Global Motion Research Articles

A Fully Coupled CFD-DMB Approach on the Ship Hydroelasticity of a Containership in Extreme Wave Conditions

In this paper, we present a fully coupled computational fluid dynamic (CFD) and discrete module beam (DMB) method for the numerical prediction of nonlinear hydroelastic responses of a ship advancing in regular and focused wave conditions. A two-way data communication scheme is applied between two solvers, whereby the external fluid pressure exported from the CFD simulation is used to derive the structural responses in the DMB solver, and the structural deformations are fed back into the CFD solver to deform the mesh. We first conduct a series of verification and validation studies by using the present CFD–DMB method to investigate the global ship motion, vertical bending moments (VBMs), and green water phenomenon of the ship in different regular wave conditions. The numerical results agreed favourably with the CFD–FEA model and experimental measurements. Then, the extreme ship motions are studied in focused wave conditions to represent extreme sea conditions that a ship may experience in a real sea state. According to the conclusion drawn from the numerical simulations, it is founded that the focused wave case will lead to the increase of the longitudinal responses of the hull compared to regular wave condition, i.e., the heave, pitch, and total VBMs rise about 25%, 20% and 9%, respectively. In focused wave conditions, intensive ship responses and severe waves cause stronger slamming phenomena. It is found that the instantaneous impact pressure from the focused wave is higher and sharper compared to the regular waves and comes along with the obvious green-water-on-deck phenomena.

Deep-learning-based image preprocessing for particle image velocimetry

Particle image velocimetry (PIV) is an important measurement technique for estimating global fluid motion by capturing particle motion from a pair of images. Hence, obtaining high-quality particle images is critical for a PIV estimation. The purpose of this study is to propose a deep-learning-based technique for PIV image preprocessing. Specifically, we first designed a deep convolutional network called Bilateral-CNN for a pair image preprocessing task, which embeds residual learning and batch normalization. Bilateral-CNN fully considers the feature connections between image pairs. Second, to train our model, we generate a synthetic dataset that includes Gaussian noise and various light intensities. Considering a real fluid scene, different background information such as rising bubbles is superposed into the images, which makes the particle images more challenging. Finally, we conducted experiments on synthetic and experimental particle images. Extensive results indicate that our method can effectively overcome the particle image interference noise under different situations. In addition, the processed particle images are calculated using cross-correlation WIDIM algorithm, and a high-precision velocity field is obtained by fast inference speed.

GMNet: an action recognition network with global motion representation

GMNet: an action recognition network with global motion representation

A comparison of equivalent noise methods in investigating local and global form and motion integration.

Static and dynamic cues within certain spatiotemporal proximity are used to evoke respective global percepts of form and motion. The limiting factors in this process are, first, internal noise, which indexes local orientation/direction detection, and, second, sampling efficiency, which relates to the processing and the representation of global orientation/direction. These parameters are quantified using the equivalent noise (EN) paradigm. EN has been implemented with just two levels: high and low noise. However, when using this simplified version, one must assume the shape of the overall noise dependence, as the intermediate points are missing. Here, we investigated whether two distinct EN methods, the 8-point and the simplified 2-point version, reveal comparable parameter estimates. This was performed for three different types of stimuli: random dot kinematograms, and static and dynamic translational Glass patterns, to investigate how constant internal noise estimates are, and how sampling efficiency might vary over tasks. The results indicated substantial compatibility between estimates over a wide range of external noise levels sampled with eight data points, and a simplified version producing two highly informative data points. Our findings support the use of a simplified procedure to estimate essential form-motion integration parameters, paving the way for rapid and critical applications to populations that cannot tolerate protracted measurements.

Daily fluctuations in visual motion discriminability contribute to daily fluctuations in continuous visuomotor performance.

