17 results about "Local scale" patented technology

The invention provides an optical and acoustic image registration method for underwater structure crack detection, and the method comprises the steps: collecting a multi-mode image through the vision field and time synchronization, building a one-to-one correspondence relation according to the pose and time metadata, and improving the image quality through the denoising, distortion correction and other means; optical and acoustic multi-scale features are respectively extracted through a dual-channel network, and cross-modal semantic alignment is realized in combination with a shared weight and a channel attention mechanism; generating a high-resolution scale offset field by using local cross-correlation and micro-upsampling, and introducing manifold regularization constraint to ensure that a vector field is smooth and continuous; according to the method, the local scale field and the affine parameters are combined, non-rigid space mapping is achieved through thin-plate spline interpolation, feedback iterative optimization based on edge structure consistency is assisted, the registration precision of a key structure is enhanced, the automatic registration effect of the underwater multi-modal image is improved, and the method has high robustness and practical application value.

The invention relates to the technical field of machine vision, and particularly discloses a length measurement method and system based on machine vision, and the method comprises the steps: collecting the geometric data of a sequence image recognition clamp to form a geometric constraint set, and extracting the candidate edge of the end part of a pole piece; executing constraint deformation regression based on the geometric constraint set to obtain time-varying distortion mapping and generate a geometric correction image; triggering a focal plane perturbation to collect an auxiliary image, calculating a parallax field, solving a local scale factor, and generating a mapping relation from pixels to actual lengths; executing sub-pixel edge fitting in the geometric correction image to obtain pole piece endpoint coordinates; calculating the extension amount of the measured pole piece; according to the method, time-varying distortion is eliminated through online correction of geometric drive of the clamp, depth-related scale change is compensated by using active focal plane perturbation, and the precision problem of length measurement in a cross-medium environment is effectively solved by combining sub-pixel positioning and time sequence processing, and extra calibration equipment is not needed.

The invention discloses a typesetting method and typesetting equipment capable of printing while shooting, a medium and a program product, and relates to the technical field of image processing and intelligent printing. On the basis of converting an original image into structured data and performing global mapping, the typesetting device divides consumables into a main visual area, an auxiliary visual area and an avoidance area, constructs a semantic adhesion cluster in combination with spatial adjacency between objects, and can detect whether an image-text module with close logic association can be split by a fold line or shielded by a hole. If conflicts occur, the limitation of global scaling is broken, and local scaling and offset coordinates are independently calculated for the adhesion clusters for adjustment. According to the derivation process of multi-dimensional feature interaction, the problems of visual blind areas and semantic segmentation of traditional two-dimensional typesetting in a complex three-dimensional pasting scene are effectively avoided, and the integrity and transmission accuracy of graphic and text information of special labels (such as flag-shaped labels and winding labels) in actual physical application are improved.

The application provides a packaging box anomaly detection method and device based on semantic constraints and geometric priors, and relates to the technical field of defect detection. The method extracts local geometric description information from three-dimensional point cloud data, inputs the information into a geometric perception measurement module to generate a geometric reliability coefficient, and uses the coefficient to adaptively modulate a local scale parameter predicted based on a direction vector to construct a multi-modal anomaly measurement constrained by a geometric prior. The method inputs a sample to be detected into a trained anomaly detection model, calculates local anomaly measurement values of each region through the multi-modal anomaly measurement, generates an anomaly score map reflecting the degree of anomaly, and performs threshold segmentation and connected component analysis to realize detection and positioning of packaging box anomalies. The method can realize high-precision packaging box anomaly detection without relying on a large amount of manually labeled data, and significantly improves the reliability and efficiency of packaging box quality detection.

The invention provides a packaging box anomaly detection method and device based on semantic constraint and geometric prior, and relates to the technical field of defect detection.The method comprises the steps that local geometric description information is extracted from three-dimensional point cloud data and input into a geometric perception measurement module to generate a geometric reliability coefficient; performing adaptive modulation on a local scale parameter based on direction vector prediction by using the coefficient, and constructing a multi-modal anomaly metric constrained by geometric prior; and inputting a to-be-detected sample into the anomaly detection model obtained by training, calculating a local anomaly metric value of each region through the multi-modal anomaly metric, generating an anomaly score graph reflecting an anomaly degree, and performing threshold segmentation and connected domain analysis to realize detection and positioning of the anomaly of the packaging box. According to the method, high-precision packaging box anomaly detection can be realized under the condition of not depending on a large amount of manual annotation data, and the reliability and efficiency of packaging box quality detection are remarkably improved.

