227 results about "Motion field" patented technology

A system for dynamic latent space adaptation using spatiotemporal kernel context for multiscale rendering with hierarchical and Lorentzian autoencoders. The Spatiotemporal Kernel Estimator (SKE) analyzes media through motion field, temporal recurrence, frequency band, and scene semantics analyzers to generate adaptive kernel parameters encoding content-specific importance distributions. The system dynamically adapts latent manifold geometry by modifying metric tensor properties according to kernel context, enabling content-aware compression that allocates representational capacity based on visual significance. A multiscale cache implements kernel-adaptive retention policies prioritizing important regions. An adaptive renderer provides intelligent level-of-detail selection based on zoom level and kernel-estimated importance, optimizing processing allocation. The self-optimizing architecture continuously refines kernel context and geometric adaptation based on user interaction and performance feedback, achieving superior compression ratios and perceptual quality. Applications include bandwidth-efficient video streaming, virtual reality, scientific visualization, and cognitive video analytics requiring intelligent context-aware visual processing.

The invention discloses a self-adaptive low-delay motion scene live broadcast method and system, and particularly relates to the technical field of scene live broadcast. Through unified mapping and exception suppression of multi-source time sequence data, a multi-scale sliding window predictor and a short-time autoregression and long-time trend sensing algorithm are combined; a more accurate bandwidth prediction result with interval confidence description is generated, characteristics such as offset cumulant, fluctuation intensity and error residence time are extracted by using a residual trajectory, threshold crossing frequency, switching amplitude, direction alternation rate and critical zone residence duration are analyzed synchronously with a parameter switching log, critical oscillation characteristics are formed, and the bandwidth prediction accuracy is improved. The risk identification is more accurate, the high-frequency oscillation risk score of the system is calculated through a normalization and time sequence risk identifier, the risk assessment result is mapped into an executable stable intervention strategy, the system state observation sequence after adjustment execution is subjected to short-time assessment, and a feedback packet is formed to write back a closed loop. And online optimization of the weight, the decision threshold and the cooling time of the bandwidth predictor is realized.

The invention discloses a road video event rapid detection method based on edge calculation, particularly relates to the field of computer vision and image processing, and is used for solving the problem of motion evidence distortion caused by frame-level sequential disorder and content gaps in a road monitoring video. The method comprises the following steps of: reading a video clip, constructing a time sequence correction graph according to inter-frame content similarity and boundary continuity, rearranging to generate a corrected sequence, outputting a frame consistency index, estimating a stable motion field and a continuity score on the corrected sequence, and writing back an image stabilization amplitude correction time anchor point; generating an event evidence sequence according to the stable motion field, combining track breakpoints according to a time sequence correction graph, initiating backtracking revision to return candidate event segments, and if consistency verification is executed on candidate events, performing cross-level revision on the time sequence correction graph and image stabilization amplitude to trigger the stable motion field to re-estimate and output a confirmation event; and issuing a trigger result and establishing a mapping index as a new fragment to initialize priori acceleration time sequence correction and judgment convergence.

The invention provides a traffic scene multi-target detection method and system based on deep learning, and relates to the technical field of traffic, and the method comprises the steps: carrying out the semantic prior driven multi-scale feature extraction of a multi-frame image, and carrying out the point-by-point fusion; obtaining a motion field through optical flow estimation and feature similarity calculation, and executing motion compensation to obtain a moving target mask; obtaining a static target boundary by using boundary regression decoupling and geometric consistency constraint; and finally, moving and static target results are combined, and quadratic regression is executed based on consistency evaluation. The dynamic and static targets in the traffic scene can be effectively detected, the boundary regression precision is improved, and the false detection rate caused by shielding is reduced.

