38 results about "Level set function" patented technology

The embodiment of the invention discloses a bill key field identification method and device. The method comprises the following steps: acquiring a bill image; defining the key field, wherein the key field comprises at least one of an invoice number, an amount and a date; fractional order differential calculation is carried out on the bill image, an enhanced image and fractional order differential features are generated, and the fractional order differential features comprise a fractional order gradient field and a multi-scale feature vector; semantic features of the bill image are extracted according to a predefined bill semantic template, semantic priori is generated, and the semantic priori represents the relative position and expected content of the key field; a level set function is initialized based on the fractional order differential features and the semantic priori, an energy function is constructed, the energy function is minimized to drive the level set function to evolve, and a segmentation result is generated; and performing feature extraction on the segmentation result to generate structured data. According to the invention, the accuracy and efficiency of key field identification are improved.

The invention relates to the technical field of image processing, and discloses a polyp image segmentation method and a computer readable storage medium, and the method comprises the steps: inputting a to-be-segmented polyp image into a backbone network to extract multiple layers of initial feature maps, and sequentially inputting the initial feature maps into a level set edge enhancement module, performing explicit modeling and dynamic enhancement on the edge by using the learnable parameters and geometric characteristics of the level set function to inhibit feature confusion and obtain an optimized feature map; inputting the optimized feature map into a double-branch attention module, cooperating global and local information to adaptively calibrate a channel weight, inputting the optimized feature map into a cross-layer fusion module to perform multi-scale feature complementation, and outputting an attention enhancement feature map; and based on a cross-layer fusion module, deep feature aggregation and layer-by-layer feature aggregation, carrying out layer-by-layer fusion to obtain a segmentation result graph. According to the method, the problems of over-segmentation and under-segmentation of the polyp image caused by edge blurring and feature confusion are effectively solved, and the accuracy of a polyp segmentation task is remarkably improved.

The embodiment of the invention discloses a power grid optimization method and device based on a level set function, and the method comprises the steps: collecting the topological data of a power grid, and constructing a power grid topological data set containing multi-dimensional features according to the topological data; according to the power grid topological data set, constructing physical constraint parameters representing power grid electric energy transmission efficiency and stability; performing dimensionality reduction coding on the power grid topology through a variational auto-encoder to generate a low-dimensional potential space representation, and constructing an initial level set function based on the low-dimensional potential space representation by using the physical constraint parameter; analyzing the initial level set function by using a variational auto-encoder decoder module, and determining potential features and dynamic constraint conditions of the power grid topology; and optimizing parameters of the level set function according to the potential features and the dynamic constraint conditions, and generating improved power grid layout data based on the optimized level set function. The power transmission efficiency and stability of the power grid are remarkably improved.

The invention discloses a level set field reconstruction method and system based on a three-dimensional finite element grid and a medium. Relates to the technical field of geometric reconstruction. Positioning a space Cartesian grid area intersected with the boundary surface grid based on a separation axis theory; calculating a geometric space point-surface position relation, calculating a directed projection distance from a space Cartesian grid center to a two-dimensional boundary surface grid, and correcting the positive and negative characteristics of a Cartesian grid level set value based on a three-dimensional eight-direction rapid scanning method, so that the grid level set value is consistent with a region boundary value; according to the method, the geometric reconstruction problem of the deformable body which is complex in appearance and free of explicit analytic expression can be efficiently and accurately solved, a coupling approach between a finite element grid and a Cartesian grid is established under a non-conformal grid system, and the flexibility and universality of explicit calculation of a level set function under the Cartesian grid are improved.

The application discloses a LoG operator-based active contour model image segmentation method and system, and belongs to the technical field of computer vision, and the technical points are as follows: firstly, an edge fitting function EFF is used to replace a traditional local data fitting function; secondly, an energy functional model is created; finally, two new functions are introduced to serve as the influence of a distance regularization term and a length term, so as to prevent the level set function from being too flat or too stepped, and to smooth or shorten the curve, thereby realizing image segmentation. The LoG operator-based active contour model image segmentation method and system have the advantages that, compared with a segmentation evaluation database and a model of the prior art, the model has a faster segmentation speed, improved robustness to an initial contour, and strong anti-noise capability.

