415 results about "Geometric distortion" patented technology

The invention discloses a welded pipe surface defect detection system, particularly relates to the technical field of metal pipe surface quality detection, and is used for solving the problem of defect misjudgment and missing detection caused by mixed reflection interference under single light source irradiation in the existing visual detection technology. An annular light source is adopted for time-sharing pulse triggering through a light source control module, reflected light images at different angles are synchronously collected, a feature extraction module constructs a diffuse reflection intensity ratio and mirror reflection angle distribution matrix to generate a three-dimensional reflection feature spectrum, and reflection anomaly characterization of a defect area is enhanced; the area positioning module screens candidate areas and inhibits interference by combining strength ratio circumferential deviation degree and reflection angle gradient change, and the distortion correction module compensates geometric distortion errors based on curvature radius and station movement parameters, filters pseudo defects of abnormal time sequence fluctuation, and improves the accuracy of the distortion correction. And the defect judgment module utilizes a gradient-gray scale space coupling classification model to distinguish the types of cracks, scratches and corrosion, and finally judges the defect authenticity in combination with reflection characteristic deviation vector superposition.

The invention discloses a real-time single-stage remote sensing image correction target detection method based on YOLOV8, and relates to the technical field of remote sensing image processing. According to the method, a deformable convolution dynamic prediction local geometric distortion parameter is embedded based on a YOLOv8 backbone network, an adaptive deformation field is generated, pixel-level real-time correction is realized, shallow details and high-level semantic features are fused through a bidirectional path aggregation network, and channel attention and a space gating mechanism are combined, so that the real-time correction of the image is realized. The small target detection capability is enhanced, background noise is suppressed, angle prediction is divided into discrete classification and continuous residual error regression tasks through a decoupling type rotation detection head, angle periodic errors are eliminated in combination with a direction sensitive loss function, and the rotation frame positioning precision is improved. And constructing a dynamic multi-task collaborative loss function, introducing gradient distribution consistency constraint to jointly optimize correction and detection tasks, and realizing feature semantic alignment and model self-enhancement through end-to-end closed-loop training. And the rotating target detection precision and the complex scene robustness are obviously improved.

The invention relates to the field of image comparison, and discloses a multi-layer PCB alignment deviation detection system and method based on image comparison, and the method comprises the steps: obtaining high-resolution image data before and after lamination of a multi-layer PCB, carrying out the unified registration preprocessing of an original image through the combination of a multi-mode image fusion algorithm and a geometric distortion correction technology, and obtaining a multi-layer PCB alignment deviation detection result; constructing a standardized image registration input set; key alignment feature extraction is carried out on the image registration input set, and a multilayer structure graph model is constructed based on a graph neural network; in combination with the alignment reference map, dynamically adjusting an image comparison window and a search region by adopting a region attention mechanism and a local adaptive matching algorithm, and constructing an alignment deviation mapping map; constructing a deviation evolution model by using a time sequence behavior recognition network according to the constructed alignment deviation mapping graph and historical process deviation data; and performing comprehensive evaluation on the image registration input set based on the deviation evolution model and the generated early warning information. The method has the advantage of improving the accurate detection level.

The invention relates to the technical field of industrial product surface quality detection, in particular to a flexible display module surface defect image recognition method, which comprises the following steps: acquiring a plurality of surface images of a flexible display module under different light sources and carrying out distortion removal processing on the surface images; reconstructing and generating three-dimensional reference point cloud data representing the current curved surface form of the module; re-projecting the distorted image to the ideal rigid plane according to the relationship, generating a plurality of corrected images, and generating a plurality of corrected images to eliminate geometric and luminosity distortion introduced by flexible deformation; obtaining a defect candidate area binary image; and extracting a multi-dimensional feature vector of the defect candidate region from the binary image of the defect candidate region, and classifying the feature vector by using a pre-trained defect classification model to obtain a defect identification result. Through the three-dimensional reference point cloud reconstruction and image re-projection technology, the problem of geometric distortion caused by surface deformation of the flexible display module is effectively solved, and misjudgment and missed judgment are avoided.

