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3D human pose estimation (HPE) has become increasingly important in baseball analytic, but there are several difficulties pertaining to pose estimation in real-world baseball pitching. First, in-the-wild baseball pitching lacks related 3D pose datasets and contains lots of joints occluded by other body parts. Second, baseball pitching contains dramatic velocity changes during arm acceleration phases. Due to these properties of pitching, it is difficult to use common filters to remove random noises while preserving high-frequency critical joint movements in pitching. To solve these problems, we propose joint-wise volumetric triangulation to reconstruct 3D human poses by utilizing the information of multi-view 2D joint heatmaps generated by 2D HPE methods. We also designed a baseball-customized filter system to remove noisy signal from pose movement while preserving the high-frequency pitching motion. Our proposed pose reconstruction scheme yields a 33.1 mm average position error and 0.35m/s (1.28 km/h) average velocity error on baseball pitching motion. Our work can be directly applied to estimate human poses either in indoor environment or real-world baseball field.

Parallel corpora for the languages of Myanmar (Beik, Burmese, Rakhine) are extremely scarce but a necessary requirement for machine translation R&D. Previous studies have proved that pivoting leads to better translation quality if the bridge language is closely related to the source and target language pair. The baseline study is conducted based on the three major approaches of machine translation; Weighted Finite State Transducer (WFST), Phrase-Based Statistical Machine Translation (PBSMT) and Deep Recurrent Neural Network (Deep-RNN). Based on the baseline results, this paper mainly investigated the pivot language technique for PBSMT with Burmese dialects. We employed two different pivot translation methods: transfer (sentence level) and triangulation (phrase level). We present the experimental results on Dawei-Beik, Beik-Dawei translations and Beik-Rakhine, Rakhine-Beik translation via Burmese. Both the transfer and triangulation approaches outperformed the baseline (direct translation), specifically for the Rakhine-Beik language pair. Moreover, the results of the average BiLingual Evaluation Understudy (BLEU), Character n-gram F-score (chrF), and Word Error Rate (WER) scores of the 10-fold cross-validation experiments proved that the triangulation pivot has significantly better acceleration than the transfer pivot. We plan to release the parallel corpora of Burmese dialects and present several avenues for further research.

Event location in power systems is quite essential information for system operators to enhance control-room situational awareness capability. Therefore, it is of great importance to develop an event location estimation algorithm for transmission systems with high accuracy. With the development of wide-area measurement system (WAMS) such as FNET/GridEye, and the synchrophasor measurement devices (SMDs) such as frequency disturbance recorders (FDRs), the synchronous measurement data including frequency, voltage amplitude and phase angle can be collected and used for event location estimation. First, the phase angle and rate of change of frequency (RoCoF) trajectories are respectively used for determining two sets of wave arrival time associated with each FDR. Then, a convolutional neural network (CNN) is utilized to determine the wave arrival order to select the more suitable set of wave arrival times for a given case and to perform corresponding modifications. Next, the oscillation intensity associated with each FDR is determined based on phase angle trajectories in the center of inertia (COI) coordinate system. Finally, the multiple criteria for event location estimation are represented. Case studies and comparisons between the proposed and previous algorithms using actual and confirmed cases in U.S. power systems are performed to demonstrate the effectiveness and improvement of the proposed algorithm in practical applications. [ABSTRACT FROM AUTHOR]

Triangulation algorithms that conform to a set of non-intersecting input segments typically proceed in an incremental fashion, by inserting points first, and then segments. Inserting a segment amounts to: (1) deleting all the triangles it intersects; (2) filling the so generated hole with two polygons that have the wanted segment as shared edge; (3) triangulate each polygon separately. In this article we prove that these polygons are such that all their convex vertices but two can be used to form triangles in an earcut fashion, without the need to check whether other polygon points are located within each ear. The fact that any simple polygon contains at least three convex vertices guarantees the existence of a valid ear to cut, ensuring convergence. Not only this translates to an optimal deterministic linear time triangulation algorithm, but such algorithm is also trivial to implement. We formally prove the correctness of our approach, also validating it in practical applications and comparing it with prior art. [ABSTRACT FROM AUTHOR]

Active three-dimensional(3-D) thermography combines 3-D shape and active thermography. Thus, it enables the provision of the intuitive thermographic inspection results for composite parts with a complex geometry. However, conventional 3-D thermography acquires thermographic information and 3-D shape through at least two independent sensors and requires complex cross-modal image registration algorithm based on the keypoint detection and matching. In this article, an active 3-D thermography system is proposed using only one thermal camera for moving objects. This system does not require an independent 3-D sensor while the thermal camera acts as the 3-D sensor. Nature behind that is that a mathematical model is proposed to unify the line scanning thermography and laser triangulation in the dynamic scanning process, taking a line laser as both heat excitation for active thermography and spatial coding for 3-D reconstruction. Furthermore, the model enables the feature-free registration of thermogram sequence and 3-D shape, so that the registration is fast and robust without keypoint features. The experiments on standard height specimens, 3-D printing glass fiber composites, and carbon fiber intake tube have shown the error is calibrated within 0.25 mm in the range of 1 to 150 mm and evidenced the capability for subsurface defects detection. The ease and robustness of the proposed active 3-D thermography have a bright future for 3-D measurement, defects detection, and quality control in the production line. [ABSTRACT FROM AUTHOR]

3D models are commonly used in computer vision and graphics. With the wider availability of mesh data, an efficient and intrinsic deep learning approach to processing 3D meshes is in great need. Unlike images, 3D meshes have irregular connectivity, requiring careful design to capture relations in the data. To utilize the topology information while staying robust under different triangulations, we propose to encode mesh connectivity using Laplacian spectral analysis, along with mesh feature aggregation blocks (MFABs) that can split the surface domain into local pooling patches and aggregate global information amongst them. We build a mesh hierarchy from fine to coarse using Laplacian spectral clustering, which is flexible under isometric transformations. Inside the MFABs there are pooling layers to collect local information and multi-layer perceptrons to compute vertex features of increasing complexity. To obtain the relationships among different clusters, we introduce a Correlation Net to compute a correlation matrix, which can aggregate the features globally by matrix multiplication with cluster features. Our network architecture is flexible enough to be used on meshes with different numbers of vertices. We conduct several experiments including shape segmentation and classification, and our method outperforms state-of-the-art algorithms for these tasks on the ShapeNet and COSEG datasets. [ABSTRACT FROM AUTHOR]

