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Journal articles on the topic 'Neighborhood of a point'
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Beketayev, Kenes, Damir Yeliussizov, Dmitriy Morozov, Gunther H. Weber, and Bernd Hamann. "Measuring the Error in Approximating the Sub-Level Set Topology of Sampled Scalar Data." International Journal of Computational Geometry & Applications 28, no. 01 (2018): 57–77. http://dx.doi.org/10.1142/s0218195918500036.
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This paper studies the influence of the definition of neighborhoods and methods used for creating point connectivity on topological analysis of scalar functions. It is assumed that a scalar function is known only at a finite set of points with associated function values. In order to utilize topological approaches to analyze the scalar-valued point set, it is necessary to choose point neighborhoods and, usually, point connectivity to meaningfully determine critical-point behavior for the point set. Two distances are used to measure the difference in topology when different point neighborhoods and means to define connectivity are used: (i) the bottleneck distance for persistence diagrams and (ii) the distance between merge trees. Usually, these distances define how different scalar functions are with respect to their topology. These measures, when properly adapted to point sets coupled with a definition of neighborhood and connectivity, make it possible to understand how topological characteristics depend on connectivity. Noise is another aspect considered. Five types of neighborhoods and connectivity are discussed: (i) the Delaunay triangulation; (ii) the relative neighborhood graph; (iii) the Gabriel graph; (iv) the [Formula: see text]-nearest-neighbor (KNN) neighborhood; and (v) the Vietoris–Rips complex. It is discussed in detail how topological characterizations depend on the chosen connectivity.
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Wang, Lei, Ming Huang, Zhenqing Yang, et al. "LBNP: Learning features between neighboring points for point cloud classification." PLOS ONE 20, no. 1 (2025): e0314086. https://doi.org/10.1371/journal.pone.0314086.
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Inspired by classical works, when constructing local relationships in point clouds, there is always a geometric description of the central point and its neighboring points. However, the basic geometric representation of the central point and its neighborhood is insufficient. Drawing inspiration from local binary pattern algorithms used in image processing, we propose a novel method for representing point cloud neighborhoods, which we call Point Cloud Local Auxiliary Block (PLAB). This module explores useful neighborhood features by learning the relationships between neighboring points, thereby enhancing the learning capability of the model. In addition, we propose a pure Transformer structure that takes into account both local and global features, called Dual Attention Layer (DAL), which enables the network to learn valuable global features as well as local features in the aggregated feature space. Experimental results show that our method performs well on both coarse- and fine-grained point cloud datasets. We will publish the code and all experimental training logs on GitHub.
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Shen, Yaqi, Le Hui, Haobo Jiang, Jin Xie, and Jian Yang. "Reliable Inlier Evaluation for Unsupervised Point Cloud Registration." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 2 (2022): 2198–206. http://dx.doi.org/10.1609/aaai.v36i2.20117.
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Unsupervised point cloud registration algorithm usually suffers from the unsatisfied registration precision in the partially overlapping problem due to the lack of effective inlier evaluation. In this paper, we propose a neighborhood consensus based reliable inlier evaluation method for robust unsupervised point cloud registration. It is expected to capture the discriminative geometric difference between the source neighborhood and the corresponding pseudo target neighborhood for effective inlier distinction. Specifically, our model consists of a matching map refinement module and an inlier evaluation module. In our matching map refinement module, we improve the point-wise matching map estimation by integrating the matching scores of neighbors into it. The aggregated neighborhood information potentially facilitates the discriminative map construction so that high-quality correspondences can be provided for generating the pseudo target point cloud. Based on the observation that the outlier has the significant structure-wise difference between its source neighborhood and corresponding pseudo target neighborhood while this difference for inlier is small, the inlier evaluation module exploits this difference to score the inlier confidence for each estimated correspondence. In particular, we construct an effective graph representation for capturing this geometric difference between the neighborhoods. Finally, with the learned correspondences and the corresponding inlier confidence, we use the weighted SVD algorithm for transformation estimation.Under the unsupervised setting, we exploit the Huber function based global alignment loss, the local neighborhood consensus loss and spatial consistency loss for model optimization. The experimental results on extensive datasets demonstrate that our unsupervised point cloud registration method can yield comparable performance.
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Tian, Fujing, Zhidi Jiang, and Gangyi Jiang. "DNet: Dynamic Neighborhood Feature Learning in Point Cloud." Sensors 21, no. 7 (2021): 2327. http://dx.doi.org/10.3390/s21072327.
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Neighborhood selection is very important for local region feature learning in point cloud learning networks. Different neighborhood selection schemes may lead to quite different results for point cloud processing tasks. The existing point cloud learning networks mainly adopt the approach of customizing the neighborhood, without considering whether the selected neighborhood is reasonable or not. To solve this problem, this paper proposes a new point cloud learning network, denoted as Dynamic neighborhood Network (DNet), to dynamically select the neighborhood and learn the features of each point. The proposed DNet has a multi-head structure which has two important modules: the Feature Enhancement Layer (FELayer) and the masking mechanism. The FELayer enhances the manifold features of the point cloud, while the masking mechanism is used to remove the neighborhood points with low contribution. The DNet can learn the manifold features and spatial geometric features of point cloud, and obtain the relationship between each point and its effective neighborhood points through the masking mechanism, so that the dynamic neighborhood features of each point can be obtained. Experimental results on three public datasets demonstrate that compared with the state-of-the-art learning networks, the proposed DNet shows better superiority and competitiveness in point cloud processing task.
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Zhu, Yushu, and Qiang Fu. "Deciphering the Civic Virtue of Communal Space." Environment and Behavior 49, no. 2 (2016): 161–91. http://dx.doi.org/10.1177/0013916515627308.
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Drawing on a citywide survey of 39 urban neighborhoods and a qualitative case study of a neighborhood in Guangzhou, China, this research addresses how communal space, social capital, and neighborhood attachment (NA) jointly shape neighborhood participation (NP). Communal space is strongly and significantly associated with NP. Furthermore, we find that communal space is related to NP in two ways: promoting place-based social relations (the social-capital mechanism) and nurturing place attachment (the intrapsychic mechanism). These findings point to the significance of communal space as a civic focal point in community building and place making.
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Witczak, Tomasz. "Topological and Multi-Topological Frames in the Context of Intuitionistic Modal Logic." Bulletin of the Section of Logic 48, no. 3 (2019): 187–205. http://dx.doi.org/10.18778/0138-0680.48.3.03.
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We present three examples of topological semantics for intuitionistic modal logic with one modal operator □. We show that it is possible to treat neighborhood models, introduced earlier, as topological or multi-topological. From the neighborhood point of view, our method is based on differences between properties of minimal and maximal neighborhoods. Also we propose transformation of multitopological spaces into the neighborhood structures.