The invention relates to the technical field of medical image processing, in particular to a medical equipment optical system calibration method and system based on image processing, and the method comprises the following steps: capturing a calibration image in medical equipment, analyzing the boundary structure of an optical calibration grid, screening the pixel distribution characteristics of a segmentation boundary, and extracting the geometric description characteristics of the calibration image; and generating a geometric distortion distribution diagram. According to the invention, by capturing the calibration image and analyzing the boundary of the optical calibration grid, the geometric distortion area is accurately identified, the accuracy of the distortion compensation image is improved, the non-linear distortion is compensated in a partition manner, the image quality of each area is optimized, the continuity of the image boundary is improved, and the structural symmetry and curvature change area are optimized. The distortion of the detail area is restored, the edge details are smoothed, the image quality is improved, the amplitude distortion of the optical calibration grid is effectively controlled, the edge detail distortion is reduced, the image integrity and consistency are ensured, and the image precision and the reliability of an optical system are remarkably improved.

The invention provides a hull surface defect detection system based on machine vision, and relates to the technical field of data processing. The image correction module is used for carrying out illumination equalization processing and geometric distortion correction; the region construction module is used for identifying a defect-free stable region and generating reference region data which comprises a brightness model and a texture model; the candidate generation module is used for detecting a region where texture interruption or abnormal bright spots exist locally to form candidate defect data, and the candidate defect data comprise pixel positions and local contrast parameters; the stability judgment module is used for carrying out projection matching in the multiple frames of images and simultaneously carrying out joint comparison with the brightness model and the texture model of the reference area data to form real defect data and false defect data; the result output module is used for generating a detection result containing defect coordinates, defect contours, image frame numbers and interference sample prompts; the accuracy of hull surface defect detection is improved.

The invention discloses a time-space dynamic weight-based building monitoring data accurate registration method, which comprises the following steps of: establishing a response model considering nonlinear behaviors of materials by analyzing deformation characteristics in multi-source monitoring data; constructing a dynamic weight function based on a structural stress state, and realizing self-adaptive distribution of sensor data reliability weight; geometric deformation correction is carried out by using the material nonlinear response model, and geometric distortion caused by material nonlinear behaviors is corrected; space-time registration of multi-source data is realized by adopting a weight fusion and deformation gradient constraint method; and parameter adaptive optimization is carried out through an online learning mechanism. According to the method, the technical problems of low geometric correction precision of a high-stress area, dynamic change of sensor reliability and the like are solved, and submillimeter-level precision building monitoring data registration is realized.

The invention belongs to the field of image generation, particularly relates to a three-dimensional image generation method based on spherical reflection cavity parameter optimization, and aims to solve the problems of low imaging precision and poor adaptability caused by lack of a collaborative optimization mechanism in the prior art. The method comprises the following steps: acquiring optical characteristics of the inner wall of a cavity to generate a reflection path offset comparison table, measuring deformation deviation of the cavity to construct a geometric compensation mapping relation, and monitoring environmental disturbance in real time and converting the environmental disturbance into a noise intensity coefficient; inputting the target three-dimensional image into a pre-compensation processor, synchronously executing optical path compensation, geometric distortion correction and environmental noise suppression, and outputting a pre-corrected image to projection equipment; capturing an actual projection through an image sensor, extracting difference data with the pre-corrected image, and decomposing the difference data into a system error component and an environment interference component; and feeding back the updated parameters to the processor to regenerate a final three-dimensional image. According to the invention, multi-error-source cooperative correction and dynamic optimization are realized, and the imaging precision and adaptability are significantly improved.

