3D Target Research Articles

A Chaotic Parallel Artificial Fish Swarm Algorithm for Water Quality Monitoring Sensor Networks 3D Coverage Optimization

In recent years, the increasingly severe water pollution problem encouraged researchers to optimize water quality monitoring sensor networks (WQMSNs) by creating new underwater sensor coverage algorithms. Since the sensor is limited by the monitoring range and the number of targets, optimizing the 3D target coverage of heterogeneous multisensors is essential to maximize the 3D target coverage rate of the monitored waters. To enhance the target coverage rate, the target allocation needs to be searched in all possible combinations. To optimize the 3D coverage of underwater targets, this research proposes a chaotic parallel artificial fish swarm algorithm (CPAFSA). CPAFSA uses chaotic selection to initialize parameters and integrates the global search capabilities of parallel operators. It also applies the elite selection which effectively avoiding local optimization and solving the problem of 3D target coverage. Ultimately, CPAFSA is compared with genetic algorithm (GA) and particle swarm optimization (PSO). The results of the simulation experiment demonstrated the excellent performance of CPAFSA in achieving underwater 3D target coverage.

Research on underwater target tracking based on Gaussian Hermitian Kalman Particle filter algorithm

The underwater target tracking environment usually has the characteristics of strong nonlinearity and non-Gaussian. The target tracking problem usually uses the nonlinear filtering algorithm, which combines the nonlinear measurement model with the linear system dynamics. The primary goal of target tracking is to extract accurate information about the real-time state of the target from the noise nonlinear observation obtained by the sensor. A new Gaussian Hermitt Kalman particle filter algorithm (GHKF-PF) is used to improve the tracking accuracy of the particle filter algorithm (PF). GHKF-PF uses the GHKF sampling method to sample particles from the posterior distribution of the target, calculates the mean and covariance for each particle, and uses the mean and variance to guide sampling. In the simulation experiment, the uniform linear motion model of underwater 3D target in the environment of Gaussian mixture noise (GMN) is established, and the application of GHKF-PF algorithm in 3D space is realized, and the accuracy is higher than that of PF. In order to further verify the effectiveness of the algorithm, a six-dimensional uniform acceleration motion model is established and compared with the extended particle filter (EPF) algorithm and the unscented particle filter (UPF) algorithm. The simulation results show that the performance of the GHKF-PF algorithm is similar to that of the UPF algorithm and better than the EPF algorithm.

IN-PROCESS PORTABLE PHOTOGRAMMETRY USING OPTICAL TARGETS FOR LARGE SCALE INDUSTRIAL METROLOGY

Portable photogrammetry methods are being used more and more as a 3D technique for large scale industrial metrology applications. Optical targets are placed on an object and images are taken around it, where measuring traceability is provided by precise off-process pre-calibrated digital cameras and scale bars. According to the 2D target image coordinates, target 3D coordinates and camera views are jointly computed. Numerical approaches such as bundle adjustment have usually been adopted for computing the 3D scene, with high computation time, leading in practice to time consuming and user dependent iterative review and re-processing measuring procedures until an adequate set of images is taken, limiting the potential of photogrammetry for fast, easy-to-use, and precise measurements. In a previous work, a new efficient computing procedure was presented for solving the bundle adjustment problem in portable photogrammetry. In this paper, further steps are presented towards increasing the computational efficiency of the in-process approach in portable photogrammetry. Measurement of raw part surface geometry prior to its machining is adopted as the industrial case for evaluation, with large scale measuring volumes (100 m3). An increase of efficiency in the in-process computing capability is demonstrated on a consumer grade desktop PC, with a total in-process computing time ranging 1 second per image for all the images taken at all the computed industrial scenarios. Additionally, a method for the self-calibration of camera and lens distortion is integrated into the in-process approach, enabling the use of low cost non-specialized digital cameras for precise industrial metrology, ranging at the same performance reported for portable photogrammetry with precise off-process pre-calibrated cameras, with a relative precision of 1/10,000 (e.g. 0.1 mm error in 1 m). Keywords: metrology; precision; machine tool; image; photogrammetry; calibration

Coverage Aware Scheduling Strategies for 3D Wireless Video Sensor Nodes to Enhance Network Lifetime