The present invention discloses an efficient driving interaction method and system based on backward reachable set safety assurance, wherein the method includes the following steps: S1. Dividing the sampled values ​​of the vehicle state vector according to a certain grid size, offline calculating and storing the level set function values ​​by solving the level set equation, constructing a mapping relationship between the vehicle state vector and the level set function value to form a discrete table; when the level set function values ​​are all zero, it corresponds to a zero level set, which corresponds to the safe distance boundary of the interacting vehicle relative to the ego vehicle; S2. Obtaining the ego vehicle's state vector online and searching the corresponding level set function value according to the discrete table; S3. Comparing the found level set function value with a set threshold and entering different control constraints based on the comparison result; S4. Generating control instructions based on the control constraints to control the ego vehicle's driving. The present invention improves vehicle traffic efficiency in interactive scenarios.

The present invention belongs to the field of image detection technology and is a SAR image coastline detection method based on level sets. The level set function is initialized as a signed distance function, the grayscale means of the foreground and background are calculated according to the initialized level set function, and each point of the level set function is evolved using the grayscale means of the foreground and background estimated previously. The iterative step size of the level set function in the evolution process is adaptively changed by a step size control function. If the level set function value of the current point is greater than 100 or less than -100, the step size is changed from the original 0.1 to 1, otherwise it remains unchanged. The level set function is then continuously evolved. According to the definition, foreground>0 and background<0, if the grayscale value of the current point is close to the foreground valuation, the value of the level set is correspondingly increased, otherwise it is decreased. The level set function is reinitialized according to the penalty term to make it conform to the signed distance function. Finally, it is checked whether the level set function converges, and if not, it is re-evolved.

The present invention relates to an image processing method based on morphology and non-linear Poisson equation, which includes: fitting an intensity differential fitting function of the contours on both sides of the input image according to gray-scale morphological erosion and dilation operations, and defining an energy function containing the image contour line; obtaining a new energy function according to the level set function and taking the derivative to obtain a gradient descent flow equation; decomposing the data term therein and introducing a non-linear Poisson equation for representation; constructing a canonical function for non-linearly stretching the value range of the data term function; continuously updating the level set function, and after regularization and neighborhood average filtering processing, obtaining the final level set function; outputting the same and the image segmentation result; adopting mathematical morphology to increase the anti-noise ability, effectively eliminating the influence of noise on image segmentation, reducing the amount of computation and the image segmentation time; constructing a non-linear Poisson equation to weaken disturbances such as non-uniformity, introducing a level set model to complete image segmentation, and increasing the accuracy of image segmentation; constructing a canonical function to improve the robustness.

The present application relates to the field of additive manufacturing, and specifically discloses a kind of generation method of manufacturing three-dimensional Chinese character cup model based on energy driving.The present application constructs the smooth time evolution path from multiple outline initial region to single target outline, adopts level set function to describe the outline evolution process, and establishes energy functional including curve length regularization term, adaptive inflation term and shape prior term;Through introducing the dynamic coupling control mechanism based on area difference of region, shape error and boundary length, the adaptive adjustment of inflation coefficient is realized;Then the level set evolution equation is obtained by variational derivation, and after numerical discrete solution, two-dimensional evolution sequence is added to three-dimensional volume field along time axis;Finally, through isosurface extraction and thick shell processing, the 3D printable three-dimensional Chinese character cup model with complete structure and uniform wall thickness is generated.

This application belongs to the field of structural optimization technology and specifically discloses a parameterized level set topology optimization method for porous structures that takes geometric nonlinearity into account. The method comprises: constructing a level set function to implicitly describe the structural boundary, and using compactly supported radial basis function spline interpolation to obtain the parameterized level set function; establishing an equilibrium equation for geometric nonlinear topology optimization, and performing geometric nonlinear finite element analysis to solve the structural response under large deformation conditions; approximating and simplifying local volume constraints through local constraint domains and p-norm functions; establishing a topology optimization model based on maximizing the geometric nonlinear porous filling stiffness; performing sensitivity analysis through the adjoint method, and calculating the partial derivatives of the objective function of the topology optimization model with respect to the expansion coefficient to determine the optimization direction of each finite element in maximizing the geometric nonlinear porous filling stiffness; and performing iterative optimization to obtain the optimized topology structure. This application can realize the topology optimization of porous fillings under geometric nonlinear conditions.