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Weinmann, M., B. Jutzi, and C. Mallet. "Semantic 3D scene interpretation: A framework combining optimal neighborhood size selection with relevant features." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-3 (August 7, 2014): 181–88. http://dx.doi.org/10.5194/isprsannals-ii-3-181-2014.
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3D scene analysis by automatically assigning 3D points a semantic label has become an issue of major interest in recent years. Whereas the tasks of feature extraction and classification have been in the focus of research, the idea of using only relevant and more distinctive features extracted from optimal 3D neighborhoods has only rarely been addressed in 3D lidar data processing. In this paper, we focus on the interleaved issue of extracting relevant, but not redundant features and increasing their distinctiveness by considering the respective optimal 3D neighborhood of each individual 3D point. We present a new, fully automatic and versatile framework consisting of four successive steps: (i) optimal neighborhood size selection, (ii) feature extraction, (iii) feature selection, and (iv) classification. In a detailed evaluation which involves 5 different neighborhood definitions, 21 features, 6 approaches for feature subset selection and 2 different classifiers, we demonstrate that optimal neighborhoods for individual 3D points significantly improve the results of scene interpretation and that the selection of adequate feature subsets may even further increase the quality of the derived results.
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Chen, Dong. "On total flexibility of local structures of Finsler tori without conjugate points." Journal of Topology and Analysis 11, no. 02 (2019): 349–55. http://dx.doi.org/10.1142/s1793525319500158.
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Cope, Michael R., Jorden E. Jackson, Scott R. Sanders, Lance D. Erickson, Tippe Morlan, and Ralph B. Brown. "The Manifestation of Neighborhood Effects: A Pattern for Community Growth?" Societies 10, no. 1 (2020): 16. http://dx.doi.org/10.3390/soc10010016.
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Neighborhood effects, or the development of community by neighborhoods, are often studied in an urban context. Previous research has neglected to examine the influence of neighborhoods in nonurban settings. Our case study, however, contributes to the existing literature as it takes place in a small, rural-to-urban town at an important point in time where the town was urbanizing. We find that neighborhood effects also influence community satisfaction and attachment in Creekdale, an urbanizing town. Using survey data (N = 1006) drawn from the Creekdale Community Citizens Viewpoint Survey (CCVS), we find that, contrary to conventional wisdom, population size and density does not matter necessarily for an individual’s community attachment and satisfaction; community experience is shaped by neighborhood effects.
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Gorbunova, Lidia A., Jens Ambrasat, and Christian von Scheve. "Neighborhood Stereotypes and Interpersonal Trust in Social Exchange: An Experimental Study." City & Community 14, no. 2 (2015): 206–25. http://dx.doi.org/10.1111/cico.12112.
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Recent research indicates that segregation is, in addition to many other undesirable consequences, negatively associated with social capital, in particular, generalized trust within a community. This study investigates whether an individual's residential neighborhood and the stereotypes associated with this neighborhood affect others’ trusting behavior as a specific form of social exchange. Using an anonymous trust game experiment in the context of five districts of the German capital, Berlin, we show that trusting is contingent on others’ residential neighborhood rather than on deliberate assessments of trustworthiness. Participants show significantly greater trust toward individuals from positively stereotyped neighborhoods with favorable sociodemographic characteristics than to persons from negatively stereotyped neighborhoods with unfavorable sociodemographics. Importantly, when stereotypes and sociodemographic factors point in opposite directions, participants’ trust decisions reflect stereotype content.
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Liao, Zhongping, Tao Peng, Ruiqi Tang, and Zhiguo Hao. "Point Cloud Registration Algorithm Based on Adaptive Neighborhood Eigenvalue Loading Ratio." Applied Sciences 14, no. 11 (2024): 4828. http://dx.doi.org/10.3390/app14114828.
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Traditional iterative closest point (ICP) registration algorithms are sensitive to initial positions and easily fall into the trap of locally optimal solutions. To address this problem, a point cloud registration algorithm is put forward in this study based on adaptive neighborhood eigenvalue loading ratios. In the algorithm, the resolution of the point cloud is first calculated and used as an adaptive basis to determine the raster widths and radii of spherical neighborhoods in the raster filtering; then, the adaptive raster filtering is implemented to the point cloud for denoising, while the eigenvalue loading ratios of point neighborhoods are calculated to extract and match the contour feature points; subsequently, sample consensus initial alignment (SAC-IA) is used to carry out coarse registration; and finally, a fine registration is delivered with KD-tree-accelerated ICP. The experimental results of this study demonstrate that the feature points extracted with this method are highly representative while consuming only 35.6% of the time consumed by other feature point extraction algorithms. Additionally, in noisy and low-overlap scenarios, the registration error of this method can be controlled at a level of 0.1 mm, with the registration speed improved by 56% on average over that of other algorithms. Taken together, the method in this study cannot only ensure strong robustness in registration but can also deliver high registration accuracy and efficiency.
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Li, Yong, Guofeng Tong, Xiance Du, Xiang Yang, Jianjun Zhang, and Lin Yang. "A Single Point-Based Multilevel Features Fusion and Pyramid Neighborhood Optimization Method for ALS Point Cloud Classification." Applied Sciences 9, no. 5 (2019): 951. http://dx.doi.org/10.3390/app9050951.
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3D point cloud classification has wide applications in the field of scene understanding. Point cloud classification based on points can more accurately segment the boundary region between adjacent objects. In this paper, a point cloud classification algorithm based on a single point multilevel features fusion and pyramid neighborhood optimization are proposed for a Airborne Laser Scanning (ALS) point cloud. First, the proposed algorithm determines the neighborhood region of each point, after which the features of each single point are extracted. For the characteristics of the ALS point cloud, two new feature descriptors are proposed, i.e., a normal angle distribution histogram and latitude sampling histogram. Following this, multilevel features of a single point are constructed by multi-resolution of the point cloud and multi-neighborhood spaces. Next, the features are trained by the Support Vector Machine based on a Gaussian kernel function, and the points are classified by the trained model. Finally, a classification results optimization method based on a multi-scale pyramid neighborhood constructed by a multi-resolution point cloud is used. In the experiment, the algorithm is tested by a public dataset. The experimental results show that the proposed algorithm can effectively classify large-scale ALS point clouds. Compared with the existing algorithms, the proposed algorithm has a better classification performance.
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Liu, Liman, Jinjin Yu, Longyu Tan, Wanjuan Su, Lin Zhao, and Wenbing Tao. "Semantic Segmentation of 3D Point Cloud Based on Spatial Eight-Quadrant Kernel Convolution." Remote Sensing 13, no. 16 (2021): 3140. http://dx.doi.org/10.3390/rs13163140.
