59 results about "Spatial reconstruction" patented technology

A spatial-type de-interlacing process to be applied to a digital image for obtaining a spatial reconstruction. Furthermore, to the digital image there are also applied one or more temporal-type motion compensation de-interlacing processes for obtaining one or more temporal reconstructions, and the spatial reconstruction and the one or more temporal reconstructions are sent to a decision module. The decision module applies a cost function to the spatial reconstruction and the temporal reconstructions and chooses from among the spatial reconstruction and the temporal reconstructions the one that minimizes the cost function. Preferential application is to display systems, in particular displays of a cathode-ray type, liquid-crystal type, and plasma type which use a mechanism of progressive scan.

The invention discloses a multi-floor indoor structured three-dimensional modeling method and system based on laser point cloud, and the method comprises the steps of carrying out the segmentation ofa connection region between floors based on a point cloud elevation histogram in a vertical direction after inputting the multi-floor point cloud; carrying out single-floor room segmentation on each floor, carrying out single-floor indoor space unit division based on vector wall surface lines, and carrying out vector indoor plane graph construction based on vector and grid superposition; and according to Boolean intersection, deleting an overlapping surface between the floor and the communication area, and establishing a multi-floor three-dimensional model. According to the method, through thehistogram segmentation of a peak value-valley-peak value strategy, a multi-floor indoor structure reconstruction problem is converted into a single-floor indoor reconstruction problem and an inter-floor communication space reconstruction problem; through the means of first segmentation and then local reconstruction, the multi-floor building indoor three-dimensional model can be rapidly and efficiently constructed, the point cloud data in the indoor complex environment can be better processed, and the indoor structured modeling requirement is met.

The invention relates to a PIV wind tunnel test method for ship air flow field measurement, which comprises the steps that a large number of tracer particles are scattered in the flow field of a test wind tunnel, the tracer particles are enabled to move along with the flow field, a generated laser beam is expanded into a sheet beam lighting flow field through a combined lens, a cross-frame CCD camera is utilized to continuously shoot flow field pictures, and multiple groups of front and back tracer particle images are acquired; thus the flow field vorticity, a flow field flow line and a flow field equal velocity line are acquired; then the surface of a ship model of a waterline model made of a metal material is coated with matte black paint; CCD cameras are parallelly placed, test measurement is performed in a mode that the axis of each CCD camera is perpendicular to a laser sheet light surface, a PIV two-dimensional measurement mode is adopted, an included angle of a central longitudinal line of the ship model and an incoming flow wind direction in the wind tunnel and a rolling angle or a pitching angle of the ship model are respectively changed; and thus multiple groups of measurement data of the tracer particles in different measurement sections of the ship model are acquired, multiple air flow field velocity cloud charts and air flow field flow line charges are generated finally so as to complete spatial reconstruction for different measurement sections of the ship model.

To carry out de-interlacing of digital images there is provided a spatial-type de-interlacing process to be applied to a digital image for obtaining a spatial reconstruction. Furthermore, to the digital image there are also applied one or more temporal-type de-interlacing processes for obtaining one or more temporal reconstructions, and the spatial reconstruction and the one or more temporal reconstructions are sent to a decision module. The decision module applies a cost function to the spatial reconstruction and the temporal reconstructions and chooses from among the spatial reconstruction and the temporal reconstructions the one that minimizes the cost function. Preferential application is to display systems, in particular displays of a cathode-ray type, liquid-crystal type, and plasma type which use a mechanism of progressive scan.

The invention belongs to the technical field of space-to-ground observation, and particularly discloses a method for calculating a three-dimensional intersection angle of satellite images. The method comprises the following steps of A, acquiring image point coordinates of same-name points of a three-dimensional satellite image pair by using a manual measurement or automatic satellite image matching method; B, implementing an RFM (rational function model)-based area network compensation method by utilizing the coordinates of the same-name points of the satellite images to realize the optimization of RPC (rational polynomial coefficient) parameters; C, calculating spatial rectangular coordinates corresponding to object-space points formed by projecting the same-name points onto projection surfaces at different elevations by using a projection trajectory method to finish the spatial reconstruction of same-name light; and D, calculating the three-dimensional intersection angle of the same-name light by utilizing direction vectors of the reconstructed same-name light. The method is applied to the calculation of the three-dimensional intersection angle of any three-dimensional model formed by overlapped satellite images without any ground control point, can be used for the mapping accuracy evaluation of the three-dimensional model, and has remarkable economic and social benefits.

