191 results about "Gaussian blur" patented technology

The invention relates to an optimization method and device for sparse view angle three-dimensional Gaussian splash, and belongs to the technical field of three-dimensional reconstruction in computer vision, and the method comprises the steps: collecting a sparse view angle image; a multi-view stereoscopic vision model based on deep learning generates a geometrically consistent depth map for the sparse view image, converts the depth map into point clouds and fuses the point clouds to obtain dense point clouds; sampling dense point clouds by adopting voxel-guided farthest point sampling to obtain initialized point clouds, and constructing a three-dimensional Gaussian field; rendering the three-dimensional Gaussian field through an enhanced geometric renderer to obtain a rendering depth and a rendering normal; constructing a multi-level geometric regularization loss function, and optimizing the three-dimensional Gaussian field; and performing optimization adjustment on the three-dimensional Gaussian field based on a shape-scale constraint criterion and a two-stage adaptive opacity constraint strategy to obtain an optimized three-dimensional Gaussian field. According to the method, the problems of initialization failure, insufficient geometric supervision and element out-of-control of 3D Gaussian splashing under the sparse view angle are solved.

The invention discloses a virtual-real fusion exhibition display interaction system and a multi-mode perception method, and belongs to the technical field of exhibition display interaction. The system collects audience eyeball fixation points, gesture actions and ambient light data through AR / VR equipment, analyzes coordinates of a region of interest through an eyeball fixation point attention mechanism, a gesture space-time encoder and a multi-modal fusion unit, triggers holographic projection explanation and virtual exhibition stand light and shadow dynamic adjustment (including illumination intensity, color, Gaussian blur and the like) based on a threshold value, and performs real-time display on the virtual exhibition stand. And multi-user collaborative interaction is realized through federal learning. According to the method, reinforcement learning is adopted to optimize an event-driven threshold value, and virtual and real visual splitting is eliminated in combination with ambient light adaptive mapping. The problems of low participation degree, insufficient single-mode interaction information and multi-user cooperation of traditional exhibition are solved, interest analysis accuracy is improved through multi-mode fusion, personalized experience is enhanced through dynamic interaction, the method is suitable for multiple scenes such as museums and science and technology museums, and exhibition intellectualization, immersion and group interaction efficiency are effectively improved.

The invention discloses an improved filtering and adaptive edge detection steel plate gap identification method for a hull surface image, and the method comprises the steps: 1, carrying out the denoising of a hull surface image containing a steel plate gap through employing a local weighted Gaussian blur and dynamic bilateral filtering fusion algorithm, and obtaining a denoised hull surface image; 2, carrying out the edge detection of a local region of the denoised hull surface image through employing a self-adaptive dynamic threshold Canny edge detection method, and obtaining a binary image; and step 3, carrying out edge extraction on the binary image to obtain a steel plate gap image. According to the method, through a local weighted Gaussian blur and dynamic bilateral filtering fusion denoising algorithm, the noise in the image is efficiently removed in a proportional fusion mode, meanwhile, through self-adaptive adjustment of filtering parameters, the noise influence of a local area is improved in a targeted mode, loss of detail information is avoided, and the image quality and detail presentation are remarkably improved.

The invention provides a pathology image segmentation method and device based on category labels and a readable storage medium, and the method comprises the steps: segmenting a full-view digital pathology (WSI) image, and extracting a feature map containing local textures and global semantics through a ResNet, ViT or a pathology-dedicated pre-training large model UNI; calculating the contribution degree of an image block to a classification result through an attention mechanism, and extracting the actual influence of features on a classification decision in combination with gradient analysis; and fusing the two weights by adopting a sorting weighting strategy, generating a lesion area mask through Gaussian blur and Otsu method threshold segmentation, and finally superposing the lesion area mask to an original image to output an interpretable segmentation result. According to the method, weak supervision segmentation can be realized only through slice-level labels, the labeling cost is remarkably reduced, the positioning precision is improved through complementary fusion of attention and gradient information, a visual and visual lesion area is generated, the model transparency and clinical practicability are enhanced, and the method is suitable for various pathological image analysis scenes.

