266 results about "Motion correction" patented technology

The invention discloses a slope deformation monitoring method and system, a medium and equipment, and relates to the field of slope deformation monitoring, and the method comprises the steps: obtaining a slope image sequence with a fixed time interval; extracting features of the first image, tracking the features of the first image in the to-be-detected image by using a Lucas-Kanade optical flow method, estimating a view field translation amount and a rotation angle of the to-be-detected image relative to the first image, and performing view field motion correction on the to-be-detected image; performing semantic segmentation on the first image and the corrected to-be-detected image to obtain an image pair with a slope region mask; obtaining a feature point matching set of the image pair based on LoFTR; performing feature point mismatching elimination on the feature point matching set by using a two-stage mismatching elimination method to obtain a matching point pair set M [beta]; and calculating the displacement of each point pair in the M [beta], and obtaining a sub-pixel-level slope deformation field through Gaussian interpolation. The scheme is low in cost, small in calculation amount and high in precision.

The invention relates to a ball action evaluation method, device and equipment based on a large model, and a medium, and the method comprises the steps: collecting the three-dimensional coordinates of the joints of a student and the trajectory data of sports equipment, and generating a standardized action sequence through space-time compensation and noise suppression; based on inverse kinematics and differential kinematics technologies, human body joint rotation features and instrument motion parameters are separated and extracted, and a multi-dimensional motion feature vector is constructed; calculating a dynamic mahalanobis distance and a key joint contribution degree by adopting a comparative learning mechanism and a standard action template, and generating a quantitative evaluation result; and finally, through inverse kinematics optimization and three-dimensional visual rendering, outputting an action correction scheme. According to the method, the limitation of traditional single-mode evaluation is broken through, deep analysis of instrument-human body collaborative motion features is realized, the technical problems of inaccurate action detail capture, single evaluation dimension and insufficient feedback visualization in a high-speed scene are effectively solved, and the scientificity and real-time performance of training guidance are remarkably improved.

The invention provides a motion capture and feedback adjustment method and system for smart motion of meta universe, and relates to the technical field of meta universe, and the method comprises the steps: collecting actual motion data of a user through a plurality of inertial measurement units, establishing a mapping relation with skeleton nodes, evaluating and optimizing the motion based on a multi-dimensional scoring matrix, and constructing a user virtual image. And performing time sequence analysis on the virtual motion data to obtain local and global motion features, comparing the local and global motion features with standard motion features to generate an adjustment amount, and converting the adjustment amount into a motion correction instruction to guide a user to adjust a posture, so that accurate capture and real-time feedback can be realized, and a motion learning effect is improved.

Described here are systems and methods for generating microvessel images from image data acquired with an ultrasound system while analyzing the image data in realtime, or retrospectively, to generate a performance descriptor that can be used to assess data quality and / or motion correction quality; to adaptively suppress noise in the data; or both.

The invention discloses a multi-frame photoacoustic image reconstruction method based on optical flow alignment and depth feature fusion. The method comprises the following steps: S1, obtaining photoacoustic signal data; s2, reconstructing a photoacoustic cross-sectional image; s3, performing optical flow calculation and image alignment; and S4, training the deep feature fusion network. According to the method, through optical flow motion correction and a potential space learning mechanism, space-time information and complementary features in the aligned multiple frames of images are dynamically integrated, complementary information in the multiple frames of images is adaptively fused, noise is suppressed, and finally reconstruction of high signal-to-noise ratio and high spatial resolution images of biological tissues is achieved. Experimental results show that the method can significantly improve image quality, recover image distortion and detail loss caused by motion and noise, and provide a new effective scheme for promoting robust clinical application of a photoacoustic imaging technology.