In ball sports such as table tennis, in which a ball moving at high speed is hit, an athlete's brain needs to process the motion information of the ball, predict the arrival point, and form a motor command to direct the racket there. Therefore, day-to-day fluctuations in visuomotor performance may be ascribed to fluctuations in visual motion discriminability, but it is not clear how the two are related. To examine this point, university table tennis players performed a motion direction discrimination (MDD) task and continuous visuomotor (CVM) task over 10 days as an estimation of visual motion discriminability and visuomotor performance, respectively. In the MDD task, using a joystick, participants distinguished the direction of a global coherent motion of target dots moving in the same direction on a PC monitor from innumerable dots moving in random directions. In the CVM task, participants hit sequential targets moving fast from right to left on the PC monitor by operating the cursor on the left side of the monitor up and down using the prehensile force of their thumb and index finger. The scores in the MDD and CVM tasks fluctuated day by day and showed a significant and moderate correlation between the MDD task score for the visual field in which the participants captured the target in the CVM task and the CVM task score. This correlation was confirmed even with the target moving from left to right. The fluctuations in the onset latency and the endpoint position of the cursor movement approaching the target were correlated with those of the visual motion discriminability, suggesting the contribution of motion vision to the speed and accuracy of the visuomotor performance. Moreover, these relationships were prominent in veteran players. For table tennis athletes, especially experienced players, fluctuations in the visual motion discrimination performance in a visual field specific for capturing a ball may be responsible for the fluctuations in continuous visuomotor (striking) performance.

Quantification of left ventricular strain and torsion by joint analysis of 3D tagging and cine MR images.

Cardiovascular magnetic resonance (CMR) imaging is the gold standard for the non-invasive assessment of left-ventricular (LV) function. Prognostic value of deformation metrics extracted directly from regular SSFP CMR images has been shown by numerous studies in the clinical setting, but with some limitations to detect torsion of the myocardium. Tagged CMR introduces trackable features in the myocardium that allow for the assessment of local myocardial deformation, including torsion; it is, however, limited in the quantification of radial strain, which is a decisive metric for assessing the contractility of the heart. In order to improve SSFP-only and tagged-only approaches, we propose to combine the advantages of both image types by fusing global shape motion obtained from SSFP images with the local deformation obtained from tagged images. To this end, tracking is first performed on SSFP images, and subsequently, the resulting motion is utilized to mask and track tagged data. Our implementation is based on a recent finite element-based motion tracking tool with mechanical regularization. Joint SSFP and tagged images registration performance is assessed based on deformation metrics including LV strain and twist using human and in-house porcine datasets. Results show that joint analysis of SSFP and 3DTAG images provides better quantification of LV strain and twist as either data source alone.

Incremental Translation Averaging

Translation averaging is known to be more difficult than rotation averaging due to scale ambiguity, estimation sensitivity, and solution uncertainty. Existing approaches have exposed their limitations in terms of accuracy, robustness, simplicity, or efficiency. To tackle this tough problem, a simple yet effective translation averaging pipeline, termed as Incremental Translation Averaging (ITA), is proposed in this paper. It combines the advantages of high accuracy and robustness in incremental parameter estimation pipeline and the advantages of high simplicity and efficiency in global motion averaging approach. Unlike the traditional translation averaging methods which estimate all the absolute camera locations simultaneously and suffer from inaccuracy in parameter estimation and incompleteness in scene reconstruction, our ITA computes them novelly in an incremental way with higher accuracy and robustness. Thanks to the introduction of incremental parameter estimation thought into the translation averaging pipeline, 1) our ITA is robust to measurement outliers and accurate in parameter estimation; and 2) our ITA is simple and efficient because of its less dependency on complicated optimization, carefully-designed preprocessing, or additional information. Comprehensive evaluations on the 1DSfM dataset demonstrate the effectiveness of our ITA and its advantages over several state-of-the-art translation averaging approaches.

