105 results about "Motion correction" patented technology

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 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 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.

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.

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.

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 discloses a sonar image geometric correction method based on image optimization, and relates to the technical field of underwater sonar imaging, and the method comprises the steps: collecting a multi-source underwater observation data packet including sonar image original data, platform motion state data and underwater environment data, and carrying out the preprocessing; extracting sonar image original data from the multi-source underwater observation data packet, performing geometric coarse correction to obtain a pre-corrected sonar image, and performing motion compensation on the obtained pre-corrected sonar image to generate a motion corrected sonar image; constructing a joint optimization objective function, carrying out geometric fine tuning on the motion correction sonar image by adopting a sliding time window strategy, and outputting a geometric fine correction sonar image; geometric distortion caused by non-uniform motion of a platform, attitude disturbance and acoustic propagation distortion is effectively eliminated, inter-frame consistency and submarine topography continuity of sonar images are remarkably improved, and the method is suitable for high-precision operation scenes such as underwater surveying and mapping, target recognition and three-dimensional reconstruction.

The invention belongs to the technical field of artificial intelligence and medical rehabilitation, particularly relates to an intelligent training optimization system for orthopedic rehabilitation, and aims to solve the problems of insufficient individuation, feedback lag, subjective evaluation and the like in traditional rehabilitation training. The system collects motion and physiological data through a wearable sensing unit, and generates and adjusts a training scheme in combination with a self-adaptive optimization algorithm driven by cloud individualized modeling, dynamic risk assessment and reinforcement learning; real-time motion correction is achieved through multi-modal feedback, doctor-patient cooperation and group intelligent analysis are supported through a clinical management platform, and rehabilitation safety, effectiveness and accessibility are improved.

The embodiment of the invention provides an angular velocity correction method and device and a robot, and relates to the technical field of motion correction, and the method comprises the steps: determining an angular velocity zero offset according to motion data measured by an inertial measurement unit, and taking the angular velocity zero offset as a zero offset observation value; determining the weight of the zero offset observation value and the weight of the zero offset prediction value based on the discrete degree information of the historical zero offset observation value and the uncertainty information of the zero offset prediction value; updating the zero offset predicted value through the zero offset observed value, the zero offset predicted value, the weight of the zero offset observed value and the weight of the zero offset predicted value; and correcting the angular velocity included in the motion data by adopting the updated zero offset predicted value. The angular velocity zero offset adopted for correcting the angular velocity is determined based on the motion data measured by the inertial measurement unit instead of a pre-calibrated fixed angular velocity zero offset, so that the accuracy of the corrected angular velocity can be improved.

In a case where motion information of a subject (3) during scanning is acquired from an image pair, and a CT image is generated by performing motion-corrected image reconstruction, erroneous recognition of a motion component and excessive smoothing are suppressed to improve accuracy of motion correction. A motion information acquisition unit (330) includes a filtering unit (331) that reduces noise of an image pair, a registration unit (333) that performs registration processing by using the image pair after filtering, and a processing condition adjustment unit (335) that adjusts conditions of filtering processing and the registration processing in accordance with a tube current ratio of the image pair. The processing condition adjustment unit refers to a table in which a relationship between the tube current ratio and a parameter of each processing is predetermined, selects parameters corresponding to a tube current ratio of a target image pair, and determines the parameters for filtering and registration. The motion information acquisition unit acquires motion information by performing filtering and registration by using the determined parameters, and the image reconstruction unit performs motion-corrected image reconstruction by using the motion information.

Disclosed herein is a method of magnetic resonance imaging. The method comprises receiving (200) groups of k-space data descriptive. The groups of k-space data comprise group specific imaging k- space data and group specific navigator k-space data. The method further comprises determining (202) group specific subject positions using the group specific navigator k-space data of the groups of k- space data. The method further comprises initializing (204) group specific motion parameters of a joint optimization using the group specific subject positions. The method further comprises reconstructing (206) a motion corrected magnetic resonance image by performing the joint optimization on the group specific imaging k-space data of the groups of k-space data. The joint optimization is configured to simultaneously reconstruct the motion corrected magnetic resonance image while determining the group specific motion parameters. The method further comprises providing (208) the motion corrected magnetic resonance image descriptive of the field of view.