The invention relates to the technical field of computer vision, in particular to an intelligent conference video frame dynamic coding method based on multi-modal semantic understanding, which comprises the following steps: acquiring a video stream sequence and a synchronous audio stream in a conference scene in real time; performing semantic analysis and decoupling on the video stream sequence, and extracting key frames and subsequent frames; extracting a sparse motion field from a subsequent frame, and segmenting a video frame into candidate visual areas including a face, a mouth shape and a background; extracting audio semantic features, executing cross-modal semantic correlation analysis, calculating semantic correlation between the sparse motion field distribution features and the audio semantic features, and positioning a pronunciation area highly related to the voice content; and calculating a quantization offset value of each candidate visual area according to the semantic relevancy, applying the quantization offset values in different areas, and packaging the quantization offset values into a variable-code-rate video code stream. According to the invention, the multi-mode semantic understanding model is constructed to carry out deep semantic analysis on the video frame content so as to realize the dynamic coding of the conference video frame.

The invention relates to the technical field of face verification, and discloses a face identity verification data processing method based on dynamic feature extraction, which comprises the following steps: acquiring a continuous face video frame sequence and calculating a full-pixel instantaneous velocity vector to generate an original dense optical flow field, selecting rigid region anchor points to calculate a rigid affine transformation matrix and construct a theoretical rigid motion field, performing differential stripping on the theoretical rigid motion field from the original dense optical flow field, and extracting a non-rigid micro-motion residual field; mapping the non-rigid micro-motion residual field to a facial muscle topological grid to generate a time sequence feature tensor, and calculating a geodesic line distance between a covariance matrix of the time sequence feature tensor and a reference dynamic feature in a Riemannian manifold space; when the geodesic distance is smaller than a threshold value, verification is passed, through a rigid-non-rigid orthogonal decomposition mechanism, the special viscoelastic micro-motion and cooperation law of biological soft tissue is captured by utilizing a residual field, and the high-simulation mask is effectively defended.

Implementations are described for determining, for a block being encoded in a picture, at least one predictor candidate, determining for the at least one predictor candidate, one or more corresponding control point generator motion vectors, based on motion information associated to the at least one predictor candidate, determining for the block being encoded, one or more corresponding control point motion vectors, based on the one or more corresponding control point generator motion vectors determined for the at least one predictor candidate, determining, based on the one or more corresponding control point motion vectors determined for the block, a corresponding motion field, and encoding the block based on the corresponding motion field.

The invention discloses an event-image dual-mode fusion video turbulence correction method, medium and system, and belongs to the field of digital image processing, and the method comprises the steps: synchronously obtaining an event voxel of a target and a to-be-corrected image sequence; inputting the event voxels into a pre-selected coding and decoding structure to obtain features, projecting the features along the optical flow direction, and performing three-dimensional reconstruction to obtain target motion features; extracting background scene representation; fusing the target motion feature and the background scene representation in a channel dimension to obtain an edge guide feature; inputting the image sequence and the image sequence into a pre-trained video restoration network to obtain a turbulence-corrected video; the training loss of the coding and decoding structure comprises an optical flow field estimated according to an image sequence without turbulence disturbance and a target motion field obtained by projecting the feature along the optical flow direction. The method can accelerate the recovery process and improve the recovery quality.

The invention provides an automatic equipment anomaly monitoring method and system based on image processing, and relates to the technical field of automatic equipment anomaly monitoring, and the method comprises the steps: converting an equipment operation video into a multi-dimensional physical field feature: in a motion field dimension, based on optical flow field analysis, extracting a full-period displacement statistical histogram and a space thermodynamic diagram, the motion instability characteristic of the mechanical transmission system is accurately quantified; in a vibration field dimension, an energy spectrum and a vibration thermodynamic diagram are generated innovatively through time-frequency transformation of a displacement signal of a frequency spectrum monitoring point, and frequency domain feature visualization of a hidden vibration fault is achieved; in the dimension of a structure field, edge gradient analysis and texture feature extraction technologies are fused, a time sequence structure thermodynamic diagram sequence is constructed to capture a progressive damage evolution rule, a three-field abnormal index dynamic weighting fusion mechanism overcomes the limitation of traditional single-point monitoring, connected domain analysis of a fused thermodynamic diagram is combined with an LBP texture and morphological feature decision tree, and the defect of the prior art is overcome. Automatic equipment abnormity monitoring based on image processing is realized.