Exploration of motion inhibition for the suppression of false positives in biologically inspired small target detection algorithms from a moving platform

Detecting small moving targets against a cluttered background in visual data is a challenging task. The main problems include spatio-temporal target contrast enhancement, background suppression and accurate target segmentation. When targets are at great distances from a non-stationary camera, the difficulty of these challenges increases. In such cases the moving camera can introduce large spatial changes between frames which may cause issues in temporal algorithms; furthermore targets can approach a single pixel, thereby affecting spatial methods. Previous literature has shown that biologically inspired methods, based on the vision systems of insects, are robust to such conditions. It has also been shown that the use of divisive optic-flow inhibition with these methods enhances the detectability of small targets. However, the location within the visual pathway the inhibition should be applied was ambiguous. In this paper, we investigated the tunings of some of the optic-flow filters and use of a nonlinear transform on the optic-flow signal to modify motion responses for the purpose of suppressing false positives and enhancing small target detection. Additionally, we looked at multiple locations within the biologically inspired vision (BIV) algorithm where inhibition could further enhance detection performance, and look at driving the nonlinear transform with a global motion estimate. To get a better understanding of how the BIV algorithm performs, we compared to other state-of-the-art target detection algorithms, and look at how their performance can be enhanced with the optic-flow inhibition. Our explicit use of the nonlinear inhibition allows for the incorporation of a wider dynamic range of inhibiting signals, along with spatio-temporal filter refinement, which further increases target-background discrimination in the presence of camera motion. Extensive experiments shows that our proposed approach achieves an improvement of 25% over linearly conditioned inhibition schemes and 2.33 times the detection performance of the BIV model without inhibition. Moreover, our approach achieves between 10 and 104 times better detection performance compared to any conventional state-of-the-art moving object detection algorithm applied to the same, highly cluttered and moving scenes. Applying the nonlinear inhibition to other algorithms showed that their performance can be increased by up to 22 times. These findings show that the application of optic-flow- based signal suppression should be applied to enhance target detection from moving platforms. Furthermore, they indicate where best to look for evidence of such signals within the insect brain.

A novel method for computing self-motion manifolds

For kinematically redundant robots, self-motion manifolds that represent all the inverse kinematic solutions for a given end-effector location play a significant role in global motion planning. However, the efficient computation of self-motion manifolds, especially the computation of high-dimensional manifolds, is still a problem that needs to be solved. In this paper, the grid elements modeling criteria are formulated, and the specific modeling method of a grid element in the configuration space is developed. Based on the idea of cellular automata, the problem of computing self-motion manifolds is transformed into a dynamic model searching problem, and an evolution strategy is proposed to realize the efficient computation of self-motion manifolds. Finally, two examples are used to verify the effectiveness of the proposed method in computing high-dimensional self-motion manifolds. The results show that compared to the conventional methods, the proposed method can correctly identify all the self-motion manifolds with 94% reduced computational time on average.

Fully Automated Bead Art Assembly for Smart Manufacturing Using Dynamic Compensation Approach

In this study, we demonstrate the implementation of make-to-order bead art assembly without human intervention using dynamic compensation approach to achieve accurate real-time positioning and long-term adaptation for robotic automation in smart manufacturing. In the proposed framework, an industrial robot was designed to perform coarse global motion to implement low-bandwidth adaptation. Simultaneously, fine local motion to tackle real-time online uncertainties was achieved using an add-on robotic module to implement accurate positioning. The effectiveness of the proposed method was verified through experimental evaluations.

Camera Motion Agnostic Method for Estimating 3D Human Poses.

Although the performance of 3D human pose and shape estimation methods has improved considerably in recent years, existing approaches typically generate 3D poses defined in a camera or human-centered coordinate system. This makes it difficult to estimate a person’s pure pose and motion in a world coordinate system for a video captured using a moving camera. To address this issue, this paper presents a camera motion agnostic approach for predicting 3D human pose and mesh defined in the world coordinate system. The core idea of the proposed approach is to estimate the difference between two adjacent global poses (i.e., global motion) that is invariant to selecting the coordinate system, instead of the global pose coupled to the camera motion. To this end, we propose a network based on bidirectional gated recurrent units (GRUs) that predicts the global motion sequence from the local pose sequence consisting of relative rotations of joints called global motion regressor (GMR). We use 3DPW and synthetic datasets, which are constructed in a moving-camera environment, for evaluation. We conduct extensive experiments and prove the effectiveness of the proposed method empirically.