The invention belongs to the field of machine vision, and relates to a novel circle detection method in a complex environment. The method comprises the following steps: firstly, preprocessing an input color image, converting the color image into a grayscale image, then performing Gaussian blur processing, and applying a Canny edge detection algorithm to obtain an edge image; then, a findContours function of OpenCV is used for detecting a contour in the edge image, and traversal processing is carried out on the contour; secondly, circle detection is conducted on each contour through a findcircle () function, and circle fitting is conducted through a three-point circle determination method or a least square circle fitting method; performing accuracy verification on the fitted circle; and finally outputting the number and parameters of the identified circles, such as circle center coordinates and radiuses. The method is low in operand, can rapidly and accurately detect a circle in a complex environment with a weak light source and noise, does not need to occupy high computing resources, and has high detection speed and precision.

The invention relates to the technical field of image recognition, in particular to a packaging film defect detection system based on image recognition, which comprises a candidate region extraction module, a form enhancement module, a feature extraction module, a fuzzy evaluation module and a defect judgment module. According to the method, a self-adaptive binarization value is generated by calculating a pixel local neighborhood gray average value and a standard deviation, background texture noise and illumination non-uniform interference are effectively suppressed by using a dynamic threshold, a defect entity contour is reconstructed by combining polygonal broken line approximation and morphological filling operation, key geometric angular points are reserved, and internal fracture textures are repaired. Constructing a multi-dimensional feature vector set covering a texture energy entropy value and illumination uniformity, deeply analyzing a defect surface microscopic optical structure, introducing a Gaussian fuzzy membership function and an information entropy weight distribution mechanism, objectively quantifying the affiliation probability of feature components to different defect categories, and determining the defect surface microscopic optical structure. The limitation of a single rigid criterion is broken through, and accurate classification of fuzzy boundary defects is realized.

The present invention relates to a spatio-spectral fusion method for non-paired hyperspectral and SAR images with dual-domain alignment, including: upsampling the hyperspectral image and performing feature extraction, performing Gaussian blur and feature extraction on the SAR image; approximating the radiation distribution of the SAR features to the radiation distribution features of the hyperspectral image; adjusting the spatial geometric information of the SAR features; extracting the change information between the image features, and predicting the low-spatial-resolution hyperspectral image through information injection; fusing the low-spatial-resolution hyperspectral image and the original SAR image after alignment to obtain the final high-spatial-resolution hyperspectral image. The beneficial effects of the present invention are: the present invention improves the spatio-spectral fusion accuracy of hyperspectral and SAR images, providing reliable support for subsequent applications.

The invention relates to the technical field of anti-counterfeiting, and provides an agricultural product anti-counterfeiting traceability method and device, electronic equipment and a storage medium. The anti-counterfeiting traceability method for the agricultural products comprises the following steps: generating a first agricultural product code based on information of each producing area; encrypting each first agricultural product code, and generating a color two-dimensional code from the encrypted first agricultural product code; performing Gaussian blur on the color two-dimensional code to form a first anti-counterfeiting code; receiving a second anti-counterfeiting code on the agricultural product scanned by the user terminal, and decoding the second anti-counterfeiting code to obtain a second agricultural product code; and if the second agricultural product code is the same as any first agricultural product code in the agricultural product code database, sending production place information corresponding to the first agricultural product code to the user terminal. According to the agricultural product anti-counterfeiting traceability method, the second anti-counterfeiting code is matched with the first agricultural product code in the agricultural product code database, so that the production place information of the agricultural product can be obtained, and anti-counterfeiting traceability is realized.

The invention relates to a portable device and a method for identifying and rapidly detecting particles in insulating oil. The portable device and the method are combined with a micro-flow imaging technology and a Raman spectrum analysis technology. The device can detect the particles in the insulating oil with high precision and high speed and obtain the physical characteristics and chemical components of the particles. Physical characteristics of particles are detected through a microflow imaging technology, and meanwhile chemical component information of the particles is analyzed through a surface enhanced Raman scattering technology. The device has a real-time online monitoring function, data transmission and remote control can be realized through the wireless communication module, and the detection efficiency and the intelligent level are remarkably improved. And the high-resolution CMOS camera and the SERS module are adopted, so that the detection sensitivity and precision are remarkably improved. In combination with Gaussian blur and histogram equalization technologies, noise can be effectively removed, the contrast of the particle image can be enhanced, and the particles can be accurately identified even under the condition of weak light or low contrast.