High-density wireless video sensor nodes (VSNs) having limited battery power are deployed randomly in the disaster-hit area for capturing visual data, but its local processing and transmission consume high energy. High deployment density of those VSNs results in a larger overlap in the coverage area across VSNs that can be utilized to cover the sensing region of some VSNs and shut off such VSNs to decrease energy consumption and increase network lifetime without losing much area coverage. Two advanced approaches (APP_5 and APP_6) with realistic 3D rectangular pyramid camera coverage of VSN monitoring 2D target area is proposed in this paper. These approaches reduce the number of active VSNs in the target area and energy consumption maintaining the overall coverage area above some threshold value ensuring network connectivity. The approaches are compared with the three state-of-the-art approaches EX_1, EX_2 and EX_3 in the same simulation setup. Observed that for 150 deployed VSNs over the target area of size $75\times 75$ square meters, APP_5 and APP_6 reduce energy consumption by 6.98% and 18.6% respectively from the existing approach EX_3 (producing a better result among three existing approaches in terms of energy consumption). Reducing the number of active VSNs helps decrease energy consumption at the expense of reduced area coverage. For the same node density, both APP_5 and APP_6 lose a little amount of area coverage (i.e. 0.93% and 0.95%) than the existing approach EX_2 (producing a better result among three existing approaches in terms of percentage of area coverage). Additionally, both the proposed approaches (having the same communication overhead) establish superiority by 3.19%/7.83%/4.25% from EX_1/EX_2/(EX_3) in terms of communication overhead for 100 deployed VSNs on the same target area. Finally, APP_6 substantiates superiority in terms of reduced energy consumption (11.97%) than APP_5 losing a very little percentage (0.02%) of area coverage for 150 deployed VSNs.

A summary statistical representation influences perceptions but not visually or memory-guided grasping.

A statistical summary representation (SSR) is a phenomenon wherein a target property (e.g., size) is encoded based on the average of the stimulus-set to which it belongs. An SSR has been demonstrated in obligatory judgment tasks; however, to our knowledge no work has examined whether it influences grasps to 3D targets. Here, participants completed a method of adjustment task, and visually and memory-guided grasps in conditions wherein differently sized 3D targets (widths: 20, 30 and 40mm; height and depth=10mm) were presented with equal frequency (i.e., control) and when the smallest (i.e., 20-mm: small-target) and largest (i.e., 40-mm: large-target) targets were presented five times as often as the other targets in the stimulus-set. In the method of adjustment task, responses for the small- and large-target weighting conditions were smaller and larger than the control condition, respectively. In other words, an SSR biased perceptions in the direction of the most frequently presented target in the stimulus-set - a result consistent with the view that perceptions are supported by relative visual information laid down by the ventral visual pathway. In contrast, grip apertures were refractory to target-weighting and was a finding independent of the presence (i.e., visually guided) or absence (i.e., memory-guided) of visual feedback. Furthermore, two one-sided tests showed that peak grip apertures for the different target weighting conditions were within an equivalence boundary. Accordingly, an SSR does not influence 3D grasps and is a finding adding to a growing literature reporting that actions are supported by the absolute visuomotor networks of the dorsal visual pathway.

Development and validation of a reconstruction approach for three-dimensional confined-space tomography problems.

This work reports the development and validation of a new tomography approach, termed cross-interfaces computed tomography (CICT), to address confined-space tomography problems. Many practical tomography problems require imaging through optical walls, which may encounter light refractions that seriously influence the imaging process and deteriorate the three-dimensional (3D) reconstruction. Past efforts have primarily focused on developing open-space tomography algorithms, but these algorithms are not extendable to confined-space problems unless the imaging process from the 3D target and its line-of-sight two-dimensional (2D) images (defined as "projections") is properly adjusted. The CICT approach is therefore proposed in this work to establish an algorithm describing the mapping relationship between the optical signal field of the target and its projections. The CICT imaging algorithm is first validated by quantitatively comparing measured and simulated projections of a calibration plate through an optical cylinder. Then the CICT reconstruction is numerically and experimentally validated using a simulated flame phantom and a laminar cone flame, respectively. Compared to reconstructions formed by traditional open-space tomography, the CICT approach is demonstrated to be capable of resolving confined-space problems with significantly improved accuracy.