The spatial resolution of captured plenoptic images is enhanced. In one aspect, the plenoptic imaging process is modeled by a pupil image function (PIF), and a PIF inversion process is applied to the captured plenoptic image to produce a better resolution estimate of the object.

The invention provides a method for predicating a coincident index between a distributed wind power generation output and a heating load, wherein the method relates to the field of wind power generation technology. According to the method, after phase spatial reconstruction is performed on a nonlinear time sequence of a system parameter of a wind-generated power heating system, distributed wind power generation output and heating load synchronous index mathematical modeling are performed through using the nonlinear time sequence as a sample set. A fuzzy neural network is utilized for solving the mathematical model. Predication is performed on system synchronism at a next time period for aiming at data after phase spatial reconstruction, and a predicated value of the coincident index between the distributed wind power generation output and the heating load. The method provided by the invention performs functions of performing real-time monitoring on a heat-and-power cogeneration system, measuring a system operation parameter and a geographical environment parameter of the system, predicating the coincident index between the distributed wind power generation output and the heating load, controlling the heat-and-power cogeneration system according to a calculation result, effectively using wind energy, and remarkably improving reliability and economical performance in operation of the heat-and-power cogeneration system.

To carry out de-interlacing of digital images there is provided a spatial-type de-interlacing process to be applied to a digital image for obtaining a spatial reconstruction. Furthermore, to the digital image there are also applied one or more temporal-type de-interlacing processes for obtaining one or more temporal reconstructions, and the spatial reconstruction and the one or more temporal reconstructions are sent to a decision module. The decision module applies a cost function to the spatial reconstruction and the temporal reconstructions and chooses from among the spatial reconstruction and the temporal reconstructions the one that minimizes the cost function. Preferential application is to display systems, in particular displays of a cathode-ray type, liquid-crystal type, and plasma type which use a mechanism of progressive scan.

A semantic textual information recognition system is provided. The system includes a memory configured to store instructions and results. The system includes a processor operatively coupled with the memory configured to receive a plurality of text elements along with respective text element coordinates, wherein the processor includes a spatial reconstruction module configured to identify the plurality of text elements on an information axis based on the text element coordinates. The processor also includes a semantic clustering module configured to determine a plurality of semantic clusters of the plurality of text elements using the plurality of text elements on the same information axis and a semantic data model. The processor further includes a rank clustering module configured to generate a plurality of rank clusters by ranking the plurality of semantic clusters. The processor further includes a machine learning module configured to update the semantic data model based on the feature set.

The invention belongs to the field of elevator detection, in particular to an elevator guide rail surface abrasion degree detection method and device based on computer vision. The method comprises thefollowing steps that a laser projector projects a controllable light strip to a surface of an elevator guide rail according to measurement needs to form a feature point; images of feature points on the surface of the elevator guide rail are shot, then the images are processed, and pixel coordinates corresponding to the feature points are extracted and obtained; the pixel coordinates of the extracted feature points are converted into spatial coordinates of the points according to the conversion parameters obtained by calibration, and the spatial reconstruction of the elevator guide rail is completed; and the three-dimensional shape of the elevator guide rail is obtained through the spatial reconstruction, and the surface abrasion degree of the elevator guide rail is judged. According to the elevator guide rail surface abrasion degree detection method and device based on the computer vision, the image processing is carried out, so that the recognition rate is high, the measurement result is accurate, and the problem that the existing detection technology is incomplete is overcome.

The spatial resolution of captured plenoptic images is enhanced. In one aspect, the plenoptic imaging process is modeled by a pupil image function (PIF), and a PIF inversion process is applied to the captured plenoptic image to produce a better resolution estimate of the object.

