642 results about "Image splicing" patented technology

The invention relates to an injection molding product defect detection method based on machine vision, which comprises the following steps: collecting material information of a to-be-detected injection molding product in real time, and dynamically matching and adjusting light source parameters according to spectral reflection characteristics of materials to ensure image collection quality; secondly, the collected images are preprocessed, edge features and texture features are extracted, a three-dimensional model is constructed through multi-view image splicing, and three-dimensional defect features are extracted; thirdly, the multi-dimensional features are input into a deep learning model, the defect probability is calculated through feature fusion and forward propagation, and whether the product has defects or not is judged; if the defect exists, further identifying the defect category, and calculating the number and size of the defect; and generating a standardized detection report based on the defect information. According to the method, the image adaptability of products made of different materials is improved through dynamic light source adjustment, the two-dimensional and three-dimensional features are fused, the defect recognition accuracy is improved, and full-process automation from qualitative judgment to quantitative analysis of the defects is achieved.

The invention relates to the technical field of visual automatic calibration, and discloses a joint manipulator automatic calibration method and device based on a visual system. The method comprises the following steps: carrying out multi-view image acquisition on a joint manipulator of the five-axis robot to obtain original image data; performing occlusion region extraction on the original image data to obtain a joint occlusion region set; on the basis of the joint occlusion region set, disordered image splicing and mark point feature extraction are carried out on the original image data, and joint feature image data are obtained; constructing an adaptive calibration equation based on the joint feature image data, and solving a target transformation relation between a global visual coordinate system and a local visual coordinate system; multi-joint collaborative calibration and joint chain constraint optimization of the five-axis robot are executed according to the target transformation relation, and joint error correction parameters are generated, high-precision joint error correction parameter calculation is achieved, and the positioning precision and the movement precision of a joint manipulator of the five-axis robot are greatly improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses an image generation method, device and equipment based on theme information and a medium. The method comprises the steps that input information is analyzed to generate theme information and copywriting information, a cue word set is generated, and a figure image set and a background image set are generated; fitting the segmented figure image with the background image to form a head image candidate set, and selecting a head image matching template frame to generate a basic image; decomposing the copywriting to generate a sub-module initial picture set, and adding a gradual change effect to form a sub-module picture set; the adjusted sub-module pictures are obtained based on size adjustment, and a pre-synthesized image is generated through splicing; and identifying the blank area to draw a title text to obtain a final image. Through theme analysis, template matching, image splicing, modular copywriting processing and blank drawing, poster generation efficiency is improved, layout flexibility is enhanced, and visual unification is realized.

The invention discloses a multi-robot cooperative target identification and tracking method in a dynamic environment. The method comprises the following steps: identifying a target person in real time; if the confidence coefficient of the target person is lower than a threshold value and an effective detection frame exists, a re-identification module is started, and identity confirmation is carried out by utilizing a TCN network in combination with time sequence characteristics and a motion mode; if the target is lost, a multi-view collaborative mechanism is started, a global panoramic perception view is constructed through a multi-view image splicing and distributed target fusion technology, and meanwhile, the problem of tracking interruption caused by target shielding is solved by utilizing the multi-robot view complementary characteristic; if the confidence coefficient of the target person is higher than a threshold value, the target position is obtained in real time, an optimized APF and PID control strategy is adopted to establish a closed-loop system from target recognition to motion control, and a tracking strategy is kept. According to the method, the problems of view shielding, target loss and dynamic interference in a complex environment are effectively solved, and high-precision and high-stability target tracking is realized.

The invention discloses a monitoring method and system for identifying crop drought based on leaf morphology, and relates to the field of agricultural drought monitoring, and the method comprises the following steps: selecting a completely unfolded healthy leaf, cleaning with ultrapure water, blow-drying with nitrogen, and fixing on a standard objective table to obtain a standardized leaf sample; based on the standardized leaf sample, a high-resolution scanner is used for obtaining a front gray image and a back gray image of the leaf, and a binary image with a clear vein network is obtained through image splicing and binarization processing; skeletonizing the binary image with the clear vein network, and calculating the vein fractal dimension by adopting a box counting method to obtain a fractal dimension change rate; the method is in a fixed detection area of a standardized leaf sample. Through innovative leaf vein fractal dimension analysis and contact angle dynamic measurement, a crop drought monitoring system is constructed, and in the aspect of leaf vein analysis, skeletonization processing and a box counting method are adopted to realize accurate quantification of a leaf vein network topological structure.

