607 results about "Transformation matrix" patented technology

The invention discloses a data encryption and dynamic key management method based on a quantum security protocol, and the method comprises the steps: 1, selecting an initial quantum random seed and a classical entropy source seed, and generating a key at an initial moment; at each moment, forming an update increment through a nonlinear chaos increment and a fractal function, constructing a key update function, and updating the key; 2, preprocessing the original message into an integer vector; constructing a dynamic transformation matrix by using a secret key at the current moment, sampling from multi-dimensional Gaussian distribution to obtain a short vector, and constructing a public random matrix; encrypting the integer vector by combining the dynamic transformation matrix, the short vector and the public random matrix to obtain a ciphertext; and step 3, decrypting by using the secret key at the current moment and the dynamic transformation matrix which are synchronously updated, and recovering the original message. The invention provides a data encryption scheme with high security and quantum attack resistance.

A parallel-in-time disturbance region update method for dynamic characteristics of flight vehicles includes reading-in the data; obtaining the components of Chebyshev transformation matrix; initializing the flow field; establishing a dynamic computational domain; solving the flow-governing equations of all time layers simultaneously in the dynamic computational domains; updating the dynamic computational domain; judging whether the parallel computation in the current time period converges and whether the computation completes; if so, outputting the results; if the computation converges but not completes, jumping to the step that initializing the flow field, and performing the computation in next time period; if not, jumping to the step that solving the flow-governing equations of all time layers simultaneously in the dynamic computational domains, and entering the next iterative step.

The invention discloses a multi-agent robust specified time state estimation method based on discrete continuous mixing, and belongs to the field of distributed state estimation of an agent system, and the method comprises the following steps: S1, analyzing the uncertainty of a multi-agent system and a measurement noise model, and building a system dynamic equation; s2, designing a transformation matrix based on a null space and a system dynamic equation, and constructing a multi-agent system consistency error; s3, constructing a state space equation of the state observer based on the consistency error of the multi-agent system; and S4, based on the state space equation of the state observer, realizing specified time convergence of the estimation error through the power return-to-zero characteristic of the power zero matrix. According to the multi-agent robust specified time state estimation method based on discrete continuous mixing, state estimation is carried out only by using relative output information between neighbors without depending on relative input information between the neighbors, use of communication channels is avoided, and the method has the characteristic of network attack immunity.

The invention discloses a road and bridge settlement displacement monitoring system and method based on image detection, and the method comprises the steps: selecting a plurality of static background reference points in a stable background region of a monitoring scene, so as to construct a virtual and stable image internal reference system; furthermore, by accurately tracking image coordinate changes of the background reference points in the initial reference frame and the current frame, a transformation matrix capable of accurately describing disturbance of the camera from the initial pose to the current pose is reversely calculated, and the transformation matrix is applied to observation coordinates of a monitored target point; therefore, the virtual displacement component introduced by the camera disturbance is accurately stripped from the total displacement, and finally the real image displacement generated only by the motion of the structure is obtained. By means of the mode, the system can effectively resist interference of external factors such as environment vibration and temperature change, it is ensured that the height of the finally calculated physical displacement is close to the real settlement value of the structure, and therefore the accuracy and reliability of the monitoring result are greatly improved.

A method for slide image registration includes receiving slide images, detecting features contained in the slide images, comparing pairs of the slide images using the detected features, creating a distance matrix that reflects a respective difference between each of the pairs of the slide images, ordering the slide images to create a slide image series using the distance matrix, determining transformation matrices for registering the slide images, where a respective transformation matrix rigidly aligns a first slide image (Ii) to a second slide image (Ii−1), wherein i is the position of a slide image within the slide image series, and the respective transformation matrix that rigidly aligns the first and second slide images is determined using a set of the detected features shared by neighboring slide images in the slide images series, and rigidly aligning the slide images using the transformation matrices.