The invention discloses a robot imitation learning method based on a diffusion model, and belongs to the technical field of robot learning and body intelligence. Comprising the following steps: carrying out standardization processing on input image observation data and a robot state, and carrying out image enhancement by adopting gray scale transformation, random erasure and Gaussian blur; based on the enhanced image and the robot state, training a diffusion model to generate an action sequence, including forward diffusion, conditional feature fusion, noise prediction and joint loss optimization; in real-time control, features are extracted through an image enhancement network, sampling is accelerated through DDIM to generate an action sequence, noise scheduling coefficients are dynamically adjusted based on visual feature differences, and closed-loop optimization is achieved. According to the method, the image enhancement technology and the diffusion strategy are deeply fused, joint optimization of visual features and action sequences is achieved, the action generation accuracy and robustness of the robot under the complex visual interference and small sample conditions are remarkably improved, and the success rate is improved by 18% compared with a base line under 40 sample sizes.

The invention discloses a ground penetrating radar data fusion optimization method for tunnel detection. The method comprises the following steps: step 1, selecting an antenna frequency; step 2, cutting data; step 3, preprocessing the data; step 4, multi-frequency data space-time standardization; step 5, multi-frequency data fusion; step 6, Gaussian blur processing; and 7, image screening: selecting an optimized image with the best robustness. The method disclosed by the invention is specially used for tunnel detection radar data, interferences of metal davits, expansion joints and the like can be effectively removed, the fused multi-frequency data naturally transits near the boundary, the boundary and double images basically disappear, and the problems in practical application of tunnel engineering detection are effectively solved. According to the method, contradictory unification of large detection depth and high resolution is realized on the same image, targets such as steel bars and layers in the image are more easily and automatically identified by an artificial intelligence algorithm, and a technical foundation is laid for subsequent AI interpretation.

The invention discloses a panoramic image splicing method, system and device, and relates to the technical field of image processing, and the method comprises the steps: collecting images around a vehicle at different visual angles; using the MASK masks to generate overlapping region masks of the different view angle images, and performing local feature detection on the overlapping region masks to generate a transformation matrix; aligning the images of different visual angles according to the transformation matrix; a Canny edge detection operator is fused in splicing seam energy detection, the energy weight of the image edge area of the aligned image is enhanced, and a splicing seam is guided to actively avoid an object with remarkable structural features; b spline interpolation is introduced to optimize an initial splicing seam path; constructing a Laplacian pyramid along Gaussian blur of a splicing seam, and sequentially carrying out gradual fusion to generate a seamless panoramic image; according to the method, the basic function of eliminating the visual blind area is realized, a clear and visual panoramic view can be provided for a driver, and the driving safety is effectively improved.

The invention discloses an ultrasonic image enhancement method based on an ultrasonic Gaussian kernel and non-sharpening mask sharpening. The method comprises the following steps: acquiring an original image; removing the noise of the original image by using an ultrasonic Gaussian kernel to obtain a denoised image; performing Gaussian blur processing on the denoised image to obtain a Gaussian blur image; extracting high-frequency information in the original image; sharpening the extracted high-frequency information part to obtain a sharpened image; performing weighted fusion on the sharpened image and the original image to obtain a weighted image; the weighted image is converted to a Lab color space, and the brightness channel contrast is enhanced; and restoring the contrast-enhanced image to the RGB color space to generate a final enhanced image. According to the method, the Gaussian kernel denoising and the non-sharpening mask sharpening technology are combined, noise in the ultrasonic image is effectively removed, image details are enhanced, and the identifiability of the image is further improved through self-adaptive contrast enhancement.

An image edge smoothing processing method based on an ink droplet diffusion principle can be divided into three parts: a first part, performing various image preprocessing on a mask part of a specified image, including graying, binaryzation, opening operation, median filtering and connected domain analysis, so as to corrode and eliminate small noise points and convex regions; the second part is that local closed operation is performed on the preprocessed image to fill up the concave area, the local closed operation may cause significant distortion of the image, and only the part which is small in filling area and does not cause significant deformation of the image is reserved through image subtraction and connected domain analysis; and a third part, performing bilinear interpolation and Gaussian blur on the image to resist aliasing, and then applying a super-resolution algorithm to obtain a high-definition image with smooth edges. According to the method provided by the invention, the problem that the image edge is uneven in the color separation treatment process of template manufacturing in the textile field is solved, so that the working efficiency of factory template manufacturing is improved.

The invention provides a meteorological radar radial speed feature extraction method for near strong wind forecasting, which comprises the following steps: converting radial speed data into a digital image, obtaining a plurality of retreating / approaching wind speed zones by searching color block contours with different threshold values, and expanding corresponding wind speed zones. And judging whether the speed extreme value and the center distance of the two meet characteristic conditions or not, and finally obtaining radial speed characteristics conforming to convection strong wind. According to the invention, radar radial speed data is converted into a digital image, and image identification and contour extraction are carried out, so that the processing speed is greatly improved; the space continuity of the data is enhanced through a Gaussian blur method, so that the subsequent image recognition is more accurate; the retreating wind speed area and the approaching wind speed area are combined together as a group of wind speed areas for treatment, so that the characteristics of the wind field are reflected more comprehensively; by judging the contour overlapping relation and de-weighting according to the speed value, the feature with the large speed value is reserved, noise interference can be effectively removed, and the reliability of the final extraction result is improved.

