591 results about "Image matching" patented technology

The invention discloses an automobile part quality detection method and system based on artificial intelligence visual inspection, and belongs to the field of artificial intelligence machine visual inspection, and the method comprises the steps: firstly, carrying out the registration of a collected RGB image and a depth image, and extracting a part region through a saliency detection network; two-dimensional key points are extracted based on an RGB region image and are matched with key points of a three-dimensional model, an initial three-dimensional attitude is obtained by adopting a PnP algorithm, iterative registration is performed with the three-dimensional model in combination with a point cloud generated by a depth image, and a fine three-dimensional attitude is obtained. And calculating a geometric transformation matrix from the part to a standard front view attitude according to the attitude, and performing attitude correction on the RGB and depth region image. And then matching the corrected image with a standard template image by using a feature detection and matching network so as to correct the position of the detection window. And finally, the three-dimensional size of the part is calculated in the corrected detection window in combination with the depth value, and tolerance judgment is carried out. And the precision, the robustness and the automation level of online detection of the automobile parts can be obviously improved.

The invention discloses a cylindrical roller trajectory tracking method and device, and the cylindrical roller trajectory tracking method comprises the following steps: obtaining an actual measurement displacement sequence of a key point on the end surface of a roller under a global coordinate system based on a multi-reference matching digital image correlation method; drawing a key point displacement curve about key points and time according to the actually measured displacement sequence; judging the motion state of a roller according to the shape characteristics of the key point displacement curve, dividing a smooth part in the key point displacement curve into a rolling area, and dividing a part with jumping or sharp points into a sliding area; calculating the actual revolution angular velocity and the actual rotation angular velocity of the roller based on the actually measured displacement sequence corresponding to the sliding area; according to the method and the device, the image matching precision can be improved, meanwhile, the rolling and sliding components of the roller are accurately distinguished, the precision of the track of the cylindrical roller is improved, and high-precision and visual experimental data are provided for existing dynamics and tribology models.

The invention provides a camera pose estimation method based on 2D Gaussian splash. The camera pose estimation method comprises the following steps: acquiring a depth map and a normal map from a training image pose based on a pre-trained 2D Gaussian splash model; acquiring a grid ray starting point based on the depth map; generating a main ray and a hemispherical ray for each sampling point based on the normal diagram and the ray starting point; constructing a ray and image matching network and a lightweight convolutional neural network, and performing network training through a loss function; estimating the position of the camera through the trained ray and image matching network and the lightweight convolutional neural network based on the ray features of the main ray and the hemispherical ray and the image features of the query image, and constructing a rotation matrix to realize initial camera pose estimation; and optimizing the initial camera pose by adopting a depth-guided pose optimization method to obtain a final optimized camera 6D pose estimation result. The method gets rid of the dependence of an initial value, and has the advantages of high precision, strong robustness, efficient calculation and wide application.

The invention discloses an automatic generation method for health science popularization articles, and belongs to the crossing field of computer technology and health science popularization. The method comprises the following steps that S1, health authority information is collected through a multi-source crawler, and an original material pool is obtained through Sension-BERT vectorization duplicate removal and BioBERT medical entity labeling; s2, classifying nine types of health themes by using a RoBERTa fine tuning model; s3, multiple AI model supplementary materials are made into a structured package; s4, predetermining optimal selection questions in combination with AI three-dimensional scoring and editing; s5, calling the multi-dimensional knowledge base to generate a professional knowledge packet; s6, generating an outline and performing three-dimensional five-score system auditing of knowledge point fullness and the like; s7, the LLM expands and writes the text and marks knowledge sources; s8, performing double-stage auditing; s9, anthropomorphic draft moistening; s10, intelligently illustrating the picture; s11, when the matching degree is smaller than a preset threshold value, automatically updating the knowledge base; and S12, integrating and outputting. According to the method, the health science popularization article is generated, the manual participation time is shortened to be within 30 minutes, the medical error rate is reduced to be below 5%, the daily output of a 10-person team is improved by 3-5 times, the annual human cost is reduced by 60% or above, and the large-scale and high-quality science popularization requirements are met.