The invention provides a bridge dynamic displacement measurement method based on an unmanned aerial vehicle, and relates to the field of bridge health monitoring, and the method comprises the steps: obtaining a bridge image sequence through the unmanned aerial vehicle; obtaining transformation matrixes according to the relation between the other frames of images and the fixed measurement points of the reference frame, correcting the coordinates of the motion measurement points of the corresponding frames of images according to the transformation matrixes, obtaining a corrected bridge image sequence, and further obtaining an initial displacement signal; variational mode decomposition is carried out on the initial displacement signal, a constraint variational optimization problem is constructed, the optimization problem is solved through iteration to obtain each mode component of variational mode decomposition and a corresponding center frequency, and a low-frequency mode component is removed to obtain a displacement signal after secondary correction of the influence of the unmanned aerial vehicle; and obtaining the real displacement according to the scale factor between the image obtained by the unmanned aerial vehicle and the real world. The influence caused by the movement of the unmanned aerial vehicle is corrected, and the accuracy, reliability and stability of a measurement result are effectively improved.

The invention provides an electric energy meter correction electric quantity calculation method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining metering historical data of a target electric energy meter; analyzing the metering historical data by adopting a phase angle feature portrait algorithm, and identifying a wiring error type corresponding to the target electric energy meter; determining a corresponding correction transformation matrix based on the wiring error type; and calculating the corrected electric quantity of the target electric energy meter based on the corrected transformation matrix. The invention provides a novel calculation scheme for the corrected electric quantity of the electric energy meter, the corrected electric quantity of the electric energy meter is calculated more accurately by adopting an advanced algorithm and a mathematical model, the interference of human factors is reduced, various complex wiring error conditions can be adapted, and the calculation efficiency is improved. Therefore, the accuracy and reliability of the correction electric quantity calculation method of the electric energy meter of the power system are improved.

The invention discloses a pose estimation method and device based on point pair feature matching, a medium and a product. The method comprises the following steps: performing coarse registration on a fusion scene point cloud and each template point cloud, and calculating point pair features; according to the corresponding hash key values, retrieving from each hash table to establish registration point pairs; calculating a translation vector and a rotation quaternion matched with each registration point pair according to the reference point coordinate system and the template coordinate system, and performing clustering processing on each registration point pair to obtain each initial corresponding set; screening out a target pose candidate transformation matrix and a matched target template point cloud from the pose candidate transformation matrixes matched with the initial corresponding sets respectively; a target optimization function between the fusion scene point cloud and the target template point cloud is constructed, iterative optimization is performed on parameters in the target pose candidate transformation matrix, and the target pose of the fusion scene point cloud is obtained at the end of iteration, so that the precision, real-time performance and reliability of six-dimensional pose estimation are improved.

The invention discloses a conversion matrix determination method and device, equipment and a storage medium. According to the technical scheme, the calibration object is identified through the camera, the first coordinates of the at least nine feature points on the calibration object in the pixel coordinate system and the second coordinates of the at least nine feature points on the calibration object in the world coordinate system under each sampling position are determined, the first coordinates and the second coordinates are converted into the coordinates under the camera coordinate system, and the equation is constructed; according to the first coordinate, the second coordinate, a conversion matrix from a world coordinate system to a tail end coordinate system of the calibration object and a conversion matrix from a pixel coordinate system to a camera coordinate system, solving an equation of an equation, and determining a conversion matrix from the tail end coordinate system to the camera coordinate system, an inertial unit or a mechanical arm joint encoder is not needed to measure the pose of the tail end mechanism of the calibration object, and hand-eye calibration of a low-cost open-loop control system without a pose measuring element is achieved.

The invention relates to the technical field of camera measurement, and discloses a target positioning method and device, computer equipment and a storage medium, and the method comprises the steps: determining a model point and candidate feature points corresponding to a target point on a current frame image; determining a reference feature point according to the distance between the model point and the candidate feature point; determining matching point pairs according to the reference feature points; constructing a similar transformation matrix according to the matching point pairs, and determining a registration error corresponding to the similar transformation matrix; when the registration error is smaller than a preset threshold value, determining the similar transformation matrix as a target transformation matrix; determining a search area on the next frame of image through the covariance matrix; fitting a plurality of prediction points in the search area by using a least square method to obtain a fitting function, and solving an extreme point of the fitting function; and correcting the extreme point according to the target transformation matrix to obtain the position and speed of the target point on the next frame of image. Based on the technical scheme of the invention, the precision and efficiency of target point positioning can be improved.