The invention discloses an intelligent psychological health education system based on an education robot. The system comprises (1) AI family-school cooperation: an improved YOLOv7 algorithm captures classroom behaviors, dynamic Gaussian blur protects privacy, directionally pushes student participation images containing desensitization IDs, supports AI to generate personalized communication verbal skills, and improves the degree of participation of parents; (2) an interdisciplinary fusion engine: constructing a knowledge graph to associate psychological courses with mathematics, sports and other subjects, predicting an optimal fusion opportunity in combination with educational administration data, and generating a double-teacher collaborative teaching plan; (3) interest and emotion driven daily penetration: analyzing student interest labels based on a latent semantic model (LFM), and triggering personalized family task pushing in combination with multi-modal emotion recognition to realize intelligent penetration of psychological health education in a family scene; and (4) full-link privacy protection: adopting differential privacy, block chain and federated learning technologies to realize'all anonymity 'and'availability and invisibility' of data, and ensuring information security through three-level desensitization and quantum encryption, so that the system meets the requirements of'personal information protection method '.

The present disclosure relates to the field of chemical analysis technology, and specifically discloses methods and systems for analyzing and calculating aggregation of analytes in a chemical compound. The analysis method includes: acquiring a chemical image of analytes in a control sample and a chemical image of the analytes in at least one test sample, performing Gaussian blur on each of the chemical images, and performing brightness calibration on the obtained Gaussian blurred image; converting the brightness calibrated Gaussian blurred image into a binary image; converting the binary image into one-dimensional array; calculating a continuous length of predetermined pixels in a one-dimensional array and a frequency corresponding to each continuous length, and generating a histogram of frequency and continuous length; fitting the histogram with an exponential Gaussian distribution function to obtain an exponential attenuation parameter of the fitted exponential Gaussian distribution function; calculating an aggregation index of the test sample according to the exponential attenuation parameters corresponding to the test sample and the control sample. This technical solution can quantitatively determine the aggregation of analytes.

The invention discloses a blurred bar code image processing method and system fused with super-resolution restoration, and relates to the technical field of bar code image restoration, and the method comprises the four steps: firstly, extracting the frequency domain and texture complexity index of a target blurred bar code image, and achieving the automatic classification of dynamic, defocusing and Gaussian blur, and exclusive denoising; building a double-branch feature extraction network, extracting a structure and texture feature map, and generating a cross-scale fusion feature map; outputting a bar code image restoration first draft; and finally, constructing a dual-objective loss function for iterative optimization, and outputting a final clear image. The beneficial effects of the method are that multi-dimensional feature classification enables fuzzy type identification to be accurate, and exclusive denoising lays a high-quality foundation for subsequent restoration; the multi-modal feature fusion is combined with the layered super-resolution technology, a double-target loss function and a closed-loop verification mechanism ensure that the final image can be stably decoded, and the method is suitable for mainstream barcode types and has high practical value in industrial barcode recognition scenes.

This invention discloses an automated anterior tooth feature parameter extraction method and evaluation system based on computer vision, comprising: acquiring and visualizing a full-mouth scan image of a patient to be processed; cropping the corresponding tooth portion image; using Gaussian blur to eliminate specular reflection; using color segmentation to set thresholds for the H, S, and V channels respectively; separating the teeth and gingiva in each tooth portion image and performing morphological processing on each; extracting the tooth and gingiva portion contours using contour extraction methods; further refining the segmentation accuracy using the Chan-Vese active contour model algorithm and the energy function within the tooth portion contour; extracting corner points using the Shi-Tomasi corner detection method and then clustering them; extracting other important evaluation feature points through curve fitting and extreme value finding methods; and finally calculating relevant objective indicators for tooth evaluation and automatically generating the corresponding tooth score, thus completing the automated anterior tooth evaluation.