Accurate event identification is an essential part of situation awareness ability for power system operators. Therefore, this work proposes an integrated event identification algorithm for power systems. First, to obtain and filter suitable inputs for event identification, an event detection trigger based on the rate of change of frequency (RoCoF) is presented. Then, the wave arrival time difference-based triangulation method considering the anisotropy of wave propagation speed is utilized to estimate the location of the detected event. Next, the two-dimensional orthogonal locality preserving projection (2D-OLPP)-based method, which is suitable for multiple types of measured data, is employed to achieve higher effectiveness in extracting the event features compared with traditional one-dimensional projection and principle component analysis (PCA). Finally, the random undersampling boosted (RUSBoosted) trees-based classifier, which can mitigate the data sample imbalance issue, is utilized to identify the type of the detected event. The proposed approach is demonstrated using the actual measurement data of U.S. power systems from FNET/GridEye. Comparison results show that the proposed event identification algorithm can achieve better performance than existing approaches. [ABSTRACT FROM AUTHOR]

In this paper, a simultaneous localization and mapping (SLAM) method that eliminates the influence of moving objects in dynamic environments is proposed. This method utilizes the correlation between map points to separate points that are part of the static scene and points that are part of different moving objects into different groups. A sparse graph is first created using Delaunay triangulation from all map points. In this graph, the vertices represent map points, and each edge represents the correlation between adjacent points. If the relative position between two points remains consistent over time, there is correlation between them, and they are considered to be moving together rigidly. If not, they are considered to have no correlation and to be in separate groups. After the edges between the uncorrelated points are removed during point-correlation optimization, the remaining graph separates the map points of the moving objects from the map points of the static scene. The largest group is assumed to be the group of reliable static map points. Finally, motion estimation is performed using only these points. The proposed method was implemented for RGB-D sensors, evaluated with a public RGB-D benchmark, and tested in several additional challenging environments. The experimental results demonstrate that robust and accurate performance can be achieved by the proposed SLAM method in both slightly and highly dynamic environments. Compared with other state-of-the-art methods, the proposed method can provide competitive accuracy with good real-time performance. [ABSTRACT FROM AUTHOR]

When organizing robots into formations, the interplay between the underlying network topology of the team and the sensing and communication modalities available to the individual robots has a fundamental effect on what types of formations are possible. The purpose of this article is to characterize the available motions of formations in which relative angles between robots equipped with bearing-only sensors are maintained. First, infinitesimal shape-similarity, a property of frameworks for which maintaining certain angles between robots ensures that the formation is invariant to translation, rotation, and uniform scaling, is examined; the shape-similarity matrix is redeveloped, and results on its nullspace are presented. Second, triangulations, a class of frameworks, are shown to be infinitesimally shape-similar. Finally, the coupling between network topology and robot capabilities is examined through the design of a decentralized heterogeneous formation-control strategy for a class of triangulations in which all robots are equipped with bearing-only sensors and a single robot can measure distances; the formation-control strategy is demonstrated on a team of differential-drive robots. [ABSTRACT FROM AUTHOR]

Layout migration is a fast methodology to generate the required layout for given circuits with different device attributes or different technology. By keeping the original layout topology, the previous design experience can help to keep the circuit performance. However, routing preservation is often not mentioned in previous layout migration techniques, which requires a complete rerouting to break the original style. Pan et al. (2015) proposed a topological slicing tree and a constrained delaunay triangulation (CDT) model to keep the routing style during migration. However, this approach may incur some missing nets after migration, which still requires tedious manual works to fix those nets. In this article, we propose a sequence pair (SP)-based placement migration methodology and a novel Cartesian detection line (CDL) model to preserve the routing styles in original layouts. By using the proposed approach, the routability information and routing behaviors can be preserved during layout migration. In order to prevent from missing nets, several refinement techniques are also proposed to fix unreasonable routing nets due to block displacement. In the experiments, the missing nets after migration can be reduced to almost zero with the proposed CDL model, which greatly reduces the extra design efforts. [ABSTRACT FROM AUTHOR]

Objective: In this study, we have used whole heart simulations parameterized with large animal experiments to validate three techniques (two from the literature and one novel) for estimating epicardial and volumetric conduction velocity (CV). Methods: We used an eikonal-based simulation model to generate ground truth activation sequences with prescribed CVs. Using the sampling density achieved experimentally we examined the accuracy with which we could reconstruct the wavefront, and then examined the robustness of three CV estimation techniques to reconstruction related error. We examined a triangulation-based, inverse-gradient-based, and streamline-based techniques for estimating CV cross the surface and within the volume of the heart. Results: The reconstructed activation times agreed closely with simulated values, with 50-70% of the volumetric nodes and 97-99% of the epicardial nodes were within 1 ms of the ground truth. We found close agreement between the CVs calculated using reconstructed versus ground truth activation times, with differences in the median estimated CV on the order of 3-5% volumetrically and 1-2% superficially, regardless of what technique was used. Conclusion: Our results indicate that the wavefront reconstruction and CV estimation techniques are accurate, allowing us to examine changes in propagation induced by experimental interventions such as acute ischemia, ectopic pacing, or drugs. Significance: We implemented, validated, and compared the performance of a number of CV estimation techniques. The CV estimation techniques implemented in this study produce accurate, high-resolution CV fields that can be used to study propagation in the heart experimentally and clinically. [ABSTRACT FROM AUTHOR]

