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Analyzing free-bending pipelines in cluttered 3-D scenarios is a vital and challenging task in industrial manufacture and maintenance. It is beneficial in understanding the structure of pipelines and monitoring pipeline spacing for accurate assembly. Most existing pipeline analysis algorithms focus on regular straight pipelines and only depend on 3-D spatial cues. In this article, we propose a novel pipeline analysis framework based on 4-D spatiotemporal pipeline representation. The framework allows multiple pipeline analysis tasks ranging from detecting free-bending pipelines in point clouds and extracting 3-D skeletons to modeling complete 3-D pipelines. The 4-D pipeline representation formulates a free-bending pipeline as a 3-D cylinder sequence and incorporates 3-D spatial geometrization of each cylinder frame into temporal continuity of the frames. The multitask pipeline analysis starts from a cylinder patch and is performed on sequential pipeline frames using spatiotemporal cues. Finally, surface registration-based optimization refines pipeline skeletons, detected point cloud, and generated complete models simultaneously. The proposed algorithm is verified on real pipeline scenarios. It can deal with challenging free-bending pipeline shapes, clutter, and occlusions. Compared with existing algorithms, our algorithm achieves more comprehensive tasks and higher detection performance. The modeling of complete pipelines reaches an overall accuracy of 0.33 mm. Finally, we present a thorough analysis concerning the applicability of our algorithm from multiple aspects. [ABSTRACT FROM AUTHOR]

Façade features are important in large-scale level-of-detail 3 (LoD-3) reconstruction in urban environments, and street-view panoramas are arguably the best option for detailed 3-D façade modeling. However, despite the plethora of street-view panoramas available, few studies have explored the metric capabilities of panoramas. This is due in part to the complexities of system integration and in part to problems associated with projection (e.g., distortion at the tops of buildings), and deformation (e.g., the bending of straight structures). In an effort to solve these problems, this letter introduces a flexible and practical solution using only a single panorama. The key is to efficiently rectify panoramas using image-space line constrained deformation inspired by the as-rigid-as-possible deformation of surface meshes. The image is then re-projected using gnomonic projection on a properly selected tangent plane. The proposed approach requires a reasonable amount of user interaction to select and position the vertical line segments. The tangent point is also chosen empirically for each panorama. The rectified images can then be imported into off-the-shelf 3-D modeling solutions as reference images for interactive sketching. Experimental evaluations reveal the effectiveness of the image-space rectification: after proper scaling, the semantic-aware 3-D façade models achieve decimeter-level accuracy with respect to the reference surface mesh. [ABSTRACT FROM AUTHOR]

Surface nuclear magnetic resonance (SNMR) technique has been widely applied to noninvasive groundwater exploration and aquifer quantitative characterization. Furthermore, it has the potential for studying and detecting hydrocarbon contaminants and for water resource management. To increase the tomography resolution of groundwater and expand the application range of SNMR, we first implemented its 3-D forward modeling using a vector finite-element (FE) method based on the total electric field. The key of SNMR forward modeling is the calculation of excitation magnetic field, and the total-field algorithm is an alternative scheme to numerically simulate the electromagnetic field. In this paper, we viewed the circular loop source as the combination of a certain number of horizontal electric dipoles. The unstructured tetrahedral mesh and local refinement technology were combined to precisely delineate the distribution of circular loop source, which effectively reduces the adverse effects of field source singularity. Then, the vector FE solver based on the total electric field was used to calculate the magnetic field distribution. The accuracy of the calculated results was validated by the analytical solutions for a circular loop laid on the surface of a homogeneous half-space. After calculating the excitation field, SNMR responses of three typical synthetic 3-D groundwater models were obtained with the basic signal response formula. Finally, we studied the effects of some important factors which have significant influences on SNMR signal responses and have practical importance in groundwater issues. [ABSTRACT FROM AUTHOR]

To find the real risk points of internal dielectric charging (IDC), 3-D IDC simulation is performed to a typical dielectric structure considering GEO severe radiation environment (FLUMIC3). Charge transportation simulation is carried out by Geant4 and electric field is numerically computed according to the current conservation law. The aluminum shielding thickness is varied to investigate the shielding dependence. By adjusting the lower energy limit of the incident electron spectrum according to the shielding thickness, a technique is proposed to improve the simulation efficiency. The statistic condition in Geant4 is checked and attention is paid to the influence of mesh on the peak electric field. The simulation data show that the peak electric field occurs on the grounding edge and local mesh refinement on the critical charging point is of significance to make sure the real charging level. Increasing the shielding thickness could mitigate IDC. However, this mitigation effect is weak in the case where radiation-induced conductivity dominates the total conductivity (at low temperature). In this case, even using a 3-mm aluminum shielding, the peak electric field can reach the level of 10^7 V/m. [ABSTRACT FROM AUTHOR]

Geolocalization of crowdsourced images is a challenging task that is getting increased attention nowadays due to the rise in popularity of geotagging and its applications. Among these applications, 3-D modeling from Internet photograph collections is a very active research topic with great promise and potential. In order to automize and optimize the crowdsourced 3-D modeling process, this letter proposes a novel framework that can be used for automatic 3-D modeling of city points of interest (POIs), such as statues, buildings, and temporary artworks. Crowdsourced images related to the POI and its location are collected using a geographical Web search process based on geotags and semantic geodata. Subsequently, panoramic Google Street View (SV) images are used to geolocalize the images. If enough feature matches are found between the image and one of the SV images, the image is annotated with the location metadata of the best matching image from the SV database. Otherwise, when too few matches are found, the image most probably will not contain the POI in its field of view (FOV), and it is filtered out. For optimal performance, the equirectangular panoramic SV images are transformed into an SV data set of perspective cutouts facing the POI with different pitches and FOVs. From this data set, a basic 3-D model of the POI and its environment is generated. Finally, the geolocalized crowdsourced images refine and optimize the 3-D model using the matching matrix that is generated from the geolocalization results. Experiments show the feasibility of our approach on different types of city POIs. Our main contribution is that we can decrease the 3-D modeling computation time by more than half and significantly improve the model completeness. Finally, it is important to remark that the applicability of the proposed framework is not limited to 3-D modeling but can also be used in other domains, such as geoaugmented reality and location-based media annotation. [ABSTRACT FROM PUBLISHER]

