287 results about "Spatial transformation" patented technology

The invention provides a multi-view-oriented three-dimensional scene image reconstruction registration and optimization method and system, and relates to the technical field of image processing, and the method comprises the steps: obtaining multi-frame three-dimensional scene image data, extracting a multi-level feature set, constructing a cross-view-angle semantic association graph, building a feature corresponding relation, and calculating a multi-view-angle spatial transformation relation parameter. Performing coordinate system alignment on the image data to generate an initial three-dimensional reconstruction result, and performing optimization in combination with a multi-target joint optimization function and a dynamic adaptive weight regulation and control mechanism. According to the method, the precision and robustness of three-dimensional scene reconstruction are improved, and the problem of registration errors caused by large view angle difference in a complex scene is solved.

The invention discloses a deep learning-based multimodal image fusion method for soft tissue photoacoustic / ultrasound imaging. Steps: an ultrasound-photoacoustic imaging device acquires photoacoustic and ultrasound images of human soft tissue and performs size normalization processing; an input spatial transformation module converts the images to the YCbCr space; an input pre-convolution module modifies the number of data channels; an input multi-scale feature extraction module extracts salient features from the source images; an input filter prediction module derives multi-scale filters; and an input filter fusion and adaptive enhancement module combines the input source images to obtain the final fused result. The invention has superior fusion performance compared to several traditional fusion methods and deep learning-based fusion methods, and more importantly, it exhibits excellent real-time performance. Furthermore, various modes of photoacoustic / ultrasound fusion extension experiments have verified the effectiveness of the method proposed in the invention.

The invention belongs to the technical field of steel reinforcement framework three-dimensional reconstruction, and particularly discloses an automatic high-precision three-dimensional dense reconstruction method for a box girder steel reinforcement framework, which comprises the following steps of: dividing a scanning area into a plurality of sub-areas according to a depth image by moving an inspection robot along the steel reinforcement framework, and performing multi-angle local scanning in each sub-area; constructing a local point cloud model by combining camera poses during acquisition, further calculating a relative spatial transformation relationship by using an overlapping region between adjacent local models, constructing a pose map and implementing global optimization, and uniformly adjusting the poses of the local models in a world coordinate system; and finally, all corrected local point clouds are fused to generate a complete global three-dimensional model, so that collaborative reconstruction operation of domain-divided scanning, segmentation reconstruction and global optimization is realized, frame-by-frame accumulation of errors in traditional scanning-while-splicing reconstruction is effectively blocked, long-distance drift is remarkably inhibited, and the precision of three-dimensional reconstruction of the reinforcement cage is greatly improved.

The invention provides a friction stir welding seam intelligent correction method based on visual identification, and relates to the technical field of friction welding. Image information of a welding seam area of a to-be-welded workpiece is collected in real time, image feature points are recognized, and the spatial pose of the welding seam area is obtained through a spatial transformation model; based on the space position deviation of the actual space pose and the theoretical welding track, a dynamic deviation correction path is generated; and a multi-point iterative optimization algorithm of motion control parameters and a real-time feedback control method are further adopted, so that high-precision real-time adjustment and dynamic compensation of the welding seam track are realized. According to the method, the accuracy and the stability of friction welding seam deviation rectification are remarkably improved.

The invention discloses a method, device and equipment for generating a head model, a storage medium and a program product. The method is characterized by comprising the following steps of: receiving and verifying original three-dimensional T1 weighted image data and an original three-dimensional grid model; performing tissue segmentation based on the verified three-dimensional T1 weighted image data and the original three-dimensional T1 weighted image data to obtain a cerebral grey matter mask, a cerebral white matter mask, a scalp mask and a skull mask; performing topological structure repair on the brain grey matter mask and the brain white matter mask to obtain a brain mask; respectively converting the scalp mask, the skull mask and the brain mask into a scalp mesh model, a skull mesh model and a brain mesh model; calculating and applying a spatial transformation matrix from the verified three-dimensional grid model to the scalp grid model, and performing spatial registration and fusion on the scalp grid model, the skull grid model and the brain grid model to obtain a head model; the method has the advantages that the efficiency, precision and robustness of head model generation are effectively improved, and the method has good cross-platform deployment capability.

