292results about How to "Improve rebuild efficiency" patented technology

The large part three-dimensional reconstruction method based on the image sequence comprises the following steps that S1, an unmanned aerial vehicle carrying a camera flies around a target part, and ato-be-reconstructed image sequence is obtained; s2, adopting an SIFT algorithm and an SURF algorithm to jointly extract image feature points; s3, estimating camera motion by calculating an essentialmatrix and a basic matrix based on the sparse feature points obtained by the SIFT corner points and the SURF corner points, and registering three-dimensional space points to obtain sparse point cloudof a three-dimensional scene; s4, judging whether the optimized sparse point cloud has a symmetrical repeated structure or not; and S5, taking the sparse point cloud as seed point and reference imageinput, and performing dense reconstruction based on a dense three-dimensional point construction method of multiple views to obtain a low-resolution depth map. The three-dimensional point recovery andcorrection method based on the image sequence has the advantages that the three-dimensional point recovery and correction method based on the image sequence is provided, and construction from the image sequence to the space sparse three-dimensional points is achieved.

The invention provides a method for three-dimensional reconstruction of a liver computed tomography (CT) image. The method comprises the steps of segmenting a liver two-dimensional CT image sequence, respectively extracting segmentation sequences corresponding to every tissue in a plurality of tissues of the liver; and performing three-dimensional image reconstruction on every tissue according to the segmentation sequences according to every tissue and three-dimensional reconstruction processes corresponding to every tissue so as to perform three-dimensional reconstruction on the liver CT image. According to the technical scheme, when the three-dimensional reconstruction is performed on the liver CT image, the corresponding reconstruction processes can be selected according to the characteristics of different tissues, and accordingly reconstruction efficiency of the liver CT image and quality of the liver CT three-dimensional reconstruction image are improved.

The invention relates to a face three-dimensional reconstruction method and device, an electronic device and a storage medium, and belongs to the technical field of computers. The method comprises thesteps of obtaining an initial three-dimensional face model and a face image; performing face recognition on the face image to obtain face posture data; acquiring projection parameters of equipment ofthe face image and three-dimensional coordinates of all vertexes of the initial three-dimensional face model according to the face posture data, and acquiring two-dimensional coordinates of all the vertexes projected to an imaging plane of the equipment under the posture corresponding to the face posture data of the initial three-dimensional face model; and performing texture mapping processing on the initial three-dimensional face model according to the two-dimensional coordinates of each vertex and the face image to obtain a three-dimensional face model of the face image. According to the method, the reconstruction process is simplified, the calculation amount is small, and the three-dimensional face reconstruction efficiency is improved.

The invention discloses a teleoperation-device-based virtual reconstruction system and a teleoperation-device-based virtual reconstruction for a nuclear radiation environment. The virtual reconstruction system for the nuclear radiation environment comprises a front-end subsystem arranged on a mobile device, wherein the front-end subsystem is connected with a background subsystem through a communication cable. The virtual reconstruction system for the nuclear radiation environment acquires the information of the nuclear radiation environment through positioning measurement equipment, gamma image acquisition equipment and a gamma spectrometer, which are arranged on the front-end subsystem, and transmits the information of the nuclear radiation environment to the background subsystem for processing through the communication cable by front-end communication equipment to create a virtual scene of the nuclear radiation environment. A nuclear radiation environment field can be measured in real time, the virtual scene can be reconstructed on line, and nuclear radiation environment measurement and nuclear radiation environment reconstruction are integrated, so that reconstruction efficiency is improved; and a three-dimensional model of a radiation source and the attribute of the radiation source are associated, so that rich visual information can be provided for staff, the working hours of the staff in a radiation field are reduced, and the nuclear radiation level of the staff can be reduced as much as possible.

The invention discloses a point cloud based 3D reconstruction method for an operation scene of a distribution line. Operations including conditional filtering, de-sampling, outlier removing and segmenting are carried out on an initial point cloud to obtain point cloud of to-be-constructed parts, as a lightening arrester and a cross arm, of the distribution line, a SIFT 3D algorithm is used to extract key points, FPFH characteristics are used to construct describing vectors of the key points, coarse registering and improved ICP fine registering are used to complete registering of the point cloud, a complete 3D point cloud of the parts is obtained, an offline model library is established, the point cloud is collected in real time, the point cloud of the parts is registered with models of themodel library, 3D reconstruction is completed, curved surfaces are reconstructed via a Poisson surface reconstruction method, and a 3D model is obtained. According to the invention, semi-autonomous 3D reconstruction of the distribution line scene is realized, manual intervention is reduced, aimed registering serves as an important step of 3D reconstruction, SIFT 3D key point extraction and FPFH characteristic describing vectors are used, the quality of the key points is ensured, point pair relation is provided with weights, incorrect point pairs are eliminated, the registering speed is improved, and the 3D reconstruction efficiency is improved.