We present supervision by registration and triangulation (SRT), an unsupervised approach that utilizes unlabeled multi-view video to improve the accuracy and precision of landmark detectors. Being able to utilize unlabeled data enables our detectors to learn from massive amounts of unlabeled data freely available and not be limited by the quality and quantity of manual human annotations. To utilize unlabeled data, there are two key observations: (I) The detections of the same landmark in adjacent frames should be coherent with registration, i.e., optical flow. (II) The detections of the same landmark in multiple synchronized and geometrically calibrated views should correspond to a single 3D point, i.e., multi-view consistency. Registration and multi-view consistency are sources of supervision that do not require manual labeling, thus it can be leveraged to augment existing training data during detector training. End-to-end training is made possible by differentiable registration and 3D triangulation modules. Experiments with 11 datasets and a newly proposed metric to measure precision demonstrate accuracy and precision improvements in landmark detection on both images and video. [ABSTRACT FROM AUTHOR]

Triangulation is an important task in the 3D reconstruction of computer vision. It seems simple to find the position of a point in 3D space when its 2D perspective projections in multi-view images are given and the corresponding camera projection matrices are known. However, in practice, multiple lines in 3D space do not intersect at one point because of noise. Then how to calculate the optimal 3D point of intersection becomes difficult. While there have been multiple methods trying to solve this problem, there is no systematic comparison between them. In this paper, we reviewed various currently existing variants of triangulation method and compared them through extensive experiments. The speed and accuracy of these methods have been compared using both synthetic and real datasets. We presented the results of experiments and summarized the advantages, limitations, and applicability of these methods so that to provide a guide for users when they need to choose an appropriate triangulation method for their given applications. Moreover, based on above analysis we proposed an improved method which shows better performance.

A low-frequency angular acceleration rotary table (LFAART) is capable of sinusoidal motion around a fixed position and essential to verify dynamic characteristics of a gyroscope device. The table’s performance at different swinging frequencies and amplitudes needs to be calibrated to realize traceability. However, there has been little research on the LFAART calibration. Besides, the existing measurement methods of sinusoidal angular motion parameters are not adequate for the needs of the LFAART calibration in terms of measurement range, precision, and feasibility. This article presents a calibration scheme combining an optical shaft encoder and a laser triangulation sensor. When the rotation amplitude covers sufficient slots of the scale grating, the optical shaft encoder is applied, while the laser triangulation sensor is used for a tiny amplitude motion. An algorithm is proposed to accurately calculate the angular acceleration amplitude and frequency of the rotation utilizing sinusoidal phase-modulated signals of the optical shaft encoder output. Singular zero-crossings of the series are discarded, and intervals of reversing moments are recognized according to the signal derivative’s sign at remaining zero-crossings. Then intervals are divided into subintervals to obtain accurate reversing moments. Finally, angular displacements between two adjacent reversing moments are estimated based on the fringe counting method, and the amplitude and frequency are calculated. The measurement uncertainty analysis and experiment demonstrate that the calibration scheme, including the algorithm, possesses high accuracy and resolution, wide range, and good operability, which perfectly solves the rotation’s angular acceleration amplitude and frequency. [ABSTRACT FROM AUTHOR]

On-machine measurement (OMM) is increasingly used to inspect freeform surfaces without unloading the workpiece from the machine tool. Compared to the mechanical probe deployed in many OMM processes, a laser displacement sensor has its advantage of high scanning efficiency. However, the accuracy has been a bottleneck limiting its application in precision inspection, due to the lack of a rigorously planned path. In this work, an error compensation model is established and verified considering laser triangulation error, misalignment from calibration, tooling error, and preevaluated error. Essentially, error modeling contributes to improve the measurement accuracy of laser triangulation OMM (LTOMM). After understanding these errors, a reliable measuring path is crucial to maintain the given accuracy of LTOMM. This path generated is capable of minimizing the measurement time at a given accuracy constraint. In a particular case study, a turbine blade surface was inspected using LTOMM following generated path against a built-in probe. The measurement time was reduced by 77.4% for a constraining accuracy of $\pm 10~\mu \text{m}$. The time could be further reduced by 38.6%, for a constraining accuracy of $\pm 13~\mu \text{m}$. This study has demonstrated that LTOMM could potentially replace the on-machine probe during measurement of freeform surfaces, toward more efficient industrial applications in the future. [ABSTRACT FROM AUTHOR]

With the aims of safe, smart and sustainable future mobility, a personalized approach of trajectory planning and control based on user preferences is developed for lane-change of autonomous vehicles in this paper. First, a safe area during the lane change process is identified by using constraint Delaunay triangulation. Then, an improved rapidly-exploring Random Trees (i-RRT) is developed with B-spline to generate the feasible trajectory cluster, which is subject to the safe area boundaries and the vehicle dynamics. To extract a personalized trajectory from this cluster, we firstly adopt the fuzzy linguistic preference relation (FLPR) method to identify users’ preferences on driving, which can be reflected by their subjective objectives including driving safety, ride comfort and vehicle stability. Then, the technique for order preference by similarity to ideal situation (TOPSIS) is utilized to solve the multi-objective optimisation problem formulated by considering the user preferences. The algorithms proposed above are integrated, and both simulation and experimental validation are conducted under lane-change scenarios of autonomous driving. Simulation and experiment results show that proposed approach is able to successfully realize personalized trajectory planning and lane-change control, satisfying users’ various preferences and simultaneously ensure vehicle safety, demonstrating its feasibility and effectiveness. [ABSTRACT FROM AUTHOR]

Simultaneously locating multiple sources passively in the wireless sensor networks (WSN) is challenging in the internet of things (IoT) applications, where reducing the computation and communication load is of great importance due to the requirement on real-time processing and the energy constraint. This is especially true when the number of sources or the number of sensor nodes is large. In this paper, a localization algorithm to estimate multiple sources' positions in the three-dimensional space is proposed. With the direction-of-arrival (DOA) estimates for multiple sources obtained at each sensor node, it is crucial to discriminate which estimate corresponds to which source. To save the computation resources, a classification method based on the genetic algorithm is proposed to handle the multiple sources. A fitness function is designed to assess the clustering of the DOA estimates. Extensive simulations are carried out to analyze the algorithm performance under the various settings. Numerical examples show that the proposed method could lower the computational burden by orders of magnitude compared to the conventional method, without significantly sacrificing the estimation accuracy.