The invention discloses a modal fusion method of camera-sonar equipment under the condition of unknown external parameters, and relates to the field of underwater multi-sensor data fusion. The method comprises the following steps: firstly, acquiring synchronous underwater optical images and sonar images, and converting sonar polar coordinate imaging data into a three-dimensional point cloud under a sonar coordinate system through a geometric model; then, under the condition that camera-sonar external parameters do not need to be calibrated in advance, a spatial transformation matrix between a camera coordinate system and a sonar coordinate system is automatically estimated and calibrated through the corresponding relation between images and target features in point clouds, and alignment fusion of two kinds of modal data is achieved. According to the method, optical and acoustic information can be effectively fused in a severe water quality environment, the sensing limitation of a single sensor is made up, the robustness and accuracy of underwater dam crack and other target detection are improved, and the method has the advantages of no need of manual calibration and high adaptability.

The invention relates to the technical field of image recognition, in particular to an osteosarcoma image intelligent recognition and classification method and system based on an image recognition model. The method comprises the following steps: collecting a tumor image and carrying out multi-sequence disturbance equilibrium enhancement to generate an enhanced block tumor image, then obtaining a color standardized image through color space transformation remapping, then extracting a tumor region image and carrying out contrast inversion processing, reconstructing a tumor feature image, and finally, carrying out multi-sequence disturbance equilibrium enhancement on the tumor feature image. Based on a preset image recognition model, tumor edge features are recognized, the edge rule degree is determined, preliminary classification is carried out, a benign tumor image is obtained, surrounding metabolite signal features of the benign tumor image are analyzed, a benign reference group is formed through combination, metabolism state comparison is carried out on tumor feature images, and metabolism difference data are generated. And performing category correction according to the metabolic state mapping data so as to identify the osteosarcoma category. According to the invention, intelligent analysis of tumor features is realized, the image processing flow is optimized, and the processing efficiency is improved.

The invention relates to the technical field of three-dimensional reconstruction and rendering, and particularly discloses an underwater long-sight-distance adaptive three-dimensional reconstruction and rendering method based on a Gaussian variable-distance field, and the method comprises the steps: carrying out the initialization point cloud generation based on a motion recovery structure algorithm for RGB image data obtained by underwater image collection equipment; points on the initialized point cloud are combined with a three-dimensional Gaussian distribution model, a three-dimensional Gaussian primitive is defined, and parameter initialization about information such as a Gaussian geometric center, a three-dimensional covariance matrix, opacity and color is carried out on the three-dimensional Gaussian primitive; the method comprises the following steps: establishing a Gaussian cross-space transformation model, performing transformation from a Cartesian coordinate space to a homogeneous coordinate space on a three-dimensional Gaussian primitive, establishing a Gaussian full-parameter space mapping model, mapping color information of the three-dimensional Gaussian primitive after cross-space transformation into a spherical harmonic function, and spatial adaptive three-dimensional reconstruction and rendering in an underwater long-sight-distance environment are realized.

The invention provides a medical image registration method and equipment. The method is applied to the technical field of medical image processing. The method comprises the steps that a to-be-registered image pair used for medical image registration is acquired, the to-be-registered image pair comprises a floating image and a fixed image, the floating image is an image needing spatial transformation in the registration operation, and the fixed image is a reference standard correspondingly consistent with the spatial position and feature of the floating image in the registration operation; performing hierarchical feature extraction on the floating image and the fixed image through a registration model to obtain a first multi-scale feature pyramid and a second multi-scale feature pyramid, and performing correlation perception registration on multi-scale features in the first multi-scale feature pyramid and the second multi-scale feature pyramid to obtain deformation field data; and performing spatial transformation on the floating image based on the deformation field data to obtain a target registration image. According to the invention, efficient and accurate medical image registration is realized.

The invention discloses a substation multi-modal image fusion registration method and system, a medium and equipment, and belongs to the technical field of power system multi-modal image analysis, and the method comprises the steps: obtaining a visible light image and an infrared image of a substation power equipment region, carrying out the edge detection, and extracting feature points; performing initial matching through a nearest neighbor and secondary neighbor distance ratio constraint to obtain a first matching point pair; further screening out a second matching point pair according to the matching point pair connecting line slope; solving an affine transformation matrix by adopting a least square method based on the screened point pairs; and finally, performing spatial transformation on the infrared image by using the matrix, and generating a registered infrared image which is spatially aligned with the visible light image by combining bilinear interpolation resampling. By implementing the method, the problems of high mismatching rate of feature points and insufficient registration precision caused by large difference of multi-band image imaging principles under the complex background of a transformer substation in the prior art can be solved.