An unmanned aerial vehicle target detection tracking method based on intelligent image matching comprises the steps that an unmanned aerial vehicle collects images to make a data set, a model file is obtained through twin neural network training, the model file is loaded to detect a video stream, a current frame target position and detection frame information are obtained, whether the frame is a first frame is judged, and if yes, the target is detected; using a target position information result to initialize a kernel filtering algorithm, and constructing a measurement vector; if not, calculating correlation by using a kernel correlation filter, taking the highest response position as a prediction bit, and constructing a measurement vector; calculating a Kalman gain, and inputting the Kalman gain into a Kalman filter to obtain a predicted state estimated value = (); comparing y-axis deviations of the detector and the tracker, and setting a threshold value for judgment; according to the method, target tracking under resource limitation is realized through light-weight twin network detection in combination with kernel filtering and improved Kalman filtering, the problems of high target loss rate and low precision are solved, and the method is suitable for the fields of military reconnaissance, intelligent autonomous attack and the like.

The invention relates to the crossing field of mineralogy and geochemistry, in particular to a mineral and element relation analysis method for gold-bearing minerals based on image matching and data fusion and application of the mineral and element relation analysis method. Comprising the steps of ore sample processing, gold-bearing mineral image calibration through AMICS, trace element scanning through fixed-point EPMA and EDS, image-level matching and spatial data fusion and gold-bearing mineral-element relation analysis. According to the method, the AMICS mineral map and the EPMA element information are fused, relation analysis of trace elements in the gold ore and visual reconstruction of a mineral-element corresponding mechanism can be achieved, and effective data and image models are provided for microcosmic metallogenic information requirements in digital mine construction.

The invention discloses a text-to-three-dimensional object (3D) generation method based on dual color consistency matching, and aims to solve the problem of 3D asset quality reduction caused by multi-view color inconsistency in the prior art. According to the method, in each training iteration, a group of multi-view images are firstly rendered from a 3D model, and a high-fidelity forward view is specified as a color reference; subsequently, optimization is carried out through a double matching mechanism: 1, matching among images: aligning colors of other views to the reference view by using depth semantic features, thereby breaking circulation of error accumulation; 2, image internal matching: in a single image, color and geometric smoothness in the same semantic region are standardized by improving total variation loss; through the dual mechanism, accumulation and propagation of unnatural color distortion are effectively prevented, color inconsistency is reduced, and the visual fidelity and color consistency of the generated 3D model are remarkably improved.

The invention discloses a multi-platform SAR (Synthetic Aperture Radar) data and ground monitoring image fused earth surface deformation identification method, and belongs to the technical field of remote sensing monitoring and geological disaster early warning. According to the method, SAR images before and after deformation of a target area are obtained through a spaceborne, airborne or foundation SAR platform, and radar sight direction and azimuth direction deformation fields are extracted through preprocessing and offset tracking or interference processing; synchronously utilizing an unmanned aerial vehicle or ground photography equipment to obtain a high-resolution optical image, and calculating an east-west horizontal deformation field and a south-north horizontal deformation field through an image matching technology; after unifying the multi-source deformation fields to the same spatial resolution, constructing a deformation observation equation set based on a three-dimensional deformation decomposition model by combining radar geometric parameters, and inverting three-dimensional deformation in the vertical, east-west and north-south directions of the earth surface by adopting a weighted fusion algorithm (such as Helmert variance component estimation to determine a weight matrix); and finally, performing time sequence optimization on the deformation field by using Kalman filtering or a stress-strain model to generate a space-time continuous three-dimensional deformation product.

The invention discloses a high-precision satellite attitude correction method, system and equipment for a maximum initial positioning error, and belongs to the technical field of machine vision image matching. A proper reference region is selected to prepare a first reference image and a second reference image, and feature descriptors of the reference images are extracted offline; performing down-sampling on a remote sensing image acquired by a satellite remote sensing camera, and performing image matching on a real-time image after down-sampling and the first reference image; calculating the optimal window position of the ground calibration point of the reference area in the real-time image according to the matching result; cutting the real-time image according to the window position to obtain a high-resolution calibration point real-time image; performing image matching on the high-resolution calibration point real-time image and the second reference image; and calculating latitude and longitude coordinates of the center of the real-time image according to a matching result for satellite attitude correction. The position error of the target area is gradually reduced in a two-stage remote sensing image matching mode, the longitude and latitude coordinates of the center of the real-time image are accurately positioned, and high-precision satellite attitude correction is realized.