Effective Multi-Object Tracking via Global Object Models and Object Constraint Learning.

Effective multi-object tracking is still challenging due to the trade-off between tracking accuracy and speed. Because the recent multi-object tracking (MOT) methods leverage object appearance and motion models so as to associate detections between consecutive frames, the key for effective multi-object tracking is to reduce the computational complexity of learning both models. To this end, this work proposes global appearance and motion models to discriminate multiple objects instead of learning local object-specific models. In concrete detail, it learns a global appearance model using contrastive learning between object appearances. In addition, we learn a global relation motion model using relative motion learning between objects. Moreover, this paper proposes object constraint learning for improving tracking efficiency. This study considers the discriminability of the models as a constraint, and learns both models when inconsistency with the constraint occurs. Therefore, object constraint learning differs from the conventional online learning for multi-object tracking which updates learnable parameters per frame. This work incorporates global models and object constraint learning into the confidence-based association method, and compare our tracker with the state-of-the-art methods on public available MOT Challenge datasets. As a result, we achieve 64.5% MOTA (multi-object tracking accuracy) and 6.54 Hz tracking speed on the MOT16 test dataset. The comparison results show that our methods can contribute to improve tracking accuracy and tracking speed together.

A Novel Bio-Inspired Motion Direction Detection Mechanism in Binary and Grayscale Background

The visual system plays a vital role in the daily life of humans, as more than 90 percent of the external information received by the human brain throughout the day comes from the visual system. However, how the human brain processes the received visual information remains a mystery. The information received from the external through the visual system can be divided into three main categories, namely, shape features, color features, and motion features. Of these, motion features are considered the key to deciphering the secrets of the visual system due to their independence and importance. In this paper, we propose a novel bio-inspired motion direction detection mechanism using direction-selective ganglion cells to explore the mystery of motion information extraction and analysis. The mechanism proposed in this paper is divided into two parts: local motion direction detection neurons and global motion direction detection neurons; the former is used to extract motion direction information from the local area, while the latter infers global motion direction from the local motion direction information. This mechanism is more consistent with the biological perception of the human natural visual system than the previously proposed model and has a higher biological plausibility and greater versatility. It is worth mentioning that we have overcome the problem in which the previous motion direction detection model could only be applied in the binary background by introducing the horizontal cells. Through the association formed by horizontal cells and bipolar cells, this model can be applied to recognizing problems of motion direction detection on a grayscale background. To further validate the effectiveness of the proposed model, a series of experiments with objects of different sizes, shapes, and positions are conducted by computer simulation. According to the simulation results, this model has been proven to have high accuracy rates regardless of objects’ sizes, shapes, and positions in all experiments. Furthermore, the proposed model is verified to own more stable accuracy rates and stronger noise immunity by comparing it with the recognized superior classical convolutional neural network in a background of different percentage noise.

Cervical cytopathology image refocusing via multi-scale attention features and domain normalization.

Cervical cytopathology image refocusing is important for addressing the problem of defocus blur in whole slide images. However, most of current deblurring methods are developed for global motion blur instead of local defocus blur and need a lot of supervised re-training for unseen domains. In this paper, we propose a refocusing method for cervical cytopathology images via multi-scale attention features and domain normalization. Our method consists of a domain normalization net (DNN) and a refocusing net (RFN). In DNN, we adopt registration-free cycle scheme for normalizing the unseen unsupervised domains into the seen supervised domain and introduce gray mask loss and hue-encoding mask loss to ensure the consistency of cell structure and basic hue. In RFN, combining the locality and sparseness characteristics of defocus blur, we design a multi-scale refocusing network to enhance the reconstruction of cell nucleus and cytoplasm, and introduce defocus intensity estimation mask to strengthen the reconstruction of local blur. We integrate hybrid learning strategy on the supervised and unsupervised domains to make RFN achieving well refocusing on the unsupervised domain. We build a cervical cytopathology image refocusing dataset and conduct extensive experiments to demonstrate the superiority of our method compared with current deblurring state-of-the-art models. Furthermore, we prove that the refocused images help improve the performance of subsequent high-level analysis tasks. We release the refocusing dataset and source codes to promote the development of this field.