The embodiment of the invention provides a medical image processing method and device, equipment and a medium. The processing method comprises the following steps: firstly, inputting a supine image of a target object into a trained neural network model, wherein the output of the neural network model can be a transformation vector or a three-dimensional deformation field of a plurality of target bones; and then, under the condition that the output of the model is the transformation vector, generating a three-dimensional deformation field based on the transformation vector, or under the condition that the output of the model is the three-dimensional deformation field, carrying out spatial transformation on the supine image by utilizing the deformation field based on the deformation field to obtain an image of the standing position of the target object, so that the standing position of the target object can be obtained under the condition that actual standing scanning is not needed. And a reference basis is provided for evaluating the real form and the mechanical condition of the skeleton in a load-bearing state.

The invention discloses a multi-modal endoscope image fusion analysis method, and relates to the technical field of medical image navigation, and the method comprises the steps: collecting an original white light image and an original narrow-band image, carrying out the preprocessing, and collecting real-time pose parameters; extracting blood vessel texture features in the preprocessed original white light image and the original narrow-band image, generating a binary semantic mask, calculating a spatial transformation matrix according to the binary semantic mask, and generating preliminary visual deformation field data; and calculating microscopic deformation field data according to a pre-stored biomechanical characteristic database, the preliminary visual deformation field data and the real-time pose parameters, carrying out reverse calculation to obtain physical deformation compensation field data, and carrying out spatial superposition operation on the physical deformation compensation field data and the preliminary visual deformation field data to obtain comprehensive compensation deformation field data. According to the method, real-time quantitative evaluation and abnormal early warning of the reliability of the fusion result are realized through multi-modal fusion quality early warning judgment, and the clinical decision credibility and the operation safety are enhanced.

The invention provides an active train sensing system and method based on thunder and vision fusion. Comprising an acquisition module used for acquiring multi-path image data containing different focal length images and three-dimensional point cloud data corresponding to the front of a train; the time synchronization module is used for generating synchronous image data and synchronous three-dimensional point cloud data; the coordinate transformation module is used for establishing a spatial transformation relation between the three-dimensional point cloud coordinate system and the image coordinate system; the image sensing module is used for inputting the synchronous image data and the three-dimensional point cloud data into the target segmentation network to obtain track coordinates; the point cloud sensing module is used for obtaining three-dimensional point cloud data in front of the train; and the thunder-vision fusion module is used for distributing an accurate center representative coordinate for each obstacle, generating structured obstacle information and outputting the structured obstacle information to a vehicle-mounted train control system. The multi-source sensing information utilization rate can be improved, the track obstacle recognition and three-dimensional positioning precision is improved, and the system sensing stability in a complex scene is enhanced.

The invention provides a multi-camera external parameter calibration method. The multi-camera external parameter calibration method comprises the following steps: acquiring an image sequence acquired by each camera in a plurality of cameras of road mobile equipment in a turning time window and an actual pose data sequence of the image sequence; when it is detected that the road mobile device is in the idle state, executing the following steps: obtaining a target space point of any camera and camera coordinate system target pose data corresponding to any camera; obtaining an initial transformation parameter for representing a spatial transformation relationship between a coordinate system of each camera and a coordinate system of the road mobile device; obtaining a cross-camera feature matching relation of any two cameras; and according to the cross-camera feature matching relation of every two cameras, the target space points of all the cameras, the initial transformation parameters and the initial rotation external parameter of each camera, obtaining the rotation external parameter of each camera when the total reprojection error is the minimum value, and taking the rotation external parameter as the calibration rotation external parameter of each camera. According to the method, full-automatic and high-precision rotation external parameter calibration can be realized.

The invention discloses a self-adaptive spatial transformation network for cross-medium optical distortion, and solves the problem that the existing spatial transformation network is difficult to consider both global transformation and local nonlinear optical distortion. The network is integrated in a target detection model, and the method comprises the steps that firstly, a parameter prediction network synchronously outputs affine transformation parameters and a water surface fluctuation physical parameter diagram; then, the local distortion grid generation network generates a distortion sampling grid based on the parameter diagram and a predefined water surface fluctuation physical model; meanwhile, the grid generator generates an affine sampling grid according to the affine transformation parameters; then, a grid fusion device fuses the two grids to generate a final sampling grid; and finally, the sampler samples the input image or the feature map according to the final sampling grid to obtain an output image or a feature map. Through a space transformation mechanism guided by physical prior, optical distortion caused by water surface fluctuation can be effectively relieved, and the accuracy and robustness of a target detection model in a cross-medium underwater scene are remarkably improved.