The invention relates to an acoustic shadow image detection system and method based on multi-dimensional feature fusion and structure guidance. The system comprises a first acquisition module for acquiring ultrasonic radio frequency data and a B-mode image obtained by performing logarithmic compression on the ultrasonic radio frequency data; the second acquisition module is used for acquiring a local scale parameter diagram and a local shape parameter diagram based on the ultrasonic radio frequency data, the local scale parameter diagram comprises local scale parameters corresponding to the positions respectively, and the local shape parameter diagram comprises local shape parameters corresponding to the positions respectively; the sound shadow probability graph acquisition module is used for acquiring a sound shadow probability graph based on the local scale parameter graph and the local shape parameter graph; and the correction module takes the B-mode image as a guide image, and performs sound shadow edge correction on the sound shadow probability graph to obtain a sound shadow detection image. According to the method, a low-echo pathological structure and a normal liquid dark region can be effectively distinguished, and high-precision boundary positioning of an acoustic shadow region can be realized based on refined analysis.

The invention provides a modeling method and system for analyzing a statistical distribution rule of RSRP in a local space, and the method comprises the steps: respectively calculating approximate expressions of a direct path distance and a reflection path distance based on a two-dimensional plane geometrical relationship formed by a base station, a user and a single dominant reflection surface under the constraint that the user makes small displacement near an initial position; calculating a superposition result of the received signals, and deducing an approximate analytical expression of user side reference signal received power (RSRP) about a user displacement variable; deriving a probability density function PDF and / or a cumulative distribution function CDF of the RSRP in the local space on the basis of the approximate analytical expression under the condition that the user displacement is assumed to be uniformly distributed in the local area; and evaluating the coverage quality according to the spatial statistical distribution rule of the RSRP. According to the method, a local scale model which keeps physical interpretability and can export an approximate analytic solution is constructed, so that a quantitative basis is provided for engineering evaluation, measurement sampling interval selection and rapid simulation.

The invention discloses a large eddy simulation method based on diurnal variation characteristics of a local scale three-dimensional space wind-heat-humidity environment. The large eddy simulation method is specifically implemented according to the following steps: step 1, constructing a multi-field coupling model of the local scale wind-heat-humidity environment; step 2, acquiring parameters of the parameterized scheme; 3, setting a research area; 4, setting initial and boundary conditions; and 5, carrying out numerical simulation analysis on the daily change of the local scale wind-humidity-heat environment. According to the method, the dynamic-thermal effect of the urban vegetation layer is comprehensively considered through the integration method, a new large-eddy simulation model is constructed, and accurate numerical simulation under the local scale is achieved.

The application discloses a biological data visualization and batch correction system based on deep manifold learning, which embeds a given data set into a two-dimensional or three-dimensional visualization space based on Euclidean manifold or hyperbolic manifold under the premise of considering specific task types, corresponding to biological data of the type "time fixed" or "time evolution" respectively. Specifically, the system learns the relationship between data points based on the local scale shrinkage technique through end-to-end training, converts the data to the visualization space while preserving the data geometric structure, and corrects the batch effect. The application has better effects in discovering complex biological data relationships, revealing time trajectories and processing complex batch factors.

The application discloses a caterpillar beam weld seam track fitting method based on an improved RANSAC algorithm and particularly relates to the technical field of weld seam track identification and automatic welding path planning. Standardized point cloud data is acquired. Welding spatter points are removed by combining the normal change rate of points with the extraction of weld protruding feature points through local height gradient analysis, a multi-scale point set structure of an overall scale point set and a local scale point set is constructed, a random consistency sampling is performed on the overall scale point set to fit a weld overall trend model, and a local track model set is generated on the local scale point set with the inner point set as a constraint. A track connection graph structure is constructed according to the local curvature change value and the direction continuity score, the screening and splicing of the local track model are completed, and a weld compensation track model is obtained. The compensation track model is subjected to continuous curve fitting to generate a weld center track curve. The application can realize high-precision weld seam track fitting in the presence of welding spatter and local deformation interference.

The application discloses a waveform-protected non-source wavelet local scale traveltime inversion method, relates to a non-source wavelet full waveform inversion method established by using a convolution and deconvolution method, and is combined with local scale traveltime inversion, and simultaneously solves the period jump of full waveform inversion and the source wavelet dependence problem. First, the influence of the source wavelet in the observation data and the simulation data is eliminated by using the convolution and deconvolution method, and the original seismic data waveform characteristics are protected. Secondly, local scale traveltime information of the observation data and the simulation data is extracted, and a waveform-protected non-source wavelet local scale traveltime inversion objective function is constructed. Then, the partial derivative of the waveform-protected non-source wavelet local scale traveltime inversion objective function to the velocity parameter is deduced in detail, and the corresponding underground velocity parameter gradient operator of the adjoint state method is deduced. Finally, the stable update of the velocity model parameter is realized by using an L-BFGS optimization algorithm. Finally, the effectiveness of the method is verified by the seismic data test.