The invention relates to an accurate collision-free automatic docking control method which is characterized by comprising the following steps: S1, remote guidance: triggering an automatic docking signal, establishing a three-dimensional semantic map, completing autonomous positioning, planning a docking path, performing real-time obstacle avoidance navigation, and executing safe and stable movement to reach a pre-docking position; s2, posture adjustment: when reaching the pre-docking position, determining the target posture of the target docking object and the posture of the object to be docked, and adjusting the posture of the object to be docked to the pre-docking posture; and S3, short-range docking: based on motion compensation, detecting a motion error, generating a motion correction instruction according to error signal feedback, and realizing collision-free accurate docking. The movement function and the body position changing function of the intelligent wheelchair are achieved through the intelligent wheelchair, smooth and stable switching can be achieved in the changing process of the intelligent wheelchair, and meanwhile the intelligent wheelchair is an indoor power assisting system, has the obstacle avoiding walking function and can be in collision-free automatic butt joint with a bed body and a closestool.

The invention discloses a space-time speckle projection three-dimensional imaging method based on multi-frame optical flow alignment. Firstly, a projector based on DLP is used for projecting a space-time speckle pattern to a measured scene, and a binocular camera synchronously collects a three-dimensional space-time speckle image. The calibration parameters of the binocular camera are used to carry out stereo correction on an acquired original speckle image, and a parallax image is generated frame by frame in combination with a coarse-to-fine single-frame speckle matching strategy. And estimating a two-dimensional inter-frame displacement field between continuous disparity maps by using an optical flow method by taking an intermediate frame disparity map as a reference, compensating motion artifacts in a space-time speckle image, and ensuring strict space-time registration of a dynamic target. And based on the speckle image after motion correction, a speckle matching strategy is expanded to a time-space domain, and high-precision multi-frame three-dimensional measurement of a complex dynamic scene is realized. The method is suitable for performing rapid and high-precision three-dimensional modeling on a moving target in an unstructured environment, and can perform accurate three-dimensional measurement on a high-speed dynamic target undergoing any translation or rotation motion.

The present disclosure provides systems and methods for automated scan preparation and real-time monitoring / adjustment in medical imaging. The automated scan preparation may include positioning a target subject to be scanned by a medical imaging device, determining a rotation scheme of the medical imaging device, targeting a scan region of the target subject with an imaging isocenter of the medical imaging device, determining target position(s) of component(s) of the medical imaging device, performing a virtual scan, generating a reference subject model representing an internal structure of the target subject, or the like. The real-time monitoring / adjustment operations may include achieving automatic brightness stabilization, monitoring a posture of the target subject, adjusting the position of component(s) of the medical imaging device, estimating a dose distribution, monitoring a treatment of the target subject, performing a motion correction, or the like.

The invention provides a bridge stay cable video multi-target identification tracking and vibration extraction method based on an unmanned aerial vehicle. The method comprises the following steps: 1, constructing a bridge stay cable refined inclined slender target detection model based on a YOLOv11 model; 2, providing a multi-target tracking algorithm fusing the inclined slender target detection model and a StrongSORT algorithm; step 3, improving a displacement extraction method combining SIFT / ORB feature point matching and a sub-pixel refinement technology; 4, designing an unmanned aerial vehicle motion correction algorithm based on variational mode decomposition and time-frequency domain combined screening; and 5, constructing a joint working modal analysis algorithm for realizing combination of a natural excitation technology and a random subspace recognition algorithm. According to the method, high-precision extraction of the vibration signals of the stay cable and identification of modal parameters of the stay cable are realized, and technical support is provided for health monitoring of the large-span cable-stayed bridge.

The invention provides a heart MRI quantitative imaging motion correction and parameter graph reconstruction and segmentation method and system. The method comprises the following steps: constructing a multi-task model; and constructing a motion-free simulation signal data set of the selected quantity imaging task, and pre-training the single-pixel parameter estimation network and the single-pixel signal simulation network based on the data set. And constructing a real image data set of the selected amount of imaging tasks, fixing network parameters obtained by pre-training, and training a multi-task model to determine values of residual parameters in the model. And performing motion correction, quantitative parameter graph reconstruction and region-of-interest segmentation on a newly acquired image sequence of the selected amount of imaging task through the trained multi-task model, and outputting a reconstructed quantitative parameter graph and a segmentation mask of the quantitative parameter graph. The method is more accurate and quicker, and can realize full-automatic and one-stop post-processing of heart quantitative imaging.