A plurality of image projections are acquired at faster than cardiac rate. A spatiotemporal reconstruction of cardiac frequency angiographic phenomena in three spatial dimensions is generated from two dimensional image projections using physiological coherence at cardiac frequency. Complex valued methods may be used to operate on the plurality of image projections to reconstruct a higher dimensional spatiotemporal object. From a plurality of two spatial dimensional angiographic projections, a 3D spatial reconstruction of moving pulse waves and other cardiac frequency angiographic phenomena is obtained. Reconstruction techniques for angiographic data obtained from biplane angiography devices are also provided herein.

The invention discloses an imaging method and system of a three-dimensional image. According to the method and the system, two-dimensional images provided by at least three cameras which are located on the same line and have the consistent optical axis direction, and space information of each camera are obtained, wherein the two-dimensional images include common image regions; based on the common image regions of two to-be-paired two-dimensional images, the two-dimensional images are pre-processed based on space reconstruction; based on each pre-processed pair of the two-dimensional images and the space information, space pixel values of a to-be-displayed three-dimensional image are reconstructed; and the reconstructed space pixel values are projected on a preset screen used for displaying the three-dimensional image, and the three-dimensional image is obtained. According to the method and system, the problem of poor stereo perception of the three-dimensional image is effectively solved.

The invention discloses a marine real-time rendering system and method based on spatial multi-scale reconstruction, wherein the system comprises a marine data gridding module, a spatial multi-scale reconstruction module, a real-time response module, a data reconstruction module, and a data rendering module; the spatial reconstruction of gridding marine data is carried out through coding compression LOD multi-level details. A Mortan encoding rule is used for compressing the data, so that the memory consumption of the system is reduced; meanwhile, an LOD multi-level detail technical model is used for converting rasterized ocean data into vector data, and it is guaranteed that image rendering is not distorted due to browser proportion changes; a data response strategy is adopted, viewpoint range changes are captured in real time, loading of invalid nodes is reduced, and rendering efficiency is improved; and a data recombination method and a self-adaptive image rendering method are adoptedto draw and render the layer data in the viewpoint range, so that real-time visual rendering of the layer can be realized.

The invention discloses an X-space magnetic particle imaging deconvolution method. The method comprises the following steps: collecting an X-space original image; a neural network structure is set, convolution and deconvolution layers are set, and a jump connection layer is arranged between the corresponding convolution layer and deconvolution layer; neural network training: network input is a simulated original image, a corresponding original clear image is used as a label to train a neural network, and a mean square error is adopted as a loss function; neural network detection: selecting three indexes of a root-mean-square error, a peak signal-to-noise ratio and a structural similarity index measure to carry out quantitative evaluation on image quality, and modifying network training parameters according to an evaluation result, so that a reconstructed image is closer to an original image; and X-space deconvolution: inputting a to-be-deconvolved original image into the trained and detected neural network model for prediction to obtain a deconvolution result. According to the method, the influence of system noise on the reconstruction process is greatly reduced, and the resolution of the magnetic particle imaging system for X-space reconstruction is improved.

A non-invasive scattering imaging method beyond a memory effect range based on connected component optimization, including: calculating an autocorrelation image of collected speckle information, and obtaining an autocorrelation graph according to the autocorrelation image; obtaining a reconstruction result according to the autocorrelation graph, and optimizing the reconstruction result; calculating and normalizing autocorrelation of the optimized reconstruction result, to calculate initial autocorrelation of an object; obtaining a reconstruction result according to the autocorrelation of the object obtained in the previous step, calculating autocorrelation of an optimized reconstruction result by using a connected component and performing normalization, and using the autocorrelation graph to calculate autocorrelation of another object; and repeating the preceding steps of calculating autocorrelation of the two objects until a predetermined loop count is reached, and then using a phase recovery algorithm and the calculated autocorrelation of the two objects to perform spatial reconstruction to achieve non-invasive imaging.