The invention discloses an image stabilization method and system based on inertial measurement data, and the method comprises the steps: obtaining video frame motion optical flow data in real time through a video collection device, obtaining video optical flow motion information through image algorithm processing, and recording and transmitting motion inertia data of a camera through an IMU (Inertial Measurement Unit); the two types of data are preprocessed, and then time axis alignment is completed; accurate video data motion information is obtained through bidirectional correction; kalman filtering is introduced for forward compensation, and a motion model is established to solve a rotation translation matrix; dividing the image into local pixel spaces, calculating a transformation matrix of each region, and obtaining a radiation transformation matrix by adopting neighborhood weighting; and finally, triangular region filling is realized through an OpenGL graphic rendering library, and a stable image is output. According to the method, IMU data and visual information are creatively fused, the real-time performance and accuracy of video image stabilization are remarkably improved through multi-level motion compensation and local optimization, and the method is particularly suitable for meeting the requirement for high-quality image stabilization in a motion scene.

The invention discloses a multi-modal deep reinforcement learning gas leakage decision optimization method based on optical flow, and the method comprises the steps: carrying out the constraint and correction based on a pixel motion field in combination with wind speed estimation, and obtaining an optical flow reconstruction wind field; based on plume probability distribution, combining with an image intensity and radiation transmission model, obtaining concentration field estimation, fusing gas concentration data and an optical flow reconstruction wind field, and forming full-field state description; establishing a convective diffusion model, predicting the concentration field at the next moment through the convective diffusion model based on the full-field state, establishing a convective diffusion model, and predicting the concentration field at the next moment through the convective diffusion model based on the full-field state. According to the technical scheme of the invention, the dynamic evolution precision of gas leakage can be effectively improved by introducing the wind field reconstruction method of the optical flow and the scatter-free constraint, the prediction stability is improved, the physical consistency of the wind field and the concentration distribution is ensured, and stronger data support is provided for emergency control.

The invention provides an endoscope image stabilization control method, which comprises the following steps of: acquiring an original dithering image sequence and inertial data of an endoscope, fusing the original dithering image sequence and the inertial data, acquiring an initial mixed motion field, performing operation semantic segmentation on the original dithering image sequence, and acquiring a collaborative attention map in combination with a physical motion mechanism. Obtaining a space-time adaptive filtering kernel through the collaborative attention map, filtering the initial mixed motion field according to the space-time adaptive filtering kernel, obtaining a stable motion field, carrying out image deformation and interpolation synthesis processing on the stable motion field, and obtaining a stable image sequence; through the integrated technical scheme of operation semantic segmentation, dynamic image stabilization optimization and accurate jitter suppression, the problem of interference of endoscope jitter on the neurosurgery operation visual field is solved.

The invention provides an eye movement tracking method, a control unit and eye movement tracking equipment, and relates to the technical field of eye movement tracking. The method comprises the steps of obtaining a surface image of a target eyeball; extracting composite phase distribution from the surface image, wherein the composite phase distribution represents phase coupling information after the incident light is jointly modulated by a cornea interface and a crystalline lens interface of the target eyeball; based on eyeball geometric constraint and cornea interface smoothness prior, according to the composite phase distribution, constructing a decoupling objective function with separation of cornea phase distribution and crystalline lens phase distribution as a target; taking a minimum decoupling objective function as an optimization objective, and iteratively determining cornea phase distribution decoupled from crystalline lens phase distribution; and determining the visual axis direction of the target eyeball according to the cornea phase distribution. Through the double-interface phase decoupling technology, the visual axis error in a large view field and eyeball depth motion scene is effectively reduced, and the eye movement tracking precision is remarkably improved.

The invention discloses a comprehensive performance testing system and method for a power-assisted exoskeleton, and belongs to the technical field of robot testing. Comprising a multi-modal data acquisition layer, a scenarized test module and an intelligent processing and evaluation layer, the multi-modal data acquisition layer comprises a multi-modal data acquisition module and a hierarchical data synchronization module, and the scenarized test module selects modules in the multi-modal data acquisition module to perform data acquisition under the conditions of wearing an exoskeleton and not wearing the exoskeleton in a corresponding scene according to a pavement scene and a motion scene which need to be tested; and the intelligent processing and evaluation layer outputs a final comprehensive performance index based on a three-layer fusion strategy according to the output of the scene test module. Through multi-source data fusion analysis and multi-dimensional evaluation indexes, the auxiliary effect, the energy consumption efficiency and the use safety of the exoskeleton can be comprehensively and objectively reflected, and accurate guidance is provided for research and development optimization of the exoskeleton.