The invention discloses an unmanned aerial vehicle image seamless rapid splicing method based on multi-view geometry, and particularly relates to the technical field of unmanned aerial vehicle image splicing, and the method comprises the steps: extracting feature points of video frames through a deep learning model, and carrying out the inter-frame matching, and obtaining a matching point set; based on the matching point set, solving the three-dimensional coordinates of the 3D map points, determining the initial pose of the key frame and initializing the global map, matching the feature points of the current frame with the 3D map points in the global map, and solving the camera pose of the current frame; a new key frame is selected according to the feature coverage rate, a local optimization window is constructed for 3D map points jointly observed by the newly added key frame and the associated key frame, error correction is carried out on key frame poses and 3D map point coordinates in the window through local light beam adjustment optimization, and a global map is updated; and based on the optimized global 3D map point fitting reference surface, projecting the key frame image to the fitting reference surface, generating a panoramic image by adopting a multi-scale pyramid fusion method, and outputting a final image spliced image.

The invention discloses a building engineering multi-dimensional monitoring system based on panoramic imaging, and relates to the technical field of building engineering monitoring and sensing. Comprising a panoramic image acquisition module, an image splicing and panoramic reconstruction module, a multi-source data synchronous acquisition module, an image-data fusion construction module, an image-physical association modeling module and a risk identification and intelligent response module, an image acquisition array formed by high-resolution cameras is installed at key positions of a building structure, images of all the cameras are obtained, and an original image sequence covering a monitoring area is output. According to the invention, through fusion of the panoramic image and the multi-source sensing data, visualization, intelligent identification and risk prediction of the structure state are realized, a closed-loop monitoring system is constructed, the problems of single traditional monitoring means, data splitting and the like are solved, and the safety monitoring intelligent level of constructional engineering is improved.

The invention discloses a video transmission image stitching data enhancement method based on deep learning, and relates to the field of video image processing. The method comprises the following steps: S1, acquiring and screening images; s2, continuously screening structures and selecting key frames; s3, correcting image distortion; s4, splicing and fusing the images; s5, performing image enhancement output; firstly, a sliding time window mechanism is adopted, multi-dimensional image quality screening is combined, fuzzy, underexposure or severely-shielded inferior frames are accurately removed, and high quality of input key frames is ensured; through intelligent splicing and enhancement of a dynamic adaptive threshold strategy and semantic guidance, the image splicing precision and efficiency of the unmanned aerial vehicle and the multi-view camera in a complex environment are remarkably improved; besides, semantic segmentation guided feature extraction is combined with a multi-band fusion technology, seamless splicing is realized, the quality of an output image is improved, and the reliability of automatic analysis and decision making is remarkably improved.

The invention discloses a rice field insect pest detection method and system based on deep learning, and relates to the technical field of deep learning, and the method comprises the following steps: receiving the soil humidity and environment temperature of a rice field, and determining a temperature and humidity abnormal region of the rice field based on the soil humidity and environment temperature of the rice field; acquiring field image data in the temperature and humidity abnormal area of the rice field; processing the field image data in the temperature and humidity abnormal region of the rice field based on morphological closed operation and morphological gradient operation to obtain processed field image data, inputting the processed field image data into a pre-established YOLOv5s + target detection algorithm model, and outputting to obtain a pest and disease damage identification result; and performing image splicing generation based on the field image data in the temperature and humidity abnormal region of the rice field and the pest and disease identification result to obtain a disaster grading map.

The invention belongs to the field of application of an image processing technology and an automatic detection technology, provides a nodular cast iron pipe white sprayed code integrity detection method and system, and aims to solve the problems of low nodular cast iron pipe white sprayed code recognition accuracy, poor environmental adaptability, low processing speed, image splicing and the like in industrial production. In combination with an optimized image preprocessing technology, an advanced feature point detection and matching technology and an image fusion algorithm are applied to solve the problem of image seamless splicing. By adopting the method provided by the invention, the error and time cost in a manual identification process can be greatly reduced through prediction, and the automation and intelligence level of a production line is improved. The production efficiency can be improved, the product quality can be ensured, and the increasing industrial automation requirement is met. Finally, the system becomes an important tool for improving the product quality and the production efficiency of nodular cast iron pipe production enterprises.