The invention discloses a distributed multi-source image interactive fusion method based on multistage space-time calibration, and relates to the technical field of signal processing, comprising the following steps: step 1, time synchronization calibration; inputting an optical image and an infrared image, and synchronizing the acquisition time of the optical image and the infrared image through time synchronization calibration; step 2, performing time interpolation compensation calibration, and correcting time offset of the optical image and the infrared image; step 3, attitude calibration: aligning the optical image and the infrared image to a unified space coordinate system, and eliminating deviation caused by different visual angles; step 4, geometric distortion calibration: correcting the geometric distortion of the image; 5, interactive image fusion: fusing the optical image and the infrared image through a deep learning image processing method; according to the method provided by the invention, data deviation caused by time, space and attitude differences between different sensors and imaging systems is effectively solved.

The invention discloses an image video de-disturbing method and system based on a time domain attention mechanism. The method comprises the following steps: inputting a continuous multi-frame turbulence degraded image sequence; performing inter-frame registration and alignment to compensate for geometric distortion; extracting spatial features through a multi-scale feature extraction network; enhancing spatial feature representation through the spatial attention module, performing learnable frequency domain filtering through the frequency domain attention module and fusing time sequence features through the time domain attention module in sequence; and finally, integrating the features to reconstruct a clear image after turbulence is removed. According to the method, a time domain attention mechanism is innovatively introduced, the time sequence correlation between video frames is effectively utilized, and the problem of inter-frame inconsistency existing in video restoration of an existing single-frame processing method is solved. Experiments show that the method is obviously superior to the existing mainstream method in objective indexes such as PSNR and SSIM, and the time sequence continuity and stability of the restored video can be effectively enhanced.

The invention provides a NeRF sparse view reconstruction method based on adaptive multi-modal feature fusion, and relates to the technical field of three-dimensional scene reconstruction. The method comprises the following steps: extracting a depth feature and a semantic feature from a sparse input view, and generating a first multi-modal feature through a feature enhancement network; a mixed attention mechanism is combined with channel and space attention to perform dual selective enhancement on the features to obtain second multi-modal features; the weight distribution network based on confidence guidance dynamically fuses the multi-modal features, harmonizes conflicts among the modals through a consistency loss function, and generates final fusion features; and inputting the fusion features into an enhanced neural radiation field rendering module, decomposing a scene into multi-scale representation by using layered feature coding, and optimizing geometry and appearance details in stages by combining a progressive training strategy to realize high-quality three-dimensional reconstruction. According to the method, the problems of geometric distortion, fuzzy texture and inconsistent modes caused by sparse views in a traditional method are solved.

The invention relates to the technical field of projectors, in particular to an intelligent picture geometric distortion control method based on machine vision and an image projector, and the method comprises the steps: obtaining image data of a to-be-corrected projection picture, and carrying out the feature analysis of the image data, so as to obtain a distortion feature value; matching a correction optimization model corresponding to the distortion characteristic value, so as to call a correction parameter from a preset correction parameter library through the correction optimization model; simulating a correction process corresponding to the projection picture to be corrected according to the correction parameter; the correction parameters are adjusted according to the correction process, and the projection picture to be corrected is corrected according to the adjusted correction parameters, the corresponding correction optimization model is matched based on the characteristic values, the adaptive correction parameters can be dynamically called from the preset parameter library, the limitation of a traditional fixed algorithm is changed, and the correction efficiency is improved. The correction strategy is highly coupled with the actual distortion scene, and the correction efficiency and pertinence are remarkably improved.

The invention discloses a global enhancement method for a wide-area astronomical image. The method comprises the following steps: carrying out high-frame-frequency image acquisition on a starry sky area; converting the reference star from a star catalogue position to an observation position, and resolving a linear negative film model; geometric distortion correction is carried out based on the resolving result; based on the image after geometric distortion correction, matching and identifying a reference star in a full view field, and obtaining celestial coordinate information of all fixed star images in the view field; on the basis of celestial coordinate information, correcting a poorer astronomical effect of the astronomical image, establishing an ideal coordinate system taking the center of a view field as a tangency point, and projecting a time sequence observation image to the ideal coordinate system; translating the processed graph, accumulating all translated images, and averaging all accumulated pixel positions to realize image superposition enhancement, thereby effectively improving the dynamic range of a detector and the detection capability of a wide-area astronomical observation system; and meanwhile, the signal-to-noise ratio of the astronomical image can be effectively improved, and the astronomical image centering and light measuring precision can be further improved.