The motion estimation of an anatomical structure may be performed using a machine-learned (ML) model trained based on medical training images of the anatomical structure and corresponding segmentation masks for the anatomical structure. During the training of the ML model, the model may be used to predict a motion field that may indicate a change between a first training image and a second training image, and to transform the first training image and a corresponding first segmentation mask based on the motion field. The parameters of the ML model may then be adjusted to maintain a correspondence between the transformed first training image and the second training image and between the transformed first segmentation mask or a second segmentation mask associated with the second training image. The correspondence may be assessed based on at least a boundary region shared by the anatomical structure and one or more other anatomical structures.

The invention discloses a dynamic scene deblurring method and system based on physical information adversarial learning, and the method comprises the steps: obtaining original blurred image data, carrying out the preprocessing of the original blurred image data, and obtaining a preliminary deblurred image; inputting the preliminary deblurred image into an initial dynamic scene deblurring model for training to obtain a dynamic scene deblurring model; the initial dynamic scene deblurring model comprises a generator network, an optical flow estimation network, a three-stage progressive training strategy and a multi-scale discriminator network, the generator network is used for mapping an initial deblurred image into a deblurred image, and the optical flow estimation network is used for estimating a motion field and calculating optical flow consistency loss; the three-stage progressive training strategy is used for carrying out three-stage training on the initial dynamic scene fuzzy model in sequence, and the multi-scale discriminator network is used for judging image authenticity on different scales; and inputting a to-be-processed blurred image into the dynamic scene deblurring model to obtain a blurred image. According to the invention, the deblurring effect is improved.

Described herein are systems, methods, and instrumentalities associated with estimating the motions of multiple 3D points in a scene and predicting a view of scene based on the estimated motions. The tasks may be accomplished using one or more machine-learning (ML) models. A first ML model may be used to predict motion-embedding features for a temporal state of a scene, based on motion-embedding features for previous states. A second ML model may be used to predict a motion field representing displacement or deformation of the multiple 3D points from a source time to a target time. Then, a third ML model may be used to predict respective image properties of the 3D points based on their updated locations at the target time and / or a viewing direction. An image of the scene at the target time may then be generated based on the predicted image properties of the 3D points.

The invention provides a dysphagia screening system based on computer vision, and relates to the technical field of image recognition, and the system comprises an image collection module which collects image data of a swallowing event to obtain a time sequence image sequence; the motion field establishing module is used for extracting a motion point track sequence according to the time sequence image sequence and establishing a global motion transformation field; the motion separation module is used for carrying out registration correction on the global motion transformation field and carrying out motion separation on the stabilized image sequence to obtain local deformation components; the region segmentation module is used for carrying out key region segmentation on the local deformation components to obtain swallowing function parameters; and the risk identification module is used for identifying the obstacle risk of the swallowing function parameters and generating a swallowing obstacle screening report. According to the method and the device, the technical problem of relatively poor screening accuracy of the dysphagia based on the computer vision in the prior art can be solved, and the technical effect of improving the screening accuracy of the dysphagia based on the computer vision is achieved.

The invention relates to the technical field of computer vision and precise opto-electro-mechanical control, in particular to a motion scene intelligent recognition focus tracking system based on deep learning, which comprises a high-frequency vision acquisition discretization unit, a video stream decoupled into an RGB semantic stream and a gray-scale high-frequency stream, and a high-frequency vision acquisition discretization unit, the RGB semantic stream and the gray high-frequency stream are sent to a double-stream uncertainty prediction unit; the double-current uncertainty prediction unit is used for obtaining a target state vector and generating a transient prediction value; the motion entropy arbitration fusion unit is used for calculating the motion entropy based on the inertial predicted value, the target state vector and the covariance matrix, weighting the inertial predicted value and the transient predicted value based on the motion entropy, and generating a target point and a future reference trajectory; the entropy sensing self-adaptive control unit is used for solving the control voltage to drive the focusing module; according to the invention, focusing lock loss caused by overshoot of the control target is effectively prevented, and the tracking precision under extreme working conditions is ensured.

