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This paper aims to understand how a group of academics cite each others' work through time, considering the simultaneous co-evolution of three networks representing their scientific collaboration, the journals in which they publish and institutional membership. It argues that both social and cognitive processes contribute to these dynamics. Two types of network mechanisms are considered specifically: closures by affiliation and closures by association. To assess whether these mechanisms generate the macro features of the network under study, we propose new features for three-mode multilevel networks such as the mixed geodesic distances, mixed degree distributions, and the mixed quadrilateral census. We investigate whether a micro-level model that considers the above-mentioned network mechanisms is able to correctly reproduce these features. We apply stochastic actor-oriented models (SAOMs) for one-mode and two-mode networks to link the micro-macro processes using a dataset of a scientific community of astronomers from 2013 to 2015. The results suggest that social relationships grounded on scientific collaboration and proximity based on institutional affiliation are more accurately suited to understanding the co-evolution of the network of citations than an alternative approach that merely considers cognitive-based networks measured as the similarity in publishing in the same journals. • We investigate how a group of academics cite each others' work through time considering the co-evolution of a multilevel network. • The co-evolution of a three-mode multilevel network is analysed considering cross-level effects. • New and already available measures for diagnostics are used for statistical models for social networks to identify how micro-mechanisms trigger different structures at the macro level. • Social relationships grounded on scientific collaboration and space proximity based on institutional affiliation are more accurately suited to understand the co-evolution of the networks in a scientific network. [ABSTRACT FROM AUTHOR]

Sometimes, the land surveyor wants the geodetic coordinates for stations that were coordinated in a spreadsheet from a total station's observations. There is a large body of literature devoted to this subject, the direct problem of geodesy, along with its inverse problem, to determine the geodesic distance and directions between two stations given their geodetic coordinates. For a spheroidal Earth model, such as a reference ellipsoid, the exact solution of these problems involves determining the arc length of the geodesic among stations, which requires evaluating an elliptic integral. Some modern software ecosystems, like MathematicaTM, MatlabTM, and SciPy, provide built-in elliptic integrals, but spreadsheets still lack them. Approximate solutions abound, starting since the time of Bessel. The approximate solutions often require evaluating highly complicated truncated infinite series and/or iteration--which can be done in a spreadsheet--but our aim is to compare methods that are simple enough to implement comfortably in a single row along the top of a spreadsheet. This constraint allows surveyors to easily convert many stations' coordinates by just dragging the computational columns down the rows holding the observations. We review some classical approaches that were used before electronic calculators were available and, thus, were relatively simple, and we derive some new formulas. We provide their spreadsheet implementations, and tabulate their accuracies for the subpolar portion of the Earth. One finds the usual trade-off between simplicity and accuracy. [ABSTRACT FROM AUTHOR]

The equiangular cubed sphere is a spherical grid, widely used in computational physics. This paper deals with mathematical properties of this grid. We identify the symmetry group, i.e. the group of the orthogonal transformations that leave the cubed sphere invariant. The main result is that it coincides with the symmetry group of a cube. The proposed proof emphasizes metric properties of the cubed sphere. We study the geodesic distance on the grid, which reveals that the shortest geodesic arcs match with the vertices of a cuboctahedron. The results of this paper lay the foundation for future numerical schemes, based on rotational invariance of the cubed sphere. [ABSTRACT FROM AUTHOR]

The aircraft environmental control system (AECS) plays a vital role in the normal operation of an airplane; thus, performance degradation of the AECS may result in failure and even catastrophic consequences. Because performance degradation is inevitable, performance degradation assessment is critical to provide information for appropriate maintenance actions. This paper proposed an integrated framework for performance degradation assessment of AECS by introducing the visual cognition theory into the specific method. First, an observer is used to generate a residual error, which contains degradation information of the AECS. Second, the generated residual error is transformed into a two-dimensional image based on a permutation method. Third, inspired by the multichannel characteristic (MCC), the transformed image is decomposed into several subbands based on the nonsubsampled contourlet transform (NCST) for multiscale and multidirectional feature extraction, thus capturing the precise characteristics of degradation. Then, inspired by the manifold sensing characteristic (MSC), the manifold space is established based on diffusion maps (DM) to reveal the intrinsic evolutionary law of health degradation. Finally, the geodesic distance is employed to represent the deviation between the current state and health baseline. As a health indicator, the confidence value (CV), which is derived from the geodesic distance, is capable of representing the degradation trend of the AECS. The whole process of degradation can be tracked in real time based on the proposed method. An AECS simulation model was established in MATLAB/Simulink, and two typical faults were introduced. The results based on simulation data demonstrate that the proposed assessment method can effectively reflect the degradation process of the AECS and the constructed health indicator is reasonable. [ABSTRACT FROM AUTHOR]