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In order to deal with the problem that some existing semantic segmentation networks for 3D point clouds generally have poor performance on small objects, a Spatial Eight-Quadrant Kernel Convolution (SEQKC) algorithm is proposed to enhance the ability of the network for extracting fine-grained features from 3D point clouds. As a result, the semantic segmentation accuracy of small objects in indoor scenes can be improved. To be specific, in the spherical space of the point cloud neighborhoods, a kernel point with attached weights is constructed in each octant, the distances between the kernel point and the points in its neighborhood are calculated, and the distance and the kernel points’ weights are used together to weight the point cloud features in the neighborhood space. In this case, the relationship between points are modeled, so that the local fine-grained features of the point clouds can be extracted by the SEQKC. Based on the SEQKC, we design a downsampling module for point clouds, and embed it into classical semantic segmentation networks (PointNet++, PointSIFT and PointConv) for semantic segmentation. Experimental results on benchmark dataset ScanNet V2 show that SEQKC-based PointNet++, PointSIFT and PointConv outperform the original networks about 1.35–2.12% in terms of MIoU, and they effectively improve the semantic segmentation performance of the networks for small objects of indoor scenes, e.g., the segmentation accuracy of small object “picture” is improved from 0.70% of PointNet++ to 10.37% of SEQKC-PointNet++.
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West, Samuel J., Diane Bishop, Derek A. Chapman, and Nicholas D. Thomson. "Comparing forms of neighborhood instability as predictors of violence in Richmond, VA." PLOS ONE 17, no. 9 (2022): e0273718. http://dx.doi.org/10.1371/journal.pone.0273718.
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Violence events tend to cluster together geospatially. Various features of communities and their residents have been highlighted as explanations for such clustering in the literature. One reliable correlate of violence is neighborhood instability. Research on neighborhood instability indicates that such instability can be measured as property tax delinquency, yet no known work has contrasted external and internal sources of instability in predicting neighborhood violence. To this end we collected data on violence events, company and personal property tax delinquency, population density, race, income, food stamps, and alcohol outlets for each of Richmond, Virginia’s 148 neighborhoods. We constructed and compared ordinary least-squares (OLS) to geographically weighted regression (GWR) models before constructing a final algorithm-selected GWR model. Our results indicated that the tax delinquency of company-owned properties (e.g., rental homes, apartments) was the only variable in our model (R2 = 0.62) that was associated with violence in all but four Richmond neighborhoods. We replicated this analysis using violence data from a later point in time which yielded largely identical results. These findings indicate that external sources of neighborhood instability may be more important to predicting violence than internal sources. Our results further provide support for social disorganization theory and point to opportunities to expand this framework.
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Wang, Zhecheng, Haoyuan Li, and Ram Rajagopal. "Urban2Vec: Incorporating Street View Imagery and POIs for Multi-Modal Urban Neighborhood Embedding." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (2020): 1013–20. http://dx.doi.org/10.1609/aaai.v34i01.5450.
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Understanding intrinsic patterns and predicting spatiotemporal characteristics of cities require a comprehensive representation of urban neighborhoods. Existing works relied on either inter- or intra-region connectivities to generate neighborhood representations but failed to fully utilize the informative yet heterogeneous data within neighborhoods. In this work, we propose Urban2Vec, an unsupervised multi-modal framework which incorporates both street view imagery and point-of-interest (POI) data to learn neighborhood embeddings. Specifically, we use a convolutional neural network to extract visual features from street view images while preserving geospatial similarity. Furthermore, we model each POI as a bag-of-words containing its category, rating, and review information. Analog to document embedding in natural language processing, we establish the semantic similarity between neighborhood (“document”) and the words from its surrounding POIs in the vector space. By jointly encoding visual, textual, and geospatial information into the neighborhood representation, Urban2Vec can achieve performances better than baseline models and comparable to fully-supervised methods in downstream prediction tasks. Extensive experiments on three U.S. metropolitan areas also demonstrate the model interpretability, generalization capability, and its value in neighborhood similarity analysis.
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Cui, Yuxia, Zhipeng Sun, and Xianlun Wang. "Research on robot scene recognition based on improved feature point matching algorithm." ITM Web of Conferences 47 (2022): 02028. http://dx.doi.org/10.1051/itmconf/20224702028.
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A new feature description method based on the fusion of fast retina keypoint (FREAK) and the rotation-aware binary robust independent elementary features (rRBRIEF) is proposed to realize the effective combination of efficiency and accuracy of the two feature descriptions. In addition, in the elimination stage of mismatched point pairs, by setting the base point and its neighborhood, an improved neighborhood parallel random sample consensus (RANSAC) algorithm is proposed to achieve efficient parallel operation of the algorithm in multiple local neighborhoods. The improved feature point matching algorithm and the existing algorithm were tested in different scales, different rotations, different illuminations, and different fuzzy data sets. The experimental results show that the improved algorithm improves the average scene recognition accuracy by 18.21%, improves the efficiency by 15.58%, and shows good robustness.
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Liu, Shou Bin, and Kun Feng. "Point Cloud Segmentation Based on Moving Probability." Applied Mechanics and Materials 380-384 (August 2013): 1796–99. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1796.
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This paper presents a novel automatic algorithm for point cloud segmentation by using moving probability. An arbitrary point in point cloud is selected as the first seed point. Starting from the seed point, moving probability between the starting point and each of neighborhood points is estimated. Once one or more points with probabilities greater than a given threshold are identified, the starting point will move to these neighborhood points and new starting points are generated. Moving probabilities are estimated again and starting points move continually until all calculated probabilities are less than the threshold. Visited points are segmented from point cloud data. The second seed point is selected arbitrarily from the rest of points and the process is repeated. As a result, point cloud is segmented into individual feature regions. Experimental results show the effectiveness of the proposed algorithm.
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Roitenberg, V. Sh. "On Singular "Semifocus" Type Point Bifurcations of Piecewise Smooth Dynamical System." Mathematics and Mathematical Modeling, no. 5 (November 12, 2018): 57–70. http://dx.doi.org/10.24108/mathm.0518.0000140.
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For the processes described by dynamical systems, closed trajectories of dynamical systems are in line with periodic oscillations. Therefore, there is a considerable interest in describing the bifurcations of the generation of closed trajectories from equilibrium when the parameters change. In typical one-parameter and two-parameter families of smooth dynamical systems on a plane, closed trajectories can be generated only from equilibrium – weak focus. In mathematical modeling in the theory of automatic control, in mechanics and in other applications, piecewise smooth dynamical systems are often used. For them, there are other bifurcations of the generation of closed trajectories from equilibrium. The paper describes one of them, which is a typical family of dynamical systems specified by a piecewise smooth vector field on a two-dimensional manifold depending on two small parameters. It is assumed that for zero values of the parameters the vector field has a singular point O on the line of discontinuity of the field, and the point O is stable; in one half-neighborhood of the point O the field coincides with a smooth vector field for which the point O is a weak focus with positive (negative) first Lyapunov value, and in the other half-neighborhood it coincides with a smooth vector field directed at the points of the line of discontinuity inside the first of the semi-neighborhoods. The paper describes bifurcations in the neighborhood of the point O as the parameters change, in particular, indicating the regions of the parameters for which the vector field has a stable closed trajectory.
