256 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 method for identifying impurities and bubbles in a wine bottle based on multi-feature comprehensive judgment, and belongs to the technical field of image processing and computer vision, and the method comprises the steps: employing a high-resolution industrial camera to be combined with an LED light source, and continuously shooting multiple frames of images of the wine bottle under the consistent shooting angle and illumination condition; carrying out the preprocessing of the collected image through employing a self-adaptive histogram equalization and Gaussian blur method; extracting the edges of impurities and bubbles in the wine bottle on the image by adopting a Canny edge detection algorithm to obtain an edge image; segmenting the edge image into a plurality of target areas by using a contour detection technology, and extracting possible bubble and impurity areas from the edge image; extracting target area features including circularity, gray average, gray uniformity, texture features and color space; and according to the extracted features, bubbles and impurities are distinguished by applying a comprehensive judgment rule. According to the method, the bubbles and the impurities are comprehensively judged by adopting multi-feature combination, so that the accuracy and the robustness of detection are greatly improved.

The invention relates to an endoscope image reflection restoration method based on detection network and filtering fusion, and belongs to the technical field of medical image computer processing. The method aims at improving detection and removal of specular reflection in an endoscope image, a variant of TransUnet is adopted in the detection stage, and U-Net and Transform architectures are combined; the method comprises Atrous spatial pyramid pooling (ASPP), information bottleneck (IB) and a convolutional block attention module (CBAM). The enhancement functions improve the identifiability of specular highlight and solve the problem of weak generalization in endoscope data concentration. In the removal stage, the invention designs an enhanced median filtering module for removing specular reflection. The module first applies Gaussian blur to the repaired region and then calculates color information from the non-reflective region. According to the method, mirror reflection is effectively eliminated from the endoscope image, clearer and more accurate visual information is provided for clinicians, and improvement of diagnosis precision is facilitated.

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 discloses an X-ray detection image enhancement method and system, and the method comprises the following image enhancement steps: carrying out the gray preprocessing of an image, carrying out image enhancement preprocessing, carrying out homomorphic filtering enhancement processing, carrying out USM sharpening processing, taking a filtered image Ifiltering as an input, carrying out the processing of an input image through Gaussian blur, generating an enhanced image, and finally carrying out the tone adjustment. And carrying out color gradation adjustment on the enhanced image Ishadow to obtain a final target image Itarget. According to the method, firstly, multiple parameters such as exposure, brightness and contrast are adjusted for the original image, so that the overall quality of the image can be improved more comprehensively. Compared with the prior art, the multi-dimensional adjustment provides greater flexibility, so that the image can reach the optimal basic state before entering the subsequent processing. And a foundation can be laid for subsequent image post-processing, image analysis, image classification and image library and map establishment.

The invention discloses an image anonymization processing method, device and equipment and a computer readable storage medium, and the method comprises the steps: carrying out the training of a preset CNN regression model according to an obtained to-be-trained data set, and generating a corresponding anonymization processing model; verifying the anonymization processing model according to an obtained verification data set, and determining whether the anonymization processing model is an optimal anonymization processing model; if it is determined that the anonymization processing model is the optimal anonymization processing model, processing a to-be-anonymized image based on the anonymization processing model, and predicting Gaussian blur kernel parameters of the to-be-anonymized image; and anonymizing the to-be-anonymized image through the Gaussian blur kernel parameter to generate an anonymized image, thereby solving the technical problem of poor anonymization effect in related technologies, realizing full-automatic intelligent anonymization, improving the anonymization effect and the automation degree, and reducing the manual intervention demand.

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 provides a lace texture image diffusion generation method based on an ADM and a dynamic denoising filtering model, the model combines a learnable Gaussian blur layer and a high-pass filtering technology, and the Gaussian blur layer aims at adaptively adjusting the blur degree of the image according to different input images and reducing high-frequency noise in the lace texture image. According to the high-pass filtering technology, key areas such as the contour and the edge of a main pattern in the image are enhanced, so that main texture features of the image are highlighted. The module is added into a down-sampling layer of an ADM network, the phenomenon that lace texture images generated by an original model are too smooth is improved, and the texture layering sense of the images is enhanced.