An overset boundary condition-enforced immersed boundary method for incompressible flows with large moving boundary domains

Conventional immersed boundary methods (IBMs) have greatly simplified the boundary condition treatment by interpreting boundaries as forces in the source terms of governing equations. In conventional IBMs, uniform meshes of very high resolution must be applied near the immersed boundary to treat the solid–fluid interface. However, this can induce a high computational cost for simulating flows with large moving boundary domains, where everywhere along the trajectory of the moving object must be refined isotropically. In the worst scenario, a global refinement is required when the object is moving arbitrarily in the entire computational domain. In this work, an overset boundary condition-enforced immersed boundary method (overset BC-enforced IBM) is proposed to simulate incompressible flows with large moving boundary domains efficiently. In the proposed overset BC-enforced IBM, a locally refined uniform mesh is applied and fixed on the moving object to account for the local motions, e.g., the rotation and deformation of the object, while the global motion of the object is handled by embedding the locally refined mesh in a coarser background mesh. Both the local mesh and the global background mesh can be generated automatically using the Cartesian approach to avoid the cumbersome boundary treatment. Since the mesh refinement is local, considerable computational savings can be achieved. The overset BC-enforced IBM is combined with the lattice Boltzmann flux solver to simulate various fluid–structure interaction problems with rigid and deformable boundaries.

Optimization of Spinal Reconstructions for Thoracolumbar Burst Fractures to Prevent Proximal Junctional Complications: A Finite Element Study.

The management strategies of thoracolumbar (TL) burst fractures include posterior, anterior, and combined approaches. However, the rigid constructs pose a risk of proximal junctional failure. In this study, we aim to systemically evaluate the biomechanical performance of different TL reconstruction constructs using finite element analysis. Furthermore, we investigate the motion and the stress on the proximal junctional level adjacent to the constructs. We used a T10-L3 finite element model and simulated L1 burst fracture. Reconstruction with posterior instrumentation (PI) alone (U2L2 and U1L1+(intermediate screw) and three-column spinal reconstruction (TCSR) constructs (U1L1+PMMA and U1L1+Cage) were compared. Long-segment PI resulted in greater global motion reduction compared to constructs with short-segment PI. TCSR constructs provided better stabilization in L1 compared to PI alone. Decreased intradiscal and intravertebral pressure in the proximal level were observed in U1L1+IS, U1L1+PMMA, and U1L1+Cage compared to U2L2. The stress and strain energy of the pedicle screws decreased when anterior reconstruction was performed in addition to PI. We showed that TCSR with anterior reconstruction and SSPI provided sufficient immobilization while offering additional advantages in the preservation of physiological motion, the decreased burden on the proximal junctional level, and lower risk of implant failure.

The Effects of Glare on the Perception of Visual Motion as a Function of Age.

PurposeThe purpose of this study was to determine the impact of glare, that simulated the effects of oncoming vehicle headlights, and age on different aspects of motion perception in central and peripheral vision.MethodsTwenty younger (mean age = 25 years, range = 20–32 years) and 20 older (mean age = 70 years, range = 60–79 years) visually healthy adults completed four visual motion tasks. Stimuli were presented centrally and at 15 degrees horizontal eccentricity for 2 viewing conditions: glare (continuous, off-axis) versus no glare. Motion tasks included minimum Gabor contrast required to discriminate direction of motion, translational global motion coherence, minimum duration of a Gabor to determine direction of motion (2 different size Gabors to determine spatial surround suppression), and biological motion detection in noise. Intraocular straylight was also measured (C-Quant).ResultsOlder adults had increased intraocular straylight compared with younger adults (P < 0.001). There was no significant effect of glare on motion thresholds in either group for motion contrast (P = 0.47), translational global motion (P = 0.13), biological motion (P = 0.18), or spatial surround suppression of motion (P = 0.29). Older adults had elevated thresholds for motion contrast (P < 0.001), biological motion (P < 0.001), and differences in surround suppression of motion (P = 0.04), relative to the younger group, for both the glare and no-glare conditions.ConclusionsAlthough older adults had elevated thresholds for some motion perception tasks, glare from a continuous off-axis light source did not further elevate these thresholds either in central or peripheral vision.Translational RelevanceA glare source that simulated the effect of oncoming headlights, did not impact motion perception measures relevant to driving.