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HENDERSON, MICHAEL E. "MULTIPLE PARAMETER CONTINUATION: COMPUTING IMPLICITLY DEFINED k-MANIFOLDS." International Journal of Bifurcation and Chaos 12, no. 03 (2002): 451–76. http://dx.doi.org/10.1142/s0218127402004498.
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We present a new continuation method for computing implicitly defined manifolds. The manifold is represented as a set of overlapping neighborhoods, and extended by an added neighborhood of a boundary point. The boundary point is found using an expression for the boundary in terms of the vertices of a set of finite, convex polyhedra. The resulting algorithm is quite simple, allows adaptive spacing of the computed points, and deals with the problems of local and global overlap in a natural way. The algorithm is robust (the new points need only be near the boundary), and is well suited to problems with large embedding dimension, and small to moderate dimension.
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Böhlmark, Anders, and Alexander Willén. "Tipping and the Effects of Segregation." American Economic Journal: Applied Economics 12, no. 1 (2020): 318–47. http://dx.doi.org/10.1257/app.20170579.
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We analyze how neighborhood ethnic population composition affects the short- and long-run education and labor market outcomes of natives and immigrants. To overcome the problem of nonrandom sorting across neighborhoods, we borrow theoretical insights from the tipping point literature and exploit estimated tipping thresholds as instruments for changes in ethnic population composition. Our results provide little evidence in support of the idea that living in a neighborhood with a higher immigrant share leads to worse outcomes. (JEL I20, J15, J24, R23)
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Li, Mingfeng, Lichen Zhao, Ding Tan, and Xiaozhe Tong. "BLE Fingerprint Indoor Localization Algorithm Based on Eight-Neighborhood Template Matching." Sensors 19, no. 22 (2019): 4859. http://dx.doi.org/10.3390/s19224859.
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Aiming at the problem of indoor environment, signal non-line-of-sight propagation and other factors affect the accuracy of indoor locating, an algorithm of indoor fingerprint localization based on the eight-neighborhood template is proposed. Based on the analysis of the signal strength of adjacent reference points in the fingerprint database, the methods for the eight-neighborhood template matching and generation were studied. In this study, the indoor environment was divided into four quadrants for each access point and the expected values of the received signal strength indication (RSSI) difference between the center points and their eight-neighborhoods in different quadrants were chosen as the generation parameters. Then different templates were generated for different access points, and the unknown point was located by the Euclidean distance for the correlation of RSSI between each template and its coverage area in the fingerprint database. With the spatial correlation of fingerprint data taken into account, the influence of abnormal fingerprint on locating accuracy is reduced. The experimental results show that the locating error is 1.0 m, which is about 0.2 m less than both K-nearest neighbor (KNN) and weighted K-nearest neighbor (WKNN) algorithms.
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Ma, Teng, Xiang Long, Lu Feng, Pei Luo, and Zhuangzhi Wu. "Visible neighborhood graph of point clouds." Graphical Models 74, no. 4 (2012): 184–96. http://dx.doi.org/10.1016/j.gmod.2012.04.007.
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Chen, Xijiang, Qing An, Bufan Zhao, et al. "Contour Extraction of UAV Point Cloud Based on Neighborhood Geometric Features of Multi-Level Growth Plane." Drones 8, no. 6 (2024): 239. http://dx.doi.org/10.3390/drones8060239.
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The extraction of UAV building point cloud contour points is the basis for the expression of a three-dimensional lightweight building outline. Previous unmanned aerial vehicle (UAV) building point cloud contour extraction methods have mainly focused on the expression of the roof contour, but did not extract the wall contour. In view of this, an algorithm based on the geometric features of the neighborhood points of the region-growing clustering fusion surface is proposed to extract the boundary points of the UAV building point cloud. Firstly, the region growth plane is fused to obtain a more accurate segmentation plane. Then, the neighboring points are projected onto the neighborhood plane and a vector between the object point and neighborhood point is constructed. Finally, the azimuth of each vector is calculated, and the boundary points of each segmented plane are extracted according to the difference in adjacent azimuths. Experiment results show that the best boundary points can be extracted when the number of adjacent points is 24 and the difference in adjacent azimuths is 120. The proposed method is superior to other methods in the contour extraction of UAV buildings point clouds. Moreover, it can extract not only the building roof contour points, but also the wall contour points, including the window contour points.
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Vasil’eva, Ekaterina V. "Multi-pass stable periodic points of diffeomorphism of a plane with a homoclinic orbit." Vestnik of Saint Petersburg University. Mathematics. Mechanics. Astronomy 8, no. 3 (2021): 406–16. http://dx.doi.org/10.21638/spbu01.2021.303.
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A diffeomorphism of a plane into itself with a fixed hyperbolic point and a nontransversal point homoclinic to it is studied. There are various ways of touching a stable and unstable manifold at a homoclinic point. Periodic points whose trajectories do not leave the vicinity of the trajectory of a homoclinic point are divided into a countable set of types. Periodic points of the same type are called n-pass periodic points if their trajectories have n turns that lie outside a sufficiently small neighborhood of the hyperbolic point. Earlier in the articles of Sh. Newhouse, L. P. Shil’nikov, B. F. Ivanov and other authors, diffeomorphisms of the plane with a nontransversal homoclinic point were studied, it was assumed that this point is a tangency point of finite order. In these papers, it was shown that in a neighborhood of a homoclinic point there can be infinite sets of stable two-pass and three-pass periodic points. The presence of such sets depends on the properties of the hyperbolic point. In this paper, it is assumed that a homoclinic point is not a point with a finite order of tangency of a stable and unstable manifold. It is shown in the paper that for any fixed natural number n, a neighborhood of a nontransversal homolinic point can contain an infinite set of stable n-pass periodic points with characteristic exponents separated from zero.
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Kwok, Alan H., Julia Becker, Douglas Paton, Emma Hudson-Doyle, and David Johnston. "Stakeholders’ Perspectives of Social Capital in Informing the Development of Neighborhood-Based Disaster Resilience Measurements." Journal of Applied Social Science 13, no. 1 (2019): 26–57. http://dx.doi.org/10.1177/1936724419827987.