The invention relates to a method for reconstructing a large-scale complex flyover 3D model by using airborne LiDAR data. The method includes the following steps: using inverse iteration mathematic morphological filtering and area information to extract flyover point cloud from airborne LiDAR data; in accordance with bridge floor connectivity, segmenting the flyover cloud point; adopting a central line vertical line scanning method to segment and extract a communicated bridge surface which has no bifurcation or intersection structures and has a width consistent width, acquiring a "constitutional unit"; an end place of the constitutional unit central line serving as a circular buffer area, detecting a shielding structure of the flyover through determination of point cloud height information within the buffer area; through steps of constitutional unit matching, 2D central line fitting and 3D curved surface fitting, acquiring a complete bridge surface 3D central line; and reconstructing a flyover 3D model in combination with bridge surface width information. Practice has proved that the method of the invention can effectively reconstruct the large-scale complex flyover 3D model, addresses shielding problem of the flyover point cloud, and the reconstructed 3D model has a higher correctness and completeness.

The embodiment of the invention discloses a 3D human face reconstruction method. The method includes the steps that an image for 3D human face reconstruction is obtained, information of a preset number of image feature points serving as 3D human face reconstruction feature points is obtained from the image, and the set of the image feature points is used for reflecting the contour of a human face; a 3D average human face model is acquired; corresponding points of the image feature points in the 3D average human face model are acquired, fitting operation is performed based on the information of the image feature points and the corresponding relation between the image feature points and the corresponding points in the 3D average human face model, deformation model coefficients for 3D human face reconstruction are obtained, the 3D average human face model is corrected according to the deformation model coefficients, and an initial reconstructed 3D human face is obtained; texture coordinates of all points on the initial 3D human face are obtained, and 3D human face reconstruction is achieved. The embodiment of the invention further discloses a 3D human face reconstruction device. Through the 3D human face reconstruction method and device, 3D human face reconstruction can be performed on any image.

The invention relates to a parallel realization method for high-resolution reconstruction of a spot diagram in an astronomic image by a K-T algorithm, and belongs to the field of astronomic technical image processing and computing. The method comprises the steps of firstly reading image and parameter files through a main process of a CPU and calculating related physical parameters for performing image preprocessing and data alignment; secondly segmenting a whole view field image of all frames into numerous sub-blocks according to the size of an isoplanatic region to perform image block segmentation; thirdly distributing sub-block frames to independent sub-processes, performing parallel computing processing by adopting a CUDA parallel programming model, and performing Fourier amplitude and phase reconstruction; and finally performing inverse Fourier transform on each sub-block in combination with Fourier amplitude and phase by each sub-process to obtain a reconstructed image, and performing splicing processing on all the sub-blocks by the main process to finish reconstruction. According to the method, a CPU+GPU mixed heterogeneous parallel mode and a GPU-aware MPI mechanism are applied to the problem in the high-resolution reconstruction of the spot diagram in the astronomic image, so that the problem in the high-resolution reconstruction of the spot diagram is solved; and the method has relatively high expansibility.

The invention relates to a method for reconstructing the spectral reflectance of a single-pixel detector based on sparse priors, which performs principal component analysis on the training sample set, and obtains the first three principal components of the spectral reflectance data of the training sample set as the reconstruction basis function Vector; through the single-pixel detector to collect the spectral energy of a single multi-spectral test color block, the energy value U is obtained; in the process of solving the training sample set, the basis function vector B, the basis function vector coefficient a and the specific coefficient of the measurement matrix are obtained The reflectance of the test sample is reconstructed by the obtained spectral energy U collection for a single multi-spectral test color patch. The present invention can make full use of the spatially sparse feature of spectral reflectance and the sparse prior knowledge of the relative spectral power distribution of the lighting source based on the principal component orthogonal basis, reduce the optical complexity of the multi-spectral data acquisition system, reduce the number of samples, and improve the spectrum of the reflectance Improve reconstruction efficiency and improve reconstruction accuracy.