Despite progress made in the accuracy and robustness of the dense matching technique in past years, efficient occlusion detection remains an open problem. In this paper, we present a two-step occlusion detection method to remove false matches in dense matching fields. First, a statistical dense matching method is developed by considering the correspondence between the grids to identify most occlusion regions. Second, to handle the potential misjudgment match in the occlusion boundary, a double-threshold filtering method is first used to reduce the noise in the grid image, which ensures that the gradient operator can accurately extract the boundary grid in the grid image; then, misjudgment matches in the boundary grid region are corrected based on the triangulation with descriptors. The results of the experiments comparing the proposed method and existing occlusion detection methods by, respectively, using the MPI-Sintel and KITTI datasets’ test sequence show that the proposed method has higher accuracy and better robustness.

Structured Gaussian elimination (SGE) is a class of methods for efficiently solving sparse linear systems. The key idea is to first triangulate the original linear systems. The maximum component (MC)-based strategies are widely used to implement the triangulation process. The most straightforward way to find the MC is through exhaustive search. Instead, in this letter, we propose to use a disjoint-set data structure (DSDS) to efficiently maintain the components. The extra storage and time complexity introduced by the DSDS are respectively linear to the number of unknowns and constraints involved in a linear system. Simulation results show that using the DSDS can be several times faster than doing the exhaustive search to find the components. [ABSTRACT FROM AUTHOR]

Most 3-D laser scanners are based on 3-D optical triangulation algorithms, where the location of each 3-D point is estimated as the intersection of a camera ray and a plane of light projected by a laser line generator. Since a physical laser-line generator projects a sheet of light of finite thickness, inaccurate measurements and errors result from assuming that the plane of light is infinitesimally thin. We propose a new mathematical formulation for 3-D optical triangulation based on interval arithmetic, where 3-D points are only determined within certain bounds along the camera rays, and multiple measurements are used to tighten these bounds. We propose the line segment cloud as an alternative surface representation to visualize the measurement errors within the proposed framework. We introduce the iterative line segment tightening algorithm to convert line segment clouds into point clouds, as a preprocessing step prior to surface reconstruction. We describe how to construct a low-cost laser-line 3-D scanner, where the camera is fixed with respect to the object and the laser-line generator is mounted on a high-resolution motion platform. We describe a GPU-based implementation where the large number of captured images is processed in real time. Finally, we present some experimental results. [ABSTRACT FROM AUTHOR]

We study the accuracy of triangulation in multi-camera systems with respect to the number of cameras. We show that, under certain conditions, the optimal achievable reconstruction error decays quadratically as more cameras are added to the system. Furthermore, we analyze the error decay-rate of major state-of-the-art algorithms with respect to the number of cameras. To this end, we introduce the notion of consistency for triangulation, and show that consistent reconstruction algorithms achieve the optimal quadratic decay, which is asymptotically faster than some other methods. Finally, we present simulations results supporting our findings. Our simulations have been implemented in MATLAB and the resulting code is available in the supplementary material, which can be found on the Computer Society Digital Library at http://doi.ieeecomputersociety.org/10.1109/TPAMI.2019.2939530. [ABSTRACT FROM AUTHOR]

Visible light positioning systems (VLPSs) are a feasible alternative to local positioning systems due to the technology improvement and massive use of light-emitting diodes (LEDs). Compared to other technologies, VLPSs can provide significant advantages, such as the achieved accuracy, although they still present some issues, mainly related to the reduced coverage area or the high computational load. This article proposes the design of a VLPS based on four LED lamps as transmitters and a quadrant photodiode angular diversity aperture (QADA) as a receiver. As the shape of the QADA is circular and the aperture to be installed over it is square, we derive the corresponding general equations to obtain the currents through the different pads of the QADA, regarding the angle of incidence of the light (and, inversely, how to estimate the angle of incidence from the measured currents). An encoding scheme based on 1023-bit Kasami sequences is proposed for every transmission from the LED lamps, thus providing multiple access capability and robustness against low signal-to-noise ratios and harsh conditions, such as multipath and near-far effect. A triangulation technique has been applied to estimate the receiver’s position, by means of the least-squares estimator (LSE), together with some geometrical considerations. The proposal has been validated by simulation and by experimental tests, obtaining 3-D positioning average errors below 13 and 5.5 cm for separations between the transmitters’ plane and the receiver of 2 and 1 m, respectively. [ABSTRACT FROM AUTHOR]

Underwater 3-D measurement has many applications, for example, in the oil and gas industry, archeology, and biology. Systems with laser triangulation sensors (LTSs) are being currently used underwater, even though they face some strong environment influences. Among these challenging influences are poor image quality and refraction, due to optical window interfaces between water and air inside the camera chamber. The refraction effect can be modeled knowing the distance from the camera pinhole center to the surface of refraction, the axis of refraction, the refractive index of the mediums, and the thickness of the optical window. This paper analyses a method for underwater LTS measurement using real experiments. The proposed method is based on the pinhole camera model, a refraction modeling, and a fitted mathematical plane for the projected laser light plane. After the in-air calibration, a step standard is measured underwater and the window distance from the camera is optimized. The method is evaluated according to guidelines for optical systems evaluation (VDI/VDE 2634). [ABSTRACT FROM AUTHOR]

In scan-based 3-D systems, the achievable measurement time is strongly restrained by the moving mass. This limitation can be relaxed, by scanning the optical path instead of moving the entire sensor, such that a higher measurement speed can be achieved. This article deals with the design, the control, and the measurement results of a scanning triangulation sensor, in which the illumination and reflection paths of the sensor are scanned by a fast steering mirror (FSM). The system architecture is determined using ray-tracing simulations, such that the performance of the scanning system can be determined in advance. To scan the area of interest with the FSM, conventional raster trajectories and Lissajous trajectories are employed, which provide an early overview of the scan area. For tracking these trajectories, PID and dual-tone controllers are used, respectively. The experimental results demonstrate that for both scan trajectories, the sample can be captured correctly, with a frame rate of 1 frame/s, a maximum spatial resolution of 130 $\mu \text{m}$ , and a field of view of $11.5\,\,\times 18.5\,\,{\mathrm{ mm}}^{2}$. [ABSTRACT FROM AUTHOR]