The invention provides an e-commerce commodity graph differentiation generation method and device, equipment and a medium, and the method comprises the steps: obtaining an original to-be-shelved commodity graph of e-commerce, and finally obtaining the disassembly information; the method comprises the following steps: adjusting an original commodity image into 980 * 980 pixels, performing Gaussian blur processing at the same time, and then converting image pixel data into a Base64 coded character string; constructing a composite cue word according to the image data, the disassembly information and a format required by generation; inputting the composite cue word into a model to generate a new scene description; inputting the new scene description, the original commodity graph and the synthesis requirement into a model to generate a synthesis cue word, and then inputting the original commodity and the synthesis cue word into an image-text model to generate a differentiated commodity graph; setting the output resolution, the number of iterations and the learning rate of the SuperIR model, and inputting the differentiated commodity graph into the SuperIR model to generate a differentiated commodity optimization graph; the defects of low efficiency, poor scene adaptation, main body distortion and difficulty in repeated avoidance of a traditional method are overcome.

An embodiment of the present application discloses an image processing method, apparatus, system, electronic device, and storage medium. The method includes: in response to an image acquisition request, acquiring an image to be processed; performing Gaussian blur processing on the image to be processed to obtain a target image, where the target image is the image to be processed after Gaussian blur processing; determining a region to be processed in the image to be processed, and acquiring position information corresponding to the region to be processed; based on the position information, cropping the target image to obtain a Gaussian blurred image corresponding to the region to be processed; covering the Gaussian blurred image on the region to be processed of the image to be processed, performing Gaussian blur processing on the entire image to be processed, then cropping the target image corresponding to the specified region, and covering the target image on the specified region of the image to be processed, so that drawing can be performed on the original image without the need to generate a new layer, thereby reducing the consumption of image drawing.

Methods and apparatus for image processing of spherical content via hardware acceleration components. In one embodiment, an EAC image is subdivided into facets via existing software addressing and written into the memory buffers (normally used for rectilinear cubemaps) in a graphics processing unit (GPU). The EAC facets may be translated, rotated, and / or mirrored so as to align with the expected three-dimensional (3D) coordinate space. The GPU may use existing hardware accelerator logic, parallelization, and / or addressing logic to greatly improve 3D image processing effects (such as a multi-band blend using Gaussian blurs.)

The application discloses a GAN neural network multiple distortion suppression model based on a coordinate attention mechanism, obtains an original neutron radiographic image from a neutron source; proposes a brand-new neutron radiographic image degradation model; adds Gaussian blur, Gaussian noise, Poisson noise and noise obeying gamma distribution to the clear neutron radiographic image; uses real white spot noise to train the GAN neural network, simulates white spot noise and randomly adds the white spot noise to the clear neutron radiographic image; constructs a multiple distortion suppression model of the GAN neural network based on the coordinate attention mechanism; uses Huber loss to train the constructed GAN neural network based on the coordinate attention mechanism; inputs a real neutron radiographic image containing multiple distortions into the trained multiple distortion suppression model as input, predicts the original image of the real neutron radiographic image containing multiple distortions, and obtains a target result.

The invention provides an X-ray imaging detection method and device. The method comprises the following steps: correcting original image data of X-rays to obtain a corrected original image, extracting high-frequency information of the corrected original image through Gaussian blur, and performing difference and noise suppression processing to obtain preprocessed noise reduction data; carrying out binarization processing on the preprocessed noise reduction data to obtain noise reduction data; performing edge processing on the noise reduction data through a Sober operator to obtain an edge image; and after the edge image and the corrected original image are fused, the line is identified through Hough transform, a detection part image is obtained, the device comprises a module for realizing the method, through the method and the device, the ray machine device in the DR detection system is optimized, a high-frequency constant-voltage ray machine is replaced by a conventional power frequency ray machine, and the equipment cost is reduced.

The invention provides a waterproof board laying and hanging robot path recognition method based on machine vision, and relates to the technical field of image recognition. According to the method, an industrial camera carried by a waterproof board laying and hanging robot collects a waterproof board laying and hanging area image in a tunnel, perspective transformation is performed by combining internal and external parameters of the camera, and a laying operation image under an orthographic view angle is obtained; performing Gaussian blur, contrast enhancement, brightness normalization and other preprocessing operations on the laying operation image; after the laying operation image is preprocessed, a directional entropy gradient field is constructed based on local information entropy, a disturbance response is fused to generate a structure display function, and a structure feature map of the laying operation image is extracted; a path point sequence for navigation control of the laying and hanging robot is obtained through an image skeleton extraction and path point equal-interval sampling method, noise reduction processing is conducted on the path point sequence through sliding window mean filtering, smooth fitting is conducted on the path sequence through B-spline curve fitting, and a robot motion path is obtained.