The invention discloses a map matching system based on a level set method. Relates to the technical field of automatic driving, in particular to a map matching system based on a level set method. According to the method, the continuous probability field representation for constructing the road network is proposed, and the optimal path mapping of the trajectory points is realized based on the level set evolution, so that the robustness and precision of map matching are remarkably improved. The system transforms discrete road information into a probability distribution representation in a continuous space. And then, designing a level set evolution mechanism fusing multi-source observation information, controlling dynamic updating of a level set function through a partial differential equation, extracting a zero level set from the converged level set function as an optimal path candidate, and ensuring the rationality and continuity of the path with the assistance of curvature regularization and multi-scale verification. And a historical data-oriented batch processing mode and a real-time application-oriented online incremental mode are supported.

The invention discloses a high-speed aircraft aerodynamic topology design method based on a level set method. The method comprises the steps that 1, a flow field control equation containing a high-speed physical model and a structural analysis model are established; 2, defining an optimization problem taking a lift-drag ratio, a lift coefficient or a resistance coefficient as a target function and taking a volume or an area as a constraint condition; 3, implicitly describing the boundary of the aerodynamic contour of the aircraft by adopting a level set function, and converting an optimization problem into an evolution problem of the level set function; 4, efficiently calculating shape sensitivity information of the objective function about the level set function by solving the adjoint equation; and 5, based on the shape sensitivity information, constructing and solving a level set evolution equation, and driving the aerodynamic boundary to automatically evolve to a topological structure with better performance until convergence. The method solves the problem that an existing parameterization optimization method is difficult to realize automatic evolution of an aerodynamic configuration topological structure and accurate capture of shock waves in a high-speed flow field.

The present application belongs to the technical field of structure optimization design, and discloses a structure topology optimization design method and device, steps are as follows: (1) dividing a physical design domain into a background grid, based on coordinate information of background nodes of the background grid, a sparse filtering matrix is constructed by using a Gaussian kernel function with row normalization; (2) using the sparse filtering matrix to perform front filtering on design variables on the background nodes to obtain a smooth density field, and then obtaining a level set function field; (3) using an adaptive grid subdivision algorithm to cut and locally adaptively reconstruct the level set function field to obtain an analysis grid; (4) interpolating the level set function field to the analysis grid through a sparse global interpolation matrix from the background grid to the analysis grid to obtain a level set function field; (5) projecting the level set function field to obtain a pseudo-density field on the analysis grid nodes, and then obtaining an optimal topology configuration. The present application realizes good boundary smoothness while improving optimization efficiency.

The invention belongs to the technical field of aero-engine cooling design and flow heat exchange optimization, and provides a turbine blade cooling turbulence rib construction method, system and configuration based on level set topological optimization and a blade. The method comprises the steps that a fluid-solid coupling calculation model containing a cooling channel fluid domain and a blade wall surface solid domain is constructed, defining a level set function to implicitly represent the two-phase region; establishing a unified control equation set covering a full computational domain based on the function, and introducing a flow velocity suppression mechanism into a solid domain to carry out single-domain coupling solution of fluid flow and solid heat conduction; according to the physical field solution, constructing an optimization model taking the cooling performance as a target function and the volume fraction of the turbulent flow rib as a constraint, and calculating the sensitivity of the target function to a level set function; using the sensitivity to drive a level set function update to evolve a spoiler rib boundary; iterating until convergence and outputting the final geometric configuration of the turbulent flow rib. According to the invention, the irregular turbulent flow structure with high heat exchange efficiency and low flow resistance can be automatically generated without presetting a rib type.

The present invention relates to an active contour model image segmentation method based on K-medoids clustering, comprising: using a K-medoids clustering algorithm to perform binarization processing on an image to be segmented to obtain a fitting result, and acquiring a foreground image and a background image; constructing a pre-segment fitting function based on the binarization fitting result; using a zero level set that meets the Lipschitz condition to replace the contour curve of the pre-segment fitting function to obtain a KM pre-segment fitting function; using a gradient descent method to minimize the energy functional of the KM pre-segment fitting function to obtain a gradient flow equation; using an adaptive regularization function to regularize a data-driven term of the gradient flow equation; using a rulesig function to regularize the zero level set function, using a kernel function to smooth and shorten the curve of the regularized zero level set function, and outputting a level set function as a segmentation result of the image to be segmented.