In this paper, we present an approach to local coverage path (CP) planning for a circular mobile sensor. Our family of algorithms provides completed and overlapped coverage control, curvature and path length control, localization, choice of observer’s placement based on sensing range, and first-order differentiable CP. Our family of algorithms begins by partitioning the target region (TR) into several regular triangulations (RTs). Based on the size of the RT and the sensing range, the number and location of observers are determined for all RT. All observers found are then used as waypoints (WPs) to generate baseline CP with the Traveling Salesman Problem’s nearest neighbor algorithm. The proposed algorithm solved the problems of how to design a differentiable and collision-free CP for all sensing range, limited and sufficient, while providing completed coverage control, overlapped coverage control, curvature and path length control, localization, collision avoidance, and the choice of observer’s placement based on sensing range and the environment. The main technical contributions of the proposed approach is to provide a holistic solution that segments any TR, uses triangulation to determine the observation WPs and then compute the smooth and collision-free CP. Computer simulations are provided to validate the effectiveness and correctness of our algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a cost-effective localization method by using received signal strength (RSS) from single wireless access point (AP) that has only three directional antennas. To reduce the requirement for the costly database training process, a three-step parametric model-based approach is proposed. This approach estimates the AP-device angle, distance, and the device location analytically by making use of wireless path-loss models, antenna radiation patterns, and differential RSS. Meanwhile, the differential antenna gain is introduced to eliminate the need for known AP-device distances when estimating the AP-device angle. Furthermore, to circumvent the difficulty in detecting outliers in a single-AP system, this paper further integrates single-AP solutions with dead-reckoning (DR) to obtain more robust solutions. Three types of measurement models (i.e., the device location updates, AP-device angle updates, and AP-device distance updates) are built from the single-AP localization system. Static and dynamic field tests with the new Bluetooth 5 signals indicated the possibility for using RSS from three directional antennas on single AP to obtain a low-cost localization solution and the effectiveness of integrating with DR on mitigating the impact of outliers. [ABSTRACT FROM AUTHOR]

Underwater 3-D perception is crucial for autonomous manipulation and mapping. Using a mirror-galvanometer system to steer a laser line projector and using triangulation with a camera it is possible to produce full 3-D perception, while avoiding moving the sensor with respect to the scene. This paper presents a ray-based model of a laser scanner and a calibration method for such a model. The model takes into account the distortion introduced to the system by the flat viewports used for both the laser and the camera. The paper also introduces the computation of elliptical cones as a way to synthesize the ray-based model and speed up the triangulation computation. The present study reports results obtained using the real sensor in a water tank to reconstruct objects and planes to assess the sensor's accuracy, as well as a comparison between the two triangulation methods (ray-based and elliptical cone). [ABSTRACT FROM AUTHOR]

A structured light system is used to position a scientific instrument mounted on a Mars rover robotic arm relative to a Mars surface sample. Fifty laser spots are projected on the Mars surface sample. The identification of individual laser spots, the centroiding algorithm, and the calibration are discussed. Results show that the system measures the distance to the Mars surface sample more accurate than 50 μm at a nominal operating distance of 30 mm. [ABSTRACT FROM AUTHOR]

Image stitching is usually subject to projective distortion and color difference problems. This paper presents an illumination-smoothing image stitching method based on the shape-optimizing hybrid transformation. An automatic mesh generation strategy is especially designed to reduce the calculation of the hybrid transformation within a reasonable range and guarantee the accuracy of image alignment. We consider stitching multiple images while constraining the distortion with the hybrid transformation and alleviate the color difference. In this paper, the method of color correction is adapted according to the characteristics of image stitching so as to achieve illumination-smoothing results. Moreover, the triangulation of the matching feature points is used to partition the color regions of the image and then more abundant color information is obtained for the calculation of the color transformation model. For general horizontal images, our method can achieve better results with less distortion in comparison with state-of-the-art methods. Various experimental results validate our new method. [ABSTRACT FROM AUTHOR]

Localization of sensor nodes is one of the important issues in wireless sensor networks (WSNs). The location of a node can be used as the location of the occurrence of an event. Error handling and scalability are key research issues that need to be taken care of while estimating the efficiency of any localization algorithm. In this paper, we propose an approach of error correction mechanism in addition to the minimization of error in multihop system (MEMHS) for the localization algorithm. The MEMHS algorithm deals with a scalable error correction of a multiliterate localization process using a few geographical positioning system enabled nodes. The MEMHS authors assumed that an error propagates linearly and is equal in any direction. In the present study, the authors show that an error propagates nonlinearly with respect to the hop count, and the magnitude of error (X-coordinate or Y-coordinate) depends on the direction of equator lines. This paper proposes a modified algorithm of MEMHS. Furthermore, an optimum deployment strategy is introduced so that maximum number of sensor nodes can be localized. By analyzing the proposed algorithm in comparison with the MEMHS algorithm, it is found that the proposed algorithm has a better performance in terms of error correction. [ABSTRACT FROM AUTHOR]

Due to the variation in factors surrounding humans, the physiological impact of stress is reported to be different for each individual. Thus, an efficient stress monitoring system needs to assess both the physiological and psychological impact of stress on individual basis and translate these assessments into an accurate quantitative metric that is of value to the individual. Therefore, this study proposed a logistic regression based model that integrates data from psychological Stress Response Inventory, biochemical (salivary cortisol), and physiological (HRV measures) domains via a principle of triangulation for achieving high reliability and consistency during stress assessment. With the proposed model, a mental stress index (MSI) based on the correlation between salivary cortisol and HRV time-/frequency-domain features were established. A total of 30 college students were recruited to verify the feasibility of proposed method by identifying targeted stressful event. The obtained results reveal that MSI values were sensitive to acute stress, and could predict the association level of normal individual to a stress group with approximately 97% accuracy. Findings from this study could provide potential insight on self-tracking and training of individual's stress with adoption of wearable sensor system in a dynamic setting. [ABSTRACT FROM AUTHOR]