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 invention discloses a furrow identification and navigation line extraction method of a self-propelled transportation platform for ridge planting vegetables. The furrow identification and navigation line extraction method comprises the steps of image acquisition, image boundary smoothing processing, color conversion, threshold segmentation, denoising processing, edge detection, midpoint extraction and navigation line fitting. Gaussian blur filtering is used to carry out boundary blur processing on the image, then a traditional BGR image is converted into an HSV image to reduce the influence of an illumination condition on furrow identification, and a low-influence component is separated to carry out next processing; the method comprises the following steps of: enhancing gray information by adopting a Masking algorithm, effectively realizing threshold segmentation and successfully identifying a furrow position in combination with an OTSU method, further removing most noise interference in an image by utilizing a morphological denoising method, realizing deep denoising of the image on the basis of a FindContours, extracting a furrow boundary, and finally, extracting the furrow boundary. Then, the horizontal coordinates of the two boundary points of the furrow are utilized to solve the midpoint coordinate point cloud of each row in the image pixels, and finally, the point cloud coordinates are utilized to improve the RANSAC algorithm to realize the navigation line fitting of the furrow.

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 discloses a preparation liquid quality detection method, which belongs to the field of assisted reproduction, and comprises the following steps: collecting an original image of a culture solution preparation liquid, and reducing the width and the height in equal proportion; estimating the background light intensity of the reduced image based on a Gaussian blur function to obtain a background image; subtracting the background image from the shrunk image to obtain a foreground image; performing normalization and binarization processing on the foreground image; the contour breakpoints of the processed image are connected through closed operation, and a processed image is obtained; carrying out object contour searching on the processed image, extracting and representing an object contour in the image, and eliminating burrs and noisy points of a contour curve to obtain a final image; the contour area of the final image is calculated, liquid drops, bubbles and impurity contours are screened out according to the area, impurities and bubbles are distinguished according to roundness and gray values, the roundness and height of the contour of the liquid drops are calculated, the quality evaluation indexes of the existence of the impurities and the bubbles and the form quality of the liquid drops are given, the quality of the prepared liquid can be accurately and automatically evaluated, and the culture dish for completing the prepared liquid does not need to be manually screened.

The invention relates to a dual-domain aligned non-paired hyperspectrum and SAR image space-time spectrum fusion method, which comprises the following steps: carrying out up-sampling on a hyperspectrum and carrying out feature extraction, and carrying out Gaussian blur and feature extraction on an SAR image; enabling the radiation distribution of the SAR features to approach the radiation distribution features of the hyperspectral image; adjusting the spatial geometric information of the SAR features; extracting change information between image features, and predicting a hyperspectral image with low spatial resolution through information injection; and fusing the hyperspectral image with the low spatial resolution and the aligned original SAR image to obtain a final hyperspectral image with the high spatial resolution. The method has the beneficial effects that the fusion precision of the hyperspectrum and the SAR image space-time spectrum is improved, and reliable support is provided for subsequent application.

The invention discloses a rock slice analysis method for quantitative discrimination of a shale deposition structure, and the method comprises the steps: scanning and splicing a plurality of rock slice pictures, generating a complete scanning and splicing image, obtaining a corresponding RGB image, carrying out the sharpening of the image, converting the image into a gray image, carrying out the Gaussian blurring, and carrying out the recognition of the image, thereby achieving the quantitative discrimination of the shale deposition structure. Carrying out binarization processing to obtain a binarized image, carrying out morphological reconstruction operation on the binarized image to obtain a reconstructed image, and identifying a closed boundary or contour of rock particles in the reconstructed image; and constructing, analyzing and observing a rock mineral particle total number change trend chart, a rock mineral particle total area change trend chart and a rock mineral particle average area change trend chart, and performing sedimentary structure type division according to the three trend charts. According to the method, the inherent uncertainty of manual statistics is effectively avoided, and the accurate judgment of the long quartz deposition structure type of the shale is remarkably improved.

