Contour Motion Research Articles

Manual Acupuncture Manipulation Recognition Method via Interactive Fusion of Spatial Multiscale Motion Features

Manual acupuncture manipulation (MAM) is essential in traditional Chinese medicine treatment. MAM action recognition is important for junior acupuncturists’ training and education; however, there are obvious personalized differences in hand gestures among expert acupuncturists for the same type of MAM. In addition, during the MAM operations, the magnitude and frequency of the expert acupuncturists’ hand shape and relative needle-holding finger position changes are tiny and fast, resulting in difficulties in observing MAM action details. Thus, we propose a Spatial Multiscale Interactive Fusion MAM Recognition Network to solve the difficulties in MAM recognition. First, this paper presents an optical flow-based hand motion contour global feature extraction method for acupuncture hand shape. Second, to explore the motion rule between the needle-holding fingers during the MAM operations, we design a quantitative description method of the relative motion of the needle-holding fingers: an “interactive attention module,” which achieves feature fusion and mines the correlation between different scales of MAM action features. Finally, the proposed MAM recognition method was validated by 20 acupuncturists from the Beijing University of Traditional Chinese Medicine and 10 from the Beijing Zhongguancun Hospital who participated in the MAM video signal collection. The proposed recognition method achieves the highest average validation accuracy of 95.3% and the highest test accuracy of 96.0% for four typical MAMs, proving its feasibility and effectiveness.

Cartoon copyright recognition method based on character personality action

Aiming at the problem of cartoon piracy and plagiarism, this paper proposes a method of cartoon copyright recognition based on character personality actions. This method can be used to compare the original cartoon actions with the action characteristics of pirated or copied cartoons to identify whether there is piracy or plagiarism. Firstly, an image preprocessing scheme for character extraction is designed. GrabCut interactive image segmentation algorithm was used to obtain cartoon characters, and then binarization and morphological processing were performed on the results. Secondly, a feature extraction scheme based on character profile, moving character and character pose is designed. By extracting the perimeter and area of the character contour, the length-to-width ratio of the smallest rectangle and the inclination angle of the contour, the character contour features are obtained. The three-dimensional coordinates are established by the central point position of the cartoon character in the two-dimensional image and the change of the character's zoom in and out, and the character's motion angle characteristics are calculated. By skeletonizing a character to obtain the pose characteristics, and using deburring operation to remove redundant branches, then extract the skeleton joint angle information. Finally, feature fusion is performed on the extracted features. The experimental results show that the proposed method breaks the limitation of the conventional single feature based recognition, and can better extract the character features including contour feature, motion feature and pose feature through multi-feature based extraction, so as to protect the cartoon copyright.

Ice hockey puck tracking through broadcast video

Ice-hockey puck tracking is a non-trivial task in hockey video analysis as it highlights the puck in the video for broadcasting, tactical play analysis, or referee assisting. However, difficulties, such as high speed, low texture features in images and constantly changing shape, make well-developed object tracker fail to track the puck. This paper introduces a real-time online-learning ice hockey puck detection and tracking system solely depending on video input to tackle this problem. The proposed approach categorizes pucks into free-moving and control-moving states, using a combination of contour fitting, correlation filter, and motion estimation techniques to detect and track them. A thorough analysis is performed focusing on the tracking scenario using broadcast video. To our knowledge, this is the first approach addressing detection and tracking nearly invisible high-speed pucks when shooting actions take place. Experiments with a comparison between a previous work targeting puck tracking show promising results in detection and tracking the ice hockey puck through broadcast video.

StrainNet: Improved Myocardial Strain Analysis of Cine MRI by Deep Learning from DENSE.