The invention discloses a landfill leakage detection method and system based on a boundary double-voltage electric field, and the method comprises the steps: building an artificial electric field which covers the whole area of a landfill along a boundary measuring line electrode at the periphery of the landfill based on the high-resistance characteristic of an impermeable film and the conductive characteristic of leachate, and collecting the boundary potential and electric field data through the high and low double-voltage artificial electric field, the method comprises the following steps: constructing a boundary survey line data pair of physical alignment through differential denoising and normalization preprocessing, adopting an encoder-decoder architecture based on 1D-CNN, fusing spatial transformation and a Focal Loss loss function, directly learning a mapping relation between a boundary survey line potential anomaly mode and an internal leakage position, and outputting a leakage probability distribution diagram of a corresponding landfill contour. The method does not need to excavate and pre-embed the landfill pile body, the positioning precision reaches + / -2m, the single detection time consumption is less than or equal to 4h, the comprehensive cost is reduced by 70%, the electrode reuse rate is greater than or equal to 90%, the method is suitable for landfill sites with various shapes and various stages, and efficient, accurate and low-cost detection of the leakage area is realized.

The invention discloses a rigid registration method and system for visible light and infrared images of an unmanned aerial vehicle, and the method comprises the steps: gradually achieving the high-precision cross-modal image alignment through rough registration and fine registration based on a bimodal feature interaction and causal attention enhancement mechanism; therefore, a stable spatial transformation result can still be obtained under the complex conditions of low signal-to-noise ratio, local shielding and the like. According to the method, high-precision rigid registration of the infrared and visible light images of the unmanned aerial vehicle under the condition of significant modal difference and scale difference can be realized, a registration task is modeled as a long sequence dependence problem by referring to a vision mechanism of step-by-step partition comparison when people observe a large scene, and a CALS module is introduced to gradually focus a local area, so that the registration accuracy is improved. Confusion caused by information overload in traditional global modeling is avoided, and therefore balance is achieved between global scale consistency and local geometric accuracy.

The invention discloses a hydraulic concrete structure underwater surface crack detection method, and relates to the technical field of image sensors, and the method comprises the steps: carrying out the transmissivity mapping and background radiation field separation of underwater light field characterization parameters, generating a target reflectivity image, carrying out the multispectral-polarization data fusion of the target reflectivity image, and generating an enhanced feature image; performing multi-scale convolution kernel space transformation and feature pyramid fusion on the enhanced feature image to generate a crack response diagram, and performing anisotropic non-maximum suppression and morphological skeleton refinement on the crack response diagram to generate crack detection data; and performing geometric topology joint optimization and parameter quantification on the crack detection data, and outputting a structured detection report. According to the invention, by constructing the light field transmission model and synchronously carrying out polarization and depolarization ratio combined noise filtering and geometric distortion correction, adaptive noise filtering is realized, and the measurement precision is improved.

The invention provides an oral cavity scanning point cloud registration method and device, equipment and a medium, and the method comprises the steps: obtaining a plurality of groups of to-be-registered point cloud data; extracting two-dimensional texture features and three-dimensional geometric features in each group of point cloud data; based on fusion representation of the two-dimensional texture features and the three-dimensional geometric features, screening out a group of candidate key points from each group of point cloud data; for any two groups of point cloud data, calculating spatial transformation parameters between the two groups of point cloud data based on the two groups of candidate key points corresponding to the two groups of point cloud data; and registering the multiple groups of point cloud data based on the spatial transformation parameters to obtain a registered three-dimensional oral cavity scanning image. Through the method, the precision of oral cavity scanning point cloud registration can be improved.

The invention discloses a Hololens-oriented single-view texture-free object pose estimation method, belongs to the technical field of augmented reality, and solves the problem that an existing Hololens pose estimation method cannot perform accurate pose estimation on a texture-free model through single-view data under the conditions of no marker and no pre-training. According to the method, multi-modal data acquisition, self-supervised segmentation, self-adaptive point cloud denoising and geometrically-driven pose optimization are integrated, and accurate pose estimation is carried out on a texture-free object only through single-view-angle data under the condition that no marker exists and pre-training is not carried out. Through multi-modal data and spatial transformation, a point cloud reconstructed by a Depth image is mapped to a visual angle of an RGB camera, so that the point cloud is conveniently filtered by using a target mask obtained on the RGB image. Meanwhile, rendering templates under multiple visual angles are generated by rendering the CAD model, obstacles caused by no texture of the target model are avoided, meanwhile, the CAD model does not depend on pre-training, and the zero sample learning ability is provided.