The invention relates to an incremental unmanned aerial vehicle aerial photography overlapping degree detection method and system. The overlapping degree detection method comprises the following steps: acquiring exterior orientation elements of all images in a shooting area of an unmanned aerial vehicle; constructing an image connection graph according to the exterior orientation elements; extracting feature points of all the images; matching the image pair according to the feature points, and determining multi-view matching points; obtaining a camera internal reference matrix, and establishing an object space initial model according to the camera internal reference matrix; iteratively optimizing the object space initial model by using an incremental motion recovery structure method SFM, and determining an object space reconstruction model; determining a projection image according to the multi-view matching point and the object space reconstruction model; and detecting an image courseoverlapping degree and a lateral overlapping degree according to the spatial overlapping relationship between the projection images of the projection images. By adopting the overlapping degree detection method or system provided by the invention, the overlapping degree detection efficiency and the accuracy of the detection result can be improved.

The invention provides a lung multi-core MRI (Magnetic Resonance Imaging) double-domain super-resolution reconstruction method based on artificial intelligence. The method comprises the step of constructing a k-space reconstruction network and an image domain reconstruction network. And reconstructing the up-sampled multi-core low-resolution k-space domain data by using the k-space reconstruction network, and obtaining a super-resolution multi-core image by using the k-space reconstruction network and the image domain reconstruction network. According to the method, a double-domain reconstruction network is adopted, and respective characteristics of data in a k space and an image domain are effectively utilized; the image fusion technology is adopted, and the relevance of multi-core data in the space is effectively utilized. Compared with a traditional super-resolution reconstruction method, richer features can be learned, and the super-resolution reconstruction effect of the lung multi-nuclear magnetic resonance image is effectively improved.

The invention discloses a spatial reconstruction method of a video neuron node, comprising the following steps: S1, establishing an artificial video neuron node for each video sensing unit and networking to form a video neural network; S2, the massive video image information perceived by each video neuron node being added into the time axis for spatial reconstruction, and then accessed to the back-end management platform for unified management. An artificial video neuron node is established for each video sensing unit and is networked, a video neural network is formed, and the massive video image information perceived by each video neuron node is added into the timeline to form a 'one machine one file' for spatial reconstruction, and then the video image information is accessed to the back-end management platform for unified management, so as to provide the integration and sharing function for the cross-regional and cross-departmental video image information resources, realize the standardization cascade / interconnection among various heterogeneous platforms, and realize the standardization transformation of non-national standard platforms.

The invention discloses a hyperspectral image super-resolution realization method and system. The method comprises the following steps: amplifying an original hyperspectral image into a low-resolution hyperspectral image with the same size as expected output in an interpolation mode; inputting the low-resolution hyperspectral image into a spatial-spectral feature extraction network to generate a high-resolution hyperspectral image, the spatial-spectral feature extraction network comprising a spectral mapping network, a spatial reconstruction network and a spatial-spectral fusion network; inputting the low-resolution hyperspectral image into the spectral mapping network and the spatial reconstruction network, performing spectral feature extraction and spatial information feature extraction respectively in the two branch networks to reconstruct spectral information and spatial information; and using a spatial-spectral fusion network to fuse the spectral information and the spatial information output by the spectral mapping network and the spatial reconstruction network to generate a high-resolution hyperspectral image. Better performance is embodied in the aspects of spatial information recovery and spectral information storage.

The invention discloses an under-sampling lung gas MRI reconstruction method based on multi-task complex value deep learning, and the method comprises the steps of predicting complete k-space data through employing a k-space reconstruction network, obtaining a preliminary reconstruction image through employing an image domain reconstruction network, finally further enhancing the details of the image through employing a multi-task detail enhancement network combining segmentation and reconstruction, and obtaining a finally reconstructed lung hyperpolarized gas MRI image. According to the method, the plurality of convolutional layers are adopted, and phase information in the k space is effectively utilized. Compared with a traditional reconstruction method, the imaging speed is greatly increased while the reconstruction quality is improved. Compared with a network with a single training reconstruction task, the method has the advantages that the two tasks of reconstruction and segmentation are trained at the same time, the two tasks share a feature extraction layer, the segmentation task pays more attention to details and edge parts of the image, more high-frequency features can be extracted, better image details can be reconstructed, and the reconstruction quality is improved.