3D parallel tiled code implementing a modified Knuth's optimal binary search tree algorithm

This paper presents an approach to generation of 3D parallel tiled code implementing an optimal binary search tree (OBST) algorithm. We demonstrate that the features of data dependences available in the code implementing Knuth's OBST algorithm allow us to generate only 2D tiled code. We suggest a way of transformation of Knuth's OBST algorithm to a modified one exposing dependences allowing us to generate 3D parallel tiled code. The polyhedral model and the corresponding tools supporting that model are used by us to generate 3D target tiled code on the basis of the modified Knuth's OBST algorithm. Program parallelism is based on the wavefront technique and it is presented in the OpenMP C/C++ standard. Experiments carried out by us with obtained 3D tiled code demonstrate that this code considerably outperforms 2D tiled code generated on the basis of serial code implementing classic Knuth's OBST algorithm. Increased code performance is achieved due to much larger locality of 3D tiled code in comparison with that of 2D one.

3D mineral exploration targeting with multi-dimensional geoscience datasets, Tongling Cu(-Au) District, China

The Tongling Cu(-Au) district is the most important magma-skarn type Cu(-Au) polymetallic districts in the Middle-Lower Yangtze metallogenic belt of China. The previous studies in the Tongling Cu(Au) district compiled available digital geological, geochemical, geophysical, and remote sensing datasets. As the discovery of near-surface Cu(-Au) deposits becomes more and more difficult in the Tongling Cu(-Au) district, we face the challenge of exploring for blind mineral deposits in the subsurface. Three-dimensional (3D) mineral potential mapping can provide effective exploration targeting by integrating multi-source and multi-dimension geoscience datasets. In this paper, firstly, a 3D district-scale geological model was constructed from multiple geoscience datasets. Secondly, multi-level (0 m, −300 m, −600 m, −900 m, −1200 m) exploration criteria were extracted by spatial analyses. Thirdly, the weights-of-evidence method, discrete smooth interpolation, and ordinary kriging interpolation were used to integrate multi-level exploration criteria including two-dimensional (2D) surface geochemical and remote sensing data into a 3D posterior-probability model. Finally, the concentration-area and concentration-volume fractal methods were used to define posterior-probability thresholds to outline 2D and 3D targets, respectively. The results indicated that the integrating performance of ordinary kriging interpolation is better than that of discrete smooth interpolation in 3D posterior-probability modeling. The whole procedure yielded a series of high-potential Cu(-Au) targets. The methodology discussed here can be used in other magma-skarn districts in the world.

Robotic target following with slow and delayed visual feedback

Following rapidly and precisely a moving target has become the core functionality in robotic systems for transportation, manufacturing, and medical devices. Among existing targeting following methods, vision-based tracking continues to thrive as one of the most popular, and is the closest method to human perception. However, the low sampling rate and the time delays of visual outputs fundamentally hinder real-time applications. In this paper, we show the potential of significant performance gain in vision-based target following when partial knowledge of the target dynamics is available. Specifically, we propose a new framework with Kalman filters and multi-rate model-based prediction (1) to reconstruct fast-sampled 3D target position and velocity data, and (2) to compensate the time delay. Along the path, we study the impact of slow sampling and the delay duration, and we experimentally verify different algorithms with a robot manipulator equipped with an eye-in-hand camera. The results show that our approach can achieve 95% error reduction rate compared with the commonly used visual servoing technique, when the target is moving with high speed and the visual measurements are slow and incapable of providing timely information.

High-dimensional co-occurrence matrix: A new tool for 3D seismic target visualization and interpretation

The interpretation of seismic attributes has been developed for more than 40 years, but it still cannot be fully matched with the study of geologic sedimentology. There are two main bottlenecks. One is that almost no attributes can completely characterize amplitudes from a 3D perspective. Another is the influence of wavelets (frequency and phase). Accordingly, a new volumetric attribute extraction technique is presented for solving these two problems as much as possible. A high-dimensional co-occurrence matrix (HCM), as a statistical representation of the spatial structural relationship between data points, can be one of the solutions to extracting 3D attributes. By considering the global spatial distribution, the HCM fixes the directional distortion of the existing co-occurrence matrix methods and effectively suppresses vertical wavelet illusions. By realizing a rapid transformation of seismic data to 3D geologic target visualizations, this method does not rely heavily on structural information such as dip and azimuth and becomes a convenient bridge from seismic to geologic interpretation. We have applied the methodology to channel delineation in a 3D seismic survey acquired in Ordos, China. The results indicate that the attribute volume extracted from HCM expresses the geologic target convincingly and eliminates the illusions caused by the wavelets. The deposition history of the channels could be observed continuously through geologic time.