The application provides a high-resolution image segmentation method based on region probability label driven active contour, and solves the problem that an active contour model cannot process high-resolution images.The steps are as follows: firstly, a high-resolution image is down-sampled to obtain images from low to high resolution, and the following processing is performed on the images from low to high resolution: an initial level set function and statistical parameters are obtained, and a region probability label and geometric prior are calculated by using the level set function; secondly, a segmentation contour is updated under the driving of the region probability label, and the corresponding level set function and statistical parameters are updated based on the region probability label update driving; and finally, the level set function and statistical parameters of the current resolution image are transmitted to the next high-resolution image and contour segmentation is performed on the next high-resolution image.The application can greatly reduce the calculation cost and enhance the robustness of the algorithm, so that the high-resolution image can be processed.

The invention discloses a shot peening forming process planning method based on a level set algorithm, and the method comprises the steps: S1, obtaining the features of a target wallboard, and selecting the type and number of level set functions according to the features of the target wallboard, so as to describe the boundary between a shot peening region and a non-shot peening region of the target wallboard; s2, establishing a shot peening forming process optimization mathematical model based on the level set function; s3, calculating the sensitivity of an objective function of the optimization model, and deducing an iterative formula of the level set movement velocity field; s4, the optimization model is initialized, the level set function is solved, the shot blasting area boundary is updated, if the boundary is not convergent, the step S3 is executed, and if the boundary is convergent, the step S5 is executed; and S5, discretely generating a shot blasting impact point set based on the converged shot blasting area boundary. According to the method, the problems existing in shot peening forming of complex wallboards in an existing shot peening process are solved, the process planning cost is remarkably reduced, and the forming precision is improved.

This invention belongs to the field of aero-engine cooling design and flow heat transfer optimization technology. It provides a method, system, configuration, and blade for constructing turbine blade cooling fringe based on level set topology optimization. The method includes: constructing a fluid-structure interaction (FSI) computational model encompassing the fluid domain of the cooling channel and the solid domain of the blade wall; defining a level set function to implicitly represent the two-phase region; establishing a unified set of governing equations covering the entire computational domain based on this function; introducing a velocity suppression mechanism in the solid domain to perform single-domain coupling solution of fluid flow and solid heat conduction; constructing an optimization model based on the physical field solution, with cooling performance as the objective function and fringe volume fraction as the constraint; calculating the sensitivity of the objective function to the level set function; using this sensitivity to drive the level set function update to evolve the fringe boundary; iterating until convergence to output the final fringe geometry. This invention does not require pre-setting the fringe type and can automatically generate irregular fringe structures with high heat transfer efficiency and low flow resistance.

This invention discloses an intelligent identification method for the boundary features of water-induced disaster structures ahead of tunnels, relating to the field of underground engineering detection and disaster prevention technology. The method includes: acquiring tracer response curves and head observation datasets, defining source and target endpoint sets; establishing a level set function characterizing the geometric boundary of the disaster-inducing structure, and configuring travel time bias variables for each set of observations; constructing a heterogeneous parameter field based on the level set function, and constructing endpoint connectivity topological constraints to quantify the physical connectivity energy between endpoints; constructing a unified objective function including travel time residuals, head residuals, topological constraints, and sparsity constraints of bias variables; performing synchronous joint inversion, updating the level set function, physical parameter field, and bias variables, and outputting the geometric boundary of the disaster-inducing structure based on the final level set function. This invention solves the problems of fuzzy structural boundaries and missing connectivity through level set evolution and topological constraints, and eliminates the interference of adsorption delay on inversion accuracy using a bias separation mechanism.

The invention relates to an image segmentation method based on a JD-LIC active contour model, and belongs to the technical field of industrial CT image segmentation, and the method comprises the following steps: S1, inputting a to-be-segmented industrial CT image, and setting model parameters, including an initial Gaussian kernel scale sigma 0, a model driving force alpha, a window size k, a filtering weight s, an iteration number n, a time constant delta t0, and a minimum Gaussian kernel scale sigma min; s2, initializing a level set function # imgabs0 # and a bias field b; s3, iteration of a level set of the active contour model; and S4, when the iteration in the step S3 reaches a preset number n of iterations, stopping the iteration, and taking the part of the level set function equal to zero as an image segmentation result. According to the method, the overall robustness of the model is improved, the sensitivity of the zero-crossing level set is improved, and stable evolution of the level set is ensured.