The invention discloses a motion correction and registration method and device in ultrasonic microvascular imaging, a medium and a product, and relates to the field of ultrasonic microvascular imaging, and the method comprises the steps: obtaining an ultrasonic sequence image changing along with time; dividing the ultrasonic sequence image by using the threshold value of the data frame correlation coefficient; determining a reconstructed image of the static frame and a reconstructed image of the relative static frame according to a clustering result obtained by clustering the correlation coefficient between the static frames; respectively carrying out image registration on the reconstructed image of the static frame and the reconstructed image of the relative static frame to obtain a reconstructed image after registration of the static frame and a reconstructed image after registration of the relative static frame; and superposing the reconstructed image after the static frame registration, the reconstructed image after the relative static frame registration and the reconstructed image without breathing movement to obtain a registered image. According to the invention, the ultrasonic super-resolution microvascular imaging performance and quality can be improved.

The present invention relates to projectiles and munitions, and more specifically to such in flight. More particularly the present invention relates to projectiles and munitions in flight equipped with one or more image sensors adapted for acquiring image data of the environment surrounding the projection or munition in flight. The present invention further relates to systems and methods for correcting or stabilizing motion effects and artifacts present in the image data related to the movement or motion of the projectile or munition in flight, including spin or rotation of the projectile or munition.

The invention provides virtual reality-based dance teaching equipment and a teaching method thereof, and the equipment comprises a VR module which is used for providing multi-view immersive visual experience; the motion capture module is used for capturing the joint angle of the dancer in real time and the limb track feedback module and is used for simulating the ground counter-acting force in dancing through vibration or pressure, enhancing motion perception and providing a touch prompt for motion correction; the virtual scene module is used for creating a dance virtual scene; and the action analysis module analyzes the joint action deviation based on multiple view angles, generates an instant score according to the joint angle deviation and the rhythm synchronization rate, and records historical data to generate a progress curve. According to the method, the problems of resource shortage, feedback lag and other pain points in traditional dance teaching are solved, efficient, personalized and interesting dance learning experience is achieved through multi-modal interaction and a dynamic adjustment mechanism, and the application of the method is expected to promote dance education to develop in the intelligent and universal direction.

The invention provides a laser confocal ocular surface image stitching method based on deep learning. Efficient and accurate image stitching is realized through unsupervised learning. Firstly, an ocular surface image is preprocessed, and a data set is constructed; then, a multi-scale feature pyramid is constructed by using a convolutional neural network, image features are automatically extracted and pre-aligned, and image alignment is optimized through global motion aggregation and local motion correction technologies. Thirdly, constructing a content fusion generative adversarial network which comprises a generator and a discriminator to smooth the fused image and generate a spliced image conforming to human eye perception; a staged training strategy is adopted, image alignment is optimized firstly, and then image smooth fusion is achieved. And finally, automatically finishing image alignment and fusion by using the trained network to obtain a seamless large-view confocal ocular surface image. According to the method, the splicing precision and robustness are improved, the method is suitable for image splicing in different environments, and the splicing success probability is remarkably improved.

Systems and methods include acquisition of an anatomical image of an object, acquisition of molecular imaging data of the object at the plurality of photon detectors, reconstruction of a functional image based on the molecular imaging data, input of the anatomical image and the functional image to a trained neural network to generate a second functional image, and presentation of the second functional image.

The present disclosure provides systems and methods for automated scan preparation and real-time monitoring / adjustment in medical imaging. The automated scan preparation may include positioning a target subject to be scanned by a medical imaging device, determining a rotation scheme of the medical imaging device, targeting a scan region of the target subject with an imaging isocenter of the medical imaging device, determining target position(s) of component(s) of the medical imaging device, performing a virtual scan, generating a reference subject model representing an internal structure of the target subject, or the like. The real-time monitoring / adjustment operations may include achieving automatic brightness stabilization, monitoring a posture of the target subject, adjusting the position of component(s) of the medical imaging device, estimating a dose distribution, monitoring a treatment of the target subject, performing a motion correction, or the like.