Occlusion detection and drift-avoidance framework for 2D visual object tracking

This paper presents a long-term 2D tracking framework for the coverage of live outdoor (e.g., sports) events that is suitable for embedded system application (e.g. Unmanned Aerial Vehicles). This application scenario requires 2D target (e.g., athlete, ball, bicycle, boat) tracking for visually assisting the UAV pilot (or cameraman) to maintain proper target framing, or even for actual 3D target following/localization when the drone flies autonomously. In these cases, it should be expected that the target to be tracked/followed, may disappear from the UAV camera field of view, due to fast 3D target motion, illumination changes, or due to visual target occlusions by obstacles, even if the actual UAV continues following it (either autonomously, by exploiting alternative target localization sensors, or by pilot maneuvering). Therefore, the 2D tracker should be able to recover from such situations. The proposed framework solves exactly this problem. Target occlusions are detected from the 2D tracker responses. Depending on the occlusion immensity, the proposed framework decides whether to not update the tracking model, or to employ target re-detection in a broader window. As a result, the proposed framework allows continued target tracking once the target re-appears in the video stream, without tracker re-initialization.

Modified Least Squares Algorithm for Three-Dimensional Target Location Based on Wireless Communication Base Stations

The observed values of time of arrival (TOA) for the radio signals between the target and the wireless communication base stations are mainly affected by signal non-line-of-sight (NLOS) propagation in target location. TOA with NLOS makes a lot of signal noises and propagation delays, that is, location errors. For the first time, this paper focuses on the problem of modifying the Z-axis location coordinates in three-dimensional (3D) target location. A novel algorithm is proposed by establishing the modified least squares 3D location model for the accurate target location. Meanwhile, an optimal base station selection strategy is proposed by using the spectral clustering algorithm, which is based on the spatial distribution of the base stations. Compared with the existing algorithms, the proposed algorithm in this paper has better performance on the accurate target 3D location in real scenes, which has a high value of practical application. The simulations illustrate that the location error of the proposed algorithm is smaller than those of other existing algorithms based on the same simulation data and conditions.

Camera calibration with global LBP-coded phase-shifting wedge grating arrays

Non-contact optical measurement methods have been widely used in the fields of three-dimensional (3D) machine vision and photogrammetry attributed to their advantages of high precision, high speed and low cost. The calibration results directly affect the accuracy of the vision measurement system. Most of the existing calibration methods with two-dimensional (2D) targets need all of the feature points to be captured by the camera, otherwise it would be difficult to distinguish them due to the usage of identical feature points. This paper presents an active calibration target that consists of phase-shifting wedge gratings (PWGs) with three different periods and globally coded on the basis of rotation invariant local binary pattern (LBP) coding. Feature points are the intersections of several straight lines fitted by the 0 and π phase points. The sub-pixel coordinates can be accurately obtained with the phase-shifting method and the windowed linear fitting. Each feature point has its corresponding label to determine its specific position, therefore the one-to-one correspondence between the image coordinates and world coordinates can be obtained even the images are partially captured, and this characteristic can expand the flexibility in both monocular and multi-camera calibrations. In addition, owing to the phase-shifting method, calibration can be accomplished when the camera is defocused. Simulations and experiments further prove the flexibility and robustness of the proposed method.

Generation of Tactile Data From 3D Vision and Target Robotic Grasps

Tactile perception is a rich source of information for robotic grasping: it allows a robot to identify a grasped object and assess the stability of a grasp, among other things. However, the tactile sensor must come into contact with the target object in order to produce readings. As a result, tactile data can only be attained if a real contact is made. We propose to overcome this restriction by employing a method that models the behaviour of a tactile sensor using 3D vision and grasp information as a stimulus. Our system regresses the quantified tactile response that would be experienced if this grasp were performed on the object. We experiment with 16 items and 4 tactile data modalities to show that our proposal learns this task with low error.

