20 results about "Projection reconstruction" patented technology

The invention relates to the technical field of image data processing, and discloses a few-sample scene-oriented industrial product surface defect image analysis method, which comprises the following steps of: obtaining surface gray level image data of a product to be analyzed, calculating a structure tensor matrix and generating an anisotropy degree graph; searching similar blocks in a preset search neighborhood, and constructing a local texture data matrix; performing singular value decomposition on the local texture data matrix to extract a main subspace; constructing a projection operator and utilizing the projection operator to carry out orthogonal projection reconstruction on the local image block to generate a reconstructed background image block; according to the method, through an orthogonal subspace projection mechanism, good product textures and defect signals are separated, random noise is removed, meanwhile, high-frequency structural features are completely reserved, and the method is high in robustness, high in robustness and high in robustness. And the defect detection precision of a complex texture surface in a few-sample scene is improved.

The invention relates to the technical field of multi-source servo intelligent calibration, and particularly discloses a servo control system autonomous intelligent calibration method based on multi-source information fusion, and the method comprises the steps: obtaining the equipment attribute information of all servo equipment and multi-source sensor data synchronized through a global time protocol in response to a system starting or calibration instruction; and according to the physical space distribution data and the transmission connection relation data in the equipment attribute information, constructing an equipment incidence matrix representing the coupling strength between the equipment. According to the method, various cross-device coupling conflict characteristics including transmission, electromagnetism and loads can be intelligently recognized, external interference noise is accurately stripped from sensor data through dynamic weight adjustment and a coupling projection reconstruction mechanism, it is ensured that a control system only carries out calibration for the real intrinsic error of the device, and the reliability of the system is improved. Therefore, false compensation and system instability caused by coupling interference are avoided.

A sparse projection CBCT reconstruction method, device and equipment and readable storage medium, which comprises the following steps: obtaining X-ray projections of a target patient at multiple angles; obtaining a whole tissue mask prediction projection for reconstruction and a hard tissue mask prediction projection for reconstruction according to a preset tissue projection prediction model and the X-ray projections; supervising and training a preset neural field network initial model according to the X-ray projections, the whole tissue mask prediction projection for reconstruction and the hard tissue mask prediction projection for reconstruction to obtain a neural field network model corresponding to the target patient; obtaining whole tissue gray scale data of each coordinate point in a three-dimensional image to be reconstructed according to the neural field network model and coordinate information of each coordinate point in the three-dimensional image to be reconstructed, and forming the three-dimensional image to be reconstructed. Different tissues in CBCT are modeled by a multi-unit network, the interpretability and controllability of the neural field network model are improved, and the method has strong practical significance.

The application discloses a kind of multi-projection light field image synthesis method and system, it is related to image synthesis technical field, the method includes: obtaining the coordinates of projector array, the bottom of holographic screen and actual camera array;Determine multiple display restoration light;The display restoration light of each projector is carried out image synthesis, obtains multiple target images, and the holographic screen receives the projection of projector on target image and reconstructs three-dimensional light field;The display restoration light of a projector is carried out image synthesis process is: according to the column pixel of all display restoration light of projector corresponding target image determination column pixel synthesis process is: determining the ideal camera of display restoration light coordinates;According to the ideal camera coordinates and the coordinates of actual camera array determine the actual coordinates of two actual cameras;From according to display restoration light, active range and actual coordinates determine multiple actual imaging pixels.The application improves the accuracy of projection image synthesis and projection reconstruction light field display effect.

This application relates to the field of computer science and discloses an artificial intelligence task scheduling system and method for edge computing. The system includes modules for semantic manifold kernel extraction, heterogeneous resource potential field construction, gradient tensor projection reconstruction, and streaming gradient optimization scheduling. The method utilizes a potential energy function to map heterogeneous network resources into a scalar potential field and calculates negative gradient vectors; it generates a semantic manifold kernel mask based on the semantic entropy density of feature channels, and combines it with an environmental pressure tensor mapped by the negative gradient magnitude to project and reconstruct the model weights to generate a dynamic inference model; it synthesizes a scheduling vector using the negative gradient vector and Lyapunov drift correction to determine the next-hop target node. This invention achieves adaptive model compression and network load balancing while preserving key semantic features through potential field gradient collaborative optimization of the model structure and transmission path, solving the problem of balancing transmission efficiency and inference accuracy in edge computing scenarios.