Described herein is a medical system (100, 300) comprising a memory (110) storing machine executable instructions (120) and a motion estimating neural network (122, 700, 800, 900, 1000) configured for outputting trajectory data (130) in response to receiving a trial motion trajectory (128) as input. The execution of the machine executable instructions causes a computational system (104) to: receive (200) measured k-space data (124) descriptive of a subject (318); perform (202) motion estimation of the subject between the sequence of discrete acquisitions by solving an optimization problem to determine a calculated motion trajectory of the subject in the predefined coordinate system, wherein the optimization problem is modified using the trajectory data; and reconstruct (204) a final motion corrected magnetic resonance image (136) from the measured k-space data and the calculated motion trajectory in the predefined coordinate system.

The embodiment of the invention provides a multi-target tracking method and device based on camera motion correction, equipment and a medium, and the method comprises the following steps: estimating a camera motion matrix through a previous frame gray level image I1 and a current frame gray level image I2 containing a detection frame; through Kalman prediction, prediction frames of all tracking targets are obtained, and the prediction frames are corrected based on the camera motion matrix; performing one-to-one association matching on all the corrected prediction frames of all the tracking targets and all the detection frames of the current frame gray level image I2 to generate an association matching result; and dynamically adjusting Kalman filtering parameters according to the updated tracking frame information and the detection frame information matched with the updated tracking frame information, and taking the prediction frame as the updated tracking frame information at the k moment for each tracking target which fails in association matching. According to the scheme, the prediction result of Kalman filtering is corrected through the camera motion matrix, and the efficiency and accuracy of multi-target tracking are improved.

A method, system, processing circuitry, and computer program product for providing initial motion correction in magnetic resonant imaging (MRI) data that enables additional image correction to be performed on subsequently processed MRI data in the same imaging set. One such method receives k-space data including a first set of motion corrupted k-space data and a second set of k-space data (different than the first set); generates motion correction data based on the first set of motion corrupted k-space data; and generates an image based on the second set of undersampled k-space data and the motion correction data.

The invention discloses a cardiac magnetic resonance report generation method and system based on a multi-dimensional tissue feature knowledge graph. The method comprises the following steps: performing motion correction and multi-view feature extraction on a cardiac magnetic resonance multi-sequence image to obtain an original numerical attribute of an instance node; constructing a dynamic knowledge graph containing tissue characteristics and medical priori, and generating a target reasoning chain with a clinical basis in combination with neural symbol reasoning; then, in a controlled generation stage, an inference chain is injected into a prefix tree, in a decoding step, mask physical shielding logic conflict description is utilized, and a pointer network is synchronously started to directly copy original numerical value attributes into a text sequence; and finally, executing consistency logic verification through a text restoration technology, and identifying and intercepting factual errors. Through the atlas constraint and numerical unvarnished transmission mechanism, the factual illusion problem of a generative model is effectively solved, and the accuracy and reliability of a heart diagnosis report are remarkably improved.

An exemplary AI system and method are disclosed for reconstructing high-fidelity motion-corrected MRI scans from low-field, motion-corrupted MRI scans (e.g., neonatal MRI data) using a neural network (NN) model in combination with a physics-based motion model. The exemplary AI system and method can enhance image quality and remove motion artifacts which can allow for scans to be performed with significantly reduced scan time, a particular benefit for neonatal subjects as well as all patients when employing low-field MRI.

A method for performing dynamic positron emission tomography (PET) is disclosed. The method includes collecting volumetric radioactive measurement data associated with an administered radioactive tracer present in a target site of a subject over multiple scanning intervals, capturing a magnetic resonance image of the target site, and performing a motion correction process to the volumetric radioactive measurement data to produce motion corrected PET data. The method further includes co-registering the magnetic resonance image and motion corrected data to generate a co-registered dynamic PET volume, and applying a model corrected input function (MCIF) to the co-registered dynamic PET volume to calibrate an uptake amount of the radioactive tracer in the target site.

This invention proposes a method for multi-target recognition, tracking, and vibration extraction of bridge cable-stayed bridge videos based on unmanned aerial vehicles (UAVs). The method includes: Step 1: Constructing a refined tilted and slender target detection model for bridge cable-stayed bridges based on the YOLOv11 model; Step 2: Proposing a multi-target tracking algorithm that integrates the tilted and slender target detection model and the StrongSORT algorithm; Step 3: Improving the displacement extraction method by combining SIFT / ORB feature point matching and sub-pixel refinement techniques; Step 4: Designing a UAV motion correction algorithm based on variational mode decomposition and time-frequency domain joint screening; Step 5: Constructing a joint working mode analysis algorithm that combines natural excitation technology and random subspace recognition algorithm. This method achieves high-precision extraction of cable-stayed bridge vibration signals and identification of cable-stayed bridge modal parameters, providing technical support for health monitoring of long-span cable-stayed bridges.