The invention provides a rapid registration and calibration method for an AR ultrasonic navigation system, and belongs to the technical field of fusion of augmented reality and medical image.The method comprises the steps that in the system initialization stage, the relative position relation and recognition features of multiple datum points are input into a database; in the operation stage of registration and calibration, an ultrasonic probe is used for scanning a reference point, two-dimensional image features are collected, and the pose of the ultrasonic probe in a world coordinate system is recorded; processing the ultrasonic image through image recognition, matching reference point features with database information, and determining a reference point identity; calculating the real world coordinates of the reference point according to the probe pose and the calibration parameters, pairing the real world coordinates with the coordinates of the virtual model, and resolving a spatial transformation matrix; and rendering a virtual ultrasonic image by using the AR head-mounted display device according to the transformation matrix, verifying the alignment precision, and completing registration. According to the method, the virtual ultrasonic image is rendered in real time through the AR head-mounted display, the alignment condition of the virtual ultrasonic image and a real anatomical structure is verified, it is ensured that the navigation system enters a high-precision navigable state, the registration precision and operation reliability of ultrasonic navigation are improved, and the efficiency and safety of medical navigation are optimized.

The invention discloses a YOLOv8-based rail transit construction site multi-category engineering vehicle recognition device, method and equipment and a medium, and the device comprises an image obtaining module which is configured to obtain a visible light image and an infrared image of a rail transit construction site; the multi-modal feature enhancement module is configured to respectively extract texture structure features of the visible light image and temperature distribution features of the infrared image, align dual-modal features by using a spatial transformation network, and realize dual-modal feature weighted fusion through a dual-channel attention mechanism; the component-level dynamic attention mechanism module is configured to perform component-level enhancement and optimization on the bimodal features after weight fusion and output an enhanced fusion feature map; and the time sequence correlation loss function module is configured to realize target classification and bounding box prediction, output a target position distribution prediction result after time sequence correlation optimization through a target motion model in combination with a continuous frame detection result, and complete engineering vehicle identification of the rail transit construction site.

The invention provides a smart operation teaching data set acquisition method and device based on visual observation, and the method comprises the steps: preliminarily estimating the three-dimensional poses of a human hand and an object in each frame of a human hand teaching video according to a color image and a depth image in the human hand teaching video; and taking the initial pose estimation result as an initial value, performing joint optimization on the pose tracks of the human hand and the objects by utilizing an optimization objective function, and outputting the three-dimensional pose tracks of the human hand and the objects. And performing heterogeneous mapping on the three-dimensional posture track of the hand to generate a dexterous hand action sequence. And performing spatial transformation on the original track teaching data set by using the three-dimensional attitude track of each object to obtain a dexterous operation teaching data set. The dexterous hand operation teaching data is automatically extracted without an external wearable device, the teaching data acquisition efficiency is remarkably improved, and the method is suitable for various complex operation tasks.

The invention relates to the technical field of image stabilization, in particular to an electronic anti-shake method and system, and the method comprises the following steps: obtaining an angular velocity through a gyroscope, obtaining an angular displacement vector through integration, outputting inter-frame motion transformation parameters through time alignment, segmenting an image, calculating optical flow features, and outputting regional distortion feature distribution. The method comprises the following steps: performing spatial transformation to compare direction mark abnormity to generate a full-image compensation parameter mapping table, performing region transformation based on mapping to generate an anti-shake compensation image frame, performing prediction track Kalman filtering to determine a cutting boundary, and performing dynamic cutting to output a stable image sequence. Local distortion is accurately corrected by combining optical flow feature region calculation and abnormal direction comparison, self-adaptive compensation is achieved through nonlinear reverse mapping and linear translation grading processing, boundary control and view keeping are ensured through motion trail prediction and dynamic cutting, and vibration blurring and offset are remarkably reduced.

The invention relates to the technical field of medical image processing, and discloses a lung multi-temporal CT registration method and equipment, and the method comprises the steps: obtaining a to-be-registered image pair used for lung medical image registration, the to-be-registered image pair comprises a floating image and a fixed image, and the floating image and the fixed image are lung medical images of a target object in different time phases; splicing and down-sampling the floating image and the fixed image along the channel dimension to obtain a first-scale to-be-registered image; performing multi-scale deformation field prediction on the first-scale to-be-registered image by using a pre-trained multi-scale registration network to obtain target deformation field data, the multi-scale registration network being obtained by pre-training a first-scale registration network, a second-scale registration network and a third-scale registration network based on lung medical training images of different time phases; and performing spatial transformation on the floating image based on the target deformation field data to obtain a target registration image. According to the invention, the registration precision of the cross-time-phase lung CT image is improved.