The problem of temporary fastener pattern optimization in aircraft assembly is considered. Minimizing the number of fasteners while maintaining final product quality is one of the key factors for intensifying production in the aerospace industry. The considered problem is combinatorial with uncertain input data. Objective function computation requires massive contact problem solving that makes the implementation of standard black-box optimization techniques problematic. The proposed novel optimization approach avoids objective function multiple calculations by using the specifics of assembly process. Efficiency of the approach is demonstrated by comparison with optimization techniques based on local variations for optimization of wing-to-fuselage assembly process. [ABSTRACT FROM AUTHOR]

Diffusion tensor imaging (DTI) is a promising technique for characterizing organic microstructural information by mapping a spatial point as a 3 × 3 symmetric matrix (called diffusion tensor). Analyzing DTI images directly is challenging since each element of the DTI image is a 3-order matrix rather than a scalar. It is meaningful to extract the scalar-valued feature for DTI image processing and analysis. In this paper, we propose an intrinsic anisotropic feature of DTI images using global geometric attributes of diffusion tensors on the Riemannian manifold. To measure the intrinsic similarity between diffusion tensors as symmetric matrices, we convert them into symmetric positive definite (SPD) matrices and use the normalized geodesic distance in the tensor manifold to define the visible intrinsic feature. Moreover, we extend the geodesic distance to make sure our feature can measure the intrinsic properties of diffusion tensors, and the neighborhood attributes and even the global information of a DTI image are utilized to define the proposed feature. To measure the anisotropy of DTI images, we define the average diffusion sphere at each point to measure the isotropic attributes and use the difference between the diffusion tensor and its average diffusion tensor to describe the anisotropic properties. It is experimentally demonstrated that the proposed feature performs adequately in characterizing various attributes of DTI images, including but not limited anisotropy, intrinsic geometric property, and average diffusion degree, etc. Compared with the most popular features, our anisotropy feature can represent DTI image properties more robustly and effectively since the contrast of various details in the feature is improved. Moreover, the results of fiber tracking defined by the anisotropy feature indicate that our feature performs better on representing substantial anisotropic attributes of DTI images and is potentially used in DTI image processing and analysis. • An intrinsic anisotropic feature of DTI images is defined on the Riemannian manifold. • Diffusion tensors (DTs) are transformed into symmetric positive definite matrices. • Tensor manifold is used to measure the intrinsic geometric properties of DTs. • The average diffusion sphere is defined to measure the anisotropy of DTs. • The geodesic distance between DTs is extended to measure neighbourhood information. • The proposed feature can provide important and effective properties of DTI images. [ABSTRACT FROM AUTHOR]

Traditional Verlet integration frameworks have been successful with their robustness and efficiency to simulate deformable bodies ranging from simple cloth to geometrically complex solids. However, the existing frameworks deform the models as a whole. We present a Verlet integration framework which provides local surface deformation on the arbitrary selected area of the mesh without giving any global deformation impact to the whole model. The framework is designed to take the advantage of physics to deform simple triangle meshes with the position-based approach. Our framework provides an interactive selection of the deformation influence area by using geodesic distance computation based on heat kernel . Additionally, the framework computes several geometric constraints that provides an interconnected particle system simulation with environmental interactions such as collision. The proposed framework is robust and easy to implement since it is based on highly accurate geodesic distance computation and solving the projected geometric constraints. We demonstrate the benefits of our framework with the results obtained from various triangle mesh models to present its potential in terms of practicability and effectiveness. [ABSTRACT FROM AUTHOR]

In this paper we propose a new method for studying local and global clustering in networks employing random walk pairs. The method is intuitive and directly generalizes standard local and global clustering coefficients to weighted networks and networks containing nodes of multiple types. In the case of two-mode networks the values obtained for commonly considered social networks are in sharp contrast to those obtained, for instance, by the method of Opsahl (2013) , and provide a different viewpoint for clustering. The approach is also applicable in questions related to the general study of segregation and homophily. Applications to existent data sets are considered. [ABSTRACT FROM AUTHOR]