The application discloses a four-element-based three-dimensional human body posture estimation motion correction method, which comprises the following steps: 1, collecting motion data and performing data preprocessing; 2, using a convolutional neural network to extract human body joint features; 3, using the extracted joint features to construct a quaternion for rotation; 4, using three-dimensional coordinate values and a method for calculating angles in a three-dimensional space to calculate the angles between each joint and the skeleton, which are used for posture correction; and simultaneously, the speed is corrected by calculating the slope. The application improves the accuracy of posture correction in a three-dimensional space through quaternion rotation; the determination method of the motion speed in the three-dimensional space is redefined through the Euclidean distance and the speed of angle change; and the application can achieve good effects in the determination of motion speed and action recognition in the three-dimensional human body posture estimation in sports health.

This invention relates to the field of motion correction technology, specifically to a method for lower limb motion correction that integrates angle and pressure data. The method includes acquiring real-time angle and pressure data during lower limb movement and establishing an angle-pressure time-series matrix; identifying the phase difference distribution pattern between angle and pressure feature points based on the matrix; comparing the phase difference distribution pattern with an individual's standard motion pattern; generating targeted motion correction commands when abnormal phase relationships are detected; and dynamically optimizing the individual's standard motion pattern based on the execution effect of the motion correction commands. This invention achieves cross-modal fusion of motion control signals and external reaction force signals. This fusion enhances the comprehensiveness of motion anomaly detection and improves the sensitivity of identifying subtle motion imbalances or phase misalignments, making it suitable for identifying problems that conventional models struggle to capture, such as gait deviations and incorrect force application sequences.

The invention discloses a wild animal trajectory tracking method and system based on trajectory perception and motion correction, and relates to the technical field of wild animal monitoring and computer vision, and the method comprises the steps: carrying out the preprocessing of the collected GPS trajectory data of corresponding wild animals; mapping the preprocessed GPS trajectory data to a high-dimensional feature space by using a feature encoder; converting the feature coding vector into a representation sequence for capturing time sequence dependence and context information through a constructed track encoder; inputting the representation sequence into the constructed trajectory sensing network for processing to obtain scores of candidate positions in a preset candidate list of a next time step; performing real-time track correction on the candidate position with the highest score in the preset candidate list, and taking the corrected candidate position as the track position of the wild animal in the next time step; sensor noise and environmental interference are effectively suppressed, and the precision and robustness of wild animal trajectory tracking are improved.

The embodiment of the invention provides a robot control method and device, electronic equipment and a storage medium, and relates to the technical field of robotics.The method comprises the steps that expected first state information of robot joints is determined from expected action trajectory information; based on the difference between the first state information and the current second state information of the robot joint, action correction parameters are determined; correcting action description information in the first state information according to the action correction parameters; based on the corrected action description information, an action control instruction is generated, and the action control instruction is adopted to control the robot to move; wherein the first state information and the second state information comprise action description information and space description information of the robot joints, and the space description information comprises postures of the robot joints. By applying the scheme provided by the embodiment of the invention, the accuracy of action execution of the robot can be improved.

The invention discloses a robot mixed impedance control method and system for curved surface workpiece grinding, and belongs to the technical field of robot control, and the robot mixed impedance control method comprises the steps that a preset track of the tail end is generated according to an estimated geometric contour, and expected normal preset contact force is set for grinding; calculating an axial component force set value and a radial component force set value of the contact force in the preset track direction; calculating a force tracking error based on the measured value of the stress sensor, the axial component force set value and the radial component force set value, and further generating a force correction signal; scaling the force correction signal through an impedance factor, and superposing the force correction signal with a preset track to generate a correction track; based on the corrected track and the actual pose feedback, calculating a tail end motion correction amount; and through robot kinematics mapping and a driver, the tail end motion correction amount is converted into a torque instruction of a joint space and output for execution, constant-force grinding of the curved surface workpiece is achieved, and contact type machining operation can be stably and accurately completed.