Multi-body 3D–2D registration for image-guided reduction of pelvic dislocation in orthopaedic trauma surgery

Surgical reduction of pelvic dislocation is a challenging procedure with poor long-term prognosis if reduction does not accurately restore natural morphology. The procedure often requires long fluoroscopic exposure times and trial-and-error to achieve accurate reduction. We report a method to automatically compute the target pose of dislocated bones in preoperative CT and provide 3D guidance of reduction using routine 2D fluoroscopy. A pelvic statistical shape model (SSM) and a statistical pose model (SPM) were formed from an atlas of 40 pelvic CT images. Multi-body bone segmentation was achieved by mapping the SSM to a preoperative CT via an active shape model. The target reduction pose for the dislocated bone is estimated by fitting the poses of undislocated bones to the SPM. Intraoperatively, multiple bones are registered to fluoroscopy images via 3D–2D registration to obtain 3D pose estimates from 2D images. The method was examined in three studies: (1) a simulation study of 40 CT images simulating a range of dislocation patterns; (2) a pelvic phantom study with controlled dislocation of the left innominate bone; (3) a clinical case study investigating feasibility in images acquired during pelvic reduction surgery. Experiments investigated the accuracy of registration as a function of initialization error (capture range), image quality (radiation dose and image noise), and field of view (FOV) size. The simulation study achieved target pose estimation with translational error of median 2.3 mm (1.4 mm interquartile range, IQR) and rotational error of 2.1° (1.3° IQR). 3D–2D registration yielded 0.3 mm (0.2 mm IQR) in-plane and 0.3 mm (0.2 mm IQR) out-of-plane translational error, with in-plane capture range of ±50 mm and out-of-plane capture range of ±120 mm. The phantom study demonstrated 3D–2D target registration error of 2.5 mm (1.5 mm IQR), and the method was robust over a large dose range, down to 5 Gy/frame (an order of magnitude lower than the nominal fluoroscopic dose). The clinical feasibility study demonstrated accurate registration with both preoperative and intraoperative radiographs, yielding 3.1 mm (1.0 mm IQR) projection distance error with robust performance for FOV ranging from 340 × 340 mm2 to 170 × 170 mm2 (at the image plane). The method demonstrated accurate estimation of the target reduction pose in simulation, phantom, and a clinical feasibility study for a broad range of dislocation patterns, initialization error, dose levels, and FOV size. The system provides a novel means of guidance and assessment of pelvic reduction from routinely acquired preoperative CT and intraoperative fluoroscopy. The method has the potential to reduce radiation dose by minimizing trial-and-error and to improve outcomes by guiding more accurate reduction of joint dislocations.

Pemodelan Homologi Komparatif Struktur 3d Protein dalam Desain dan Pengembangan Obat

One of the strategies applied in the initial steps of drug design and discovery is by utilizing the availability of 3D protein structures. However, most of functional protein structures have not been fully obtained experimentally to date. This is due to the difficulty of procedures, the high cost and the length of time required from structural biology experiments such as X-ray crystallography. Comparative homology modeling is a computational method that is proven to be accurate in predicting the structure of 3D target proteins with a ratio of 30% similarity to the arrangement of amino acids in the structure of the template proteins. This review attempts to explain comparative homology modeling methods such as MODELLER, PHYRE2, SWISS-MODEL, its iterative steps in predicting and building 3D protein target models, its evaluation and validation of protein models as well as the examples of their application in drug design and development targeting GPCR proteins, Zika virus RNA polymerase and HIV protease. With this, comparative homology modeling can be very useful to accelerate research on drug design and development in dealing with diseases and health problems that still exist in the society.

Simultaneous identification of points and circles: structure from motion system in industry scenes

To address the issue of dynamic 3D circular features recognition in robot arm grasping, we propose a feature-based and incremental simultaneous points and circles structure from motion system. First, we represent the 3D target scene with sparse point cloud from multiple observations. The fundamental points construction pipeline combines the benefits of feature-based mapping and probabilistic depth estimation, which reduce the computational cost of generating practical 3D structures. Second, accurate 3D circles are extracted from the keyframes and are optimized in the backend. The circles construction pipeline attempts to find potential circles in the produced sparse point cloud and apply an adjusted level set method to do a novel 3D circle optimization process, which can work on keyframes smoothly. The integrated system is compared with other real-time construction systems and outperforms in industry scenes with more stable land-marks. Meanwhile the experimental results illustrate the ability of capturing circular features in target scenes.