To develop a three-dimensional (two dimensions + time) convolutional neural network trained with displacement encoding with stimulated echoes (DENSE) data for displacement and strain analysis of cine MRI. In this retrospective multicenter study, a deep learning model (StrainNet) was developed to predict intramyocardial displacement from contour motion. Patients with various heart diseases and healthy controls underwent cardiac MRI examinations with DENSE between August 2008 and January 2022. Network training inputs were a time series of myocardial contours from DENSE magnitude images, and ground truth data were DENSE displacement measurements. Model performance was evaluated using pixelwise end-point error (EPE). For testing, StrainNet was applied to contour motion from cine MRI. Global and segmental circumferential strain (Ecc) derived from commercial feature tracking (FT), StrainNet, and DENSE (reference) were compared using intraclass correlation coefficients (ICCs), Pearson correlations, Bland-Altman analyses, paired t tests, and linear mixed-effects models. The study included 161 patients (110 men; mean age, 61 years ± 14 [SD]), 99 healthy adults (44 men; mean age, 35 years ± 15), and 45 healthy children and adolescents (21 males; mean age, 12 years ± 3). StrainNet showed good agreement with DENSE for intramyocardial displacement, with an average EPE of 0.75 mm ± 0.35. The ICCs between StrainNet and DENSE and FT and DENSE were 0.87 and 0.72, respectively, for global Ecc and 0.75 and 0.48, respectively, for segmental Ecc. Bland-Altman analysis showed that StrainNet had better agreement than FT with DENSE for global and segmental Ecc. StrainNet outperformed FT for global and segmental Ecc analysis of cine MRI.Keywords: Image Postprocessing, MR Imaging, Cardiac, Heart, Pediatrics, Technical Aspects, Technology Assessment, Strain, Deep Learning, DENSE Supplemental material is available for this article. © RSNA, 2023.

Updated probabilistic seismic hazard assessment of Pakistan

A probabilistic seismic hazard analysis of Pakistan is carried out to produce seismic hazard maps showing peak ground acceleration for the 2% and 10% of probability of exceedance in 50 years and spectral acceleration for the 5% and 2% probabilities of exceedance in 50 years. The main objective of this work is to define seismic hazard definition for Pakistan based on the classical approach using updated seismic hazards analysis inputs. A composite earthquake catalogue consisting of 32,700 events was compiled and used in the analysis with a magnitude range of Mw 4.0–8.3. The computations were made at a rectangular grid of 5 km in the OpenQuake engine. Ground motion parameter values have been obtained for flat rock reference seismic site conditions having a shear wave velocity of 760 m/s. Through the logic tree, epistemic uncertainties inherent in ground motion prediction equations and the maximum magnitude potential of seismic sources were estimated. Ground motion prediction equations were assigned equal weights in the logic tree while different weights were assigned to the maximum magnitude potential models. Results of the study were expressed as ground motion contour maps and median uniform hazard spectra for important cities in Pakistan. The results showed that peak ground acceleration values in Pakistan range from 0.16 to 0.54 g for the 10% of probability of exceedance, 0.23–0.72 g for the 5% probability of exceedance and 0.32–1.02 g for the 2% of probability of exceedance in 50 years. Spectral acceleration values at 0.2 s range from 0.67 to 2.19 g for the 2% chance of exceedance in 50 years, respectively, whereas spectral acceleration at 1.0 s ranges from 0.09 to 0.52 g for the 2% chance of exceedance in 50 years. The centre of Pakistan and the southern and the northern region are exposed to high hazard compared to the central-eastern part of the country. Quetta, Chitral, Peshawar, Islamabad Ziarat and Gwadar are exposed to seismic hazard in the PGA range of 0.32–0.45 g. Comparison of the results of this study with those of similar studies suggests that the seismic estimates proposed are reliable. The study provides a demonstration that the classical seismic hazard approach applied here is rational and applicable in Pakistan.

Development of Pitch Cycle-Based Iterative Learning Contour Control for Thread Milling Operations in CNC Machine Tools