The present invention provides a polarization information-based three-dimensional reconstruction method. The invention aims to solve the technical problem of relatively complicated correction processof a surface normal in existing methods which perform three-dimensional reconstruction by using polarization information. The implementation process of the method of the invention includes the following steps that: the polarization image and depth information of an object to be reconstructed are obtained; registered depth information is obtained; the polarization degree of the object to be reconstructed is calculated; the incident azimuth and incident angle of incident light on the surface of the object to be reconstructed are calculated; the components of the polarization surface normal of the object to be reconstructed in an x-axis direction and a y-axis direction are calculated; direction correction is performed on the components of the polarization surface normal of the object to be reconstructed in the x-axis direction and y-axis direction by using the depth information; and surface integration is performed on the corrected components of the polarization surface normal of the object to be reconstructed in the x-axis direction and y-axis direction, so that the three-dimensional information of the object to be reconstructed is obtained. The method of the invention has high reconstruction efficiency and can be used for acquiring and analyzing the three-dimensional information of objects.

The invention discloses a sparse angle CT image iterative reconstruction method. The sparse angle CT image iterative reconstruction method includes the steps that original projection data with a spare angle in CT image imaging equipment are collected; a reconstruction image is initialized, and a CT image reconstruction model is constructed; the obtained original projection data with the sparse angle are substituted into the reconstruction model one by one in a two-phase mode by the utilization of the reconstruction model, the image reconstructed in the (K-1)th iteration and the image reconstructed in the (K-2) iteration are linearly combined as an initial value to enter the Kth iteration, and the reconstruction result of the Kth iteration is obtained, wherein K>=3; whether the Kth iteration reconstruction result meets preset conditions or not is judged, and if not, the image reconstructed in the Kth iteration and the image reconstructed in the (K-1)th iteration are linearly combined as an initial value to enter the (K+1)th iteration; if yes, the image data obtained in the Kth iteration is taken as the final reconstruction result, and the image is output. With the sparse angle CT image iterative reconstruction method, image quality is effectively improved, and the convergence of an algorithm is effectively accelerated.

The invention discloses a fast ortho-image generation method based on an unmanned aerial vehicle. The method comprises the steps that images are clustered by integrating the similarity of scene content and space; Rapidly generating a sparse three-dimensional reconstruction result of each group on the basis of scene clustering, wherein the sparse three-dimensional reconstruction of each group is executed in parallel; Carrying out geographic registration under the condition of no ground control point, and fusing registration results of each group; Calculating an orthoimage corresponding to eachimage by utilizing similarity transformation, and finally superposing the orthoimages into an integral orthoimage. According to the method, matching of all the images can be completed with the lineartime complexity, and the matching relation between the images can be rapidly obtained; The number of optimization parameters of a single scene can be greatly reduced, so that the reconstruction efficiency is greatly improved; Accurate geographic registration can be realized only by utilizing the POS information obtained when the unmanned aerial vehicle flies and the camera position calculated by sparse three-dimensional reconstruction, and no additional control point is needed.

The invention provides a hair follicle reconstruction method based on hair follicle stem cells and fibroblast. The method provided by the invention comprises the steps of: (1) culturing and amplifying the hair follicle stem cells; (2) culturing and amplifying the fibroblast; and (3) mixing the amplified hair follicle stem cells and the amplified fibroblast, and inoculating a mixture to a position to be subjected to hair follicle reconstruction. Because the hair follicle stem cells can be passed massively in a long term and the dermis fibroblast is convenient to obtain, the method provided by the invention has very strong actual application prospect and scientific and research potential, is an important method for reconstructing skin and hair follicles in treating large-area skin injury (such burns, scalds and the like), and is also a first choice to testify the hair follicle stem cells in scientific researches.