The invention discloses a lesion area self-adaptive segmentation method and system for a seminal vesicle endoscope image, particularly relates to the field of medical image processing, is used for solving the problems of geometric distortion and artifacts in the seminal vesicle endoscope image, and aims to eliminate geometric deviation caused by thick layer sampling through synchronous acquisition and attitude correction. Then, a resampling strategy is adjusted in a self-adaptive mode through key geometric features, the problems of inter-layer artifacts and resolution imbalance are effectively weakened, a lesion segmentation network is optimized through smooth regularization and geometric constraint, continuity and geometric accuracy of lesion boundaries are ensured, finally, the accurate lesion mask is dynamically overlaid to a real-time frame stream, and the real-time frame stream is obtained. A quantitative basis is provided for biopsy path planning and photodynamic dose scheduling; smooth and continuous images are completed and output in a strict time window, the perception ability of an operator to tiny pathological changes is enhanced, meanwhile, the method is suitable for various endoscope devices, motion blur and light spot artifacts are restrained, and focus details are kept clear.

The invention discloses an image digitization method, system and device and a storage medium, and relates to the field of data processing. The method comprises the following steps: acquiring spectral data, three-dimensional shape data and material reflectivity parameters of a target image; based on the three-dimensional shape data, analyzing surface deformation characteristics of the target image, and generating a geometric distortion distribution diagram; generating geometric correction parameters according to the surface deformation features, and reconstructing pixel positions of the geometric distortion distribution diagram to obtain reconstructed image data; and mapping the spectral data to a preset color space, matching a pre-established color database, and adjusting the color component of the reconstructed image data to obtain corrected image data. By implementing the technical scheme provided by the invention, a set of traditional Chinese painting digitization technical system considering high-precision acquisition, intelligent correction and efficient management can be developed.

The invention provides a multi-modal feature fusion reference-point-free cloud quality evaluation method, and belongs to the field of three-dimensional point cloud quality evaluation. The scheme comprises a data preprocessing step, a feature extraction step, a feature fusion step and a feature regression step. Firstly, point cloud is subjected to two kinds of preprocessing, one is that the point cloud is decomposed into sub-models, and the other is that a camera rotates around the point cloud to generate a dynamic video. And the obtained point cloud sub-model and the dynamic video are respectively sent to a feature extraction unit, and in the feature extraction stage, geometric distortion features are extracted from the point cloud sub-model, and spatial features and time features are extracted from the dynamic video. And then entering a feature fusion stage, fusing spatial features, time features and geometric distortion features by using a new cross-modal attention module to obtain final quality perception features, and finally, regressing the final quality features to a predicted quality score by using a dual full-connection layer.

The invention relates to the technical field of multispectral imaging, in particular to a multispectral imaging fused pattern recognition image acquisition method and device, and the method comprises the following steps: obtaining a multiband substrate response value through a multispectral sensor, generating response difference distribution through moving average filtering, inputting cross-correlation analysis, extracting a space offset vector, and compensating displacement; after the signal intensity of the mark points is captured, Gaussian fitting is carried out to generate registration coordinates, triangular mesh division is carried out, offset is calculated, interpolation mapping is carried out, a registration error distribution diagram is generated, bilinear interpolation remapping is carried out to collect time sequence parameters, and a multispectral imaging data set is output. According to the method, noise is eliminated through moving average filtering, offset vectors are extracted in a cross-correlation mode, array displacement is compensated, mark points are positioned through two-dimensional Gaussian fitting, a registration error graph is constructed through triangular meshes, parameters are collected through bilinear interpolation remapping, geometric distortion is corrected, feature consistency and collection synchronism are enhanced, and the imaging resolution ratio and the signal-to-noise ratio are improved.