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The cultivation of neighborhood-based social capital has gained significant attention in the disaster management sector in recent years as a means to increase community disaster resilience. However, within the sector, the concept of social capital remains unclear and its measurement is limited at the neighborhood level due to a focus on predominately top-down and quantitative approaches. By using a qualitative, inductive-driven approach, this paper proposes an integrated social capital measurement framework that combines qualitative and quantitative measures for evaluating levels of social capital in neighborhoods. Nine focus groups consisting of 58 participants across a range of socioeconomically and ethnically diverse urban neighborhoods in New Zealand and the United States were conducted. Three key themes were identified that relate to the formation, activation, and benefits of social capital resources: community demography, cultural influences on social support, and neighborhood governance. By synthesizing the study’s results and existing literature, this paper proposes a measurement framework that incorporates both quantitative indicators and contextual questions across six structural and four cognitive social capital domains. The framework can serve as a starting point for neighborhood stakeholders, emergency management practitioners, policymakers, and researchers to assess the resilience of neighborhoods and identify areas for improvement.
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Boušek, Martin, Jakub Kučera, and Hana Váchová. "Point cloud local neighborhood features - a review." Stavební obzor - Civil Engineering Journal 34, no. 1 (2025): 64–79. https://doi.org/10.14311/cej.2025.01.0005.
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Point clouds are essential for 3D spatial analysis and widely used in geodesy, photogrammetry, and remote sensing. While modern technologies simplify their collection, processing remains challenging due to data size, irregularity, and noise. Classification is critical for object identification and noise removal. This paper explores geometric features of points derived from their local 3D neighbourhoods. It examines neighbourhood definitions, feature computation via principal component analysis (PCA), and their impact on real dataset classification. Using a test point cloud with natural and anthropogenic features, we analyze feature dependencies, identify redundancies, and highlight key metrics. Additionally, we propose new approaches for noise filtering, contributing to more efficient point cloud processing and practical applications.
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Rivera, Nicolás, Carlos Hernández, Nicolás Hormazábal, and Jorge A. Baier. "The 2^k Neighborhoods for Grid Path Planning." Journal of Artificial Intelligence Research 67 (January 20, 2020): 81–113. http://dx.doi.org/10.1613/jair.1.11383.
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Grid path planning is an important problem in AI. Its understanding has been key for the development of autonomous navigation systems. An interesting and rather surprising fact about the vast literature on this problem is that only a few neighborhoods have been used when evaluating these algorithms. Indeed, only the 4- and 8-neighborhoods are usually considered, and rarely the 16-neighborhood. This paper describes three contributions that enable the construction of effective grid path planners for extended 2k-neighborhoods; that is, neighborhoods that admit 2k neighbors per state, where k is a parameter. First, we provide a simple recursive definition of the 2k-neighborhood in terms of the 2k-1-neighborhood. Second, we derive distance functions, for any k ≥ 2, which allow us to propose admissible heuristics that are perfect for obstacle-free grids, which generalize the well-known Manhattan and Octile distances. Third, we define the notion of canonical path for the 2k-neighborhood; this allows us to incorporate our neighborhoods into two versions of A*, namely Canonical A* and Jump Point Search (JPS), whose performance, we show, scales well when increasing k. Our empirical evaluation shows that, when increasing k, the cost of the solution found improves substantially. Used with the 2k-neighborhood, Canonical A* and JPS, in many configurations, are also superior to the any-angle path planner Theta* both in terms of solution quality and runtime. Our planner is competitive with one implementation of the any-angle path planner, ANYA in some configurations. Our main practical conclusion is that standard, well-understood grid path planning technology may provide an effective approach to any-angle grid path planning.
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Liu, Guangshuai, Xurui Li, Si Sun, and Wenyu Yi. "Robust and Fast Normal Mollification via Consistent Neighborhood Reconstruction for Unorganized Point Clouds." Sensors 23, no. 6 (2023): 3292. http://dx.doi.org/10.3390/s23063292.
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This paper introduces a robust normal estimation method for point cloud data that can handle both smooth and sharp features. Our method is based on the inclusion of neighborhood recognition into the normal mollification process in the neighborhood of the current point: First, the point cloud surfaces are assigned normals via a normal estimator of robust location (NERL), which guarantees the reliability of the smooth region normals, and then a robust feature point recognition method is proposed to identify points around sharp features accurately. Furthermore, Gaussian maps and clustering are adopted for feature points to seek a rough isotropic neighborhood for the first-stage normal mollification. In order to further deal with non-uniform sampling or various complex scenes efficiently, the second-stage normal mollification based on residual is proposed. The proposed method was experimentally validated on synthetic and real-world datasets and compared to state-of-the-art methods.
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He, E., Q. Chen, H. Wang, and X. Liu. "A CURVATURE BASED ADAPTIVE NEIGHBORHOOD FOR INDIVIDUAL POINT CLOUD CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 12, 2017): 219–25. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-219-2017.
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As a key step in 3D scene analysis, point cloud classification has gained a great deal of concerns in the past few years. Due to the uneven density, noise and data missing in point cloud, how to automatically classify the point cloud with a high precision is a very challenging task. The point cloud classification process typically includes the extraction of neighborhood based statistical information and machine learning algorithms. However, the robustness of neighborhood is limited to the density and curvature of the point cloud which lead to a label noise behavior in classification results. In this paper, we proposed a curvature based adaptive neighborhood for individual point cloud classification. Our main improvement is the curvature based adaptive neighborhood method, which could derive ideal 3D point local neighborhood and enhance the separability of features. The experiment result on Oakland benchmark dataset shows that the proposed method can effectively improve the classification accuracy of point cloud.
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Su, Zhigang, Shixing Du, Jingtang Hao, Bing Han, Peng Ge, and Yue Wang. "NELD-EC: Neighborhood-Effective-Line-Density-Based Euclidean Clustering for Point Cloud Segmentation." Sensors 25, no. 4 (2025): 1174. https://doi.org/10.3390/s25041174.
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For the problem that it is difficult to effectively cluster lidar point clouds with irregular shapes and uneven densities, a Neighborhood Effective Line Density (NELD)-based Euclidean Clustering (NELD-EC) algorithm is proposed in this paper. The NELD-EC algorithm first eliminates the interfering points within the neighborhood of the data point by utilizing the distance relationship and calculates the NELD of the data point using the effective neighborhood set without interfering points of the data point. The NELD of a data point is taken as the local density of that data point. Then, the NELD-EC algorithm conducts clustering processing using the NELD of all data points and uses the reciprocal of the harmonic average of the local densities of all data points within each cluster after clustering as the distance threshold for the data points within the cluster. Finally, the NELD-EC algorithm completes the clustering of the point cloud based on the adjusted adaptive distance threshold. The clustering experimental results on simulated point clouds, fixed point clouds, and sequential point clouds indicate that, compared with several other typical Euclidean clustering algorithms, the NELD-EC algorithm requires simpler parameters to be set, is less sensitive to the initial distance threshold, can effectively reduce the occurrence probabilities of over-segmentation and under-segmentation, and has strong stability in clustering performance. The NELD-EC algorithm is more suitable for processing sequential point clouds in actual dynamic and complex scenarios.