This invention discloses a flexible material feeding vision system based on an XYZ multi-module motion control system. The system includes an XYZ multi-module control subsystem, a fly-grabbing subsystem, an image processing subsystem, a motion correction subsystem, and a flexible material feeding control device. The XYZ multi-module control subsystem plans motion parameters based on preset data and historical material feeding information, driving the multi-module to complete grasping and placement. The fly-grabbing subsystem dynamically acquires grasping and placement positioning images during the movement. The image processing subsystem corrects image offsets based on flexible material feeding characteristic parameters. The motion correction subsystem achieves precision closed-loop control through secondary confirmation and real-time compensation. The flexible material feeding control device adapts to various material specifications with an adjustable tray and controllable vibration, reducing damage. Through the collaborative operation of multiple subsystems, efficient, low-damage, and high-precision material feeding of various specifications is achieved.

The embodiment of the invention provides a humanoid robot action parameter determination method and related equipment, and belongs to the technical field of robot control. The method comprises the following steps: extracting multi-modal input features; in the aerial view space, to-be-observed features generated by fusing the multi-modal input features are obtained, and static parameters of static objects and dynamic parameters of dynamic objects around the humanoid robot are determined; generating a first motion parameter of the humanoid robot based on the to-be-observed features; and determining a motion correction value by using the motion residual value, and modifying the first motion parameter by using the motion correction value to obtain a second motion parameter. According to the technical scheme, the feature information around the humanoid robot is obtained in real time, the dynamic parameters of the dynamic object and the static parameters of the static object are determined in a decoupling mode, the movable space of the mechanical arm is determined, dependence on positioning data is reduced, the obstacle avoidance success rate of the humanoid robot is increased, and the obstacle avoidance efficiency is improved. And the industrial operation efficiency and the household interaction safety of the humanoid robot are effectively improved.

The invention relates to a pilot tone signal generator, to a magnetic resonance tomography apparatus, to a method for transmitting a synchronization signal and to a computer program product. The pilot tone signal generator comprises a receiving unit for receiving a synchronization signal of a system control unit of a magnetic resonance tomography apparatus. Here, the synchronization signal comprises a clock signal and the pilot tone signal generator is designed to emit a pilot tone signal in dependence on the synchronization signal.

A method and an apparatus for correcting a 3D wind vector for velocities induced by linear and angular motions include receiving positional offsets associated with an anemometer and a motion sensor onboard a movable platform; receiving 3D wind vector data from the anemometer; receiving orientation data, angular motion data, and linear velocity data associated with the movable platform from the motion sensor; determining a transformation matrix based on the orientation data; transforming the 3D wind vector data based on the transformation matrix; calculating angular velocity of the anemometer based on the positional offsets and the angular motion data; transforming the angular velocity of the anemometer by the transformation matrix; calculating motion corrected wind vector based on the transformed 3D wind vector data, the transformed angular velocity of the anemometer, and the linear velocity data.

A method for reconstructing a motion-corrected magnetic resonance image of a subject. The method comprises receiving a motion trajectory comprising a series of motion states; determining similarities across the motion states and forming groups of motion states, such that each group contains motion states that are similar to each other, and determining a single group motion state representing each group; for each group of motion states, computing a single transformation operator and building a group-specific NuFT forward model, in which a mismatch between the coil sensitivity maps and the magnetic resonance image caused by a motion of the subject during the acquisition is reduced by operating the transformation operator on the coil sensitivity maps and / or on the magnetic resonance image; and reconstructing the motion-corrected magnetic resonance image by minimizing the data consistency error between the received k-space data and the group-specific NuFT forward models, wherein each NuFT forward model comprises the image, the coil sensitivity maps, a non-uniform Fourier Transformation, and a phase factor corresponding to the motion state held by the subject at the time of acquiring the corresponding subset of k-space data.

Accuracy in a case where body motion correction reconstruction is performed in response to body motion that occurs during imaging by an MRI apparatus is improved, and accuracy of a body motion-corrected image is improved.In a case where k-space data consisting of nuclear magnetic resonance signals is collected using an MRI apparatus, a strength of body motion correction is varied according to a position in k-space of measurement data affected by body motion that has occurred. In one aspect, in a case of determining whether measurement data acquired at a time of body motion detection is data affected by the body motion, a value of a threshold value used for body motion determination is varied according to a position in k-space of the measurement data. In another aspect, upon performing body motion correction reconstruction including iterative calculation, the body motion correction reconstruction is performed by varying a weight of processing or the like according to a position in k-space of data affected by the body motion, in a case where the data affected by body motion is incorporated into the iterative calculation as an initial value of the iterative calculation without excluding the data affected by the body motion, or during an iterative calculation process.