The invention discloses a crack image processing method based on a neural network and plane geometry image transformation, and particularly relates to the field of general image data processing driven by plane geometry image transformation and neural network identification. Comprising the steps that an image collection platform projects a structured image coding pattern to the surface of a to-be-measured target through a preset image coding pattern template, and synchronously collects an image frame containing an image mark so as to construct a space anchor point used for image geometric reference in an image plane; view angle parameter change information in an image acquisition process is acquired through an image acquisition platform, and projection configuration parameters of an image coding pattern are combined. By constructing an image offset modeling mechanism jointly driven by an image coding pattern and a space anchor point, image in-plane correction of geometric distortion features in an image frame is realized, and structural consistency and texture continuity before subsequent image analysis are improved.

The invention relates to the technical field of projection display, and discloses a multi-projection picture splicing method and system based on geometric correction. The method comprises the following steps: acquiring initial parameters and original picture data of the multi-projection equipment, wherein the original data contains an overlapped region image and is acquired according to a preset direction; geometric correction is carried out on the original image, geometric distortion feature points are extracted, and a mapping relation matrix is generated; dividing projection boundaries based on the matrix, and adjusting parameters to form a correction parameter set; performing spatial registration on the picture data according to the parameter set to generate multi-channel data; and carrying out fusion processing on the multi-channel data overlapping region, eliminating light intensity difference and color deviation, and generating a continuous splicing picture. The system comprises a data acquisition module, a geometric correction module, a parameter adjustment module, a space registration module and a fusion processing module. Through high-precision geometric correction, adaptive fusion and dynamic adjustment, the precision and visual effect of multi-projection image splicing are improved, and the method is suitable for large-size display scenes and has the advantages of being high in environmental adaptability, high in automation degree and the like.

The invention belongs to the technical field of engineering information intelligent processing, and relates to a highway bridge drawing multi-modal information extraction and semantic understanding method and system.The method comprises the steps that firstly, self-adaptive judgment is conducted on a vector drawing and a scanning drawing, geometric distortion correction, drawing frame and title bar positioning and layout segmentation are completed, and then the vector drawing and the scanning drawing are obtained; constructing a hierarchical document structure comprising texts, tables, images and two-dimensional drawing objects; then, a front-end target detection network and a rear-end cross-modal document understanding model are fused, and detection and relation reasoning of elements such as view blocks, labels, symbols and tables are achieved; and image-text feature alignment is further performed by using a visual coding network and a text coding network, structural description conforming to engineering semantics is generated by means of a multi-modal language model, an engineering parameter database is established, and parameter query and multi-modal question and answer output are supported. According to the method, the accuracy and efficiency of automatic acquisition and semantic understanding of the key information of the highway bridge drawing are improved.

The invention provides an unmanned aerial vehicle target identification and positioning method and system based on multispectral fusion. The method comprises the following steps: firstly, acquiring multispectral image data, then analyzing the multispectral image data of multiple time phases into pixel-level data, then identifying an early infection area of the plant diseases and insect pests, then constructing a geometric distortion correction model, and then determining the relative position of the early infection area of the plant diseases and insect pests through multi-view space intersection calculation. And finally, fusing the real-time differential global navigation satellite system positioning data of the unmanned aerial vehicle and the relative position of the disease and insect pest early-stage infection area to output absolute geographic coordinates of the disease and insect pest early-stage infection area. According to the technical scheme provided by the invention, accurate conversion from a local coordinate system to a global geographic coordinate system is realized, an exact spatial position basis is provided for precise agricultural operation, and precise positioning of a pest and disease damage area is realized.

The invention relates to the technical field of computer vision and medical image processing, in particular to an intra-intestinal monocular pose estimation method based on diffusion model deformation field prediction, which comprises the following steps of: inputting two frames of enteroscopy images of an intestinal capsule robot with adjacent time sequences, respectively extracting image features of front and back frames of enteroscopy images through a parameter-shared double-path depth map generation network, and generating corresponding depth maps; solving six-degree-of-freedom pose parameters of the intestinal capsule robot by utilizing the pose estimation network; generating a three-dimensional non-rigid deformation field through the non-rigid deformation field prediction network based on the physical regularization diffusion model; reconstructing a composite image aligned with the real previous frame of enteroscope image through an image synthesis module; completing self-supervised training; the problems that in an existing intestinal capsule robot positioning technology, a non-rigid deformation field lacks biophysical constraints, three-dimensional reconstruction geometric distortion is caused by monocular information, and collaborative optimization is lacked in deformation field and pose parameter decoupling are solved.