The invention relates to the technical field of unmanned aerial vehicle visual displacement monitoring, and particularly provides an airborne camera motion correction method in unmanned aerial vehicle visual displacement monitoring, comprising the following steps: installing a monitoring target and two reference targets, and collecting unmanned aerial vehicle visual image data; calculating image point coordinates of two angular points on all targets, image point displacement of a midpoint of the two angular points and scale factors of the targets; the z-axis rotation motion error of the monitoring target is calculated; the z-axis translational motion error of the monitoring target is calculated; calculating x-axis and y-axis rotation and translation motion errors of the monitoring target; and correcting rotation and translation motion errors of the z-axis, the x-axis and the y-axis to obtain an actual displacement result of the monitoring target. The method provided by the invention does not depend on the rotation center when correcting the z-axis rotation motion of the camera, is suitable for the actual situation that the rotation center deviates from the image center, and improves the applicability on the premise of effectively improving the visual displacement monitoring precision of the unmanned aerial vehicle.

The invention discloses a cone beam CT full respiratory cycle image reconstruction method based on a four-dimensional neural network. The method comprises the following steps: step 1, acquiring cone beam projection data and a respiratory signal in a respiratory state; 2, a respiratory cycle is divided into N phases, multi-phase cone beam projection data Qi are obtained, i = 1, 2,..., N, the Qi is reconstructed through a filtered back projection algorithm, and a multi-phase under-sampling CT reconstructed image # imgabs0 # is obtained; 3, # imgabs1 # is input into a four-dimensional neural network, and a three-dimensional deformation field Di is obtained; 4, performing weighted summation after deformation on the # imgabs2 # by using the Di to obtain a reference phase reconstruction image Xref after motion correction; and step 5, performing inversion on the Di to obtain a three-dimensional reverse deformation field # imgabs3 #, for any breathing phase, performing image deformation on the reference phase reconstruction image Xref by using the corresponding # imgabs4 #, and performing reconstruction to obtain a motion correction image Xi. According to the method, the breathing movement correction can be quickly carried out on the cone beam CT image.

The invention discloses a method for correcting ultrafast ultrasonic motion displacement based on a VoxelMorph model to solve motion artifacts. Comprising the following steps: acquiring ultrafast ultrasonic data, preprocessing the acquired data to obtain an initial data set, and dividing the initial data set into a training set and a test set; constructing a VoxelMorph motion correction network model based on the lightweight attention module, training the neural network model based on the training set to obtain a motion calibration network model, and testing the motion calibration network model based on the test set to obtain motion calibration data; and performing clutter filtering by using singular value decomposition to separate a blood flow signal and a tissue signal of the calibrated data, extracting the blood flow signal, and performing power Doppler blood flow imaging. According to the method, motion artifacts and random noise occurring in the ultrafast ultrasonic imaging process can be effectively restrained, blur between blood vessels is reduced, and the Doppler blood flow imaging contrast ratio is improved.

The invention belongs to the technical field of clock synchronization, and particularly relates to a high-precision clock synchronization method in an aviation cluster high-speed relative motion scene, which comprises the following steps of: S1, acquiring round-trip time RTT data based on signal transmission among nodes in an aviation cluster, and acquiring a relative radial speed v by combining an inertial navigation module; calculating by using a motion correction formula to obtain a preliminary clock skew epsilon after motion correction; in order to solve the problems of asymmetric high-speed motion path time delay, URT interference, random and nonlinear error coexistence and the like, through a four-stage cooperative scheme of RTT motion correction, URT modeling compensation, Kalman filtering smoothing and BP neural network residual optimization, the clock synchronization precision is improved to 0.1-0.3 ns, the method has strong anti-interference robustness and engineering realizability, and the method is suitable for large-scale popularization and application. The method can be widely applied to aerospace, unmanned aerial vehicle formation, satellite communication and other fields requiring high-precision clock synchronization, and effectively solves the problems of insufficient precision and poor adaptability of a traditional method in a high-speed scene.