The helical contour motion accuracy of feed drive axes is important for thread milling operations in computer numerical control (CNC) machine tools. However, the motion dynamics and external disturbances significantly affect the contour motion results, while the feed drive axes perform helical motions in thread milling operations. Although existing iterative learning contour control (ILCC) methods can improve contour motion accuracy, the problems of data recording and processing on memory usage and computational burden in control systems, wasted materials, and increased costs in thread manufacturing still limit the practical applications of ILCC. Therefore, considering the similar motion dynamics and external disturbances of the feed drive axes during the pitch cycle motions of a helical path, this study developed a pitch cycle-based iterative learning contour control (PCB-ILCC) method to address the control system and thread manufacturing problems caused by the use of ILCC. For PCB-ILCC, this study adopted contour error vector estimation by referring to the interpolated positions on the pitch cycle of the helical path to simplify the computational complexity and designed the ILCC using the cycle learning method to easily implement the ILCC structure. Thus, this study developed a permanent magnet synchronous motor (PMSM) driving control utilizing the robust control method to mitigate the problems of motion dynamics and external disturbances on the feed drive axes. Thread milling experiments performed on a five-axis CNC machining center demonstrated the feasibility of the PCB-ILCC and validated that it can significantly improve the helical contour motion accuracy of the feed drive axes and achieve an 80% contour error reduction rate in comparison with the proportional–proportional–integral control, which is extensively used in commercialized PMSM drivers and CNC controllers.

Multimodal Spatiotemporal Feature Map for Dynamic Gesture Recognition

Gesture recognition technology enables machines to read human gestures and has significant application prospects in the fields of human-computer interaction and sign language translation. Existing researches usually use convolutional neural networks to extract features directly from raw gesture data for gesture recognition, but the networks are affected by much interference information in the input data and thus fit to some unimportant features. In this paper, we proposed a novel method for encoding spatio-temporal information, which can enhance the key features required for gesture recognition, such as shape, structure, contour, position and hand motion of gestures, thereby improving the accuracy of gesture recognition. This encoding method can encode arbitrarily multiple frames of gesture data into a single frame of the spatio-temporal feature map and use the spatio-temporal feature map as the input to the neural network. This can guide the model to fit important features while avoiding the use of complex recurrent network structures to extract temporal features. In addition, we designed two sub-networks and trained the model using a sub-network pre-training strategy that trains the sub-networks first and then the entire network, so as to avoid the sub-networks focusing too much on the information of a single category feature and being overly influenced by each other’s features. Experimental results on two public gesture datasets show that the proposed spatio-temporal information encoding method achieves advanced accuracy.

Research on programmable spatial capture mechanism and its motion characteristics based on origami principle

In view of the requirements to capture non-cooperative targets in space debris removal tasks, this study designs a programmable spatial capture mechanism using the closed envelope characteristic of the origami mechanism. An enveloping origami mechanism was proposed, and a programmable parametric design was implemented. First, a kinematic model of the origami mechanism was established, and the motion law of the axisymmetric folding state of the origami mechanism was analysed. The folding performance of the origami mechanism under the three parameter characteristics of equal proportions of crease length, folding plate area, and configuration angle was analysed/established. The optimal parameters of the origami mechanism with closed edges, large envelope space, smooth envelope contour, small windup, and smooth folding motion were obtained. The driving mechanism was designed based on the motion characteristics of the axisymmetric folding origami mechanism. Adams simulation software was used to simulate the capture of space debris by origami mechanisms in a microgravity environment. The simulation results show that the origami mechanism can achieve space debris capture with high reliability, high tolerance, and enveloping.

Analysis of the Impact of Circular Contour Error Based on CNC Machine

To improve the processing accuracy of the CNC machine, the errors caused by uneven pitch spacing between the servo feeding axes of the NC machine are analyzed. With the combination of the principle of plane circular contour motion track synthesis, the influence of the pitch errors produced by the X and Y axes on the circular contour errors is analyzed. Finally, the experimental platform is adopted to analyze the data before and after screw pitch error compensation with a spherical rod. The results verify that the screw pitch errors impact the outline errors, and the proportion of mismatch errors was reduced by 96.88% after screw pitch errors compensation.