The invention provides a pipeline leakage positioning method, device and equipment and a storage medium, and belongs to the field of intelligent detection.The pipeline leakage positioning method comprises the steps that concentration data and a pipeline image are synchronously obtained when a detection probe conducts detection along a to-be-detected pipeline; performing image matching on the pipeline image and a reference image to determine spatial position data of the detection probe in a three-dimensional space, the reference image being an image obtained by photographing the pipeline by using known spatial position data in the three-dimensional space; determining a concentration distribution model of the tracer gas on the to-be-detected pipeline based on the concentration data and the spatial position data at different moments; and determining one or more leakage point positions on the pipeline based on the area with the concentration value reaching a preset condition in the concentration distribution model. In the process, by establishing a concentration distribution model, the problem that in the prior art, due to gas diffusion, a pain point is difficult to accurately position is greatly solved, and the repair efficiency and the detection reliability are remarkably improved.

The invention provides a three-dimensional image modeling method and device based on plane data, and the method comprises the steps: carrying out the full-color / multispectral band fusion, bit depth adjustment, thin cloud removal, image enhancement, stripe removal and light and color uniformity of a satellite surveying and mapping image, and achieving the real color recovery of the image; performing splicing preprocessing on the plurality of small orthographic satellite surveying and mapping images after real color recovery, performing image registration on the to-be-registered image and a reference image, performing image geometric correction on the registered image, and performing image mosaic processing on the plurality of images after image geometric correction to obtain a single large-scene image; and three-dimensional image matching, building mask extraction, three-dimensional building modeling and three-dimensional building post-processing are carried out on a single large scene image to obtain a three-dimensional digital surface model, and ground three-dimensional real image modeling is completed. By applying the technical scheme of the invention, the technical problem that the existing two-dimensional surveying and mapping data is difficult to meet the increasing application requirements of three-dimensional scenes is solved.

The invention discloses a recording method and device and electronic equipment, and belongs to the technical field of electronic equipment. The method comprises the following steps: receiving a first input for triggering a target shooting mode; under the condition that the target camera shooting mode is a delay camera shooting mode or a slow shot mode, in response to the first input, recording an audio and an image respectively in the target camera shooting mode; based on the recorded audio, determining the audio matched with the recorded image; and packaging the recorded image and the audio matched with the image to obtain a target file.

The invention belongs to the technical field of multi-modal image matching, and particularly relates to a multi-modal image matching method based on fusion features and attention state space. The method comprises the following steps: preprocessing an obtained infrared image and a visible light image, wherein the preprocessing comprises geometric correction, noise removal and brightness normalization; performing multi-scale feature extraction on the preprocessed images to obtain multi-scale features; performing rotation scale correction on the features by using a group convolution module based on a rotation group; inputting the corrected features into an attention state space model for cross-modal feature interaction, wherein the model adopts a convolution state updating mechanism and space attention fusion; on the basis of the interacted features, a rough matching result is obtained through block similarity calculation; carrying out refined optimization on a rough matching result, carrying out local feature extraction by using a small convolution kernel, and combining an attention weighting mechanism; in the training stage, a self-adaptive uncertainty loss function supervised model based on Gaussian negative logarithm likelihood is adopted for training.

The invention discloses an unsupervised pedestrian re-identification method based on a local enhancement converter and multi-granularity contrast learning. The method mainly comprises the following steps: inputting a pedestrian re-identification data set to a constructed model; extracting global attention and local enhancement features through a local enhancement converter feature extraction network and a dual-coding feature enhancement layer, and generating preliminary feature representation in combination with a dual-path feature dynamic aggregation module; dividing a feature region through a self-adaptive region division module, optimizing feature expression in combination with a space attention and constraint mechanism, and suppressing background interference; adaptively adjusting the updating momentum of the memory bank features according to the sample confidence through a confidence perception updating unit, and generating a pseudo tag in combination with clustering; and multi-granularity contrast learning is carried out, a multi-granularity contrast loss function is designed to iteratively optimize network parameters, and pedestrian image matching is completed. According to the method, the problem of pedestrian image multi-granularity representation splitting is solved, and the accuracy and robustness of pedestrian re-identification are improved.