Motion deblurring algorithm for wind power inspection images based on Ghostnet and SE attention mechanism

Aiming at the problem of motion blur in the inspection image of wind power generation equipment, an improved fast motion blur removal method based on deblurganv2 is proposed in this paper. Firstly, according to the characteristics of disparate local motion blur degrees and different global motion blur directions of the wind power inspection image, the real wind power fuzzy image data set is collected and produced. Secondly, according to the characteristics of the single background of the wind power inspection image and the requirements of fast and effective processing, the lightweight network Ghostnet is redesigned as the backbone network of the Deblurganv2 generator to reduce the number of network parameters and calculations. Finally, five SE channel attention mechanism layers are added to GhostNet to strengthen feature extraction, and the upsampling process is optimized through bilinear interpolation, to improve the deblurring performance of the wind power inspection image. The experimental results show that compared with other algorithms, the proposed algorithm has a higher peak signal-to-noise ratio and structural similarity, the network model parameters are compressed to 6.1 MB, and the reasoning speed is improved to 0.42 s.

Evaluation of approaches to estimate scapular kinematics during baseball pitching

ABSTRACT Biomechanical analyses of pitching possess limitations in accurately measuring dynamic scapular orientation and are thus unable to distinguish between glenohumeral and scapulothoracic contributions to global shoulder motion. In lieu of direct measurement, several methods to estimate scapular kinematics have been developed. This study evaluated the ability of the linear model and the double calibration acromion marker cluster (D-AMC) approaches to estimate scapular kinematics throughout a full-speed pitching motion. Each approach’s estimates were compared against scapulothoracic range of motion limits established in a non-pitching biplane fluoroscopy study involving various functional arm movements that approximate physiological limits of scapular motion. Fourteen healthy collegiate pitchers participated. Motion capture measured upper extremity joint kinematics during full-speed fastball pitches. Linear model and D-AMC approaches estimated scapulothoracic kinematics during each pitch. Linear model estimates of scapulothoracic kinematics were largely within established physiological limits on each scapular axis of motion while D-AMC estimates exceeded fluoroscopy-established bounds for more subjects and by larger, less physiologically plausible amounts. These findings demonstrate that the linear model outperforms the D-AMC and suggest that it is a viable approach to estimate scapular kinematics during pitching. Finally, these results offer additional evidence to support the accepted pattern of scapular kinematics during pitching.

Energy-based swing-up control for a two-link underactuated robot with flexible first joint

This paper concerns the swing-up control of a two-link robot moving in a vertical plane, which has a single actuator at the second joint and a linear torsional spring at the first joint. First, we present a necessary and sufficient condition such that the robot is linearly controllable at the upright equilibrium point (UEP, where two links are both upright). Second, we prove without any assumption that the robot is at an equilibrium point provided that its actuated joint angle is constant under a constant torque. Third, for the robot with its torsional stiffness of the spring being not greater than a value determined by the coefficients of its gravitational terms, we propose an energy-based swing-up controller without singular points. We conduct a global motion analysis for the robot under the proposed controller. For the case that the total mechanical energy of the robot converges to its desired value, we present the phase portrait of the closed-loop solution. For the case that the convergence is not achieved, we show that the closed-loop solution approaches an equilibrium point belonging to a set of equilibrium points, and give a sufficient condition to check its instability. From the motion analysis, we present a sufficient condition such that the robot can be swung-up close to the UEP under the proposed swing-up controller. Finally, we verify our theoretical results through a numerical simulation.