The invention discloses a tiny focus recognition method for prostate puncture and application, and the method comprises the steps: S1, collecting and preprocessing an MRI image and an ultrasonic image of a patient, and obtaining a preprocessed MRI image and a preprocessed ultrasonic image; s2, constructing a texture attention residual network, and extracting MRI texture enhancement features; constructing a multi-scale edge response gating network, and extracting ultrasonic edge enhancement features; s3, performing spatial transformation and resampling on the ultrasonic edge enhancement features to obtain aligned ultrasonic enhancement features; s4, calculating a focus area weight feature matrix, an MRI dynamic weight matrix, an ultrasonic dynamic weight matrix, redundancy-removed MRI features, redundancy-removed ultrasonic features and tiny protection features; and S5, identifying the tiny focus area, and controlling the puncture robot to puncture. The problems that a traditional method is poor in tiny focus recognition capacity, missed diagnosis of the focus is prone to occurring, and puncture accuracy is insufficient can be solved.

The invention discloses a cooperative control method for new energy power generation and a virtual power plant, and relates to the technical field of power system control, and the method comprises the steps: carrying out the operation data collection of new energy power generation, and obtaining power generation connection data; constructing a virtual regulation and control power plant for new energy power generation to carry out space conversion to obtain a regulation and control power plant space, and carrying out visual conversion on the power generation connection data based on the regulation and control power plant space to obtain a comprehensive power generation change diagram; performing fluctuation quantitative analysis on the comprehensive power generation change diagram to obtain a fluctuation capacity body; the method comprises the following steps: carrying out regulation and control node preprocessing on a fluctuation capacity body by regulating and controlling a power plant space to obtain an additional resource capacity body, carrying out opportunity positioning according to the additional resource capacity body to obtain an electric quantity regulation node, and carrying out intelligent collaborative optimization through the electric quantity regulation node to obtain a power grid balance regulation and control strategy; seamless joint and efficient operation of new energy power generation and an existing power system are achieved, and the reliability of power supply is improved.

The application provides a kind of optical tracking and depth camera coordinate calibration method based on new type calibration model, the method includes providing a chessboard with optical positioning mark, the chessboard is placed in the effective range of optical tracking device, and the front of the chessboard is towards depth camera;The spatial position and posture of optical mark point are obtained, and the position of each corner point in the optical tracking coordinate system is calculated according to the position offset of each corner point on the chessboard to the optical mark point;Image information of the chessboard is obtained, corner point coordinates of the chessboard are extracted, and the corner point coordinates are converted to depth camera coordinate system;When more than three groups of corresponding corner point coordinates are obtained, the spatial transformation matrix of depth camera coordinate system to optical tracking coordinate system is calculated, and the calibration process is completed.The application can solve the problems of traditional calibration method, such as the need for multiple manual movement of optical positioning mark and the difficulty of automatic extraction of mark point position for depth camera, and can automatically and quickly realize accurate calibration.

The application provides a high-efficiency expression migration method using base expression space transformation, comprising a preprocessing stage and a facial expression migration stage; the preprocessing stage comprises: obtaining a source model O, the source model O being a base expression model comprising expression meshes under multiple different expressions; performing facial expression reconstruction on the source model O and a target model T to obtain expression description parameters of the two; performing similarity estimation according to the expression description parameters to obtain an expression parameter conversion matrix and a similarity matrix; the facial expression migration stage comprises: inputting a character picture into the base expression model, calculating expression description parameters of a target expression model according to the expression description parameters in the base expression model and the similarity matrix, and completing expression migration.

The application discloses a kind of thermoelectric array and depth camera joint calibration method and system.The application can be simultaneously detected by thermoelectric array and depth camera with spherical calibration object, and the spherical center of the spherical calibration object is used as feature point;The spherical calibration object is set to the position so that the temperature field observed by the thermoelectric array has central symmetry;Change the space pose of the spherical calibration object and synchronously collect the first coordinate set of the spherical center in the thermoelectric array coordinate system and the second coordinate set in the depth camera coordinate system;Based on the first coordinate set and the second coordinate set, the spatial transformation relationship between the thermoelectric array and the depth camera is solved.By the application, the thermoelectric array image coordinates are not dependent on detection, but rely on aiming at a specific location, and use temperature measurement symmetry, aiming accuracy is much higher than detection, so high-precision thermoelectric array image coordinates are obtained.