Global and Local Optimization Techniques for Automatic History Matching Production and Seismic Data." SPE Journal 7, no. 02 (June 1, 2002): 123–30. http://dx.doi.org/10.2118/78353-pa.

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Binkowski, T. ANDREW, and Andrzej Joachimiak. "Protein Functional Surfaces: Global Shape Matching and Local Spatial Alignments of Ligand Binding Sites." BMC Structural Biology 8, no. 1 (2008): 45. http://dx.doi.org/10.1186/1472-6807-8-45.

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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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Wang, Tao, Xin Chen, Chang Tan, and Hao Fu. "Localization of Substation Fittings Based on a Stereo Vision Method." Journal of Advanced Computational Intelligence and Intelligent Informatics 22, no. 6 (October 20, 2018): 861–68. http://dx.doi.org/10.20965/jaciii.2018.p0861.

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We propose a novel stereo vision method based on a fast template matching strategy to improve localization accuracy and efficiency of substation fittings. First, considering the salient features of the substation fittings that can be recognized easily, the method searches for features that are similar to the ones in the matching template related to the sub-image of the substation fittings from the global image. When the substation fittings are confirmed, the method repeatedly searches for the one of screw holes in the local region of the substation fittings. It then computes the centering coordinates of the template in the source images until the screw holes are matched. The experimental results show that the proposed template matching method increases the accuracy and efficiency of the substation fitting localization from the global to local search area. Correspondingly, the accuracy and efficiency of stereo vision localization of substation fittings is improved.

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Journal articles on the topic 'Local and global matching'

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