The invention relates to a multi-modal sensing fusion-based automatic correction method and system for a welding robot body, the system comprises a three-level distributed architecture of a sensing layer, an edge layer and a control layer, the sensing layer is integrated with a millimeter wave radar, a camera, a six-dimensional force / torque sensor, a welding power supply data acquisition module and an acoustic emission sensor, and multi-modal data acquisition is realized; the edge layer runs a fusion algorithm through an embedded AI computing platform carrying a GPU to complete data processing and decision making; and the control layer receives the deviation correction instruction to drive the robot to execute motion correction. The method comprises the steps of multi-modal data real-time acquisition and synchronization, data preprocessing and feature extraction, multi-source information fusion and decision making, and real-time control and execution. The problems of single sensing dimension, response delay, weak anti-interference capability and the like in the prior art are solved, the robustness, the real-time performance and the deviation correction precision of the system are improved, full-automatic intelligent welding is realized, and the method is suitable for high-precision scenes such as shipbuilding and pressure vessel welding.

A data-driven method and system using optical tracking of natural facial features for quick and accurate pose determination during scanning using one or more cameras installed within or on a PET-CT gantry. No patient preparation, fixtures, or artificial features are needed. The pose data is analyzed and incorporated iteratively in reconstruction for reconstruction to motion correct the PET data. The present invention thus results in improved correction of blurring and artifacts caused by head motion during the PET scan.

The invention discloses a gated image adaptive noise suppression and detail enhancement method based on depth guidance, and the method comprises the steps: carrying out the noise decoupling of an original intensity image and depth map data based on depth-noise joint physical modeling, and generating a noise parameter set and a depth feature map; performing time sequence jitter compensation by using rigid constraint of the depth feature map, and generating a motion correction image through depth weighted interpolation; performing depth Fourier coefficient guided filtering processing on the motion correction image in a frequency domain, separating a base layer and a detail layer, and suppressing noise; according to the depth value, semantic regions are divided, differential enhancement is executed, and a region customization enhancement image is generated; and performing ambient light compensation based on the depth attenuation model, and outputting a final optimized image. According to the method, the image quality can be remarkably improved, the depth-of-field contradiction caused by global enhancement is solved, and the ambient light residue is radically treated.

A method is provided for acquiring a magnetic resonance image dataset of an object, using an imaging protocol in which several k-space lines are acquired in one echo train, wherein the echo train may include one or several sub echo trains, and wherein the imaging protocol includes a plurality of echo trains. Within the method, a set of additional k-space lines within a central region of k-space is acquired a number of times per echo train, wherein the number is greater than 1, in order to detect movement of the object during the echo train. The sets of additional k-space lines are acquired at pre-determined positions within the echo trains. The disclosure is also directed to a method for generating a motion-corrected magnetic resonance image dataset from the dataset thus acquired, a magnetic resonance imaging apparatus, and a computer program.

The invention discloses an anti-jitter data acquisition and obstacle recognition method for a rail transit vehicle, and relates to the technical field of rail transit vehicle data acquisition and processing, continuous four frames of original images are acquired through a binocular camera by adopting synchronous and staggered shooting, and global motion estimation is performed in combination with acquired sensor data; local pixel level alignment is realized by using a preset image layer and pixel grid division, a stable image is generated through weighted fusion, local features of the image are optimized based on a multi-head attention mechanism, the stable image is mapped to a high-dimensional vector space by using a depth encoder, motion correction information is fused, and finally, the motion correction information is obtained. A deep reconstruction network is combined with binocular parallax to generate a three-dimensional point cloud, three-dimensional coordinate changes are monitored in real time, obstacle recognition and early warning are achieved, high-quality images can be obtained in the violent vibration environment of a vehicle, the data acquisition precision and the real-time performance and accuracy of obstacle detection are improved, and the operation safety of rail traffic is effectively guaranteed.