The invention discloses a global scanning image splicing and defect positioning system for an ultra-large-diameter quartz crucible, and relates to the technical field of quartz crucibles, and the system comprises a scanning control module which constructs a fusion model through multiple sensors, evaluates a risk coefficient, obtains the fusion model, dynamically plans a multi-arm path of a robot based on reinforcement learning, and sends the multi-arm path of the robot; adaptively adjusting the acquisition frequency of the corresponding area based on the risk coefficient, and outputting multi-modal data through a multi-round feedback iteration optimization acquisition process; the image splicing module is used for acquiring multi-modal data for local splicing, and generating a panoramic image in combination with a multi-arm path of the robot; the defect positioning module is used for constructing a quartz crucible model through the panoramic image, detecting defects in a layered manner by utilizing a YOLOv5 model, tracking the defects through extended state Kalman filtering, quantifying the defect volume and the hazard level by combining point cloud semantic segmentation, and performing 3D positioning and evaluation on the defects; the output data is ensured to always meet the subsequent image splicing and defect positioning requirements, and the system stability is guaranteed.

The invention discloses a sports equipment management personnel identification method, system and equipment, and the method comprises the steps: starting and initializing a camera through detecting a sports equipment use trigger signal, and stopping automatic focusing; and shooting multiple frames of images at different focal lengths according to a preset interval, determining a clear area by using a gradient magnitude algorithm, and performing image registration and fusion to form a composite image. And if the whole area is not covered, shooting and splicing are carried out again. And through composite image splicing, distortion cutting, noise reduction and normalization processing, a final image is output for determining the identity of a person. According to the invention, through multi-frame shooting and accurate image processing, a high-quality image is obtained, and information limitation and quality defects of a single image are effectively avoided. Various algorithms are fused to ensure that the image is clear, complete and standardized, the accuracy and reliability of personnel identity recognition are remarkably improved, an accurate image basis is provided for sports equipment management, the normalization and safety of equipment use are guaranteed, meanwhile, the image collecting and processing efficiency is improved, and the management cost is reduced.

The invention discloses an airborne visible light image automatic splicing method, and relates to the field of unmanned aerial vehicle image processing, and the method comprises the steps: obtaining a plurality of images collected in the flight process of an unmanned aerial vehicle, and the corresponding spatial position information and attitude information; on the basis of the spatial position relation between the images and the image overlapping information, determining image pairs capable of being registered, and constructing a connection map with the images as nodes and the image pairs capable of being registered as edges; detecting whether spatial connection fracture caused by image missing exists in the atlas or not, if so, inserting a virtual node and constructing a virtual image comprising an edge region and a transition region; and further calculating geometric transformation parameters between the images, resampling all image contents to a unified coordinate system, and executing pixel-level fusion processing in an image overlapping region. According to the invention, the problem of discontinuous image splicing of the unmanned aerial vehicle due to the existence of a no-fly zone, a privacy protection zone and the like of the unmanned aerial vehicle is solved.

The invention relates to the technical field of image stitching, and discloses a video transmission high-definition image intelligent stitching method and system. The system comprises an image preprocessing module, a feature point matching and screening module, an image optimal splicing seam generation module and a video output module. The method comprises the following steps: firstly, acquiring a video, carrying out dynamic range equalization, carrying out filtering processing by using an adaptive noise reduction method, and carrying out image correction; secondly, extracting feature points by using a self-adaptive corner detection algorithm, matching the feature points based on a multi-dimensional spatial data index to obtain feature point matching pairs, and screening the feature points; searching an overlapping region, and introducing a dynamic search algorithm to generate an optimal image splicing path to obtain an optimal image splicing seam; and finally, dividing the image according to the dynamic grid, and realizing video output by using priority ranking. According to the method, the video images are processed and spliced, the purpose of intelligent image splicing is achieved, and the method is accurate and objective.

The invention discloses a sonar data processing and image fusion method, which belongs to the technical field of image processing, and comprises the following steps of: performing geometric correction and registration processing on forward-looking sonar data by using respective advantages of side-scan sonar data and forward-looking sonar data and taking the side-scan sonar data as a benchmark reference source, so that the imaging mode of the forward-looking sonar data is matched with that of the side-scan sonar; multiple frames of images of the forward-looking sonar are integrated through an image splicing algorithm, and a water bottom image of a complete area is constructed; and finally, the blind area of the side-scan sonar is compensated by using a splicing result, so that complete reconstruction of underwater topography information is realized. The method is easy and convenient to operate and wide in application prospect, blind area compensation of the side-scan sonar image blind area can be achieved, and a complete underwater image is obtained.