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Costa, Dora L., and Matthew E. Kahn. "Declining Mortality Inequality within Cities during the Health Transition." American Economic Review 105, no. 5 (2015): 564–69. http://dx.doi.org/10.1257/aer.p20151070.
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In the United States in the late 19th and early 20th century, large cities had extremely high death rates from infectious disease. Within major cities such as New York City and Philadelphia, there was significant variation at any point in time in the mortality rate across neighborhoods. Between 1900 and 1930 neighborhood mortality convergence took place in New York City and Philadelphia. We document these trends and discuss their consequences for neighborhood quality of life dynamics and the economic incidence of who gains from effective public health interventions.
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Chen, Ke, and Adrian Dumitrescu. "On the longest spanning tree with neighborhoods." Discrete Mathematics, Algorithms and Applications 12, no. 05 (2020): 2050067. http://dx.doi.org/10.1142/s1793830920500676.
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We study a maximization problem for geometric network design. Given a set of [Formula: see text] compact neighborhoods in [Formula: see text], select a point in each neighborhood, so that the longest spanning tree on these points (as vertices) has maximum length. Here, we give an approximation algorithm with ratio [Formula: see text], which represents the first, albeit small, improvement beyond [Formula: see text]. While we suspect that the problem is NP-hard already in the plane, this issue remains open.
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Hajialiakbari, Kaveh, Mitra Karimi, and Safiyeh Tayebi. "Toward a Paradigm Shift in Urban Planning in Tehran: Neighborhood Development Plans." Civ. Eng. Archit 9 (May 4, 2024): 2492–504. https://doi.org/10.13189/cea.2021.090733.
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Establishment of facilitation offices in deteriorated neighborhoods of Tehran for more than a decade has led to a significant transition from top-down and authoritative intervention into a bottom-up and participatory-based renewal; one of the important parts of this transition is the adoption of a distinct approach to provide neighborhood development plans (NDPs). This approach is based on the identification of the main problems of neighborhoods, attention to all dimensions, adaptation with parallel and upstream plans, and activation of the participation of local communities and collaboration of stakeholders in public, private, and third sectors. In this paper, the neighborhood development planning approach to the problem of deterioration and obsolescence in Tehran is defined; the most important parts are the content, features, process of provision, executive framework, and assessment phases of the plan. Despite the necessity of urban renewal-regeneration integrated and systematic planning, the challenge of ignoring the unique characteristics of neighborhoods will be addressed by NDPs Scaling and Framing. Actually by defining a new level in medium and short-term plans, Tehran Municipality changed the approach of urban planning. Developing a dynamic, flexible, partnership-driven and scalable framework for dealing with the urban decay to correctly identify the neighborhoods key issues and the point solutions has been the new approach's main objective.
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Kim, Boeun, Dori E. Rosenberg, Adrian Dobra, Wendy E. Barrington, Philip M. Hurvitz, and Basia Belza. "Association of Perceived Neighborhood Environments With Cognitive Function in Older Adults." Journal of Gerontological Nursing 49, no. 8 (2023): 35–41. http://dx.doi.org/10.3928/00989134-20230707-04.
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The current study examined the associations between perceptions of the social and physical neighborhood environments and cognitive function in older adults. This cross-sectional study analyzed 821 adults aged ≥65 years from the Adult Changes in Thought study. Perceived neighborhood attributes were measured by the Physical Activity Neighborhood Environment Scale. Cognitive function was assessed using the Cognitive Ability Screening Instrument. The associations were tested using multivariate linear regression. One point greater perceived access to public transit was associated with 0.56 points greater cognitive function score (95% confidence interval [CI] [0.25, 0.88]), and an additional one point of perceived sidewalk coverage was related to 0.22 points higher cognitive function score (95% CI [0.00, 0.45]) after controlling for sociodemographic factors. The perception of neighborhood attributes alongside physical infrastructure may play an important role in supporting older adults' cognitive function. [ Journal of Gerontological Nursing, 49 (8), 35–41.]
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Shan, Gui Jun. "A Dynamic Neighborhood Selection Approach for Locally Linear Embedding." Advanced Materials Research 1033-1034 (October 2014): 1369–72. http://dx.doi.org/10.4028/www.scientific.net/amr.1033-1034.1369.
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Locally linear embedding is based on the assumption that the whole data manifolds are evenly distributed so that they determine the neighborhood for all points with the same neighborhood size. Accordingly, they fail to nicely deal with most real problems that are unevenly distributed. This paper presents a new approach that takes the general conceptual framework of Hessian locally linear embedding so as to guarantee its correctness in the setting of local isometry to an open connected subset but dynamically determines the local neighborhood size for each point. This approach estimates the approximate geodesic distance between any two points by the shortest path in the local neighborhood graph, and then determines the neighborhood size for each point by using the relationship between its local estimated geodesic distance matrix and local Euclidean distance matrix. This approach has clear geometry intuition as well as the better performance and stability to deal with the sparsely sampled or noise contaminated data sets that are often unevenly distributed. The conducted experiments on benchmark data sets validate the proposed approach.
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He, Peipei, Zheng Ma, Meiqi Fei, Wenkai Liu, Guihai Guo, and Mingwei Wang. "A Multiscale Multi-Feature Deep Learning Model for Airborne Point-Cloud Semantic Segmentation." Applied Sciences 12, no. 22 (2022): 11801. http://dx.doi.org/10.3390/app122211801.
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In point-cloud scenes, semantic segmentation is the basis for achieving an understanding of a 3D scene. The disorderly and irregular nature of 3D point clouds makes it impossible for traditional convolutional neural networks to be applied directly, and most deep learning point-cloud models often suffer from an inadequate utilization of spatial information and of other related point-cloud features. Therefore, to facilitate the capture of spatial point neighborhood information and obtain better performance in point-cloud analysis for point-cloud semantic segmentation, a multiscale, multi-feature PointNet (MSMF-PointNet) deep learning point-cloud model is proposed in this paper. MSMF-PointNet is based on the classical point-cloud model PointNet, and two small feature-extraction networks called Mini-PointNets are added to operate in parallel with the modified PointNet; these additional networks extract multiscale, multi-neighborhood features for classification. In this paper, we use the spherical neighborhood method to obtain the local neighborhood features of the point cloud, and then we adjust the radius of the spherical neighborhood to obtain the multiscale point-cloud features. The obtained multiscale neighborhood feature point set is used as the input of the network. In this paper, a cross-sectional comparison analysis is conducted on the Vaihingen urban test dataset from the single-scale and single-feature perspectives.