The invention discloses an imaging method and device combining k space and image space reconstruction, and belongs to the field of magnetic resonance imaging. According to the method, firstly, a k space parallel imaging method is used for reconstructing an undersampling image frame with an automatic calibration signal, wherein the undersampling image frame is obtained in multi-frame imaging needed by magnetic resonance parameter measurement of a measured object; and then, for other under-sampling image frame data without an automatic calibration signal, carrying out reconstruction in an image space. According to the method, the accurate sensitivity graph required by the SENSE method is generated from the undersampled data, and additional acquisition is not needed. According to the method, an image which is almost consistent with a traditional GRAPPA method is generated, and no obvious artifact exists; however, the speed of the method is four times or above that of the traditional GRAPPA method. The method serves as a self-calibration reconstruction method, does not need a full sampling frame, and is particularly suitable for accelerating three-dimensional (3D) multi-frame magnetic resonance imaging.

The invention discloses a wave entropy of spontaneous dynamic activity based autism classification method. The specific implementation steps of the method are as follows: (1) cutting near infrared spectroscopy fNIRS time sequences according to sequence lengths, setting different sliding windows and step lengths, performing spatial reconstruction on each subsequence according to the sliding windowsand the step lengths, and obtaining reconstruction components; (2) performing sample entropy calculating on the reconstruction components obtained by the step (1) so as to obtain a wave entropy sequence; (3) extracting four characteristic values (including the variance, mean, maximum and minimum of the wave entropy sequence) of the wave entropy sequence to generate a new sample as the input variable of a support vector machine (SVM); and (4) inputting the new sample obtained by the step (3) into the SVM to judge ASD. The method realizes high classification on ASD diagnosis; and the method indicates that the wave entropy is a very sensitive index to distinguish the ASD and TD, which might be an fNIRS based biomarker for predicting the ASD in the future.

A method for generating an image data set of an image area located in a measurement volume of a magnetic resonance system comprising a gradient system and an RF transmission / reception system, comprises the following method steps: —reading out k-space corresponding to the imaging area, by: (a) activating a frequency encoding gradient in a predetermined spatial direction and with a predetermined strength G0 by means of said gradient system, (b) after the activated frequency encoding gradient achieves its strength G0, radiating a non-slice-selective RF excitation pulse by means of said RF transmission / reception system, (c) after a transmit-receive switch time ΔtTR following the radiated excitation pulse, acquiring FID signals with said RF transmission / reception system and storing said FID signals as raw data points in k-space along a radial k-space trajectory that is predetermined by the direction and strength G0 of the frequency encoding gradient, (d) repeating (a) through (c) with respectively different frequency encoding gradient directions in each repetition until k-space corresponding to the image area is read out in an outer region of k-space along radial k-space trajectories, said radial k-space trajectories each having a radially innermost limit kgap which depends on said switch time ΔtTR, (e) reading out a remainder of k-space that corresponds to the imaging area, said remainder being an inner region of k-space not being filled by said first region and including at least a center of k-space, in a read out procedure that is different from (a) through (d), and storing all data points read out in (d) and (e); and —reconstructing image data from the read out data points ink-space by implementing a reconstruction algorithm; In order to constrain image fidelity and optimize scan duration under given circumstances, the inner k-space region is subdivided into a core region and at least one radially adjacent shell region.

The invention relates to a method for realizing scene change video analysis and detection based on a high-speed dome camera. The method comprises the following steps: performing multi-angle high-precision detection and identification on a basic target, and performing unsupervised clustering learning according to the detected target and a driving track to complete space reconstruction of a traffic monitoring scene; analyzing the driving track model, determining a lane direction, and completing reconstruction of a traffic incident detection rule; detecting targets appearing in the traffic scene, establishing traffic flow data containing multiple target movement tracks, and judging traffic events through analysis of the traffic flow data. The invention also relates to a corresponding system and device, a processor and a computer readable storage medium. By adopting the method, the system, the device, the processor and the computer readable storage medium, difficult demands and technical problems existing in current traffic management are effectively solved, the current situation of traffic management is improved, technical bottlenecks confronted in the industry are effectively broken through, and updating and rapid development of the intelligent traffic industry are greatly promoted.