The invention discloses a power transformation oil stain defect generation method and device based on a segmentation model and a traditional method, and belongs to the technical field of power transformation equipment defect detection. According to the method, a shape library is constructed by combining segmentation of all models, accurate positioning of defect positions is realized by using a potential-changing oil leakage area segmentation model, and oil stain defect data conforming to physical characteristics is generated based on a traditional image processing technology. Generating an initial shape library by the SAM; training a potential oil leakage area segmentation model to position an oil leakage-prone area; geometric shapes in the shape library are randomly fused in the selected ROI, and the sense of reality is enhanced through brightness matching and Gaussian blur; and synchronously generating a segmentation mask corresponding to the defect. According to the method, paired data is not needed, the region-controllable high-fidelity oil stain defect image is generated, the bottleneck problem of model training caused by dependence on paired data and uncontrollable generation in an existing method is solved, the generalization ability and training efficiency of a defect detection model are remarkably improved, and traceable physical interpretation is provided at the same time.

The application discloses an active interference image anti-counterfeiting method, system, device and storage medium, which are corresponding solutions, and in the solutions, the adversarial disturbance can be adaptively distributed in an area which is not sensitive to human eyes, so that the invisibility of the disturbance is significantly enhanced, the application further introduces Gaussian blur, and the ability of the disturbance to resist image transformation is improved through adversarial training, so that the application has high robustness; in addition, the application does not depend on prompt words, an attacker is difficult to bypass the protection by using multiple prompt words, and the application only fine-tunes the weight of a variational autoencoder, so that the training resources consumed are far less than those of a traditional active interference method for a U-Net.

The invention relates to a building group contour automatic generation method based on TLS point cloud, and the method comprises the following steps: S1, obtaining the point cloud data of a building at different site positions, and carrying out the point cloud registration of the point cloud data, and obtaining a complete point cloud model of a building group; s2, carrying out downsampling, noise reduction and cutting on the complete point cloud model to obtain a preprocessed complete point cloud model; s3, carrying out point cloud slicing on the preprocessed complete point cloud model, and intercepting a middle section point cloud; s4, performing point cloud projection on the point cloud of the middle section to obtain a projection two-dimensional plane; s5, dividing the projection two-dimensional plane into grids, then converting the grids into a grey-scale map, and carrying out binarization processing; and S6, performing Gaussian blur, Canny edge detection and Hough transform processing on the binarized grey-scale map to obtain a point cloud contour. Compared with the prior art, the method has the advantages of realizing rapid and accurate building group wall line extraction and the like.

The invention relates to the technical field of chemical analysis, and particularly discloses a method and a system for analyzing and calculating the aggregation condition of an analyte in a chemical composition, and the analysis method comprises the following steps: obtaining a chemical image of the analyte in a control sample and a chemical image of the analyte in at least one test sample, carrying out Gaussian blur on each chemical image, and calculating the aggregation condition of the analyte in the chemical composition; carrying out brightness calibration on the obtained Gaussian blurred image; converting the Gaussian blurred image after brightness calibration into a binary image; converting the binary image into a one-dimensional array; counting continuous lengths when predetermined pixels appear in the one-dimensional array and frequencies corresponding to the continuous lengths, and generating a histogram of the frequencies and the continuous lengths; fitting the histogram by using an exponential Gaussian distribution function to obtain an exponential attenuation parameter of the fitted exponential Gaussian distribution function; and calculating the aggregation index of the test sample according to the index attenuation parameters corresponding to the test sample and the control sample. According to the technical scheme, the aggregation condition of the analyte can be quantitatively determined.

The invention discloses a marine surveying and mapping picture data vectorization method, which comprises the following steps of: performing coarse registration and fine registration on a historical nautical chart picture to realize spatial alignment of an image and a geographic coordinate system; based on an optimal index indexing algorithm, sensitive wavebands are selected from red, green and blue wavebands and characteristic indexes, and the characteristic indexes comprise an enhanced water body index, an ultra-green index, a normalized green and blue difference index and a visible light vegetation index; performing brightness contrast enhancement, Gaussian blur, sharpening, color clustering and binarization processing on the SWB to generate an element enhanced binary image; point element extraction: adopting a Tesseract engine to train a customized model, identifying point element characters and positions in the binary image, and generating vector point data and an attribute table; and line element extraction: deleting the point elements extracted from the binary image, performing broken line connection processing on the binary image after the point elements are deleted, and generating continuous vector line elements based on the end point distance and the tangential direction.