The invention provides a semiconductor detection method and device and a computer storage medium, and the method aims at a detection scene of an oversized crystal grain on a wafer, and comprises the steps: controlling a detector to scan a target object according to a preset view size and a track, and obtaining a to-be-detected image and the position information of the to-be-detected image in the track; according to the position information, loading a corresponding template image from a preset template set which is obtained by scanning at least one complete single crystal grain and can be spliced into a complete single crystal grain all information template image; and matching the to-be-detected image with the corresponding template image to obtain a detection result. According to the method, the template image is correspondingly matched through the accurate position information, the oversized single crystal grains can be efficiently and accurately detected, the pertinence and accuracy of detection are effectively improved, misjudgment is reduced, and the detection efficiency and quality are improved.

The invention relates to an environment sensing method based on target detection and stereo matching. According to the environment perception method based on target detection and stereo matching, targets in an environment image can be recognized through model training, and category information and position information of each target can be recognized, so that a target detection model can recognize the category information and the position information of the targets in the environment image in real time; through a local matching strategy of a target area delineated by focusing on the position information of the target, huge calculation overhead caused by full-image matching is avoided; pixel-level parallax information in the two-dimensional image is accurately converted into a physical depth distance in a three-dimensional space in combination with calibration parameters of a binocular camera in an environment sensing system; and finally outputting an environment perception image capable of displaying a detailed perception result of each detected target through information fusion. According to the environment sensing method, the efficiency and robustness of the whole sensing process are remarkably improved while the accuracy and integrity of environment sensing are ensured.

The invention provides a three-dimensional point cloud generation method and system based on multi-view remote sensing image matching, and relates to the technical field of remote sensing. By obtaining a multi-view remote sensing image set of a target area, a multi-view dynamic association initial frame is constructed, and shooting orientation association information is associated to obtain an initial association relationship; extracting feature region association attributes to generate association factors, constructing a cross-view dynamic matching constraint rule set, implementing coordinate mapping to form a three-dimensional point cloud initial unit set, extracting spatial association attributes, dividing initial unit groups, and performing progressive spatial position adjustment to obtain a three-dimensional point cloud optimization unit set; and finally, performing spatial arrangement according to the spatial distribution logic of the target area, and integrating to form a three-dimensional point cloud result of the target area, thereby effectively improving the quality and precision of three-dimensional point cloud generation.

The invention discloses an aviation multi-view three-dimensional method based on hierarchical attention and dynamic consistency filtering, electronic equipment and a computer readable storage medium. Comprising the following steps: A, data preparation and preprocessing: acquiring a collected multi-view aerial image, and preprocessing the multi-view aerial image to obtain an internal reference and image matching pair; b, dynamic feature extraction: constructing a dynamic feature extraction module based on Swin Transform, processing the input image, and extracting features to obtain a feature map; c, multi-scale feature fusion: inputting the extracted features into a feature fusion module to obtain a multi-stage feature map; and D, cost body regularization and depth estimation: constructing a cost body based on the output fusion features, and regularizing the cost body by using a mixed attention mechanism to generate a final three-dimensional point cloud. The method has the advantages that the feature matching reliability in complex aviation scenes such as weak textures and large-scale changes is remarkably improved.

The invention discloses a diffusion-guided image cross-modal matching method. The method comprises the following steps: firstly, preprocessing an optical image and an SAR image to be matched; obtaining a coarse-to-fine multi-scale feature pair of the optical image and the SAR image and a multi-scale enhancement feature of the SAR image by using a diffusion gating feature pyramid network; on the thickest scale, an optical flow field probability distribution model is constructed through a Gaussian process matching module, and an initial optical flow field and a confidence map are output in combination with a Transform decoder; then, a multi-scale convolution refining network is adopted, displacement embedding and local correlation calculation are combined, step-by-step optimization of the optical flow field and confidence is achieved through iterative updating, and finally a fine matching result is output. According to the method, speckle noise in the SAR image is effectively suppressed and geometric distortion is compensated through feature enhancement guided by the diffusion model, Gaussian process modeling, Transform decoder prediction and a multi-scale progressive refinement strategy, and the robustness and precision of cross-modal image matching in a complex scene are remarkably improved.