The application discloses a kind of self-adapting direction and step of multi-energy spectrum CT iterative reconstruction method with scattering correction, comprising: step 1, setting multi-energy spectrum CT projection reconstruction model with scattering correction;Step 2, using iterative algorithm to solve out the increment of base material distribution projection value angle by angle, update the current base material image to new base material image;Wherein, through self-adapting direction and step to quickly obtain convergent base material projection image.The scheme provided by the application is used to remove scattering, and the result of base material decomposition is more accurate, and the reconstruction result artifact is less, which lays a foundation for quantitative analysis.

The present application relates to a kind of step edge high-precision three-dimensional reconstruction method, using the parallel single-pixel imaging principle based on projection reconstruction, in combination with edge detection algorithm and adaptive bidirectional projection strategy solve the light ray aliasing problem of step edge, to realize the high-precision acquisition of step edge three-dimensional stereo matching point.First, the sequence of phase-shifted fringe pattern of bidirectional periodic continuation is generated, is projected to measurement scene by projector in turn, and the coded image of scene is obtained by camera synchronous acquisition.Then, the lower frequency image in fine positioning fringe image is extracted to calculate absolute phase map, carries out edge extraction, generates edge mask image.Secondly, the one-dimensional projection function calculation of bidirectional is carried out to the edge pixel in mask image, further calculate the intersection of projection line to obtain multiple intersection points of bidirectional projection line as candidate matching point, and two direct matching points from upper and lower surfaces of step edge are screened out by epipolar distance constraint.Finally, using calibration parameter and matching point, three-dimensional reconstruction point cloud of step edge is obtained by solving stereo matching algorithm.The application effectively solves the light ray aliasing problem of step edge caused by camera visual angle obstruction in triangulation camera-projector system, reconstructs the upper and lower surfaces of step edge completely, and improves the three-dimensional measurement precision of step edge.Based on the optimized bidirectional projection reconstruction parallel single-pixel imaging, while ensuring the measurement accuracy, the measurement efficiency is improved, which is conducive to the application and popularization of the method in actual industrial measurement field.

The invention discloses a remote sensing image semantic segmentation method and system guided by linear positioning attention, and the method comprises the following steps: carrying out the direction-sensitive feature projection reconstruction of an input feature map through a linear positioning attention mechanism, and adjusting the direction distribution of a feature vector through a nonlinear power positioning function, the low-rank limitation is broken through in combination with depth separable convolution, and direction enhancement features are generated; a deformable edge attention mechanism is used, a geometric self-adaptive edge attention graph is generated based on a deformable multilayer perceptron, edge features are enhanced through serial secondary attention modulation, and edge enhancement features are obtained; a symmetric coding and decoding network is constructed, an encoder uses ResNet18 to extract multi-scale features, a decoder uses a multistage linear positioning ViT block to perform step-by-step up-sampling and fuse with jump connection features, shallow jump connection introduces edge enhancement features, and finally a pixel-level semantic segmentation map is output through a segmentation head.

The invention belongs to the technical field of computer vision and pattern recognition, and relates to a face recognition method and device based on deep semantic perception, and the method comprises the steps: obtaining a face image training set, and constructing a visual feature matrix of the face image training set; performing singular value decomposition on the matrix to obtain a first-order standard projection matrix; introducing a learnable matrix to construct a second-order projection matrix, constructing a two-stage projection reconstruction matrix, and introducing a pre-trained face representation network to construct a loss function; iteratively solving an optimal learnable matrix according to the loss function, and obtaining an optimal second-order projection matrix according to the matrix; obtaining a to-be-recognized face image, and recognizing the to-be-recognized face image according to the matrix and the face image training set to obtain a face recognition result; according to the method, the sample is projected into the high-level semantic space of the network for subspace learning, so that the discrimination stability and interpretability under complex disturbance are improved.

The invention provides a single-view projection fast fluorescence tomography reconstruction system based on a double-path condition guide diffusion model, and the system comprises a view collection module which generates an original data set composed of a fluorescence three-dimensional distribution image and a single-view fluorescence projection measurement image, and collects single-view fluorescence projection data in a real experiment; the model construction module is used for defining a condition model framework, a diffusion model framework and a dual-path condition guiding mechanism to obtain an initial single-view projection reconstruction model; and the model optimization module is used for carrying out training updating and hyper-parameter setting and optimization on the initial reconstruction model by utilizing the original data set to obtain a single-view projection reconstruction model, and then verifying and evaluating the single-view projection reconstruction model through the test set. According to the method, the aggravated ill-conditioned problem caused by extremely sparse single-view data and overlapped depth information is solved, rapid dynamic living body three-dimensional imaging of target monitoring target molecules can be realized, and high-quality reconstruction is realized.