Method for detecting spatial coupling comprising the steps of: a. providing a set of data, b. identifying and segmenting a first and a second sets of objects of interest, wherein the objects of the second set are assimilated to punctual objects, c. determining, using a level set function, an expected number of objects of the second set present within a specified range of distances to at least one given object of the first set in case there were no interactions between said at least one given object of the first set and the objects of the second set, d. determining, using a level set function, an actual number of objects of the second set within the same range of distances to the at least one given object of the first set, and e. comparing said expected amount and said determined amount.

This invention relates to the field of image processing technology and discloses a method for polyp image segmentation and a computer-readable storage medium. First, the polyp image to be segmented is input into a backbone network to extract multi-layer initial feature maps. Then, the initial feature maps are sequentially input into a level set edge enhancement module. Learnable parameters and the geometric properties of the level set function are used to explicitly model and dynamically enhance the edges to suppress feature confusion and obtain an optimized feature map. The optimized feature map is then input into a dual-branch attention module, which coordinates global and local information to adaptively calibrate channel weights. This optimized feature map is then input into a cross-layer fusion module for multi-scale feature complementarity, outputting an attention-enhanced feature map. Based on the cross-layer fusion module, deep feature aggregation, and layer-by-layer feature aggregation, layer-by-layer fusion is performed to obtain the segmentation result map. This invention effectively solves the problems of over-segmentation and under-segmentation of polyp images caused by edge blurring and feature confusion, significantly improving the accuracy of polyp segmentation tasks.

The invention discloses an image segmentation method of a regional active contour model based on a Laplacian operator. According to the method, an adaptive-Laplacian operator is introduced, and a corresponding energy functional is used as a length regular term of a level set function, so that effective regularization of the level set function is realized. The regularization mechanism not only can suppress false contours generated in the curve evolution process, but also can adaptively adjust the diffusion process, so that noise interference in the level set evolution process is effectively suppressed, image edge structure details are kept, and segmentation robustness and accuracy are improved. Experimental results show that the method has high segmentation precision and robustness in complex natural image segmentation.

The invention discloses an isogeometric non-probabilistic reliability topological optimization and geometric post-processing integration method, and belongs to the technical field of structural topological optimization design. The method comprises the following steps: constructing a geometric model by using the same group of NURBS primary functions, analyzing the model and parameterizing a level set function, and realizing unification of CAD and CAE models; for material attributes and loads with interval uncertainty, non-probability displacement reliability constraints are established based on a vertex combination method and an optimized feature distance; taking the minimization of the structural volume as a target, performing sensitivity analysis in combination with a shape derivative and an adjoint method, and driving boundary evolution of a level set to obtain a topological configuration meeting a reliability requirement; and automatically outputting the explicit geometric model through zero level set boundary extraction and parameterized projection-based boundary repair. According to the method, integration from reliability topological optimization to manufacturable geometric model is realized, and the safety and design efficiency of the structure in an uncertain environment are improved.

The invention discloses an intelligent identification method for boundary features of a water diversion disaster-causing structure in front of a tunnel, and relates to the technical field of underground engineering detection and disaster prevention and control. The method comprises the following steps: acquiring a tracing response curve and a water head observation data set, and defining a source and target endpoint set; establishing a level set function representing the geometric boundary of the disaster-causing structure, and establishing a travel time offset variable for each group of observation configuration; constructing a heterogeneous parameter field based on a level set function, and constructing an endpoint connectivity topology constraint for quantifying physical connectivity energy between endpoints; constructing a unified objective function including travel time residual, water head residual, topological constraint and bias variable sparse constraint; and executing synchronous joint inversion, updating the level set function, the physical parameter field and the bias variable, and outputting a disaster-causing structure geometric boundary based on the final level set function. Through level set evolution and topological constraint, the problems of fuzzy construction boundary and connectivity deficiency are solved, and the interference of adsorption delay on inversion precision is eliminated by using a bias separation mechanism.