This invention claims protection for an improved scale fusion-based brain tumor segmentation method, aiming to improve scale feature fusion by aggregating scale information within and between encoder layers, thereby achieving accurate brain tumor image segmentation. This method belongs to the field of computer vision. The method includes the following steps: Step 1. This invention proposes a global-local feature mixer, which achieves global-local scale information mixing by capturing local detail features and global context information in parallel, to solve the problem of uncertain brain tumor location. Step 2. This invention designs a multi-scale expansion initialization module, which can effectively capture multi-scale shape features, aiming to improve the network's ability to adapt to different brain tumor shapes. Step 3. This invention designs a multi-scale feature aggregation module, which can effectively aggregate multi-scale features from different encoder layers, helping to alleviate the problem of uncertain brain tumor size.

The invention provides a local-scale atmospheric pollution tracing method based on big data, and belongs to the technical field of atmospheric pollution tracing. The method comprises the following steps: S1, acquiring environmental basic information; s2, grid division is carried out on an area to be subjected to atmospheric pollution traceability, the distance x from each grid point to a pollution source in the wind direction axis direction is calculated, and the distance y from each grid point to the pollution source in the direction perpendicular to the wind direction axis is calculated; s3, calculating source intensity, a transverse diffusion coefficient and a longitudinal diffusion coefficient based on the environment basic information, the distance x and the distance y; s4, calculating the pollution contribution concentration of each pollution source to the grid points based on the environmental basic information, the distance y, the source intensity, the transverse diffusion coefficient and the longitudinal diffusion coefficient through a Gaussian diffusion model; and S5, carrying out local-scale atmospheric pollution tracing based on the pollution contribution concentration. The method has the advantages that the accuracy and comprehensiveness of local scale atmospheric pollution tracing are greatly improved.

The application provides a text pedestrian representation learning and matching method and system, comprising: 1) using a pre-trained ResNet model to generate a primary feature map for each input picture, and performing cutting processing on the feature map based on different scales; 2) using a Bottleneck Transformer as a representation learning network to perform self-attention calculation on different visual regions; 3) learning each word embedding through a pre-trained BERT model with fixed parameters; 4) further processing the word embedding through a hybrid branch network combining a residual network and a Transformer; 5) optimizing the representation of text and image by means of a cross-modal projection matching function (CMPM), and aligning the visual representation and the text representation from a local scale, a medium scale and a global scale respectively; 6) using the combined representation of the three scales as the final representation for retrieval. The application can extract good semantic information, has high accuracy and fast retrieval speed.

The application relates to the technical field of measurement remote sensing, and particularly discloses a cross-view geographical positioning method and system based on a multi-scale feature fusion pyramid, which comprises the following steps: acquiring target image data; extracting global scene features and multiple local detail features by adopting global and multiple local scale pooling on the target image data, performing convolution dimension reduction and fusion on the multiple local detail features, and obtaining local fusion features; performing linear transformation on the global scene features and the local fusion features to generate a feature mapping matrix, determining global attention weights and local attention weights based on the feature mapping matrix; determining spatial self-adaptive enhanced features based on the global attention weights and the local attention weights, performing fusion by adopting multi-feature residual connection, and obtaining a multi-dimensional feature vector; and outputting target positioning information based on the multi-dimensional feature vector and a preset satellite image database, so that the positioning accuracy is improved, and the matching process is optimized to reduce the complexity.

The invention relates to the technical field of measurement remote sensing, and particularly discloses a cross-view geographic positioning method and system based on a multi-scale feature fusion pyramid, and the method comprises the steps: obtaining target image data; global scene features and multiple local detail features are extracted from the target image data through global and multi-local scale pooling, convolution dimension reduction and fusion are carried out on the multiple local detail features, and local fusion features are obtained; performing linear transformation on the global scene feature and the local fusion feature to generate a feature mapping matrix, and determining a global attention weight and a local attention weight based on the feature mapping matrix; determining a spatial adaptive enhancement feature based on the global attention weight and the local attention weight, and performing fusion by adopting multi-feature residual connection to obtain a multi-dimensional feature vector; and outputting target positioning information based on the multi-dimensional feature vector and a preset satellite image database, and optimizing a matching process to reduce the complexity while improving the positioning precision.

The application provides a site-metro tunnel network-building earthquake damage analysis method and device, and relates to the technical field of civil engineering structure earthquake resistance. The method comprises the following steps: selecting a typical metro tunnel form of a target city, establishing a fine site-metro tunnel model of a local scale; establishing simplified site-metro tunnel dynamic models of different simplified forms; calculating the transfer functions of the fine site-metro tunnel model and the simplified site-metro tunnel dynamic models, determining the simplified site-metro tunnel dynamic model with the highest matching degree and the corresponding parameter determination method; determining a non-uniform grid division mode according to the metro tunnel position and the precision requirement, and completing the construction of the earthquake source-site-metro tunnel-building group model of the site and the building in the target city according to the non-uniform grid division mode and the parameter determination method. The application can realize reliable analysis of the three-dimensional coupling of the metro tunnel network-soil-building group on a kilometer scale.