The invention discloses an image matching method and device, equipment, a storage medium and a product, and relates to the technical field of image matching, and the image matching method comprises the steps: obtaining different-source to-be-matched images at different visual angles; sequentially performing feature extraction on the images to be matched through a preset feature point matching model to obtain key points and descriptors in each image to be matched, and performing matching on the key points based on the descriptors to obtain initial matching points obtained through initial matching, the number of channels in a convolutional layer of the preset feature point matching model is negatively correlated with the spatial resolution of the to-be-matched image, the change degree of the number of channels in a first stage is smaller than the change degree of the number of channels in a second stage, and the first stage and the second stage are determined based on the spatial resolution; and performing error filtering on the initial matching point to obtain a filtered target matching point. According to the invention, when an image with high spatial resolution is processed at an early stage, the calculation cost can be significantly reduced by reducing the number of channels.

This invention discloses a method, device, storage medium, and program product for calibrating and aligning a light field camera and an RGB camera. The deviation value is calculated by matching the images from the two cameras and fed back to the engineer, allowing for more precise fine-tuning. The deviation value provided by this invention is accurate and objective, unaffected by human factors. With objective guidance, engineers can significantly improve the efficiency of fine-tuning camera positions, thereby enhancing the efficiency of this task in actual production assembly.

The application provides a kind of automatic stamping method and system based on invisible watermark, comprising: the electronic text of automatic office unit uploading stamping file and the position information needing stamping;Automatic office unit replaces the electronic text without invisible electronic watermark in process with electronic text;Server informs automatic stamping robot unit to automatically print the electronic text in automatic office unit electronic stamping process;Automatic stamping robot extracts token information in hidden electronic watermark from paper text page by page;If each page token authentication passes, pass through OCR and image matching;Automatic stamping robot stamps information in server through the token of each page text, confirms whether each page text is stamped and where to stamp;Automatic stamping is carried out in the corresponding position of text document, and after stamping is completed, paper document is output.The application can efficiently realize the comparison of paper document and electronic document, and avoid the inconsistency between stamping file and electronic document in approval process.

The application provides a complex terrain area elevation inversion method based on small unmanned aerial vehicle borne interferometric SAR. N-track small unmanned aerial vehicle flight tracks are set, the X-axis and Y-axis coordinates of the starting point and ending point of each track small unmanned aerial vehicle are unchanged, and the value of the Z-axis is gradually increased along the vertical direction. N-track echo data are obtained by the small unmanned aerial vehicle borne SAR according to the set tracks, and BP algorithm is used for imaging processing to obtain N SAR images. The offset of the SAR images of adjacent short baseline small unmanned aerial vehicles is obtained. The offset of the SAR images of long baseline small unmanned aerial vehicles is calculated based on the offset of the SAR images of small unmanned aerial vehicles, and the SAR images of long baseline are image matched. The SAR images after image matching are processed to obtain corresponding interference phase images, and the interference phase images are filtered by using Goldstein algorithm. Multi-baseline phase unwrapping is performed based on the interference phase of the interference phase images. The complex terrain area elevation inversion method of SAR is realized.

The present invention enables more efficient positioning control of a bed on which a patient is placed. A bed position control device that controls a position of a bed on which a patient is placed includes: an image acquisition unit that acquires a fluoroscopic X-ray image obtained by imaging the patient by X-ray fluoroscopy; a pseudo fluoroscopic X-ray image acquisition unit that acquires a pseudo fluoroscopic X-ray image created from a three-dimensional image of the patient; a second region-of-interest creation unit that, from a first region-of-interest set in the pseudo fluoroscopic X-ray image so as to include a region of a target structure, creates a second region-of-interest including the region of the target structure in accordance with a positional deviation of the patient; and an image matching unit that matches an image in the second region-of-interest in the pseudo fluoroscopic X-ray image with an image in the second region-of-interest in the fluoroscopic X-ray image, determines a position of the bed necessary for aligning the position of the patient in the fluoroscopic X-ray image, and moves the bed to the determined position of the bed.