The invention relates to the technical field of computer vision and optical character recognition, and discloses a clustering-based character component matching method and system. The character component matching method based on clustering comprises the following steps: performing optical character recognition processing on a to-be-processed image to obtain a plurality of characters; the method comprises the following steps: firstly, performing spatial clustering processing on all characters to obtain at least one initial space group, and then performing angle clustering processing on the characters in each initial space group to obtain at least one character subgroup; performing projection reconstruction on the character subgroup, and determining the arrangement sequence of all characters; sorting all the character subgroups according to the sorting sequence of all the characters to obtain an ordered character string sequence; according to the method, the discrete OCR recognition result is reconstructed into an ordered character string sequence by combining double clustering with a dynamic projection reconstruction technology, so that the character sequence reconstruction accuracy and the automation degree are effectively improved.

The invention provides an underground cable three-dimensional imaging method based on terahertz data and X-ray data, and the method comprises the steps: solving dielectric constant distribution and material density distribution according to the terahertz data and the X-ray data, building the incidence relation between the dielectric constant distribution and the material density distribution, and determining projection reconstruction initial parameters; a fitness function is constructed according to the projection data and the projection reconstruction initial parameters, dielectric constant distribution and material density distribution are reconstructed through a quantum genetic optimization algorithm based on the fitness function and the incidence relation, and an initial reconstruction result is obtained; performing cross iterative optimization on the dielectric constant distribution and the material density distribution by using a regularization item constructed by an initial reconstruction result until convergence; and based on the converged dielectric constant distribution and material density distribution, after an X-ray reconstruction image and a terahertz reconstruction image are generated, data fusion and feature optimization are carried out, and a three-dimensional image of the underground cable is obtained. Therefore, a high-precision three-dimensional image of the underground cable is generated by fusing the optimized bimodal image data.

The invention relates to a multi-camera 360-degree panoramic image splicing method based on depth information guidance. The method comprises the following steps: calibrating internal and external parameters of a multi-camera system and constructing a panoramic coordinate system; the images are subjected to distortion removal and then paired according to an adjacent sequence, feature points are extracted and matched, the real depth under a camera coordinate system is calculated through binocular intersection, and a sparse real depth map is obtained; meanwhile, obtaining an affine invariant depth map by using a monocular depth estimation network, carrying out scale alignment on the sparse real depth map and the corresponding affine invariant depth map to obtain a global scale factor and a depth offset, and recovering a dense real depth map; performing depth projection on the three-dimensional point clouds reconstructed by back projection of the plurality of dense depth maps on a spherical surface to obtain a panoramic depth map; and establishing a pixel mapping relation between the panoramic image and each camera image by means of panoramic depth image information, constructing a pixel value mapping table, and generating a 360-degree panoramic image. According to the method, the artifact problems of image dislocation, ghosting and the like caused by parallax can be effectively relieved.

A method for calibrated X-ray imaging is described, comprising an X-ray imaging system (10) with a variable-pose radiation source (1) (P1) and an X-ray detector (2) with a variable-pose position (P2) that can be controlled independently of the variable-pose position (P1) of the radiation source (1). In this method, an initial calibration (KAL0) is first performed by projecting an X-ray image of an X-ray phantom (PH) onto the X-ray detector (2) for a plurality of different positions (P1(n), P2(n)) of the radiation source (1) and the X-ray detector (2), thereby determining a plurality of initial projections (C(n, R0, t0)).The X-ray imaging system (10) is then used to acquire X-ray projection data (PD) of an object under investigation (OB). During the acquisition of the X-ray projection data (PD), optical image data (OBD1) representing relative poses (k(i)) of the X-ray detector (2) to the X-ray source (1) are determined for a plurality of different poses (P1(n), P2(n)) of the X-ray source (1) and the X-ray detector (2). Based on the relative poses (k(i)) of the X-ray detector (2) to the X-ray source (1) determined from the optical image data (OBD1) and based on the initial calibration (KAL0), corrected projections (C(n, R1, t1)) are determined. Finally, a reconstruction of X-ray image data (BD) is determined based on the acquired X-ray projection data (PD) and the corrected projections (C(n, R1, t1)). A calibration and control device (50) is also described. Furthermore, an X-ray imaging system (10) is described.

The application discloses a kind of binary Fourier single-pixel imaging method, system, device and medium, method includes: based on error diffusion template, by m kind of pixel scanning order to Fourier base pattern is error diffusion, obtains m group binary Fourier base pattern;M group binary Fourier base pattern is projected respectively to processing, and corresponding m two-dimensional target image is reconstructed to obtain;Select n two-dimensional target image to carry out fusion processing, and obtain target image.The application obtains multiple groups of binary Fourier base pattern by different scanning order, reconstructs by the projection of binary Fourier base pattern, finally, the reconstructed two-dimensional target image is fused, effectively reduce the quantization noise and environmental noise of image, while the high-frequency information of image is preferably retained, so that the imaging quality is greatly improved, while realizing the function of good noise reduction, without losing image detail part due to excessive smoothing, can be widely applied in optical imaging technology field.