In situ visualization of laparoscopic ultrasound in both conventional and robot-assisted laparoscopic surgery requires robust and efficient computation of the pose of the laparoscopic ultrasound probe with respect to the laparoscopic camera. Image-based intrinsic methods of computing this relative pose need to overcome challenges due to irregular illumination, partial feature occlusion, and clutter that are unavoidable in practical laparoscopic surgery. In this paper, we propose an accurate image-based method that is robust to partial occlusion of the fiducials and outliers. The method is extended to multi-view imaging model with applications in stereoscopic laparoscopy and robot-assisted surgery. Rather than treating the model-to-image correspondence and pose computation as separate problems, we solve them jointly using the Kalman Filter-based framework that demonstrates video rate running time (~24fps). By keeping the optical tracking measurements as a reference, we demonstrate that the proposed methods result in clinically acceptable tracking accuracy, reaching target registration errors well below 1.5mm on average. In addition, our multi-view tracking method is compared to a conventional stereo triangulation-based pose estimation scheme that commercial optical tracking systems are based on, to experimentally demonstrate its superiority in terms of accuracy. Finally, we qualitatively demonstrate the suitability of our methods for practical laparoscopic applications by conducting a phantom-based experiment. [ABSTRACT FROM AUTHOR]

Search and rescue missions still typically rely on teams of people tirelessly searching for victims. Advances in technology have seen the use of drones to help the rescuers in their search. These drones primarily use visual systems, but cost and visibility are major drawbacks with those systems. The system described in this paper attempts to resolve the localization problem for drones used in search and rescue operations by making use of acoustic source localization. The source location is estimated in terms of an azimuth estimation (the bearings or angle toward the source) as well as the distance. The proposed system is shown the promising results for both the azimuth and distance estimations. [ABSTRACT FROM AUTHOR]

Reconstructing dense, volumetric models of real-world 3D scenes is important for many tasks, but capturing large scenes can take significant time, and the risk of transient changes to the scene goes up as the capture time increases. These are good reasons to want instead to capture several smaller sub-scenes that can be joined to make the whole scene. Achieving this has traditionally been difficult: joining sub-scenes that may never have been viewed from the same angle requires a high-quality camera relocaliser that can cope with novel poses, and tracking drift in each sub-scene can prevent them from being joined to make a consistent overall scene. Recent advances, however, have significantly improved our ability to capture medium-sized sub-scenes with little to no tracking drift: real-time globally consistent reconstruction systems can close loops and re-integrate the scene surface on the fly, whilst new visual-inertial odometry approaches can significantly reduce tracking drift during live reconstruction. Moreover, high-quality regression forest-based relocalisers have recently been made more practical by the introduction of a method to allow them to be trained and used online. In this paper, we leverage these advances to present what to our knowledge is the first system to allow multiple users to collaborate interactively to reconstruct dense, voxel-based models of whole buildings using only consumer-grade hardware, a task that has traditionally been both time-consuming and dependent on the availability of specialised hardware. Using our system, an entire house or lab can be reconstructed in under half an hour and at a far lower cost than was previously possible. [ABSTRACT FROM AUTHOR]

This paper proposes a new technique for two-view camera pose estimation applied to vehicular monocular visual odometry. The proposed method is based on the resection–intersection concept using Gauss–Newton non-linear regression to minimize projection residuals. Different from other solutions, the proposed method does not suppose fixed positions of points during resection but rather takes into account the adjustment in the 3-D position of points when calculating the new camera pose increment. A simplified fast two-view triangulation method is used to mitigate the cost of intense 3-D point position recalculation. The method is integrated into a monocular visual odometry solution and tested on the KITTI public dataset. The final system does not use loop closing or key frame selection, and RANSAC is replaced by sequential backward selection. The resection–intersection method presents robustness to errors in initial camera pose and rapid convergence rate, reaching the final camera pose in up to three iterations in 65% of the cases. A single thread Python/OpenCV implementation of the monocular visual odometry system on an Intel Core i7 spent around 75 ms per frame, of which one third was used in the resection–intersection step. The achieved results presented better accuracy than all other published monocular odometry works in the KITTI benchmark, in a simple solution using only two consecutive frames for camera pose estimation. [ABSTRACT FROM AUTHOR]

Multiple-view triangulation by $\ell _\infty$ minimisation has become established in computer vision. State-of-the-art $\ell _\infty$ triangulation algorithms exploit the quasiconvexity of the cost function to derive iterative update rules that deliver the global minimum. Such algorithms, however, can be computationally costly for large problem instances that contain many image measurements, e.g., from web-based photo sharing sites or long-term video recordings. In this paper, we prove that $\ell _\infty$ triangulation admits a coreset approximation scheme, which seeks small representative subsets of the input data called coresets. A coreset possesses the special property that the error of the $\ell _\infty$ solution on the coreset is within known bounds from the global minimum. We establish the necessary mathematical underpinnings of the coreset algorithm, specifically, by enacting the stopping criterion of the algorithm and proving that the resulting coreset gives the desired approximation accuracy. On large-scale triangulation problems, our method provides theoretically sound approximate solutions. Iterated until convergence, our coreset algorithm is also guaranteed to reach the true optimum. On practical datasets, we show that our technique can in fact attain the global minimiser much faster than current methods. [ABSTRACT FROM AUTHOR]

Design of contemporary antenna structures is heavily based on full-wave electromagnetic (EM) simulation tools. They provide accuracy but are CPU intensive. Reduction of EM-driven design procedure cost can be achieved by using fast replacement models (surrogates). Unfortunately, standard modeling techniques are unable to ensure sufficient predictive power for real-world antenna structures (multiple parameters, wide parameter ranges, and highly nonlinear responses). Here, a design-oriented modeling technique is introduced, in which the most critical part is the domain definition. The surrogate is constructed in the region based on a set of reference designs, optimized with respect to user-selected figures of interest such as operating frequencies, bandwidth, and substrate permittivity. The domain is spanned by the simplexes, obtained by triangulation of the reference designs, further extended into their orthogonal complements. Restricting the model domain as above permits dramatic reduction of the number of training data samples necessary to build a reliable model, as compared to the conventional approach. The proposed modeling framework is demonstrated using three examples, a UWB monopole, and two dual-band antennas. Comprehensive benchmarking as well as application studies and experimental verification are also provided. [ABSTRACT FROM AUTHOR]