The application discloses a fan shell front and rear hole position positioning method and system based on image recognition, comprising the following steps: S1: collecting a fan shell image through a calibrated binocular camera, and performing stereo correction and illumination invariance enhancement processing to obtain binocular enhanced image data; S2: performing instance segmentation on the binocular enhanced image data based on an instance segmentation neural network to obtain a hole position pixel region set with front and rear semantic labels; S3: performing data-driven hole position coarse matching on the hole position pixel region set through a graph neural network, and constructing a physical constraint rule model to optimize and verify the matching result, and outputting an accurate image matching pair set of the front and rear hole positions; and S4: based on the accurate image matching pair set, constructing a cost function to solve the shell pose and obtain final world coordinates, and realizing the front and rear hole position positioning of the fan shell.

The application discloses a high-precision space positioning method and system based on multi-source image automatic matching, and the method comprises the following steps: acquiring a tower video image and a satellite remote sensing image from a satellite remote sensing image acquisition module and a tower camera module; selecting homonymic feature points of the tower video image and the satellite remote sensing image, and constructing a projection transformation relationship of the satellite remote sensing image and the tower video image; obtaining image 1 and image 2 according to the projection transformation relationship and selected points to be solved; inputting the image 1 and the image 2 into an improved SURF algorithm feature matching module to obtain a transformation matrix; and performing mutual conversion through the transformation matrix to output a satellite remote sensing image coordinate or a tower video image coordinate. The method greatly improves the image matching success rate, greatly reduces the influence of environmental factors such as weather and season on image matching, removes the matching link of a video image to be positioned and a reference library video image, and does not need to perform a large amount of image matching work.

A Display System and Display Method for Augmented Reality, comprising a display device displays a light-emitting mark, and an augmented reality wearable device comprising a wearable body equipped with a light sensor. The light sensor detects the light-emitting mark and a flashing signal, then passes both the light-emitting mark and the flashing signal to a processor. The processor compares the flashing signal and a reference flashing signal, and when they match and the light-emitting mark matches a reference light image, it controls an image capturing device on the wearable body to capture a light-emitting mark image. The processor calculates a distance information and an angular information between the image capturing device and the light-emitting mark based on the light-emitting mark image, generating a projection parameters accordingly. The processor controls the projection device to adjust a projection image of an object according to the projection parameters.

The application relates to a component tilt detection method, device, system and readable storage medium. The method comprises the following steps: extracting a plurality of component images corresponding to components from a to-be-detected two-dimensional circuit board image of a to-be-detected circuit board, and extracting a first component point cloud matched with the plurality of component images from a to-be-detected three-dimensional point cloud of the to-be-detected circuit board; performing clustering processing on the to-be-detected three-dimensional point cloud to obtain a plurality of second component point clouds; detecting a target component type corresponding to the plurality of second component point clouds according to the component types corresponding to the plurality of component images and the matching degrees between the plurality of first component point clouds and the plurality of second component point clouds; for each component, generating tilt influence parameter information corresponding to the component based on the target component type and the second component point cloud, and performing tilt detection on the component according to the tilt influence parameter information. The method can accurately detect the tilt of the components on the circuit board.

The application relates to a kind of body image matching network assisted internal three-dimensional deformation field measurement method, belong to deformation measurement field.The application implementation method is: in reference body image, define calculation region and calculation voxel point;Using three-dimensional body image feature extraction network, key three-dimensional features are extracted simultaneously.Using three-dimensional feature matching network, key three-dimensional features are matched.From matched three-dimensional feature point pair, the feature point pair in the preset three-dimensional neighborhood range around each calculation voxel point is extracted, and the three-dimensional deformation initial value of the calculation voxel point is calculated when more than four groups of feature point pairs are judged;Less than 4 groups are directly assigned using the deformation initial value of other successfully calculated points with the spatial position of the calculation voxel point nearest to the interpolation or, as the three-dimensional deformation initial value of the point.Using three-dimensional reverse combination Gauss-Newton nonlinear iterative optimization method, high-precision internal three-dimensional deformation field measurement value is calculated, that is, internal three-dimensional deformation field measurement is realized.