An adaptive transformation matrix generation is used simultaneous compression and encryption of data. The system continuously monitors input data streams to detect changes in distribution patterns, dynamically updating transformation matrices in response to these changes. Using performance evaluation criteria, the system selects and deploys optimal matrices that maintain both compression efficiency and cryptographic security as data characteristics evolve. The system implements configurable adaptation policies that govern when and how matrices are updated, ensuring system stability while maximizing performance. By employing sliding window analysis and distribution variance metrics, the system can quantify data drift and trigger appropriate adaptations. The system maintains compatibility with various operational modes including lossless, lossy, and modified lossless configurations, applying mode-specific optimization criteria to each scenario. This adaptive approach offers a resilient solution for data transmission and storage scenarios where both data reduction and security must be maintained across diverse and evolving data streams.

The invention discloses an IMU (Inertial Measurement Unit)-assisted moving target detection method under a dynamic condition, which comprises the following steps of: firstly, extracting feature points of a target image for matching to obtain feature point pairs; then, relative coordinate conversion is carried out on IMU sensor data, a corresponding basic matrix or homography matrix is solved, and then corresponding polar lines or mapping projection feature points are obtained; classifying the extracted feature points into two parts, namely static background feature points and dynamic target feature points; an affine transformation matrix is solved according to the corresponding relation of the static background feature points of the two adjacent frames of images and the motion parameter model, an inter-frame difference method is used for carrying out difference on the two frames of images and carrying out image binarization and morphological processing, and a corresponding motion target frame is obtained; and finally, traversing the moving target frame to carry out overlap ratio matching, and screening out a detection object of which the overlap ratio is greater than a set threshold value, namely an actual moving target in the current image. The method provided by the invention can effectively improve the precision and accuracy of moving target identification in a dynamic background.

The invention relates to the technical field of image precision optimization, in particular to a surveying and mapping image mathematical precision evaluation method based on dense point cloud matching, which comprises the following steps: acquiring and preprocessing image data, synthesizing an image gradient modulus length and a maximum principal curvature, and introducing a surface normal vector partial derivative to obtain a surface normal vector partial derivative; calculating a curvature-driven depth discretization step length, and generating an initial point cloud data set; constructing a point cloud complex network structure diagram and calculating the topology durability of a point cloud topology structure to obtain an optimized point cloud data set; based on the optimized point cloud data set, in combination with the target image information, the adaptive curvature gradient weight and the error diffusion control value, constructing an adaptive matching cost function, and calculating a point cloud matching cost matrix; and calculating an optimal point cloud transformation matrix based on the point cloud matching cost matrix. According to the surveying and mapping image mathematical precision evaluation method based on dense point cloud matching, low-texture region point cloud matching error suppression and structure optimization of high-curvature region point cloud on a complex curved surface are realized.

The invention provides an inspection-oriented structure three-dimensional model data optimization method, which comprises the following steps of: analyzing assembly body model data generated by a three-dimensional design platform, and extracting an original data set containing a BREP topological structure, PMI labeling information and a hierarchical relationship; dynamically removing welding nodes, process auxiliary lines and non-geometric attribute data; performing multi-precision curved surface reconstruction on the BREP entity; grid fusion optimization based on material attributes is implemented, triangular patches of the same material are combined, and a vertex index table is reconstructed; constructing a lightweight assembly relation tree, and converting the spatial poses of the parts into a relative coordinate system transformation matrix; generating a lightweight metadata file containing measurable geometric parameters; and outputting a JT format lightweight model which accords with the ISO 14306 standard. According to the technical scheme, on the premise that the model states before and after lightweight processing are consistent, the data size of the model can be reduced, reduction of the overall data size of the three-dimensional model is promoted, and the opening and loading speed of the called model is greatly increased.

The invention provides an object grabbing method and system based on point cloud deep learning, and the method comprises the steps: obtaining a target object point cloud of a target object, carrying out the point cloud matching of the target object point cloud through a point cloud registration model based on a deep learning network, obtaining an initial matching position, optimizing the matching position through a nearest iteration algorithm, and obtaining an optimal matching position; obtaining an accurate transformation matrix of the target object relative to the template under a camera coordinate system; based on a pre-obtained hand-eye transformation matrix, calculating a transformation relation of the point cloud attitude of the target object relative to a tool coordinate system through teaching; the hand-eye transformation matrix is used for indicating a transformation relation between a sensor coordinate system and a mechanical arm tail end coordinate system; and according to the conversion relation and the accurate transformation matrix, the grabbing position coordinates of the target object under the mechanical arm base coordinate system are solved. According to the invention, point cloud registration is carried out through the deep learning network, and the position of the target object can be determined more accurately.