The invention discloses a cable detection method based on picture processing, which is used for detecting dust and dirt on the surface of a cable, and comprises the following steps of: acquiring a sequence image of a fixed cable in a box body without external light interference for each section of cable by an area-array camera which rotates by 180 degrees by taking the cable as a central axis, converting a gray image, intercepting a rectangular image, and determining the dust and dirt on the surface of the cable. Gaussian fuzzy filtering, contrast enhancement, threshold segmentation, edge detection, contour detection, calculation of the contour surrounding area of the cable image and judgment of whether dust and dirt exist in the section of the cable are performed. According to the method, whether dust and dirt exist or not is judged through the cable image contour surrounding area, the radian size of the cable arc-shaped surface and details of the arc-shaped surface do not affect judgment, and meanwhile the dust and dirt details of the cable edge can be better highlighted through backlight shooting. The influence of the external environment and the arc-shaped surface of the cable on dust and dirt detection can be greatly reduced, and the detection precision, accuracy and working efficiency are improved.

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 discloses a method for extracting angular points in a checkerboard calibration object image. Firstly, rough positions of angular points of a checkerboard calibration object are obtained; converting the rough position of the angular point into a supersampled image coordinate system, and intercepting an image window at the rough position of the angular point; calculating a response value of a pixel in the central area of the window, and generating a response diagram; gaussian blur is carried out on the response diagram to enable the response diagram to be smooth, and an area near an angular point in the blur response diagram is fitted into an elliptical paraboloid; calculating the sub-pixel extreme value position of the bright spot according to the parameters of the elliptical paraboloid; and finally, carrying out coordinate conversion to obtain the accurate position of the angular point. Compared with a checkerboard calibration object angular point position extraction method based on an angular point boundary, the method provided by the invention can realize higher angular point position extraction precision, thereby improving the precision of a camera calibration result, and facilitating the accurate realization of computer vision application.

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 relates to a high dynamic range infrared image enhancement method and device based on SKWGIF. The method comprises the following steps: acquiring a 16-bit original infrared image to be enhanced, and filtering the original infrared image to obtain a base layer image corresponding to the original infrared image; enhancing the contrast of the base layer image by using a CLAHE algorithm to obtain an enhanced base layer image; performing Gaussian blur processing on the base layer image to obtain a secondary base layer image; generating a detail layer image based on the secondary base layer image and the original image; enhancing the detail layer image by using an automatic gain control method to obtain an enhanced detail layer image; combining the enhanced base layer image and the enhanced detail layer image according to a preset fusion coefficient to obtain an enhanced image corresponding to the original image; and enhancing the dynamic range of the image through linear compression to obtain an 8-bit output image. By adopting the method, the contrast of the enhanced image can be improved, and the details of the enhanced image are enriched.

The invention discloses a method and system for automatically extracting a sewing track from a visual edge and application, and belongs to the technical field of image recognition, and the method comprises the following steps: a CCD (Charge Coupled Device) is matched with a light source to obtain and store an original image of a first cutting piece; carrying out gray scale and binarization processing on the image; weighting pixel points of the original image by using Gaussian blur to obtain an output image; detecting the edge contour of the output image by using edge detection; after the contours are determined, a convex hull is obtained, and a final image containing two contours is obtained; the first piece, the second piece, the third piece and the like are sequentially overlaid on the first cutting piece, and the following cutting pieces of the first cutting piece are on the basis of the previous cutting piece, and the steps are repeated until the sample is completed. The air bag cutting piece sewing machine solves the problem that in the traditional air bag cutting piece sewing process, sewing routing is prone to being too large or too small.