The invention provides an image compression method based on a down-sampling optimization algorithm and a compression sensing theory. The method comprises the steps that the compression sensing theory and the image down-sampling and interpolation technology are combined, and down-sampling optimization is carried out on original high-resolution images first, wherein after image down-sampling is carried out with interpolation as guidance, pixel points in the generated low-resolution images include effective information of pixel points adjacent to pixel points in the original images; then, compression sensing sampling is carried out on the low-resolution images; finally, interpolation reestablishment is carried out on the reestablished low-resolution images, and the high-resolution images are obtained. According to method, efficient image compression sensing sampling and reestablishment are achieved by increasing the down-sampling process in the compression sensing sampling process and increasing the interpolation process in the reestablishment process; the complexity in the image down-sampling and interpolation processes is increased limitedly, so that the implementation complexity does not increase obviously, while the image signal reestablishment effect is remarkably improved.

The invention discloses a CT system and a CT image reconstruction method. A ray detector detects ray of a ray source, converts the ray into a signal and transmits the signal to a data acquisition module; the data acquisition module transmits the received signal to a processor module; and the data acquisition module obtains projection data acquired by CT scanning. The method comprises the steps of performing non negative processing on a target image to obtain a non negative image of the target image; then performing nonlinear decomposition on the non negative image to obtain a first non negative image and a second non negative image; and finally performing sparsification processing on the first non negative image and the second non negative image to obtain an optimal sparse solution, and realizing CT image reconstruction according to the optimal sparse solution. Therefore, the number of dimensions of an image matrix in a calculation process is reduced and the image reconstruction efficiency is improved.

The invention belongs to the field of medical molecular imaging, and relates to an autofluorescence tomography and computed tomography bimodal fusion method, in particular to a bimodal fusion tomography method based on iterative shrinkage. The technology is used for quantifying the intensity of a light source in a reconstructed target body and positioning the light source, and solving the problemof negative direction of all internal light intensity distributions acquired by inversing the limited light intensity distributions on the surface of the target body. The technical scheme has the main points that: the surface light intensity information obtained in autofluorescence tomography and the internal geometric structure information obtained in computed tomography are fused, a complicatedmulti-dimension optimization process in reconstruction is converted into a one-dimension parallel optimization high-efficiency cyclic process by iterative shrinkage, and accurate reconstruction results of regular parameter, lp norm, noise and initial value robustness are acquired integrally. The technology can be effectively applied to research on the systemic physiologic metabolism of a target body, has a high reconstruction efficiency and is suitable for a condition with lower imaging system performance.

The invention discloses a three-dimensional fine blood vessel reconstruction method. The three-dimensional fine blood vessel reconstruction method comprises the following steps of 1, loading a three-dimensional fine blood vessel original image; 2, performing blood vessel property-based image preprocessing; 3, performing blood vessel property-based characteristic matching; and 4, performing image filling and boundary processing. The invention furthermore discloses a three-dimensional fine blood vessel reconstruction system. With the adoption of the three-dimensional fine blood vessel reconstruction method and system provided by the invention, a filling region for repairing a missing part is obtained by mapping a position relationship and calculating an overall matching degree, and the missing part is repaired through the filling region to obtain a complete high-precision image, so that the accuracy of three-dimensional fine blood vessel reconstruction is high.

The invention discloses a video high-temporal-spatial-resolution signal processing method combining an optical flow method and a deep network. The video high-temporal-spatial-resolution signal processing method adopts a signal and information processing algorithm-based mode to recover and reconstruct a high-spatial-resolution and high-temporal-resolution video sequence, i.e., a video super-resolution reconstruction method. The video high-temporal-spatial-resolution signal processing method comprises the following steps: taking frame sequences of videos in sequence; starting from the third frame of the video, performing optical flow method motion estimation on each frame and front and back frames of the frame; synthesizing the four generated motion estimation images and the intermediate frame into high-dimensional image blocks of five images; constructing an OF deep convolution super-resolution network, extracting image information by a shallow network, and reconstructing a super-resolution image by a last sub-pixel convolution layer; sending the high-dimensional image blocks into a deep convolutional network for training; and finally, sending the degraded video frame or the video frame with relatively low resolution into a network for reconstruction. According to the video high-temporal-spatial-resolution signal processing method, the reconstruction quality is good, and the reconstruction speed is high, and the reconstruction effect is good compared with a traditional video super-resolution model, and real-time video reconstruction can be carried out.