The application discloses a respiratory motion estimation method, device and equipment and a medium, relates to the technical field of motion estimation analysis, adopts an unsupervised sparse-dense motion estimation framework with sequence motion consistency constraints, and can quickly and accurately realize motion estimation of a liver ultrasound image sequence under the influence of respiratory motion. A sparse-dense coarse-to-fine registration strategy based on sparse point guidance is used to accurately predict the motion field between adjacent respiratory state images, a sparse key point automatic detection guided rigid registration network is designed to automatically detect sparse key points from the images in an unsupervised manner, and a multi-source structured feature guided deformation densification network is constructed to predict the dense elastic deformation component of the motion between the images. In combination with the idea of motion decomposition and coincidence, sequence motion consistency constraints are constructed based on a small motion sequence between a plurality of adjacent respiratory states, the continuity of motion in the time flow is strengthened, and the motion estimation accuracy is further improved.

Projected motion field hole filling includes determining a motion field of a current frame using a block true subset of the current frame, the motion field including, for each block in the block true subset, a respective motion vector projected onto a reference frame. For a block in the current frame that does not have a motion vector in the motion field, a respective motion vector of a spatial domain neighboring block of the proper subset of the block is reused as a projected motion vector of the block within the motion field. A list of motion vector candidates may be determined using the motion field. A reference motion vector for the current block may be selected from the motion vector candidate list. The current block may be encoded into an encoded bitstream or decoded from an encoded bitstream using the reference motion vector.

A system for dynamic latent space adaptation using spatiotemporal kernel context for multiscale rendering with hierarchical and Lorentzian autoencoders. The Spatiotemporal Kernel Estimator (SKE) analyzes media through motion field, temporal recurrence, frequency band, and scene semantics analyzers to generate adaptive kernel parameters encoding content-specific importance distributions. The system dynamically adapts latent manifold geometry by modifying metric tensor properties according to kernel context, enabling content-aware compression that allocates representational capacity based on visual significance. A multiscale cache implements kernel-adaptive retention policies prioritizing important regions. An adaptive renderer provides intelligent level-of-detail selection based on zoom level and kernel-estimated importance, optimizing processing allocation. The self-optimizing architecture continuously refines kernel context and geometric adaptation based on user interaction and performance feedback, achieving superior compression ratios and perceptual quality. Applications include bandwidth-efficient video streaming, virtual reality, scientific visualization, and cognitive video analytics requiring intelligent context-aware visual processing.

The utility model discloses a testing device for simulating a rotary motion imaging scene, and the device comprises a roller assembly which is externally provided with a fixing plate for fixing an object to be imaged; the transmission shaft is fixed at a rotating shaft of the roller assembly; and the driving assembly is used for driving the transmission shaft to rotate so as to drive the roller to rotate. The device can continuously operate when a motion scene is simulated in an imaging test, and the requirement of the imaging test under a long-time continuous stable condition is met.

A sparse helical CT image reconstruction method based on a differentiable helical reconstruction operator. First, actual helical scanning geometric parameters of a subject and corresponding full-angle helical projection data are acquired, and a final reconstructed image is acquired by means of seven steps. In the present invention, actual scanning geometry is used to perform forward projection on a reconstructed sparse-angle image, thereby providing geometric prior guidance for missing projections; moreover, on the basis of the similarity and redundancy characteristics of adjacent projections in helical scanning, a projection completion network is constructed, and by learning bidirectional motion fields of adjacent angles and in combination with geometric prior projections, intermediate missing projection data is jointly synthesized; in addition, the global streak artifact restoration of the image is realized; and finally, the joint training of a projection domain and an image domain is realized, thereby facilitating integral restoration by using projection-image dual-domain information, and data collected within two pitches is used for restoration, thereby effectively avoiding an excessive computational load.