The invention relates to a multi-modal image matching method and system based on learning features and epipolar geometric constraints. The method comprises the following steps: carrying out edge enhancement processing on an input image through wavelet transform; extracting a multi-scale dense feature map based on the transformed convolutional neural network, and generating a feature descriptor with rotation and scale invariance in combination with principal direction normalization; adopting an FLANN algorithm and dynamic distance constraint to realize preliminary feature matching; and introducing a basic matrix construction and epipolar geometric consistency verification mechanism, and eliminating mismatching point pairs in combination with an RANSAC affine constraint model. According to the method, image enhancement, deep learning and geometric verification strategies are fused, the problems of radiation nonlinearity and geometric distortion caused by imaging mechanism differences among multi-modal images are effectively solved, the matching precision and robustness are improved, and the method is suitable for remote sensing application scenes such as optical-SAR registration, multi-source image fusion and earth surface change detection.

The invention discloses a farmland furrow three-dimensional point cloud data correction method and system based on a laser radar, and relates to the technical field of three-dimensional image processing based on the laser radar, in particular to a three-dimensional image geometric correction technology. The problems that original point cloud data directly obtained under the dynamic operation condition of an existing agricultural machine has geometric distortion, and the shape of a furrow cannot be truly reflected are solved; meanwhile, the problems that furrow quality evaluation seriously depends on low-efficiency manual means, the adaptability of an existing automatic technical scheme to an unstructured field environment is insufficient, and robust, real-time and high-precision perception of a real three-dimensional shape of a furrow cannot be achieved are solved. According to the method, dynamic tilt correction is carried out through real-time acquisition of point cloud and agricultural machine attitude information, boundary feature points are extracted, boundary lines are fitted, and a three-dimensional point cloud set of a single furrow is reconstructed by fusing a self-adaptive clustering algorithm of spatial prior. The method is suitable for the fields of farmland tillage and soil preparation quality detection, precision agricultural management, agricultural machinery intelligent operation and the like under the dynamic operation condition of agricultural machinery.

The invention relates to a multi-modal perception fusion target following method and system. The method comprises the following steps: adopting an improved KCF algorithm to realize a closed-loop process of target tracking, multi-scale space construction, feature fusion, response calculation, optimal scale decision and model updating; designing a four-level shielding processing mechanism fusing motion prediction and depth verification, and realizing tracking recovery in a shielding scene through shielding judgment, motion prediction, fine search and template protection; constructing a target distance mapping function fusing geometric distortion correction and attitude compensation, and realizing high-precision distance estimation based on monocular vision; laser radar point cloud information is integrated, an obstacle threat degree model is constructed, and cooperative path planning of following and obstacle avoidance is realized in combination with an improved TEB algorithm; and designing a linear velocity control law and an angular velocity control law based on the distance deviation and the azimuth angle deviation, and driving the robot to complete target following motion. According to the invention, high-precision and robust following of the robot to the target can be realized.

The invention discloses a high-precision global quasi-static strain vision measurement method, and relates to the technical field of strain vision measurement, and the method specifically comprises the steps: 1, obtaining high-resolution continuous image deformation data, correcting the geometric distortion of a deformed image through an ortho-rectification method, and carrying out the correction of the geometric distortion of the deformed image; the method comprises the following steps: step 1, acquiring high-resolution and continuous image deformation data through a high-precision image acquisition device, and accurately correcting geometric distortion existing in a deformed image by using an orthographic correction method, step 2, carrying out noise reduction processing on the image through a singular value decomposition technology, carrying out image displacement analysis on the image subjected to the noise reduction processing, and carrying out image displacement analysis on the image subjected to the image displacement analysis. The method comprises the following steps: step 1, carrying out deep noise reduction processing on an image by using an advanced singular value decomposition technology to effectively remove various noise interferences in the image, and step 2, carrying out further processing on data obtained after strain calculation is completed, and visually displaying a strain result through visual output. The invention provides an efficient and reliable solution for complex structure global strain measurement.