The concept of Minimum geodetic subgraphs in Graph theory was introduced by G. Chartrand, F. Harary and P. Zhang in 2001. In this paper,the idea is extended to fuzzy graphs using geodesic distance.An upper and lower bound for geodesic number of fuzzy graphs is obtained using which it is established that the geodesic number of a fuzzy graph exceeds or is equal to that of its minimum geodetic fuzzy subgraph. A necessary condition for the geodesic number of a fuzzy graph to be 2 depending on the nodes of its minimum geodetic fuzzy subgraph is obtained and a study on the minimum geodetic fuzzy subgraph of a fuzzy tree is conducted. [ABSTRACT FROM AUTHOR]

Statistical models used in geophysical, environmental, and climate science applications must reflect the curvature of the spatial domain in global data. Over the past few decades, statisticians have developed covariance models that capture the spatial and temporal behavior of these global data sets. Though the geodesic distance is the most natural metric for measuring distance on the surface of a sphere, mathematical limitations have compelled statisticians to use the chordal distance to compute the covariance matrix in many applications instead, which may cause physically unrealistic distortions. Therefore, covariance functions directly defined on a sphere using the geodesic distance are needed. We discuss the issues that arise when dealing with spherical data sets on a global scale and provide references to recent literature. We review the current approaches to building process models on spheres, including the differential operator, the stochastic partial differential equation, the kernel convolution, and the deformation approaches. We illustrate realizations obtained from Gaussian processes with different covariance structures and the use of isotropic and nonstationary covariance models through deformations and geographical indicators for global surface temperature data. To assess the suitability of each method, we compare their log-likelihood values and prediction scores, and we end with a discussion of related research problems. [ABSTRACT FROM AUTHOR]

Purpose: We aim at studying intrinsic structures of the intracranial subarachnoid space. Material and methods: Magnetic resonance images were obtained using the SPACE sequence, and the segmentation of the superior intracranial subarachnoid space was performed using geometrical features and a topological assumption of the shapes. Given such segmentation results, we present a method based on a geodesic propagation technique, which allows us to make an intrinsic profile of the space. Intrinsic profiles are then analyzed qualitatively and quantitatively, in particular for classification into healthy and pathological cases based on their intrinsic bilateral asymmetry and histogram moments. Results: The proposed method was applied to a clinical dataset of 15 subjects, of which 7 were healthy volunteers and 8 were hydrocephalus patients. The intracranial cerebrospinal fluid is not (intrinsically) bilaterally asymmetric for healthy volunteers, while hydrocephalus would cause asymmetry. We also observed that the results of a two-class classification (healthy or not) based on histogram moments were suitable; sensitivity, specificity and precision are all 100%. Conclusions: The effectiveness of the proposed method of intrinsic profiling analyses is shown by preliminary experiments on healthy adults and hydrocephalus patients. [ABSTRACT FROM AUTHOR]

This paper considers a multivariate spatial random field, with each component having univariate marginal distributions of the skew-Gaussian type. We assume that the field is defined spatially on the unit sphere embedded in R 3 , allowing for modeling data available over large portions of planet Earth. This model admits explicit expressions for the marginal and cross covariances. However, the n -dimensional distributions of the field are difficult to evaluate, because it requires the sum of 2 n terms involving the cumulative and probability density functions of a n -dimensional Gaussian distribution. Since in this case inference based on the full likelihood is computationally unfeasible, we propose a composite likelihood approach based on pairs of spatial observations. This last being possible thanks to the fact that we have a closed form expression for the bivariate distribution. We illustrate the effectiveness of the method through simulation experiments and the analysis of a real data set of minimum and maximum surface air temperatures. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of classifying human activities that are typical in a daily living environment from videos. We propose a novel method based on Riemannian manifolds that uses a tree structure of two layers, where nodes in each tree branch are on a Riemannian manifold. Each node corresponds to different part-based covariance features, and induces a geodesic-based kernel machine for classification. In the first layer, activities are classified according to the dynamics of body pose and the movement of hands or arms. Activities with similar body pose and motion but different human-object interaction are coarsely classified into the same category. In the second layer, the coarsely classified activities are further fine classified, according to the appearance of local image patches at hands in key frames. This is based on the observation that interacting objects as discriminative cues are likely to be attached to hands. The main novelties of this paper include: (i) Motion of body parts for each video activity is characterized by global features. More specifically, the features are distances between each pair of key points and the orientations of lines that connect them; (ii) Human-object interaction is described by local features. That is, the appearance of local regions around hands in key frames, where key frames are selected using the proximity of hands to other key points; (iii) Classification of human activities is formulated by a geodesic distance-induced kernel machine. This is done by exploiting pairwise geodesics on Riemannian manifolds under the log-Euclidean metric. Experiments were conducted on 2 video datasets. The first dataset, made on our university campus, contains 8 activities with a total number of 943 videos. The second dataset is from a publicly available dataset, containing 7 activity classes and a total of 224 videos. Our test results on the first video dataset have shown high classification accuracy (average 94.27%), and small false alarm rate (average 0.80%). For the second video dataset, test results from the proposed method are compared with 6 existing methods. The proposed method has outperformed all these existing methods. Discussions are given on the impact of detected skeleton points from Kinect on the performance of activity classification. [ABSTRACT FROM AUTHOR]