The invention discloses a radiation correction method and system for an airborne hyperspectral imager. The radiation correction method and system are used for improving the radiation correction precision in complex weather. The method comprises the following steps: calculating a theoretical sun position based on unmanned aerial vehicle initial coordinates, ephemeris data and atmospheric parameters; performing course and differential compensation on a solar azimuth angle by combining IMU course and wind speed and direction data, performing refraction correction on an elevation angle, and outputting a high-precision sun position parameter; hyperspectral images and irradiance data are synchronously acquired, quaternion interpolation is combined with IMU data to realize space-time alignment, and attitude disturbance is eliminated; a hemispherical space irradiance model is constructed based on alignment data, cloud layer modes (clear sky, thin cloud and thick cloud) are divided according to atmospheric optical thickness variance, and Lambert body proportion correction, a discrete longitudinal standard method or a deep convolutional spectral network are respectively adopted for radiation correction. The method remarkably improves the correction precision and generalization ability, supports large-area image splicing, and is suitable for quantitative application of geological exploration, environment monitoring and the like.

The embodiment of the invention provides a data enhancement method and device, electronic equipment, a computer readable storage medium and a program product, and relates to the technical field of image processing. The method comprises the following steps: performing semantic segmentation on a construction image to obtain a corresponding semantic mask; the image cutting and splicing operation is guided through the semantic mask, the integrity of a small target is protected, it can be ensured that the generated sample image conforms to the actual physical rule of the construction scene based on the logic judgment of the semantic mask, the image splicing reasonability is improved, unreasonable splicing is eliminated, and therefore the data quality is improved and enhanced; according to the method, the training requirement of the target detection model is better met, the generalization ability and the detection precision of the target detection model are improved, meanwhile, the missing detection problem caused by random cutting of a small target and the recognition missing problem of a construction phenomenon which does not conform to an engineering physical rule are avoided, and the construction accident risk caused by detection missing is reduced.

The invention provides a panoramic image intelligent splicing method based on context semantics. The method comprises the following steps: firstly, obtaining and preprocessing a sequence image; extracting a depth feature map of the image by using a pre-trained depth network; performing semantic-guided feature matching and image alignment based on the feature map; an optimal suture line is generated according to the semantic region boundary, and fusion is carried out by adopting a semantic weighted multi-band fusion algorithm; and finally, detecting and repairing the semantic inconsistent region to ensure the semantic coherence of the panoramic image. According to the method, matching is carried out by using the high-dimensional feature tensor rich in semantic information extracted by the deep neural network, the problem of feature matching ambiguity caused by repeated textures and weak texture regions such as sky or white walls is solved, the semantic information is used as a strong constraint, regions with similar appearances but different semantics can be effectively distinguished, and the accuracy of feature matching is improved. Therefore, correct matching point pairs with consistent semantics are screened out from massive candidates, and the matching accuracy is greatly improved.

The invention provides a multi-view eye fundus image splicing method suitable for multiple modalities, and the method comprises the steps: obtaining a plurality of to-be-spliced images of different modalities, carrying out the corresponding preprocessing according to the imaging characteristics of the modalities to which the images belong, and obtaining a preprocessed image group; selecting one of the preprocessed image groups as a reference image, and performing image registration on the other images and the reference image to obtain corresponding registration image groups; on the basis of a preset registration precision threshold value and the proportion of overlapping areas between the images, screening out the images meeting the requirements from the registration image group; and performing multi-scale fusion processing according to the spatial relationship between the screened images to obtain a spliced eye fundus image. According to the multi-view eye fundus image splicing method provided by the invention, through preprocessing, registration, screening and multi-scale fusion, images of different modes can be efficiently integrated, the splicing precision and the image quality are improved, and the method is suitable for accurate eye fundus analysis and medical diagnosis.

The invention relates to the technical field of industrial defect detection, and discloses an aluminum surface micro defect detection system for optimizing an RT-DETR model in combination with an attention mechanism, which comprises an image acquisition module, an image preprocessing module, an improved RT-DETR detection module and a result output module. According to the aluminum surface micro-defect detection system for optimizing the RT-DETR model in combination with the attention mechanism, small target copying is executed through an image preprocessing module to increase micro-defect samples, defects and matching backgrounds are fused through image splicing, illumination is optimized through brightness adjustment, Gaussian filtering noise reduction is conducted, and original defect information is strengthened; an orthogonal attention feature extraction module in the improved RT-DETR detection module strengthens channel features, a multi-scale deformable frequency hierarchical attention coding module processes the features according to high and low frequencies, an ELAHS-FPN feature fusion module fuses the multi-scale features, a super-resolution auxiliary branch improves the feature resolution, a prediction frame is corrected in combination with an Inner-GIoU bounding box loss function, and the detection precision is improved. And weak characteristics such as paint bubbles, scratches and jet flow are effectively extracted.