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

The invention belongs to the technical field of radar signal processing, and particularly relates to a Wald-based distance extension target detection method under an interference and clutter background. Assuming that the interference subspace is linearly independent of the signal subspace and coordinates of the two subspaces are unknown, modeling clutter components as complex Gaussian vectors with a mean value of zero; and solving the maximum likelihood estimation of an unknown clutter covariance matrix, an interference coordinate vector and a signal coordinate vector by using a dimensionality reduction subspace method and an oblique symmetry transformation matrix structure, thereby establishing an RPD-2Wald detector for target detection. Simulation analysis shows that under the background of interference and unknown clutters, the detection performance of the provided distance expansion target subspace detector is superior to that of a contrast detector, and the distance expansion target subspace detector has a good inhibition effect on interference.

The invention discloses a three-dimensional modeling method, electronic equipment, a storage medium and a computer program product, and relates to the technical field of data processing, and the method comprises the steps: retrieving a four-dimensional bilateral affine grid based on a spatial dimension index and a color dimension index of a three-dimensional Gaussian point of a training view to obtain an affine transformation matrix, adjusting the color dimension index through the affine transformation matrix to obtain an adjusted color dimension index; rendering the training view based on the adjusted color dimension index through a three-dimensional Gaussian splash model to obtain a rendered image; and based on the luminosity loss between the rendering image and the training view, performing parameter optimization on the four-dimensional bilateral affine grid and the three-dimensional Gaussian splash model to obtain an optimized three-dimensional Gaussian splash model, and performing three-dimensional modeling on the multi-view image based on the optimized three-dimensional Gaussian splash model. According to the method, the floating objects in the scene are reduced, and the geometric modeling accuracy of the scene is improved, so that the rendering quality and the reconstruction accuracy in the three-dimensional modeling process are improved.

The invention relates to the technical field of industrial vision and intelligent monitoring, and discloses a loom control system operation state monitoring and fault diagnosis method based on machine vision, which comprises the following steps: acquiring a video stream in a loom shed area and constructing a two-dimensional space-time slice tensor; performing global motion compensation processing on the space-time slice tensor by using a homography transformation matrix, mapping a compensated dynamic texture feature sequence to a three-dimensional phase space by using a time delay embedding algorithm, and reconstructing a closed phase space trajectory representing periodic operation logic of the loom; the discrete Frechet distance between the phase space trajectory of the current operation cycle and the preset reference trajectory is calculated, and a control instruction is generated. The health degree of the sequential logic of the system is directly quantified on the premise that specific components are not recognized by using the invariant characteristic of the phase space manifold topology; the technical problems that small phase lag is difficult to perceive and nonlinear faults cannot be early warned in a strong noise environment are solved.

The invention belongs to the technical field of intelligent early warning, and particularly relates to an intelligent early warning method for a DMF waste liquid purification and recovery control platform, and the method comprises the steps: carrying out the principal component analysis of long-period historical data, and constructing a principal component transformation matrix of a static reference model; for a moment to be diagnosed, calculating a reconstruction value by using the static reference model to obtain a residual vector, carrying out eigenvalue decomposition on a covariance matrix of a residual matrix of a sliding time window, calculating a drift coherence index according to the distribution of drift eigenvalues, modulating a drift principal component vector of the sliding time window by combining the residual vector, and carrying out diagnosis on the moment to be diagnosed; and obtaining a drift compensation vector, superposing the drift compensation vector with a reconstruction value of a real-time data vector at a to-be-diagnosed moment to obtain an adaptive reconstruction value at the to-be-diagnosed moment, calculating a reconstruction error, comparing the reconstruction error with a fault alarm threshold, judging whether a fault exists at the to-be-diagnosed moment, and performing early warning. According to the invention, the early warning accuracy and robustness are improved.

The invention discloses a multi-scale linear array camera splicing method and system based on point cloud. The method comprises the following steps: completing acquisition and preprocessing of point cloud data and image data of a target area; determining an overlapping region range between adjacent images; extracting spatial structure characteristics in the point cloud data, and performing multi-scale hierarchical decomposition on the point cloud through a multi-scale segmentation method; meanwhile, multi-scale image feature extraction is carried out on the images of the linear array camera; solving gradients in X and Y directions by adopting an optical flow method aiming at any pixel in the overlapping region, and calculating a motion vector between the pixels; fusing the optical flow information obtained under each scale, and constructing a globally consistent optical flow vector field; according to the fused optical flow vector, calculating to obtain a geometric transformation matrix of the whole overlapping region; and after image transformation and alignment are completed through the transformation matrix, fusion processing is carried out on overlapped areas. And the unification of the visual effect and the spatial integrity of the spliced image is ensured.