A framework for co-registering multi-temporal 3D point cloud surfaces (PCSs) is presented, which addresses the coregistration of urban and non-urban 3D surfaces formed by 3D points. These surfaces are acquired from different surface measurement sensors and are in different coordinate systems. No prior information about initial transformation parameters or proximate matching is assumed. A keypoint matching approach is proposed to co-register two PCSs. First, surface curvature information is utilized for scale-invariant keypoint extraction. Then, every keypoint is characterized by a scale, rotation, and translation invariant surface descriptor called the radial geodesic distance-slope histogram. Keypoints with similar surface descriptors on the two PCSs are matched using bipartite graph matching. Given scale, rotation and translation changes between PCS pairs, co-registration tests on multi-sensor urban and non-urban datasets gave rotation errors from 0.017° to 0.023°, translation errors from 0.007 m to 0.013 m and scale factor errors from 0.0002 to 0.0014. [ABSTRACT FROM AUTHOR]

The “near-repeat” effect is a well-known criminological phenomenon in which the occurrence of a crime incident gives rise to a temporary elevation of crime risk within close physical proximity to an initial incident. Adopting a social network perspective, we instead define a near repeat in terms of geodesic distance within a criminal social network, rather than spatial distance. Specifically, we report a statistical analysis of repeat effects in arrest data for Chicago during the years 2003–2012. We divide the arrest data into two sets (violent crimes and other crimes) and, for each set, we compare the distributions of time intervals between repeat incidents to theoretical distributions in which repeat incidents occur only by chance. We first consider the case of the same arrestee participating in repeat incidents (“exact repeats”) and then extend the analysis to evaluate repeat risks of those arrestees near one another in the social network. We observe repeat effects that diminish as a function of geodesic distance and time interval, and we estimate typical time scales for repeat crimes in Chicago. [ABSTRACT FROM AUTHOR]

Scene understanding is an important class of computer vision problems that is an enabler for a wide variety of applications such as advanced driver assistance systems, autonomous vehicles or mobile assistive robots. Semantic segmentation is one of the common ways to address this problem. Unlike the more standard approaches based on a probabilistic graphical model, in this paper we present a two stage classification framework based on the concept of pixel neighborhoods . In the first stage, every pixel is classified based on its appearance. The output of the first classifier in a specific region around every pixel, which we call the pixel neighborhood , is summarized by a novel voting histogram feature and given as input to a second classifier. We show how to define the pixel neighborhood by using the geodesic distance in a way that it is able to capture both local image context as well as more global object relations. We perform a quantitative and qualitative evaluation on six well-known and challenging datasets and show that our model is able to natively handle both 2D and 3D data. We compare our method to several baselines and multiple closely related methods and show state-of-the-art performance. We also present a real world application of our method in a system that automatically detects parking spaces from a moving vehicle in real time. [ABSTRACT FROM AUTHOR]