In recent years, electrical field distribution is the area of major concern in the service life of Polymeric Insulators. Several methodologies are proposed to analyze the electrical field along the insulators both experimentally and numerically of which the former might be very time consuming requiring a number of trial-error tests to determine the optimum dimensions. The advent of super computers makes numerical analyses feasible on any complex geometry modeled in 3-D and the overall optimization techniques to control the electrical field become quite easier. However, the main drawbacks in 3-D analyses are the more computation time and memory, making it desirable to simplify the structure. Hence a methodology based on relationship between electrical field profiles of 2-D (axi-symmetry model of insulator with corona ring) and 3-D model (insulator with corona ring, tower, 3 phase hardware fittings and conductors) for a dimension and location of corona/grading rings is proposed. Based on the relationship, electrical field profiles of 3-D models are predicted for different dimension and location of rings using its equivalent 2-D model electrical field profile. Commercially available 2-D and 3-D finite element based electrostatic field computation program is used. A 220 kV suspension type I string assembly is used for this study. Later, the approach is validated for 132 kV and 400 kV insulators. The type of tower, string configuration is kept constant throughout the analyses. [ABSTRACT FROM AUTHOR]

The field reconstruction method (FRM) is a recent technique for modeling electrical machines. This method offers the same accuracy as the finite element analysis at a lower computational cost by using a few prestored results, which are known as the basis functions. This paper describes an enhanced three-dimensional (3-D) FRM for modeling an axial flux permanent magnet machine. This machine has a special structure, which warrants the use of 3-D modeling for completely describing its behavior. The proposed method can be an efficient solution for the 3-D modeling of electrical machinery, especially from the viewpoint of time-varying and iterative optimization problems. Experimental and simulation results are provided to validate the proposed method's precision. [ABSTRACT FROM AUTHOR]

3-D modeling, object detection, and pose estimation are three of the most challenging tasks in the area of 3-D computer vision. This paper presents a novel algorithm to perform these tasks simultaneously from unordered point-clouds. Given a set of input point-clouds in the presence of clutter and occlusion, an initial model is first constructed by performing pair-wise registration between any two point-clouds. The resulting model is then updated from the remaining point-clouds using a novel model growing technique. Once the final model is reconstructed, the instances of the object are detected and the poses of its instances in the scenes are estimated. This algorithm is automatic, model free, and does not rely on any prior information about the objects in the scene. The algorithm was comprehensively tested on the University of Western Australia data set. Experimental results show that our algorithm achieved accurate modeling, detection, and pose estimation performance. [ABSTRACT FROM PUBLISHER]

Image registration (IR) is a challenging topic in both the computer vision and pattern recognition fields; its main aim is to find the optimal transformation to provide the best overlay or fitting between two or more images. Usually, the success of well-known algorithms, such as iterative closest point, highly depends on several assumptions, e.g., the user should provide an initial near-optimal pose of the images to be registered. In the last decade, a new family of registration algorithms based on evolutionary principles has been contributed in order to overcome the latter drawbacks. However, their performance highly depends on carefully tuning (usually by hand) the control parameters of the algorithm, which is an error-prone and a time-consuming task. In this paper, we propose a new self-adaptive evolution model to deal with IR problems. To our knowledge, this is the first time a self-adaptive approach has been used for tuning the control parameters of evolutionary algorithms tackling computer vision tasks. Specifically, we introduce a novel design of the proposed self-adaptive approach facing pair-wise range IR problem instances, which is a challenging real-world optimization problem. In addition, several classical approaches, as well as state-of-the-art evolutionary IR methods, have been considered for numerical comparison. [ABSTRACT FROM AUTHOR]

Multitemporal hyperspectral images are gaining an ever-increasing importance revealed by the ambition of the remote sensing community to develop new generation of sensors. Therefore, multitemporal images classification and change detection issues are greatly relevant in several research topics. In this paper, we propose a novel approach for modeling the temporal variation of the reflectance response as a function of time period and wavelength; summarizing the spectral signature of hyperspectral pixels as a 3-D mesh. This approach is adopted for hyperspectral time series analysis leading to the main following contribution: an advanced form of the temporal spectral signature defining the reflectance at each pixel as a congregation of the spatial/spectral/temporal dimensions. Afterward, by formulating the temporal data set in an adequate multidimensional feature space of contextual data, an innovative processing scheme exploiting the theoretical backgrounds of 3-D surface reconstruction and matching is adopted for data interpretation. Finally, an improved method for multitemporal endmember extraction and spectral unmixing based on multilinear algebra methods is introduced. A case study, in a region located in southern Tunisia, is conducted on a multitemporal subset of Hyperion images. Up to 89.86% of sampling sites have been correctly predicted by the proposed approach, outperforming conventional classifiers. The good performances obtained, on simulated multitemporal images and over various real experimental scenarios, illustrate the effectiveness and the generalization capacities of the proposed approach. [ABSTRACT FROM AUTHOR]