Determining crack kinematics from imaged crack patterns

Determining the relationship between the cause of damage and the subsequent structural behavior of infrastructure systems requires an accurate characterization of the propagation of cracks, which represents the evolution of the damage state. When no information about the cause of damage is available, kinematic approaches can be used to describe the motion of crack contours. Current image-based approaches to derive crack kinematics use digital image correlation (DIC) on a set of sequential images as the crack propagates. However, DIC is invasive in that the structure surfaces must be painted with random speckle patterns, limiting its use primarily to controlled experiments. In this paper, we propose a novel image-based methodology for computing crack opening in Mode I or Mode II. As an input, this method takes a binary image from a semantic segmentation of an image of a crack pattern. This binary image is used to detect the opposite edges along the crack, which are then registered using an optimization algorithm based on the Euclidean transformation model and non-linear least squares. As a final output, this method produces displacement maps in the tangential and normal directions to the crack skeleton. To demonstrate its performance, we validate our methodology first with synthetic crack patterns and then with real crack patterns. Because this methodology for determining crack openings requires only simple data (just a binary crack pattern image), it is straightforward, robust, and adaptable, thus contributing to the development of structural image-based damage assessments. The computational codes and datasets are available to the public for future research and benchmarking on https://github.com/eesd-epfl/crack_kinematics and https://doi.org/10.5281/zenodo.6632071.

Object Extraction of Tennis Video Based on Deep Learning

Moving target detection and tracking technology is the core technology in the field of computer vision. It integrates image processing, pattern recognition and intelligence, and artificial intelligence and automatic control are the keys to an intelligent video surveillance system. The method acquires video image signals through visible light or infrared sensors, performs digital image processing on the video images, detects moving targets, and then extracts moving targets for target recognition. Then, the moving target is predicted and tracked according to the image features and spatiotemporal features, and the contour shape, position, and motion trajectory of the target are obtained, which provides data support for subsequent tasks. This paper uses the convolutional neural network model HyperNet as the technical support to study the deep learning (DL) tennis video target extraction. The final experimental results show that the loss value of the loss function in the training process of this method is stably maintained between 1.5% and 2.5%, and the speed performance is also greatly improved. The number of boxes for extracting candidate regions is significantly reduced, the calculation time for each frame will not exceed 1.8 s, the orientation accuracy of target extraction is 96.32%, and the size accuracy is 91.05%.

Topological Friction and Relaxation Dynamics of Spatially Confined Catenated Polymers.

We study catenated ring polymers confined inside channels and slits with Langevin dynamics simulations and address how the contour position and size of the interlocked or physically linked region evolve with time. We show that the catenation constraints generate a drag, or topological friction, that couples the contour motion of the interlocked regions. Notably, the coupling strength decreases as the interlocking is made tighter, but also shorter, by confinement. Though the coupling strength differs for channel and slit confinement, the data outline a single universal curve when plotted against the size of the linked region. Finally, we study how the relaxation kinetics changes after one of the rings is cut open and conclude that considering interlocked circular polymers is key for isolating the manifestations of topological friction. The results ought to be relevant for linked biomolecules in experimental or biological confining conditions.

Direct Fuzzy CMAC Sliding Mode Trajectory Tracking for Biaxial Position System

High-precision trajectory control is considered as an important factor in the performance of industrial two-axis contour motion systems. This research presents an adaptive direct fuzzy cerebellar model articulation controller (CMAC) sliding mode control (DFCMACSMC) for the precise control of the industrial XY-axis motion system. The FCMAC was utilized to approximate an ideal controller, and the weights of FCMAC were on-line tuned by the derived adaptive law based on the Lyapunov criterion. With this derivation in mind, the asymptotic stability of the developed motion system could be guaranteed. The two-axis stage system was experimentally investigated using four contours, namely, circle, bowknot, heart, and star reference contours. The experimental results indicate that the proposed DFCMACSMC method achieved the improved tracking capability, and so reveal that the DFCMACSMC scheme outperformed other schemes of the model uncertainties and cross-coupling interference.

Lateralization in Reverse Shoulder Arthroplasty.