Geodetic forward and inverse computations are traditionally computed on the mathematical ellipsoid. Although software is readily available for those computations, the complexity of the geodetic equations can be intimidating. The 3D global spatial data model (GSDM) provides an alternate method for computing the inverse. Computations in this article start at a specified point and use numeric integration to establish the latitude and longitude of a second point—both on the ellipsoid. That means the geodesic distance is known and can be used to check the GSDM inverse accuracy. With the latitude and longitude of the endpoints known, the geocentric X/Y/Z coordinates of both points are computed and used to obtain the 3D spatial separation—that is, a chord distance. Standard chord/arc equations provide a circular arc distance between the points. For lines of "short" or "moderate" length, this arc distance very nearly approximates the known geodesic distance. A comparison of differences for various geodesic line lengths and configurations is provided. Values for the 3D azimuth are also compared with geodetic line azimuths computed using Clairaut's constant. Validity of the 3D geodetic inverse is established for short and medium length lines. Additional research is needed to establish a reasonable trade-off between possible accuracies for line lengths over about 50 km. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of Beijing Institute of Technology is the property of Beijing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Accurate segmentation of ground-glass opacity (GGO) is an important premise for doctors to judge COVID-19. Aiming at the problem of mis-segmentation for GGO segmentation methods, especially the problem of adhesive GGO connected with chest wall or blood vessel, this paper proposes an accurate segmentation of GGO based on fuzzy c-means (FCM) clustering and improved random walk algorithm. The innovation of this paper is to construct a Markov random field (MRF) with adaptive spatial information by using the spatial gravity Model and the spatial structural characteristics, which is introduced into the FCM model to automatically balance the insensitivity to noise and preserve the effectiveness of image edge details to improve the clustering accuracy of image. Then, the coordinate values of nodes and seed points in the image are combined with the spatial distance, and the geodesic distance is added to redefine the weight. According to the edge density of the image, the weight of the grayscale and the spatial feature in the weight function is adaptively calculated. In order to reduce the influence of edge noise on GGO segmentation, an adaptive snowfall model is proposed to preprocess the image, which can suppress the noise without losing the edge information. In this paper, CT images of different types of COVID-19 are selected for segmentation experiments, and the experimental results are compared with the traditional segmentation methods and several SOTA methods. The results suggest that the paper method can be used for the auxiliary diagnosis of COVID-19, so as to improve the work efficiency of doctors. • The adaptive spatial information MRF based FCM CT image clustering algorithm is proposed. • The geodesic distance is added to redefine the weight function of the random walk. • The proportion of gray feature and spatial information is adaptively calculated in the weight. • The adaptive Gaussian kernel of snowfall model is proposed to reduce the noise of CT image. [ABSTRACT FROM AUTHOR]

Accurate segmentation of ground-glass opacity (GGO) is an important premise for doctors to judge COVID-19. Aiming at the problem of mis-segmentation for GGO segmentation methods, especially the problem of adhesive GGO connected with chest wall or blood vessel, this paper proposes an accurate segmentation of GGO based on fuzzy c-means (FCM) clustering and improved random walk algorithm. The innovation of this paper is to construct a Markov random field (MRF) with adaptive spatial information by using the spatial gravity Model and the spatial structural characteristics, which is introduced into the FCM model to automatically balance the insensitivity to noise and preserve the effectiveness of image edge details to improve the clustering accuracy of image. Then, the coordinate values of nodes and seed points in the image are combined with the spatial distance, and the geodesic distance is added to redefine the weight. According to the edge density of the image, the weight of the grayscale and the spatial feature in the weight function is adaptively calculated. In order to reduce the influence of edge noise on GGO segmentation, an adaptive snowfall model is proposed to preprocess the image, which can suppress the noise without losing the edge information. In this paper, CT images of different types of COVID-19 are selected for segmentation experiments, and the experimental results are compared with the traditional segmentation methods and several SOTA methods. The results suggest that the paper method can be used for the auxiliary diagnosis of COVID-19, so as to improve the work efficiency of doctors. • The adaptive spatial information MRF based FCM CT image clustering algorithm is proposed. • The geodesic distance is added to redefine the weight function of the random walk. • The proportion of gray feature and spatial information is adaptively calculated in the weight. • The adaptive Gaussian kernel of snowfall model is proposed to reduce the noise of CT image. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of Beijing Institute of Technology is the property of Beijing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Rationale and Objectives Accurate segmentation of brain tumors, and quantification of tumor volume, is important for diagnosis, monitoring, and planning therapeutic intervention. Manual segmentation is not widely used because of time constraints. Previous efforts have mainly produced methods that are tailored to a particular type of tumor or acquisition protocol and have mostly failed to produce a method that functions on different tumor types and is robust to changes in scanning parameters, resolution, and image quality, thereby limiting their clinical value. Herein, we present a semiautomatic method for tumor segmentation that is fast, accurate, and robust to a wide variation in image quality and resolution. Materials and Methods A semiautomatic segmentation method based on the geodesic distance transform was developed and validated by using it to segment 54 brain tumors. Glioblastomas, meningiomas, and brain metastases were segmented. Qualitative validation was based on physician ratings provided by three clinical experts. Quantitative validation was based on comparing semiautomatic and manual segmentations. Results Tumor segmentations obtained using manual and automatic methods were compared quantitatively using the Dice measure of overlap. Subjective evaluation was performed by having human experts rate the computerized segmentations on a 0–5 rating scale where 5 indicated perfect segmentation. Conclusions The proposed method addresses a significant, unmet need in the field of neuro-oncology. Specifically, this method enables clinicians to obtain accurate and reproducible tumor volumes without the need for manual segmentation. [ABSTRACT FROM AUTHOR]