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Borgmann, B., M. Hebel, M. Arens, and U. Stilla. "USING NEURAL NETWORKS TO DETECT OBJECTS IN MLS POINT CLOUDS BASED ON LOCAL POINT NEIGHBORHOODS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W7 (September 16, 2019): 17–24. http://dx.doi.org/10.5194/isprs-annals-iv-2-w7-17-2019.
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<p><strong>Abstract.</strong> This paper presents an approach which uses a <i>PointNet</i>-like neural network to detect objects of certain types in MLS point clouds. In our case, it is used for the detection of pedestrians, but the approach can easily be adapted to other object classes. In the first step, we process local point neighborhoods with the neural network to determine a descriptive feature. This is then further processed to generate two outputs of the network. The first output classifies the neighborhood and determines if it is part of an object of interest. If this is the case, the second output determines where it is located in relation to the object center. This regression output allows us to use a voting process for the actual object detection. This processing step is inspired by approaches based on implicit shape models (ISM). It is able to deal with a certain amount of incorrectly classified neighborhoods, since it combines the results of multiple neighborhoods for the detection of an object. A benefit of our approach as compared to other machine learning methods is its low demand for training data. In our experiments, we achieved a promising detection performance even with less than 1000 training examples.</p>
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Luna-Torres, Joaquín. "Filters and compactness on small categories and locales." Open Journal of Mathematical Sciences 6, no. 1 (2022): 1–13. http://dx.doi.org/10.30538/oms2022.0174.
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In analogy with the classical theory of filters, for finitely complete or small categories, we provide the concepts of filter, \(\mathfrak{G}\)-neighborhood (short for "Grothendieck-neighborhood") and cover-neighborhood of points of such categories, to study convergence, cluster point, closure of sieves and compactness on objects of that kind of categories. Finally, we study all these concepts in the category \(\mathbf{Loc}\) of locales.
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Cheshmehzangi, Ali, Ayotunde Dawodu, Wangyang Song, Yuzhu Shi, and Yuwei Wang. "An Introduction to Neighborhood Sustainability Assessment Tool (NSAT) Study for China from Comprehensive Analysis of Eight Asian Tools." Sustainability 12, no. 6 (2020): 2462. http://dx.doi.org/10.3390/su12062462.
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In comparison to city-level and building-level sustainability research, neighborhood-level sustainable urban development is less studied. One of the ways of achieving sustainability at this level is the use of the Neighborhood Sustainability Assessment Tool (NSAT), which focuses on the sustainable urban development of districts, communities, and neighborhoods. NSAT is comprised of urban sustainable indicators and associated points ascribed towards achieving specific urban agendas, called headline sustainability indicators (HSIs) and themes. In China, neighborhood-level sustainability agenda has just been recently established in 2017. Hence, there is an immediate need for NSAT development of multiple cities responding to specific regions of different climate zones in China. As an example, this study utilizes the case of Ningbo City, located in east China, for such NSAT development. This paper provides a comprehensive analytical and comparison study of eight Asian NSATs to highlight compatibilities and extract specific indicators for a new NSAT development for China. The results from this comparative and analytical study, developed through a multidimensional approach of sustainable pathway model (SPM) inform a new NSAT development in a new context. This novel contribution is significant in a context where neighborhood sustainability measures are recently developed. This study serves as the starting point for future research of NSATs in China and other countries.
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LIN, Sen, and Qiang ZHANG. "Point cloud registration using neighborhood point information description and matching." Optics and Precision Engineering 30, no. 8 (2022): 984–97. http://dx.doi.org/10.37188/ope.20223008.0984.
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BLÁZQUEZ, M., and E. TUMA. "STRANGE ATTRACTORS OF THE SHIL'NIKOV TYPE IN CHUA'S CIRCUIT." International Journal of Bifurcation and Chaos 03, no. 05 (1993): 1293–98. http://dx.doi.org/10.1142/s0218127493001033.
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The chaotic behavior of the solutions of Chua's circuit is studied in the neighborhood of a homoclinic orbit to an equilibrium point of the saddle-focus type and in a neighborhood of two heteroclinic orbits to saddle-focus points which form a closed contour.
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You, Bo, Hongyu Chen, Jiayu Li, Changfeng Li, and Hui Chen. "Fast Point Cloud Registration Algorithm Based on 3DNPFH Descriptor." Photonics 9, no. 6 (2022): 414. http://dx.doi.org/10.3390/photonics9060414.
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Although researchers have investigated a variety of approaches to the development of three-dimensional (3D) point cloud matching algorithms, the results have been limited by low accuracy and slow speed when registering large numbers of point cloud data. To address this problem, a new fast point cloud registration algorithm based on a 3D neighborhood point feature histogram (3DNPFH) descriptor is proposed for fast point cloud registration. With a 3DNPFH, the 3D key-point locations are first transformed into a new 3D coordinate system, and the key points generated from similar 3D surfaces are then close to each other in the newly generated space. Subsequently, a neighborhood point feature histogram (NPFH) was designed to encode neighborhood information by combining the normal vectors, curvature, and distance features of a point cloud, thus forming a 3DNPFH (3D + NPFH). The descriptor searches radially for 3D key point locations in the new 3D coordinate system, reducing the search coordinate system for the corresponding point pairs. The “NPFH” descriptor is then coarsely aligned using the random sample consensus (RANSAC) algorithm. Experiment results show that the algorithm is fast and maintains high alignment accuracy on several popular benchmark datasets, as well as our own data.
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Shanli, Halil Ibrahim, and Ayda Zeinali Farid. "Examining New Urbanism Principles in Cold and Semi-Arid Climate Regions (Kayseri, Keçi Köy Neighborhood Example)." Architecture Image Studies 6, no. 1 (2025): 64–105. https://doi.org/10.62754/ais.v6i1.107.