Indications for Reverse Shoulder Arthroplasty (RSA) have been extended over the last 25 years, and RSA has become the most frequently implanted shoulder arthroplasty worldwide. The initial Grammont design with medialization of the joint center of rotation (JCOR), placement of the JCOR at the bone–implant interface, distalization and semi-constrained configuration has been associated with drawbacks such as reduced rotation and range of motion (ROM), notching, instability and loss of shoulder contour. This review summarizes new strategies to overcome these drawbacks and analyzes the use of glenoid-sided, humeral-sided or global bipolar lateralization, which are applied differently by surgeons and current implant manufacturers. Advantages and drawbacks are discussed. There is evidence that lateralization addresses the initial drawbacks of the Grammont design, improving stability, rates of notching, ROM and shoulder contour, but the ideal extent of lateralization of the glenoid and humerus remains unclear, as well as the maximal acceptable joint reaction force after reduction. Overstuffing and spine of scapula fractures are potential risks. CT-based 3D planning as well as artificial intelligence will help surgeons with planning and execution of appropriate lateralization in RSA. Long-term follow-up of lateralization with new implant designs and implantation strategies is needed.

Intelligent Recognition Method of Athlete Wrong Movement Based on Image Vision

Current methods of human body movement recognition neglect the depth denoising and edge restoration of movement image, which leads to great error in athletes’ wrong movement recognition and poor application intelligence. Therefore, an intelligent recognition method based on image vision for sports athletes’ wrong actions is proposed. The basic principle, structure, and 3D application of computer image vision technology are defined. Capturing the human body image and point cloud data, the three-dimensional dynamic model of sports athletes action is constructed. The color camera including CCD sensor and CMOS sensor is selected to collect the wrong movement image of athlete and provide image data for the recognition of wrong movement. Wavelet transform coefficient and quantization matrix threshold are introduced to denoise the wrong motion images of athletes. Based on this, the feature of sports athlete’s motion contour image is extracted in spatial frequency domain, and the edge of the image is further recovered by Canny operator. Experimental results show that the proposed method can accurately identify the wrong movements of athletes, and there is no redundancy in the recognition results. Image denoising effect is good and less time-consuming and can provide a reliable basis for related fields.

Research on the Contour Capture Algorithm of the Key Action of Dance with Computer-Aided

In order to solve the problem that the current key dance motion contour capture algorithm cannot effectively capture the concave part of the key dance motion contour, which leads to large contour capture error and long response time, a computer-aided key dance motion contour capture algorithm is proposed. The action sequence without background is obtained from the dance video, and the action sequence is optimized to reduce the interference of contour capture. Algorithm tracks the posture change of the object in the dance action sequence, determines the contour capture area, and completes the contour capture of the dance key action. Experimental results show that the proposed method can effectively improve the capture accuracy and shorten the response time.

Position domain control technology for six-joint robots

In the context of the rapid development of intelligent manufacturing, high-precision motion control of industrial robots has attracted increasing research attention. High-speed and high-precision motion control is a development trend associated with the current industrial robots. Industrial transformation and upgradation can be accelerated only via the independent research and development of robot core technologies, including robot high-precision control systems, ensuring the transition of the manufacturing industry in China toward intelligent and digital development. Currently, the contour tracking error associated with majority of the multiaxes CNC machine tools and multijoint industrial robots is a common problem. A good tracking performance can be achieved in time with respect to each joint axis when the space trajectory of the serial multijoint industrial robot changes under multiaxes linkage. The ideal contour trajectory cannot be fully ensured because of the mechanical and electrical hysteresis effects. Therefore, the synchronization of the motion of each servo axis in the geometric space is very important. In this paper, the existing kinematics and dynamics of industrial robots were combined with the traditional PID control theory for studying position domain control algorithm of the six-joint robot to resolve the problem of low accuracy associated with the robot end profile, which can be attributed to the lag between the motion instruction and the actual position. The algorithm uses the method of real-time establishment of the master–slave motion relation for controlling the spatial contour trajectory of the robot. It further transforms the synchronization control method of each servo joint in the time domain into the master–slave follow control method of each servo joint in the position domain. While realizing synchronization control in the position domain, PD control based on position domain was introduced to reduce the following error of the master–slave following control, improving the overall accuracy of the contour motion of the robot end. The method proposed in this paper has been tested on the Linux CNC numerical control system with a company’s HSR-JR605 robot, indicating that the position of the domain control method employed positively affects the accuracy of six articulated robot space trajectory.