The utility model provides a motion simulation device which is used for solving the problem that a device capable of simulating head motion wearing an object to be detected does not exist in the market. The motion simulation device comprises a head mold and a motion mechanism, the head die is movably connected with the movement mechanism; the head mold is used for wearing a to-be-detected article; the movement mechanism is used for simulating the movement scene of the to-be-detected object. In the implementation process of the scheme, the to-be-detected article is worn through the head mold of the motion simulation device, and the motion scene of the to-be-detected article is simulated by using the motion mechanism of the motion simulation device, so that the motion simulation function of head motion is realized.

The embodiment of the invention provides a depth video frame interpolation method and device based on occlusion perception and dynamic optimization, a storage medium and electronic equipment, and relates to the field of video frame interpolation, and the method comprises the steps: obtaining two frames of target images to be interpolated, a pre-calculated occlusion image and an optional reference intermediate frame, carrying out normalization and edge filling preprocessing on the target image and the shielding image; based on the preprocessed target image and the potential spatial dimension of the diffusion model, initializing random noise capable of being subjected to gradient optimization as a potential mark; designing an iterative optimization framework, and performing dynamic optimization on the potential mark by using the preprocessed target image and the shielding image; and inputting the optimized potential mark into a diffusion model to generate a final intermediate frame, and restoring the intermediate frame to the size of the original target image through reverse filling operation to complete frame insertion. According to the method, the core problem that blurring and artifacts are easy to generate when shielding areas and complex motion scenes are processed in the prior art is solved.

The application provides a ball game project general training method and an auxiliary referee system based on a deep learning technology for motion target detection, and comprises the following steps: establishing a basic weight parameter, establishing a trained weight parameter through deep learning, optimizing a parameter of a specific motion scene, collecting actual motion scene images through a designed parameter optimization module when an error occurs in the basic weight parameter, performing optimization training, and establishing an optimized weight parameter of the specific scene. The application establishes a gridized motion index parameter, feeds back a grid motion index change in real time during training or competition, and assists a coach in optimizing or improving a training effect according to the grid motion index change. An integrated technology of a multi-camera which can be arbitrarily stacked and expanded can be used for auxiliary refereeing during competition, and can also be used for playing back high-speed high-definition images of any viewing angle during training to help athletes analyze action postures and the like.

The invention discloses an intelligent equipment linkage control method and system based on video content analysis, and relates to the field of video content analysis, and the intelligent equipment linkage control method based on video content analysis comprises the following steps: S1, obtaining video parameters, and carrying out the preprocessing; s2, pixel motion vectors are extracted, and a time sequence motion field is formed; s3, extracting a pixel dominant motion information set, calculating an average speed value, and generating a time sequence speed parameter set; s4, performing zero crossing point identification on the time sequence speed parameter set, and generating a time sequence displacement control signal; and S5, sending the time sequence displacement control signal to the external intelligent equipment, and driving the external intelligent equipment to generate physical motion with the video content by adopting the displacement signal. According to the method, by introducing preprocessing means such as multi-scale image analysis, resolution compression and graying conversion, key information is efficiently and accurately extracted from the video, and the precision and efficiency of subsequent analysis are improved.

The invention discloses an image registration method based on grouped motion estimation and neighborhood refining sampling. The method comprises the following steps: constructing an image registration network comprising a shared feature extraction module, a sparse motion module and a dense motion module; a source image and a target image are obtained to train the image registration network, and shared feature extraction, grouping motion estimation and neighborhood refining sampling are sequentially carried out until a loss function converges to complete training; a to-be-registered source image and a target image thereof are processed through a network, and finally a registration motion stream is obtained to register the target image to the source image. According to the method, sparse modeling is carried out on input features by introducing grouping motion estimation, the local deformation expression ability is enhanced in combination with an adaptive fusion mechanism, meanwhile, a neighborhood refining sampling method is designed, a learnable local weighted correction mechanism is introduced in the motion flow sampling process, and the local deformation expression ability is improved. The boundary definition and detail precision of the dense motion field are effectively improved, and the precision and robustness of image registration are remarkably improved.