A method of motion correction image reconstruction for photon-counting CT images includes scanning a subject via a photon-counting CT scanner device to obtain measured projection data; performing, via motion correction circuitry of the motion correction system, a LLE motion correction algorithm on the measured projection data to obtain motion correction data; generating, via reconstruction circuitry of the motion correction system, reconstructed image data from the motion correction data; and outputting corrected image data based, at least in part, on the reconstructed image data. A binary bad pixel mask is applied to exclude contributions from the bad pixels to the measured projection data. An unreliable volume mask is applied to exclude contributions from X-ray beams that passed through unreliable portions of the reconstructed image data. A virtual static object removal algorithm is performed to remove a static object from the measured projection data.

The invention belongs to the technical field of image processing, and discloses a three-dimensional radar sub-aperture imaging deformation monitoring device and method, and the device comprises a radar which is used for collecting echo signals passing through a preset position, processing the echo signals according to frequency mixing and analog-to-digital conversion, obtaining a three-dimensional echo matrix, and outputting the three-dimensional echo matrix; the three-dimensional echo matrix and a sampling ending signal are sent to a motion compensation module; the motion compensation module is used for resampling the azimuth direction of the three-dimensional echo matrix by adopting an interpolation algorithm; the distance matching filtering module is used for performing pulse compression on the distance direction of the three-dimensional echo matrix; the migration correction module is used for carrying out migration correction on the distance direction of the three-dimensional echo matrix after pulse compression; the sub-aperture imaging module is used for calculating a three-dimensional echo matrix after migration correction according to a sub-aperture imaging algorithm; and the deformation monitoring module is used for performing interference difference processing on the three-dimensional complex image to obtain deformation data. The imaging quality and the deformation monitoring precision can be improved.

The present description relates to an image sequence motion correction method and apparatus based on intensity correction optical flow registration. The method comprises: determining a reference image of each deformed image in a deformed-image sequence, and performing motion correction on the deformed image by means of adjusting the weight of a mechanical regularization term between the reference image and the deformed image, so as to obtain a displacement field of the deformed image relative to the reference image; on the basis of the displacement field and an intensity correction value of a finite element node, performing intensity correction on the reference image; adjusting the weight of the mechanical regularization term, performing motion correction on the deformed image, performing intensity correction on a first corrected reference image on the basis of an optimized displacement field, repeatedly executing the process until an image residual function converges, determining that the deformed image has been corrected, and recording the optimal solution of a weight value of the mechanical regularization term of the deformed image; and on the basis of the optimal solution of the weight value, determining the actual displacement of the deformed image. In the present description, the mechanical model behind physiological motion and an intensity correction technique are integrated into an image registration algorithm to reduce image noise, thereby improving the algorithm accuracy.

A method for acquiring a magnetic resonance image dataset of an object includes using an imaging protocol in which a number of k-space lines are acquired in one shot. The imaging protocol includes a plurality of shots. A plurality of additional k-space lines are acquired in at least a subset of the shots, such that movement of the object is detected throughout the imaging protocol. A method for generating a motion-corrected magnetic resonance image dataset from the dataset thus acquired, a magnetic resonance imaging apparatus, and a computer program are also provided.

The invention relates to a radar data correction method and device, computer equipment and a storage medium. The method comprises the steps of determining an installation deviation angle of an external meteorological station according to a fitting result of an azimuth angle and a radial wind speed in multiple groups of first radar data measured by a continuous wave radar in a preset period; according to the installation deviation angle, symbol information of radial wind speed in each group of first radar data is determined; and performing motion correction on the first radar data according to a preset turbulence correction algorithm and inertia measurement data measured by an inertia measurement unit to obtain target radar data. The method can improve the accuracy of wind direction and wind speed measurement results.