The invention discloses a rotating plate double-panel PET system based on polygon view symmetry. The system can obtain multi-angle projection data and solve the problem that a panel PET system can not obtain complete angle projection data. The invention further discloses an imaging method based on the system. The method comprises the following steps that a data collecting module is used for obtaining the complete angle projection data; a view, a coding crystal and a response line are divided, and required voxel and corresponding line codes are determined and calculated; a polygon view system response matrix is calculated, and the system response matrix and the projection data are utilized for carrying out iteration reconstruction; the polyhedral voxel is converted into the cube voxel, and image display is carried out. According to the method, the symmetry of the polygon view structure is utilized, limitation on the symmetry of the rotating system from the square view structure is broken, the calculation amount of the system matrix is reduced, and the reconstruction efficiency of the PET system is improved; by adjusting the size of sectors in the view, the method can be widely applied to the rotating panel PET system.

The invention provides a real-time three-dimensional point cloud reconstruction method based on multiple cameras. The method is characterized in comprising the following steps that: constructing a multi-camera synchronous collection system, carrying out multi-camera system calibration, establishing a projection lookup table and carrying out image collection, object contour segmentation and three-dimensional reconstruction. The invention also provides a real-time three-dimensional point cloud reconstruction system based on multiple cameras. The system comprises a sealed three-dimensional space, a plurality of cameras, a plurality of lamp sources and a computer, wherein the plurality of cameras and the plurality of lamp sources are fixedly installed around the three-dimensional space; the cameras are connected with the computer; and the computer comprises a camera calibration module, a projection lookup table calculation module, an object outline segmentation module and a three-dimensional reconstruction module. The three-dimensional reconstruction disclosed by the invention has the advantages of being higher in accuracy, higher in efficiency, universally applicable and simpler in calculation.

The invention discloses an image reconstruction method and device based on a computer tomography imaging system. The method comprises steps of acquiring acquisition parameters in a scanning protocol, and determining reconstruction parameters used for image reconstruction according to the acquisition parameters; according to the acquisition parameters and/or reconstruction parameters, determining a target reconstruction algorithm and according to the target reconstruction algorithm, generating a reconstruction protocol; and based on the acquisition parameters, acquiring scanning data of a to-be-detected object, and according to the scanning data and the reconstruction protocol, carrying out image reconstruction. According to the invention, through the acquisition parameters and the reconstruction parameters, the target reconstruction algorithm is determined, so linkage between the reconstruction parameters and the acquisition parameters can be achieved; according to the target reconstruction method, a reconstruction protocol adaptable to the current scanning protocol is generated, so problems of algorithm idling and repeated calculation of parts of algorithms caused by use of fixed parameters and fixed topology structures in the current reconstruction protocol are overcome and efficiency of image reconstruction is effectively improved.

The invention discloses an airborne laser radar point cloud based three-dimensional reconstruction method for a single-stage single power lead wire. A three-dimensional geometric shape of the power lead wire is described through combination of a linear segment and a parabolic segment. The method comprises the following steps: step 1, loading airborne laser radar point cloud data of the single-stage single power lead wire; step 2, fitting a linear segment model by utilizing information of a two-dimensional projection point set of airborne laser radar point cloud of the power lead wire on an xy plane; step 3, fitting a parabolic segment model by utilizing elevation information of the airborne laser radar point cloud of the power lead wire and scale factor information of the two-dimensional projection point set; and step 4, in combination with the linear segment model and the parabolic segment model, generating a three-dimensional reconstruction model of the three-dimensional visual power lead wire. According to the method, the complexity of the three-dimensional reconstruction model is lowered and the reconstruction efficiency and precision of three-dimensional reconstruction of the single-stage single power lead wire are improved.

The invention discloses a high-quality reconstruction method of a spectral imaging system based on a convolutional neural network, and belongs to the field of computational photography. The method isapplied to a snapshot spectral imaging system based on a coded aperture, spatial correlation and spectral correlation between images are considered in the reconstruction process of a hyperspectral image, residual error learning is used for accelerating the training speed and the convergence rate of a network, and a GPU is used for completing optimization solving of the whole network. Network parameters are updated by using a random gradient descent method; and block-by-block processing is performed to complete reconstruction of the hyperspectral image. According to the method, hyperspectral image reconstruction of the CASSI spectral imaging system can be completed in a high-quality mode, it is guaranteed that a reconstruction result has high spatial resolution and high spectral fidelity, meanwhile, the efficiency of hyperspectral image reconstruction is greatly improved, and the application range of hyperspectral images is expanded. The method can be applied to the fields of geologicalexploration, agricultural production, biomedicine and the like.