Copyright of Biomédica: Revista del Instituto Nacional de Salud is the property of Instituto Nacional de Salud of Colombia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Highlights: [•] We extensively review the notion of resistance distance. [•] We interpret closeness and betweenness centrality in terms of resistance distance. [•] We discuss the benefits of this original interpretation. [Copyright &y& Elsevier]

Superpixels can capture redundancy in the image and greatly reduce the complexity of subsequent image processing tasks. Any segmentation algorithm can extract superpixels, however, most of them produce highly irregular superpixels, and the number of superpixels is not easily decided. To solve this problem, a simple and linear complex algorithm is proposed to generate superpixels based on geodesic distance transform, which can generate desired number of superpixels with approximately uniform size and shape. Experimental results illustrate that the method proposed has the ability to generate superpixels with different complex images and results in exact and robust effects. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a novel method for 2D and 3D shape matching that is insensitive to articulation. It uses the eccentricity transform, which is based on the computation of geodesic distances. Geodesic distances computed over a 2D or 3D shape are articulation insensitive. The eccentricity transform considers the length of the longest geodesics. Histograms of the eccentricity transform characterize the compactness of a shape, in a way insensitive to rotation, scaling, and articulation. To characterize the structure of a shape, a histogram of the connected components of the level-sets of the transform is used. These two histograms make up a highly compact descriptor and the resulting method for shape matching is straightforward. Experimental results on established 2D and 3D benchmarks show results similar to more complex state of the art methods, especially when considering articulation. The connection between the geometrical modification of a shape and the corresponding impact on its histogram representation is explained. The influence of the number of bins in the two histograms and the respective importance of each histogram is studied in detail. [Copyright &y& Elsevier]

Distance transforms and the saliency maps they induce are widely used in image processing, computer vision, and pattern recognition. The minimum barrier distance (MBD) has proved to provide accurate results in this context. Recently, Géraud et al. have presented a fast-to-compute alternative definition of this distance, called the Dahu pseudo-distance. This distance is efficient, powerful, and have many important applications. However, it is restricted to grayscale images. In this article we revisit this pseudo-distance. First, we offer an extension to multivariate image. We call this extension the vectorial Dahu pseudo-distance. We provide an efficient way to compute it. This new version is not only able to deal with color images but also multi-spectral and multi-modal ones. Besides, through our benchmarks, we demonstrate how robust and competitive the vectorial Dahu pseudo-distance is, compared to other MB-based distances. This shows that this distance is promising for salient object detection, shortest path finding, and object segmentation. Secondly, we combine the Dahu pseudo-distance with the geodesic distance to take into account spatial information from the image. This combination of distances provides efficient results in many applications such as segmentation of thin elements or path finding in images. • The Dahu pseudo-distance, a recent minimum barrier pseudo-distance, is revisited. • We extend it to multivariate images and combine it with a geodesic distance. • A fast algorithm devoted to compute this Dahu pseudo-distance is provided. • The robustness of this pseudo-distance to noise and blurring is demonstrated. • The efficiency of our extensions for image processing is widely illustrated. [ABSTRACT FROM AUTHOR]

This article proposes a new building extraction method from high-resolution remote sensing images, based on GrabCut, which can automatically select foreground and background samples under the constraints of building elevation contour lines. First the image is rotated according to the direction of pixel displacement calculated by the rational function Model. Second, the Canny operator, combined with morphology and the Hough transform, is used to extract the building's elevation contour lines. Third, seed points and interesting points of the building are selected under the constraint of the contour line and the geodesic distance. Then foreground and background samples are obtained according to these points. Fourth, GrabCut and geometric features are used to carry out image segmentation and extract buildings. Finally, WorldView satellite images are used to verify the proposed method. Experimental results show that the average accuracy can reach 86.34%, which is 15.12% higher than other building extraction methods. [ABSTRACT FROM AUTHOR]