The invention discloses a transparent glass pose detection method and system based on multi-modal data and a glass grabbing method, and relates to the technical field of glass pose detection. Geometric distortion characteristics of a glass contour are captured by using a camera, a relative pose relationship between the geometric distortion characteristics and a camera plane is analyzed, and a mechanical arm is driven to be roughly adjusted to a roughly parallel pose; based on distance information collected by an ultrasonic sensor array, a glass plane equation is fitted through space geometric solution, the normal vector direction and distance parameters of glass are accurately obtained, and closed-loop fine adjustment of the pose of the mechanical arm is achieved. The detection bottleneck that a transparent medium is difficult to reflect an optical signal is broken through, high-precision pose detection can be realized without installing auxiliary equipment on the surface of the glass, and the damage of a contact mark to the surface of the glass is avoided. The detection reliability is remarkably improved through multi-modal data complementation, meanwhile, a low-cost sensor is adopted to replace high-precision laser equipment, and industrial-grade precision and economical efficiency are both achieved.

The invention discloses a traffic accident scene digital reconstruction method based on ground-air cooperation. The method comprises the following steps: acquiring image data of an accident scene by using a five-point calibration method and a three-direction camera shooting method; performing denoising, enhancement and geometric distortion correction on the image data of the accident scene to obtain preprocessed image data; key feature points are extracted from the preprocessed image data, and feature point matching is carried out between different images to obtain a feature point matching result; and reconstructing an accident scene digital reconstruction model according to a feature point matching result and the preprocessed image data. According to the invention, the precision of a measurement result can be ensured, and the safety of traffic accident scene investigation work and the comprehensiveness of investigation data are ensured.

The invention discloses a self-adaptive text extraction method and system based on artificial intelligence, and the method comprises the steps: carrying out the analysis of the document structure entropy of an example document set, quantifying the noise density, geometric distortion degree and background complexity of the example document set, and carrying out the self-adaptive selection of a preprocessing assembly line intensity grade according to the above; dynamically configuring image preprocessing parameters and AI recognition model parameters, and generating a recognition engine instance to output a preliminary recognition text; after regularized coarse screening extraction is carried out based on key field description, a multi-candidate generation strategy is started for low-confidence-coefficient candidate text fragments, a multi-person cooperative verification process is triggered for lower-confidence-coefficient fragments, finally all the fragments are processed through a text standardization module, and structured text extraction information is output. According to the method, accurate adaptation of processing intensity is achieved through document quality quantitative evaluation, the extraction accuracy and system robustness of complex heterogeneous documents are effectively improved through a multi-level confidence coefficient verification mechanism, and the identification error risk caused by image quality fluctuation or rule solidification is reduced.

The invention discloses a glomerular segmentation method based on multi-scale Mama and dynamic sampling, and relates to the technical field of medical images. According to the method, the input multi-scale features are efficiently represented by using a continuous state transition mechanism, the structural association information extraction capability among different scales is enhanced, in addition, the problem of geometric distortion in dynamic sampling is solved by introducing position coding into the dynamic sampling method and adding position constraint information, and the dynamic sampling efficiency is improved based on a U-Net model. An MS-Mama module is gradually added in the encoder stage, and an existing bilinear interpolation method is replaced by a position sensing dynamic sampling module; and then, serialized representation is performed on multi-scale features based on a selective state space mechanism, the dependence of Mama on specific scale features is reduced, the ability of extracting heterogeneous morphological features of lesion glomerulus is enhanced, the boundary positioning precision of lesion glomerulus is enhanced by adding position constraint information, and the geometric distortion problem in dynamic sampling is solved.