The application discloses a zooming static CT system and an image reconstruction method, wherein the system comprises a cylindrical detector array arranged in an arc and an X-ray source array arranged in a preset shape; the X-ray source array and the detector array remain static during scanning; projection data is acquired at different angles by sequentially emitting beams of different X-ray sources; the distance from the X-ray source to the detector is variable; a data acquisition system is used for controlling the detector to acquire data; a control system controls the emission of the X-ray source and the acquisition of data by the detector; and an image processing system processes acquired image projection data by using an image reconstruction algorithm. The image reconstruction algorithm in the embodiment of the application adopts a weighted filter back-projection reconstruction algorithm, which can optimize the system geometry, improve the efficiency and accuracy of the reconstructed image, and improve the applicability and reliability of the system.

The application relates to the technical field of image data processing, and discloses an industrial product surface defect image analysis method for a few-sample scene, which comprises the following steps: acquiring surface gray image data of a product to be analyzed, calculating a structure tensor matrix and generating an anisotropy degree graph; searching for similar graph blocks in a preset search neighborhood, and constructing a local texture data matrix; performing singular value decomposition on the local texture data matrix to extract a main subspace; constructing a projection operator and performing orthogonal projection reconstruction on the local graph blocks by using the projection operator to generate reconstructed background graph blocks; splicing the reconstructed background graph blocks into a full-width reconstructed background image, calculating a gray difference graph, and performing adaptive threshold segmentation to extract defect features; and through the orthogonal subspace projection mechanism, the good product texture and the defect signal are separated, the random noise is removed, the high-frequency structural features are completely reserved, and the defect detection precision of a complex texture surface in a few-sample scene is improved.

This invention provides a method and apparatus for measuring the volume of a material pile in a construction scenario, relating to the fields of computer vision and building engineering technology. The invention acquires binocular image data and generates left and right view pairs through epipolar correction; then, it uses a cross-cost aggregation algorithm to estimate disparity in the corrected binocular image data to generate a disparity map; it performs back-projection reconstruction on the disparity map, extracts color information and confidence values, and generates a binocular point cloud image, then segments and extracts the scaled point cloud; next, it inputs the binocular point cloud image into an AtlasNet network architecture to generate a scale-free point cloud; it aligns and restores the scale of the scale-free point cloud, and finally fuses the restored point cloud with the scaled point cloud to calculate the fused height value and the volume of the material pile. This invention generates a complete 3D model with realistic physical scale by jointly registering and fusing scale-free and scaled point clouds, achieving high-precision material pile volume calculation and significantly improving measurement accuracy and efficiency.

The application discloses a brain perfusion parameter prediction blood vessel recanalization grade method and system, and belongs to the technical field of medical image processing. The method comprises the following steps: acquiring a preoperative CTP parameter map and a CTA maximum density projection reconstruction map of an acute large blood vessel occlusive stroke patient and a dynamic scanning sequence of CTP scanning; extracting functional perfusion features from a CTP channel and morphological structure features from a CTA channel through a multi-modal feature fusion network; extracting a collateral compensation vector based on a CTP dynamic sequence through a pre-constructed collateral circulation embedding module; modeling six discrete grades into an ordered category by using an mTICI hierarchical ordered regression prediction head of a cumulative link model; and modulating a prediction output by the collateral compensation vector through an attention gate mechanism, and outputting an mTICI grade prediction probability distribution. The application realizes preoperative prospective prediction of an mTICI grade after mechanical thrombectomy.

The invention discloses a nonlinear back projection reconstruction method for open structure magnetic particle imaging, and belongs to the technical field of medical image processing and reconstruction, and the method specifically comprises the steps: S1, building a zero field line nonlinear change model; s2, deriving a nonlinear filtering back projection reconstruction formula; and S3, signal acquisition and image reconstruction. According to the method, the nonlinear bending rule of the zero field line in the open structure MPI system in the scanning process is constructed; meanwhile, the nonlinear model is embedded into a filtering back projection reconstruction framework, and a new nonlinear zero field line back projection reconstruction method is deduced; and rebuilt image edge distortion caused by zero field line bending is corrected through an algorithm, and high-precision imaging under an open-structure MPI large view field is realized.