The invention relates to the technical field of image processing, solves the problems that in the prior art, UV flat plate printing needs multiple cameras to cover the whole printing platform, and the precision is not high, and provides a scanning image processing method and device based on a single camera, equipment and a medium. The method comprises the steps that a camera is controlled to scan a calibration graph in the main scanning direction to obtain a first area image, the first area image is analyzed to correct distortion, target images are obtained, then the camera moves in the auxiliary scanning direction and repeatedly scans to obtain a second area image, distortion correction is conducted as well, and overlapped parts of the two target images are compared to obtain a target image. And longitudinal shooting overlapping parameters are calculated, and adjacent target area images are spliced by using the two groups of longitudinal overlapping parameters through a mapping splicing technology. Scanning of the whole printing platform can be completed through a single camera, hardware configuration is simplified, image splicing is carried out after multiple times of scanning and distortion correction, and the overall printing precision is improved.

The invention discloses a substation equipment data defect identification method and system, and relates to the technical field of substation part defect detection. The method comprises the steps of porcelain bushing state analysis: scanning a target porcelain bushing through a camera carried by an unmanned aerial vehicle to generate a three-dimensional point cloud model, and judging the state and flight environment of the porcelain bushing; a shooting angle coarse adjustment step: adjusting the shooting angle of the thermal imager according to the state of the porcelain bushing; a flight path planning step of presetting a shooting point position according to the three-dimensional point cloud model and planning a flying path; an image acquisition and splicing step: acquiring and splicing thermal imaging images; the method has the advantages that through strategies such as porcelain bushing state analysis, point position supplementation and obstacle avoidance in flight path planning and panorama complementation in image splicing, it is ensured that complete and effective thermal imaging information can still be obtained under the complex conditions that the porcelain bushing is upright or inclined, and flying obstacles exist; accurate judgment of the abnormal condition and the abnormal area of the porcelain bushing is realized.

The invention relates to the technical field of image processing, in particular to a panoramic image splicing method for a curtain wall building facade. The method comprises the following steps: performing three-dimensional scanning on a curtain wall building facade to obtain entity point cloud data of the facade, and selecting a plurality of discrete points from the entity point cloud data as reference anchor points; shooting a plurality of local images of the curtain wall building facade, and in the shooting process of each local image, recording the spatial correspondence between the image viewing range and the reference anchor point; and according to the spatial correspondence, aligning the image area corresponding to the reference anchor point in each local image to the position of the reference anchor point in the entity point cloud data to form a preliminary spliced image. According to the invention, through an image processing technology, zero-offset binding of the panorama and the BIM is completed through one-time shooting, so that the millimeter-level positioning precision and the inspection efficiency of the curtain wall facade are improved.

The invention discloses a forest stand tree species identification method and system based on multi-source remote sensing data fusion, and the method comprises the following steps: obtaining data which comprises forest land remote sensing image data and ground data; carrying out preprocessing on the obtained data; a dual-path CBAM-UNet network model is constructed; inputting the preprocessed data into the double-path CBAM-UNet network model to carry out tree species classification prediction and obtain a tree species classification chart; performing image splicing on the tree species classification map to obtain a tree species classification grid map; the system comprises a data acquisition module, a data preprocessing module, a prediction module and an image processing module. According to the forest stand tree species identification method and system based on multi-source remote sensing data fusion provided by the invention, multi-scale feature capture and feature fusion of multi-source heterogeneous data can be realized through the constructed dual-path CBAM-UNet network model, high-precision identification can be performed on forest stand tree species under complex links, and high-quality data can be provided for forest management.

The invention is suitable for the technical field of automobile auxiliary driving, and provides a quick panoramic image splicing method based on a star flash wireless communication technology, which comprises the following steps of: S1, calibrating internal parameters and distortion coefficients of a camera by adopting a Zhang Zhengyou calibration method, calibrating external parameters by combining perspective transformation, and mapping each view angle image to a unified overlook coordinate system; s2, acquiring an image through a camera supporting a star flash protocol, and transmitting the image to a vehicle body domain controller by using a star flash technology; and S3, after distortion removal and perspective transformation are performed on the received image, a local pyramid fusion strategy is adopted: binary mask marking and multi-band fusion are performed on an overlapping region, original data are reserved in a non-overlapping region, and a panoramic image is finally output. Through combination of high-precision calibration, local pyramid fusion and a star flash communication technology, high-quality and low-delay splicing of vehicle-mounted panoramic images is realized, and the stability and practicability of the system in a complex environment are improved.