The invention discloses a laser imaging radar target echo signal simulation method and a simulator, which belong to the field of photoelectric information technology. The laser pulse receiving device of the present invention converts synchronous laser pulses into synchronous signals and initial laser pulses; generates control instructions according to the target trajectory data, and generates a series of step delay signals with the same delay difference under the trigger of the synchronous signal, representing respectively A series of equal-delay surfaces; the laser pulse delay spatial reconstruction device spatially distributes the step-delay signals to their respective equal-delay planes to form a spatial two-dimensional delay signal array; the projection optical system The time signal is projected to the entrance pupil of the tested lidar receiving lens to realize the echo signal simulation. The invention can solve the problems in the existing target echo signal simulation technical scheme that different working wavelengths cannot be used universally, the number of optical echo channels is small, the distance resolution is poor, and there is an uncertain delay, etc., and is mainly used in the semi-physical simulation of laser imaging radar guided weapons system.

The invention discloses an imaging method and device combining k-space and image space reconstruction, belonging to the field of magnetic resonance imaging. This method firstly uses the k-space parallel imaging method to reconstruct the subsampled image frame with automatic calibration signal obtained in the multi-frame imaging required for the measurement of magnetic resonance parameters; then for other undersampled image frames without automatic calibration signal Image frame data, reconstructed in image space. The present invention generates the sensitivity maps required for an accurate SENSE method from undersampled data without additional acquisition. This method produces images that are almost identical to the traditional GRAPPA method, with no visible artifacts; however, it is four times or more faster than the traditional GRAPPA method. As a self-calibrating reconstruction method that does not require full-sampled frames, the method is especially suitable for accelerating three-dimensional (3D) multi-frame magnetic resonance imaging.

The application relates to a method, a device, a computer device and a storage medium for monitoring atmospheric water vapor content. The method includes: acquiring first atmospheric water vapor data and second atmospheric water vapor data within the time period to be measured in the target area; performing time reconstruction and / or spatial reconstruction on the second atmospheric water vapor data to obtain spatially complete second atmospheric water vapor data; Perform quality screening on the spatially complete second atmospheric water vapor data to obtain the target second atmospheric water vapor data; resample the first atmospheric water vapor data to obtain the target first atmospheric water vapor data; use the enhanced adaptive spatio-temporal fusion algorithm to generate fusion data, according to the fusion The data and the target second atmospheric water vapor data calculate the atmospheric water vapor content of the target area in the time period to be measured. This method obtains time-space continuous fusion products by fusing high-resolution and low-resolution atmospheric data, which can display the spatial details of atmospheric water vapor changes and has high product accuracy.

The invention belongs to the technical field of space-to-ground observation, and particularly discloses a method for calculating a three-dimensional intersection angle of satellite images. The method comprises the following steps of A, acquiring image point coordinates of same-name points of a three-dimensional satellite image pair by using a manual measurement or automatic satellite image matching method; B, implementing an RFM (rational function model)-based area network compensation method by utilizing the coordinates of the same-name points of the satellite images to realize the optimization of RPC (rational polynomial coefficient) parameters; C, calculating spatial rectangular coordinates corresponding to object-space points formed by projecting the same-name points onto projection surfaces at different elevations by using a projection trajectory method to finish the spatial reconstruction of same-name light; and D, calculating the three-dimensional intersection angle of the same-name light by utilizing direction vectors of the reconstructed same-name light. The method is applied to the calculation of the three-dimensional intersection angle of any three-dimensional model formed by overlapped satellite images without any ground control point, can be used for the mapping accuracy evaluation of the three-dimensional model, and has remarkable economic and social benefits.