The invention provides a rock body point cloud registration method, apparatus and device, and a storage medium thereof. The method comprises the steps of obtaining source point cloud data and target point cloud data; executing a target operation based on the source point cloud data to obtain a plurality of source concave-convex descriptors, and executing the target operation based on the target point cloud data to obtain a plurality of target concave-convex descriptors; determining a plurality of pairs of available matching point pairs of which the editing distances meet a first preset threshold value from the source concave-convex descriptor and the target concave-convex descriptor; selecting three target matching point pairs from all available matching point pairs, and calculating a transformation matrix based on the target matching point pairs and a singular value decomposition algorithm; and determining an optimal transformation matrix based on all the transformation matrixes, controlling the source point cloud data to perform pose transformation based on the optimal transformation matrix to obtain an initial registration result, and adjusting the initial registration result based on the transformation matrixes to obtain a target registration result. According to the technical scheme of the embodiment of the invention, the registration precision of the two point cloud data with low overlapping rate and large angle deflection can be improved.

A GNSS data processing method based on a generalized least square filter comprises the following steps that firstly, an observation model is constructed, and then the generalized least square filter is obtained based on the least square principle; firstly, a transformation matrix is defined in each epoch so as to determine the dynamic change of the dimensionality of the state parameters, and then a dynamic model is constructed according to the real time-varying characteristics of the state parameters; initializing filtering according to a parameter minimum product estimation value of a first epoch, transmitting a state estimation value of a previous epoch to a current epoch to obtain a predicted value of a state parameter, adjusting the predicted value through an observation model, and combining original observation data to obtain an estimation value of the current epoch; the method comprises the following steps: taking an estimated value of a current epoch as a basis of prediction of a next epoch, and updating an estimated value of a state parameter in each epoch according to original observation data and a dynamic model so as to obtain real-time positioning information. The method is accurate in positioning precision and does not need to depend on an initial state value.

The invention discloses a lever image matching and positioning method based on perspective transformation, and relates to the technical field of computer vision and image recognition. The method specifically comprises the following steps: selecting a reference picture from a frame image, calculating a perspective transformation matrix, combining feature matching and instance segmentation screening, carrying out random sampling calculation and optimizing perspective transformation matrix model parameters, and estimating and evaluating matching and point mapping effects. According to the method, a perspective transformation matrix between two-dimensional images represented by multiple modes of the same object is utilized, feature point matching of a target lever image and a lever scaleplate surface area are obtained based on a feature extraction and detection method of a deep neural network, matching point pairs are screened in combination with the property of a vector, and the perspective transformation matrix is determined; and evaluating the effect of positioning the random point set on the known plane to the target image. The method does not depend on a single-standard multi-mode representation lever, the perspective transformation matrix dictionary is generated, and large errors possibly generated by single-standard operation are reduced.

The invention discloses a five-axis machining cutter location linear interpolation optimization control method, which relates to the technical field of numerical control machining, and comprises the following steps: fitting a machining contour to generate a curve expression, determining an operation point sequence based on curvature distribution, and converting the operation point sequence into an actuator pose sequence; based on the model of a five-axis machining machine tool, a total transformation matrix from a base coordinate system to an actuator coordinate system is established through a D-H method, motion parameter changes caused by five-axis elastic deformation and inertia force are established based on the nonlinear beam theory and the Alembert principle, and a nonlinear axis body coupling motion equation is established; a correction expression is generated in combination with the Jacobian matrix, optimal unknown parameters in the correction expression are determined by introducing a quantum fluctuation simulated annealing algorithm, and a correction Jacobian matrix is generated; according to the actuator pose change of the adjacent operation points in the actuator pose sequence, the corrected Jacobian matrix is combined, the inverse kinematics algorithm is adopted to derive the motion parameter vector of the previous operation point, and higher-precision machining control is achieved.