P1449 CMR-driven computational modeling of right ventricular flow dynamics

Abstract Introduction The analysis of intracardiac blood flow patterns can significantly contribute to improve the understanding and treatment of cardiovascular disease. In contrast to the substantial literature on the left side of the heart, there is currently a significant lack of knowledge about the fluid mechanics of the right heart – pulmonary circulation unit (RH-PCU), both in healthy and diseased conditions. Purpose It is conjectured that computational modeling can be a key element to enhance current imaging techniques and provide quantitative insights into the unique RH-PCU biomechanics. Here we present a novel methodology that allows personalized numerical simulations of right heart flows, through a proper combination of cardiac magnetic resonance (CMR) with computational fluid dynamics (CFD). Methods and results We developed a patient-specific pipeline from medical images to computational models, as depicted in the figure. First, the RV geometry is reconstructed from time-resolved CMR cine images, comprising short-axis and longitudinal slices of the heart, where feature-tracking techniques are used to extract the motion of the RV endocardium contours. A time-continuous description of the moving geometry is obtained through an image-registration algorithm based on diffeomorphic mappings. The moving model of the RV, including the outflow tract and proximal pulmonary arteries, is finally fed to a dedicated CFD solver. The tool is able to provide a detailed description of the velocity and pressure fields inside the right ventricle and proximal pulmonary arteries during all phases of the cardiac cycle. From these fields, global hemodynamic quantities such as vortex properties, kinetic energy, pressure gradients and hemodynamic forces can be computed. Conclusions CMR-driven computational modeling of intra-ventricular flow enables a promising approach for understanding and evaluating the biomechanical environment of the right heart. This high-fidelity framework can be applied to investigate the RV response and adaptation to abnormal pressure and/or volume load conditions. It can also be used to reproduce the virtual flow that would realize in hypothetical conditions, and therefore adds predictive capabilities to modern flow imaging. The analysis may allow to determine an association between blood flow patterns and disease progression, and ultimately lead to derive and validate imaging biomarkers of clinical significance. Abstract P1449 Figure. Pipeline for patient-specific modeling

Design of Contour Error Coupling Controller Based on Neural Network Friction Compensation

In two-axis servo contour motion control, friction and various uncertainties unavoidably exist, reducing the contour control accuracy. This paper proposes a neural network contour error coupling control method based on LuGre friction compensation, which includes a contour error calculation model, single-axis computed torque controller (CTC), and neural network friction compensation controller. The LuGre friction model can describe servo system’s complicated static and dynamic friction characteristics, and the RBF neural network has a universal approximation property to realize compensation control of friction. Simulation results indicate that the proposed contour error control method can effectively compensate for the effect of friction and improve contour control accuracy.

Perceptual integration of head and eye cues to gaze direction in schizophrenia.

The perceptual mechanisms that underlie social experience in schizophrenia are increasingly becoming a target of empirical research. In the context of low-level vision, there is evidence for a reduction in the integration of sensory features in schizophrenia (e.g. increased thresholds for contour detection and motion coherence). In the context of higher-level vision, comparable differences in the integration of sensory features of the face could in theory impair the recognition of important social cues. Here we examine how the sense of where other people are looking relies upon the integration of eye-region cues and head-region cues. Adults with schizophrenia viewed face images designed to elicit the ‘Wollaston illusion’, a perceptual phenomenon in which the perceived gaze direction associated with a given pair of eyes is modulated by the surrounding sensory context. We performed computational modelling of these psychophysical data to quantify individual differences in the use of facial cues to gaze direction. We find that adults with schizophrenia exhibit a robust perceptual effect whereby their sense of other people's direction of gaze is strongly biased by sensory cues relating to head orientation in addition to eye region information. These results indicate that the visual integration of facial cues to gaze direction in schizophrenia is intact, helping to constrain theories of reduced integrative processing in higher-level and lower-level vision. In addition, robust gaze processing was evident in the tested participants despite reduced performance on a theory of mind task designed to assess higher-level social cognition.