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In the design of historical cities, particularly in the structure of traditional neighborhoods, climate, along with the culture, identity, and sense of belonging of the neighborhood residents, holds great importance. The patterns and structural models of old settlements have been constructed over thousands of years through collective human experiences. Following the Industrial Revolution and the widespread use of automobiles, significant issues arose in urban and neighborhood design, leading to various problems alongside changes in neighborhood fabric. To address the challenges of this era, contemporary urbanism approaches (such as New Urbanism, Eco-City, Smart Growth, Sustainable Urbanism, Compact City, Neighborhood Unit, and other approaches) have been proposed. The hypothesis of this research suggests that many of these contemporary urbanism approaches already exist in Turkey’s traditional urban textures, indicating that these approaches are not entirely new. This study focuses on the evaluation of New Urbanism principles in the historical neighborhood of Kiçiköy in Kayseri, a great example of a traditional Turkish neighborhood located in a cold and semi-arid climate. This neighborhood was selected due to its preserved traditional structure and its protection by local municipalities. The research was conducted using field research methods, complemented by theoretical insights gathered from books, articles, and theses. The ten principles of New Urbanism were analyzed in the Kiçiköy neighborhood using maps, visuals, and evaluations. Subsequently, this information was assessed using a Likert scale (a five-point scale ranging from very poor to very good). Based on the Likert scale results, it was observed that the urban fabric of Kiçiköy is in full alignment with the principles of New Urbanism. These findings indicate that modern urbanism approaches, such as New Urbanism, are not entirely new concepts but have long existed within the structure of traditional Turkish neighborhoods.
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Drazic, Milan. "Influence of a neighborhood shape on the efficiency of continuous variable neighborhood search." Yugoslav Journal of Operations Research 30, no. 1 (2020): 3–17. http://dx.doi.org/10.2298/yjor190115004d.
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The efficiency of a Variable neighborhood search metaheuristic for continuous global optimization problems greatly depends on geometric shape of neighborhood structures used by the algorithm. Among the neighborhoods defined by balls in ?p, 1 ?p ? ? metric, we tested the ?1, ?2, and ?? ball shape neighborhoods, for which there exist efficient algorithms for obtaining uniformly distributed points. On many challenging high-dimensional problems, our exhaustive testings showed that, popular and the easiest for implementation, ?? ball shape of neighborhoods performed the worst, and much better efficiency was obtained with ?1 and ?2.
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Fritz, Heather, Malcolm P. Cutchin, Jamil Gharib, Neehar Haryadi, Meet Patel, and Nandit Patel. "Neighborhood Characteristics and Frailty: A Scoping Review." Gerontologist 60, no. 4 (2019): e270-e285. http://dx.doi.org/10.1093/geront/gnz072.
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Abstract Background and Objectives Frailty is highly prevalent in later life and associated with early mortality and adverse health outcomes. The neighborhood has been identified as an important contributor to individual health, and neighborhood characteristics may contribute to frailty development. A scoping review was conducted of the peer-reviewed literature to better understand how physical and social neighborhood characteristics contribute to frailty. Research Design and Methods Following an established scoping review methodology, we searched four peer-reviewed databases for relevant studies published from January 1, 2008, to December 31, 2018. Data extracted from studies included study characteristics, operationalization of neighborhood, the conceptual model of the neighborhood–frailty relationship, operationalization of frailty, and study findings for associations among neighborhood variables and frailty indicators. Results A total of 522 articles were identified and 13 articles were included in the final data charting. Existing studies suggest that neighborhood characteristics are associated with frailty in later life. Few studies articulated a conceptual model identifying exact mechanisms through which neighborhood factors affected frailty. Studies designs were mostly cross-sectional. Longitudinal studies did not measure neighborhood characteristics over time. Studies varied considerably in how they operationalized the neighborhood. Frailty was most commonly assessed using a 5-point phenotype or a frailty index approach. Discussion and Implications Findings indicate that research on the relationship between neighborhood characteristics and frailty is an emerging area of inquiry. Additional studies are needed to more definitely explicate mechanisms through which neighborhoods contribute to, or protect older adults from, frailty.
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Liu, Xinrui, Rutao Wang, and Zongsheng Wang. "Research on a Point Cloud Registration Method Based on Dynamic Neighborhood Features." Applied Sciences 15, no. 7 (2025): 4036. https://doi.org/10.3390/app15074036.
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This paper introduces a method that can enhance the accuracy and efficiency of point cloud data registration. This method selects the centroid of the point cloud as the feature point and uses the projected distance of this feature point within the dynamic neighborhood to other points as the feature information. Through this feature information, it accomplishes the registration of two sets of point cloud data. This method increases the density and integrity of point cloud data, improves the accuracy and robustness of point cloud registration, and the selection of feature points reduces the computational load thereby enhancing processing efficiency. The introduction of the dynamic neighborhood enables the method to flexibly handle point cloud data of different scales and densities. Experimental results show that the proposed method has good performance in terms of accuracy and efficiency for achieving point cloud data registration and dealing with data under various complex conditions and can effectively improve the effect of point cloud data registration and fusion.
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Becker, Jacob H. "Within-Neighborhood Dynamics: Disadvantage, Collective Efficacy, and Homicide Rates in Chicago." Social Problems 66, no. 3 (2018): 428–47. http://dx.doi.org/10.1093/socpro/spy013.
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Abstract Research on neighborhood structural conditions like concentrated disadvantage and crime largely focuses on between-neighborhood differences; for example, places with more disadvantage are expected to experience higher homicide rates. However, empirical research often does not consider within-neighborhood dynamics of structural stability and change. Furthermore, several recent studies have found cross-sectional associations between structural variables and crime outcomes can vary significantly across units, violating a key assumption of global modeling strategies. The current work explores if and how historical changes in disadvantage influence neighborhood collective efficacy and homicide rates, net of the level of disadvantage at a given time point. Collective efficacy theoretically mediates the relationship between conditions and crime, and is hypothesized to be the mechanism through which structural change influences homicide rates. It is also hypothesized that spatial variation in cross-sectional associations between disadvantage and social outcomes can be explained by accounting for within-neighborhood changes in disadvantage. Using a sample of Chicago neighborhoods and ordinary least squares and geographically weighted regression models, I find that within-neighborhood changes in disadvantage significantly predict neighborhood collective efficacy, though the effects of this change on homicide rates are not completely mediated by collective efficacy. Within-neighborhood change completely accounts for spatial variation in cross-sectional associations, offering one explanation of prior research findings. Within-neighborhood structural changes appear to disrupt collective efficacy and contribute to higher homicide rates than predicted by the level of disadvantage alone.
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Maikapar, G. I. "Fluid motion in the stagnation point neighborhood." Fluid Dynamics 35, no. 4 (2000): 549–53. http://dx.doi.org/10.1007/bf02698124.
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ANDO, K., M. MIKI, and T. HIROYASU. "Multi-Point Simulated Annealing with Adaptive Neighborhood." IEICE Transactions on Information and Systems E90-D, no. 2 (2007): 457–64. http://dx.doi.org/10.1093/ietisy/e90-d.2.457.
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Chetverikov, Dmitry. "Fast neighborhood search in planar point sets." Pattern Recognition Letters 12, no. 7 (1991): 409–12. http://dx.doi.org/10.1016/0167-8655(91)90298-z.
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