The invention discloses a method and device for eliminating moire through combined linear transformation and a storage medium. The method and device are used for improving the moire elimination effect on the basis that image feature information is reserved in the defect detection process. The method comprises the steps that a first image is acquired, and an effective area is searched in the first image; angular point coordinates of the effective area are calculated; carrying out geometric correction on the angular point coordinates to generate corrected angular point coordinates; generating a conversion matrix according to the angular point coordinates and the corrected angular point coordinates; converting the first image into a corrected image by using the conversion matrix; calculating a pixel gray value of the corrected image by adopting a bilinear interpolation method and combining the gray of the effective area of the first image, and inserting the pixel gray value into the corrected image; calculating a scaling factor according to the real magnification and the specified magnification, and generating a scaling vector according to the scaling factor; according to the zoom vector, performing affine transformation and compression on the corrected image to generate a second image; and processing the gray value of the pixel point of the second image by adopting a bilinear interpolation method.

The invention relates to a double-mechanical-arm calibration method and device based on a single camera and a storage medium. The method comprises the steps of obtaining point cloud data of a first calibration plate; a transformation matrix between a base coordinate system of the first mechanical arm and a camera coordinate system of the camera is calculated according to the first calibration plate point cloud data, and a first transformation matrix is obtained; obtaining point cloud data of a second calibration plate; calculating a transformation matrix between a base coordinate system of the second mechanical arm and the camera coordinate system according to the point cloud data of the second calibration plate to obtain a second transformation matrix; calculating a double-arm calibration transformation matrix according to the first transformation matrix and the second transformation matrix; compared with a traditional double-camera hand-eye calibration method and a calibration method based on a measuring instrument, the cost of the technical scheme is lower, and the calibration steps are simpler and more efficient.

The invention discloses a sparse polarization MIMO radar multi-parameter estimation method based on tensor decomposition, and the method comprises the steps: firstly constructing a dual-station MIMO radar system based on a polarized antenna, and calculating a covariance matrix of a receiving signal of a matched filter of a receiving end; then converting the covariance matrix into a tensor form, performing dimension transformation on the tensor, and obtaining a virtual covariance tensor of continuous virtual array elements in the difference joint array by using two transformation matrixes; dividing the virtual covariance tensor into a plurality of sub-tensors and connecting the sub-tensors along a fifth dimension and a sixth dimension; and finally, tensor decomposition is performed on the tensor obtained after connection, and multi-dimensional parameter estimation of the far-field target can be realized by using a matrix obtained through decomposition. The method has the advantages that joint estimation of the direction of arrival, the direction of departure and the polarization parameters is achieved under the underdetermined condition, and the estimation precision is high.

The invention provides a text retrieval-oriented adaptive length embedding method and system, and the method comprises the steps: encoding an original document into a high-dimensional embedding vector by using a trained embedding model, and obtaining an original document embedding matrix X belonging to Rn * d; carrying out matrix learning transformation on the embedded vector through a transformation matrix fitting module to obtain a transformed embedded vector; inputting the converted embedded vector into a hybrid coding module for hybrid coding, dividing the converted embedded vector of each document into a fixed-length dense part and a variable-length sparse part, dynamically adjusting the length of the sparse part according to the semantic complexity of the document, and then performing similarity calculation by combining the dense part and the sparse part to obtain a similarity value; and thus, self-adaptive text retrieval is realized. According to the method, the resource utilization efficiency of the system is remarkably improved, and the retrieval accuracy and robustness are also ensured. And the method is particularly suitable for a large-scale retrieval system and an application environment with strict requirements on storage and computing resources.

The invention relates to the technical field of image recognition, and particularly provides a multi-view point cloud registration method, which comprises the following steps of: performing coarse registration on a source point cloud and a target point cloud based on multi-dimensional features by using an RANSAC (Random Sample Consensus) algorithm in a coarse registration stage to obtain a coarse registration transformation matrix; in the fine registration stage, the coarse registration transformation matrix is used as an initial value, point cloud registration is carried out in at least two resolution spaces, segmented iterative optimization is carried out by using an error loss function set in each resolution space, rapid convergence is carried out in a low-resolution space through an iterative nearest point algorithm, and the point cloud registration is realized. And local geometric alignment optimization is carried out in other resolution spaces through a generalized iterative nearest point algorithm, and fine registration of the source point cloud and the target point cloud is completed after multi-resolution space progressive optimization registration. According to the method, the robustness of feature matching of the low-overlap region is remarkably improved, the registration speed and precision are balanced, and the limitation of a traditional point cloud registration method under the low-overlap and non-ideal point cloud condition is solved.