The compensation of biomechanical errors in electrogoniometric measurements of the upper extremity kinematics

The reliability of electrogoniometers as wearable sensors depends on the biomechanical characteristics of the human body. The major causes of electrogoniometric measurement errors are the complexity of movements and the anatomical features of joints. Accordingly, the main objective of this study is to enhance goniometric measurements of body kinematics as a dynamical form of calibration method. In this case study, an experiment was designed in the reachable workspace for gathering joint angles and modeling the related information among them, so a nonlinear neural network was established to compensate the goniometric measurement errors with mapping angular variations simultaneously. The results can be confirmed that the proposed method compensated measurement errors. According to the mean absolute error (MAE) criterion, the results indicated up to 4.5 cm difference between the desired and estimated values of target positions in a reachable workspace and elucidated the significant effect between the desired and estimated 3D target positions before and after using the proposed method. Consequently, since the electrogoniometer has been limited to measuring variations of joint angles separately, the most striking result to emerge from the present study is that the proposed method copes with the finding relationship among measurements of upper extremity kinematics to compensate the goniometric measurement errors.

Genetic algorithm-based inverse design of elastic gridshells

An initially two-dimensional grid of elastic rods may be actuated into a three-dimensional shell-like structure, through buckling, when the end-points of the rods are constrained to a shrunk boundary. The shape of the 3D gridshell is a joint result of elasticity and geometric constraint. We develop a discrete differential geometry-based model of elastic gridshell to investigate their form-finding process. Even though the forward process from 2D footprint to 3D gridshell can be captured by physics-based simulation, the inverse problem of obtaining the original footprint given the 3D deformed shape still lacks a generalized method. In this paper, we propose a genetic algorithm (GA)-based inverse design method to explore the planar footprint of an elastic gridshell as well as the corresponding geometric constraints. Geometric features extracted from the original planar form are encoded into various chromosomes to constitute a population in every generation. With the fitness function constructed based on the deviation of the candidate solution from the 3D target shape, the population evolves gradually until the individual of the smallest fitness value representing the optimal footprint and final boundary constraints is found under seven predefined geometric constraints. Given a series of representative target shapes, e.g., hemispherical cap, paraboloid structure, Gaussian curve shape, and semi-ellipsoid, their original footprints are quantified using a network of 10 elastic rods. Excellent agreement is obtained between the prescribed 3D shape and the simulated buckled structures as small fitness value is obtained and little difference between them is observed, which validates the effectiveness of the proposed GA-based inverse design method.

3D target localization based on multi–unmanned aerial vehicle cooperation

Ground target three-dimensional positions measured from optical remote-sensing images taken by an unmanned aerial vehicle play an important role in related military and civil applications. The weakness of this system lies in its localization accuracy being unstable and its efficiency being low when using a single unmanned aerial vehicle. In this paper, a novel multi–unmanned aerial vehicle cooperative target localization measurement method is proposed to overcome these issues. In the target localization measurement stage, three or more unmanned aerial vehicles simultaneously observe the same ground target and acquire multiple remote-sensing images. According to the principle of perspective projection, the target point, its image point, and the camera’s optic center are collinear, and nonlinear observation equations are established. These equations are then converted to linear equations using a Taylor expansion. Robust weighted least-squares estimation is used to solve the equations with the objective function of minimizing the weighted square sum of re-projection errors from target points to multiple pairs of images, which can make the best use of the effective information and avoid interference from the observation data. An automatic calculation strategy using a weight matrix is designed, and the weight matrix and target-position coordinate value are updated in each iteration until the iteration stopping condition is satisfied. Compared with the stereo-image-pair cross-target localization method, the multi–unmanned aerial vehicle cooperative target localization method can use more observation information, which results in higher rendezvous accuracy and improved performance. Finally, the effectiveness and robustness of this method is verified by numerical simulation and flight testing. The results show that the proposed method can effectively improve the precision of the target’s localization and demonstrates great potential for providing more accurate target localization in engineering applications.