The present invention proposes a hybrid level set image segmentation method based on box-level prior and weight self-constraint, which includes the following steps: step 1, training the target detection network to obtain box-level prior information and initialize the level set function; step 2, constructing a weighted edge detection energy term, using the box-level prior information obtained in step 1 to define the weight matrix, and constraining the edge detection range to the vicinity of the target area to avoid interference from the background area; step 3, constructing a weighted self-constraint energy term, introducing a variable coefficient to constrain the relationship between the level set function and the box-level prior information, so that the evolution of the level set function changes dynamically with the degree of similarity between the two; step 4, constructing a local area fitting energy term and a distance regularization term for processing grayscale unevenness and eliminating reinitialization; step 5, combining the energy terms constructed in step 2, step 3, and step 4 to obtain a total energy functional, and minimizing the energy functional to obtain a segmentation result.

The application provides a CT image segmentation method based on an NGA-RSF model, and steps are as follows: 1) obtaining a CT image to be segmented, and pre-processing the CT image to be segmented; 2) constructing an RSF image segmentation model NGA-RSF combined with a natural gradient and an AdamW algorithm, setting an objective function and initial parameters of the NGA-RSF model, wherein the initial parameters include an iteration stop condition, an AdamW parameter control condition, an AdamW algorithm adaptive adjustment parameter and an image segmentation model parameter; 3) inputting the pre-processed CT image into the NGA-RSF model, and iteratively updating a level set function; and 4) when the iteration process reaches the iteration stop condition, taking a zero level set of the level set function as a segmentation result. The application can effectively process a gray uneven image and a weak edge image, and obtain a relatively accurate sub-pixel level segmentation result; compared with a classic RSF model, the application not only maintains the sub-pixel segmentation precision of the original model, but also greatly improves the model convergence speed and robustness.

The invention relates to the technical field of volume measurement, and provides a device and a method for measuring the volume of a test pit in roadbed compaction detection. A laser scanning module scans and obtains point cloud data in a test pit and transmits it to a data preprocessing module; the data preprocessing module performs denoising according to a radius outlier filtering algorithm after receiving the point cloud data; a data calculation module receives the preprocessed data, calculates the point cloud data information, and iteratively calculates the zero isosurface of the level set function representing the surface of the test pit, and then uses Gauss-Green's theorem to perform volume calculation; a data display module displays the boundary image, volume and point cloud data image of the test pit area according to the calculation result; the invention obtains point cloud data through a laser scanning module, and after filtering and denoising by the data preprocessing module, the data calculation module constructs a level set function and determines the boundary of the test pit with the help of partial differential equation evolution, and then uses Gauss-Green's theorem to calculate the volume, so as to achieve the measurement of the precise volume of the irregular test pit.

The application discloses a kind of topological independent heat source detection method and system based on level set-physical information neural network, belong to heat conduction and heat source detection technical field, including constructing heat conduction control equation and boundary condition;Heat source boundary and heat source term are implicitly represented using level set function;Build temperature prediction and level set double network collaborative architecture;Design fusion physical constraint, data fitting and geometric regularity multi-objective composite loss function;Network is trained using pre-training, joint optimization and fine convergence stage strategy;After convergence, level set zero isoline is extracted to output heat source boundary and temperature field and performance evaluation is carried out;System includes modeling module, level set representation module, network construction module, loss function module, training optimization module, result output module and evaluation module;The topological independent heat source detection method and system based on level set-physical information neural network provided by the application have significant engineering application value.

The present invention belongs to the technical field related to structural topology optimization, and discloses a gradient lattice structure topology optimization method for matching the boundaries of multiple types of microstructures, comprising the following steps: (1) constructing a parameterized level set function of multiple types of microstructure cells; (2) obtaining a microstructure topological configuration containing multiple sub-microstructure cells; (3) obtaining a global parameterized level set function of the lattice structure; (4) decoupling the Hamilton-Jacobi partial differential equation that controls the evolution of the level set function; (5) defining a finite element analysis grid; (6) defining a global weight coefficient at the vertex of each lattice structure cell; (7) establishing a mathematical model for topology optimization of a functional gradient lattice structure; (8) performing a finite element analysis on the lattice structure; (9) performing a sensitivity analysis; and (10) updating global design variables to obtain a gradient lattice structure in which multiple types of microstructures are distributed within the same design domain. The present invention ensures a continuous transition of the geometric boundaries of microstructures of different types and different volume fractions.