In this paper, we proposed a coverage path (CP) planning approach for a mobile robot moving in a dynamically changing environment. Our family of algorithms begins by finding the first path around the center of interior obstacles/disks of a given target region (TR) through the Graham scan algorithm. The first path is then used as a foundational path to generate a collision free, first order differentiable, and observable CP through a complete set of input and output transformation algorithms which together form the rainbow CP planning approach. The last algorithm of the rainbow CP planning approach finds a sufficient number of observation points on the CP needed to observe the TR. The novelty of our rainbow CP planning approach is that it partitions the TR into different shapes with different properties needed to obtain complete coverage while achieving first order path differentiability. The main technical contributions of the proposed approach is to provide a holistic solution that segments any TR, uses triangulation to determine the line of sights and observation points, and computes the collision-free CP within a quadratic runtime. The proposed method can be readily generalized to address problems of higher dimensions, and it is scalable with respect to the size of TR and the number of robots. Computer simulations are used to illustrate the effectiveness and correctness of the proposed approach. [ABSTRACT FROM AUTHOR]

A noninvasive machine vision-based system aimed at the measurement of a refractive index (RI) of slab materials and liquids inside a cell is presented. The principle of the proposed method is based on the relaying laser stripe displacements, which occur due to the difference of the refraction across mediums, to the corresponding RI. Two of the main advantages of the proposed method are that the RI of a liquid inside a cell can be measured instantaneously and without knowing any of the optical parameters of the cell walls, respectively. Other available methods however are based on a two-step measurement (first for an empty and then for the filled cell, respectively) procedure. The RI of several well-studied liquids and aqueous solutions of NaCl was measured and the results were confirmed with the corresponding reference values. The precision of up to 10−4 in RI measurement is achieved due to the ability of localizing laser stripe displacements with the subpixel accuracy and robust image processing algorithms. The uncertainty components of the proposed method are thoroughly analyzed, and it is shown that the precision can be further increased subject to the corresponding application and budget. [ABSTRACT FROM AUTHOR]

We introduce the Hierarchical Poisson Disk Sampling Multi-Triangulation (HPDS-MT) of surfaces, a novel structure that combines the power of multi-triangulation (MT) with the benefits of Hierarchical Poisson Disk Sampling (HPDS). MT is a general framework for representing surfaces through variable resolution triangle meshes, while HPDS is a well-spaced random distribution with blue noise characteristics. The distinguishing feature of the HPDS-MT is its ability to extract adaptive meshes whose triangles are guaranteed to have good shape quality. The key idea behind the HPDS-MT is a preprocessed hierarchy of points, which is used in the construction of a MT via incremental simplification. In addition to proving theoretical properties on the shape quality of the triangle meshes extracted by the HPDS-MT, we provide an implementation that computes the HPDS-MT with high accuracy. Our results confirm the theoretical guarantees and outperform similar methods. We also prove that the Hausdorff distance between the original surface and any (extracted) adaptive mesh is bounded by the sampling distribution of the radii of Poisson-disks over the surface. Finally, we illustrate the advantages of the HPDS-MT in some typical problems of geometry processing. [ABSTRACT FROM PUBLISHER]

A novel localization framework is presented based on ultra-wideband (UWB) channel sounding, employing a triangulation method using the geometrical properties of propagation paths, such as time delay of arrival, angle of departure, angle of arrival, and their estimated variances. In order to extract these parameters from the UWB sounding data, an extension to the high-resolution RiMAX algorithm was developed, facilitating the analysis of these frequency-dependent multipath parameters. This framework was then tested by performing indoor measurements with a vector network analyzer and virtual antenna arrays.The estimated means and variances of these geometrical parameters were utilized to generate multiple sample sets of input values for our localization framework. Next to that, we consider the existence of multiple possible target locations, which were subsequently clustered using a Kim–Parks algorithm, resulting in a more robust estimation of each target node. Measurements reveal that our newly proposed technique achieves an average accuracy of 0.26, 0.28, and 0.90 m in line-of-sight (LoS), obstructed-LoS, and non-LoS scenarios, respectively, and this with only one single beacon node. Moreover, utilizing the estimated variances of the multipath parameters proved to enhance the location estimation significantly compared to only utilizing their estimated mean values. [ABSTRACT FROM PUBLISHER]

An interior penalty discontinuous Galerkin method for solving 2-D magnetostatic field problems using the magnetic vector potential A is presented. The use of the A -formulation in two dimensions results in a second-order elliptic boundary value problem. Due to the proposed method, the scheme is symmetric and the resulting mass matrix is block diagonal, whereas each block belongs to one element of the triangulation. The applicability of the method is demonstrated by solving a simple 2-D C-shaped ferromagnetic material with a coil surrounding one part of the magnetic yoke. Thereby, the permeability of the material is supposed to be homogeneous and isotropic. The solution A and the resulting field quantities are compared with the standard finite element analysis. [ABSTRACT FROM AUTHOR]

A new class of codes, Extended Product (EPC) Codes, consisting of a product code with a number of extra parities added, is presented and applications for erasure decoding are discussed. An upper bound on the minimum distance of EPC codes is given, as well as constructions meeting the bound for some relevant cases. A special case of EPC codes, Extended Integrated Interleaved (EII) codes, which naturally unify Integrated Interleaved (II) codes and product codes, is defined and studied in detail. It is shown that EII codes often improve the minimum distance of II codes with the same rate, and they enhance the decoding algorithm by allowing decoding on columns as well as on rows. It is also shown that EII codes allow for encoding II codes with an uniform distribution of the parity symbols. [ABSTRACT FROM PUBLISHER]