The invention provides a multi-camera-based AI glasses FOV extension method and system, and the method comprises the steps: transmitting a synchronization signal to a plurality of cameras, enabling the plurality of cameras to carry out the exposure at the same time, and obtaining a plurality of frames of original images at the same time; pre-processing the multiple frames of original images to obtain pre-processed images; key feature points of the preprocessed image are extracted through a feature point detection algorithm, and feature descriptors are generated; through a feature matching algorithm, feature point matching is carried out on the adjacent preprocessed images by utilizing the feature descriptors, and a local transformation matrix between the adjacent preprocessed images is calculated; based on the local transformation matrix, fusing the adjacent preprocessed images to obtain a fused image of the adjacent preprocessed images; and all the fused images are spliced to obtain a large-view-field-angle image covering the target view angle, so that the method has the advantages of effectively expanding the shooting view angle, eliminating image distortion and improving the image splicing naturalness.

The invention provides a wafer detection method and system based on multichannel C scanning, and aims to solve the problems of multichannel sensitivity difference, Z-axis height deviation, splicing artifacts and the like in a traditional system. The maximum defect point and the defect-free point of the standard sheet are selected before detection, the gain of each channel is adjusted to a unified amplitude, and the echo amplitude is recorded, so that sensitivity calibration is realized; in the detection process, a laser displacement sensor is used for collecting wafer surface height data in real time, modeling is conducted in combination with an ARIMA + Kalman filtering or Gaussian process regression algorithm, the Z-axis focusing distance is dynamically adjusted through PID feedback, and signal attenuation caused by surface warping is compensated; during image splicing, channel response is optimized by adopting three gray mapping modes of linear self-adaption, Gamma self-adaption (automatic alignment channel mean value after reference gamma setting) and manual setting, sub-pixel level offset is calculated based on a two-dimensional cross-correlation function to realize coordinate alignment, and a 5% overlapping area is set at a channel junction. Gray transition is completed through linear gradient weight weighted averaging.

The embodiment of the invention provides a multi-view-angle-based stage fusion panoramic look-around image generation method and device, equipment and a computer readable storage medium. The method comprises the following steps: acquiring a target image through a target fisheye camera; constructing a multi-view spherical projection model according to the target image; and according to the spherical projection model, generating a target visual angle panoramic spliced image. In this way, the problems of geometric distortion, obvious edge seams, inconsistent brightness, texture information loss and the like existing in a traditional double-fisheye image splicing method are solved.

The invention discloses a submarine image splicing method based on suture line optimization, and the method comprises the steps: carrying out the dual-stage feature extraction of a target submarine image based on an improved ResNet50 network, and obtaining a multi-level feature map containing global structure information and local detail features; respectively carrying out global coarse registration and local fine correction on the second feature map and the first feature map based on a deformable convolutional network to obtain a sub-pixel level aligned deformed image; and carrying out cross-scale feature interaction and dynamic mask optimization on the deformed images based on a cross-image self-attention mechanism, dividing suture line weights between the spliced images, and carrying out pixel classification according to the weights to obtain seamless spliced images. According to the method, semantic fusion is guided through deformable convolution modeling elastic deformation and a self-attention mechanism, sub-pixel-level alignment and seamless splicing are achieved, and the problems of geometric dislocation and suture line artifacts caused by refraction distortion, scattering noise and dynamic deformation in large-parallax submarine image splicing are solved.

The invention provides a digital pathological image high-color accurate splicing method and device based on feature point matching. The method comprises the following steps: obtaining a target splicing region in a target splicing image and a to-be-spliced region in a to-be-spliced image based on a scanning sequence of digital pathological images; performing affine transformation on the to-be-spliced region by using the affine transformation matrix to obtain an initial correction region; constructing a global color mapping function, and mapping the initial correction image by using the global color mapping function to obtain a color correction image; and splicing the color correction image and the target splicing image based on the scanning sequence of the pathological images to obtain a digital pathological image splicing result. According to the scheme, only the target splicing area and the to-be-spliced area are subjected to feature point matching, the cumulative distribution function of the effective feature points is calculated according to the RGB channels, and the global color mapping function is constructed and optimized, so that accurate color correction is realized, and the visual integrity and consistency of the spliced image are improved.