Sensors are key to situation awareness and response and need to maintain time and position information to tag their measurement data. While local clocks can be used for time stamping, geotagging can be challenging for sensors with no access to GPS, such as the underground environment in precision agriculture. We study the problem of sensor node localization for a hybrid wireless sensor network for precision agriculture, with satellite nodes located above ground and sensor nodes located underground. This application is quite unique in possessing multimedia and multipath features. We use received signal strength of signals transmitted between neighboring sensor nodes and between satellite nodes and sensor nodes as a means to perform the ranging measurement. The localization problem is formulated as that of estimating the parameters of the joint distribution of the received signal strength at all nodes in the network. First, we arrive at path loss and fading models for various multimedia and multipath communication scenarios in our network to model the received signal strength in terms of the propagation distance and, hence, the participating nodes’ location coordinates. We account for various signal degradation effects such as fading, reflection, transmission, and interference between two signals arriving along different paths. Then, we formulate a maximum-likelihood optimization problem to estimate the nodes’ location coordinates using the derived statistical model. We also present a sensitivity analysis of the estimates with respect to soil permittivity and magnetic permeability. [ABSTRACT FROM PUBLISHER]

The complete camera pose (location + orientation) of the Google street view (GSV) images is provided by Google. Hence, one can utilize this information to localize a query camera based on the projective geometry. The existing literature works either perform image retrieval-based rough location recognition or require high-computational power/specific features for three-dimensional fine localization. In this paper, we propose a robust 2-D method for outdoor image-based localization using the GSV database. Having found the nearest neighboring images (best matches) in the GSV database using image retrieval techniques or the GPS circle information, the proposed method can be applied for robust fine localization of pedestrians/vehicles. The proposed method first finds the common features among the three views, i.e., query view and two from the best matches. Next, for each common feature, a 2-D triangulation is performed using the retrieved database images to find the feature world coordinates. We call this procedure “2DTri.” Afterward, a novel set of nonlinear equations is solved to estimate the fine location of the query. The novel set of equations can be interpreted as a 2-D version of the well-known perspective n-point (PnP) problem, which we call “2DPnP.” Hence, the proposed method is named “2DTriPnP.” The 2DPnP step is performed in a robust way, which is more accurate and considerably less complex compared to the conventional RANSAC-based robust methods. 2DTriPnP will be demonstrated experimentally to show better localization performance compared to other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Crowd behavior analysis has recently emerged as an increasingly important and dedicated problem for crowd monitoring and management in the visual surveillance community. In particular, it is receiving a lot of attention to detect potentially dangerous situations and to prevent overcrowdedness. In this paper, we propose to quantify crowd properties by a rich set of visual descriptors. The calculation of these descriptors is realized through a novel spatio-temporal model of the crowd. It consists of modeling time-varying dynamics of the crowd using local feature tracks. It also involves a Delaunay triangulation to approximate neighborhood interactions. In total, the crowd is represented as an evolving graph, where the nodes correspond to the tracklets. From this graph, various mid-level representations are extracted to determine the ongoing crowd behaviors. In particular, the effectiveness of the proposed visual descriptors is demonstrated within three applications: crowd video classification, anomaly detection, and violence detection in crowds. The obtained results on videos from different data sets prove the relevance of these visual descriptors to crowd behavior analysis. In addition, by means of comparisons to other existing methods, we demonstrate that the proposed descriptors outperform the state-of-the-art methods with a significant margin using the most challenging data sets. [ABSTRACT FROM PUBLISHER]

Random refraction occurs in turbulence and through a wavy water-air interface. It creates distortion that changes in space, time and with viewpoint. Localizing objects in three dimensions (3D) despite this random distortion is important to some predators and also to submariners avoiding the salient use of periscopes. We take a multiview approach to this task. Refracted distortion statistics induce a probabilistic relation between any pixel location and a line of sight in space. Measurements of an object's random projection from multiple views and times lead to a likelihood function of the object's 3D location. The likelihood leads to estimates of the 3D location and its uncertainty. Furthermore, multiview images acquired simultaneously in a wide stereo baseline have uncorrelated distortions. This helps reduce the acquisition time needed for localization. The method is demonstrated in stereoscopic video sequences, both in a lab and a swimming pool. [ABSTRACT FROM PUBLISHER]

Optical motion capture is based on estimating the three-dimensional positions of markers by triangulation from multiple cameras. Successful performance depends on points being visible from at least two cameras and on the accuracy of the triangulation. Triangulation accuracy is strongly related to the positions and orientations of the cameras. Thus, the configuration of the camera network has a critical impact on performance. A poor camera configuration may result in a low quality three-dimensional (3D) estimation and consequently low quality of tracking. This paper introduces and compares two methods for camera placement. The first method is based on a metric that computes target point visibility in the presence of dynamic occlusion from cameras with “good” views. The second method is based on the distribution of views of target points. Efficient algorithms, based on simulated annealing, are introduced for estimating the optimal configuration of cameras for the two metrics and a given distribution of target points. The accuracy and robustness of the algorithms are evaluated through both simulation and empirical measurement. Implementations of the two methods are available for download as tools for the community. [ABSTRACT FROM PUBLISHER]

Flip is a simple and local operation to transform one triangulation to another. It makes changes only to some neighboring simplices, without considering any attribute or configuration global in nature to the triangulation. Thanks to this characteristic, several flips can be independently applied to different small, non-overlapping regions of one triangulation. Such operation is favored when designing algorithms for data-parallel, massively multithreaded hardware, such as the GPU. However, most existing flip algorithms are designed to be executed sequentially, and usually need some restrictions on the execution order of flips, making them hard to be adapted to parallel computation. In this paper, we present an in depth study of flip algorithms in low dimensions, with the emphasis on the flexibility of their execution order. In particular, we propose a series of provably correct flip algorithms for regular triangulation and convex hull in 2D and 3D, with implementations for both CPUs and GPUs. Our experiment shows that our GPU implementation for constructing these structures from a given point set achieves up to two orders of magnitude of speedup over other popular single-threaded CPU implementation of existing algorithms. [ABSTRACT FROM PUBLISHER]

In order to improve the quality of steel pipes, one of the main requisites is to build pipes that are as close as possible to the designed one. To accomplish this task, an optical distance sensor of reduced size and very low power consumption was developed inside a measurement system allowing reconstruction of steel pipe dimensions. The results obtained led to the construction of a working prototype configured as an active triangulation rangefinder with a gamut of 10 cm starting at 5 cm. The characterization of the novel rangefinder has confirmed the feasibility of this new system with a resolution of \sim 10~\mu \textm on a range of measurement of 10 cm, providing up to 10 000 measurements per second. [ABSTRACT FROM PUBLISHER]