218 results about "Sobel operator" patented technology

The invention relates to the technical field of image analysis, in particular to an ETFE film cutting control method and system based on image recognition, and the method comprises the following steps: enhancing an edge through multi-scale Gaussian filtering, extracting a gray level change rate pixel by a Sobel operator, generating a path segment, outputting a coordinate, calculating a gradient direction, constructing a direction continuous sequence, and recording an angle deviation; a dynamic window judges abnormal fluctuation to trigger calibration, a fluctuation segment vector is replaced to update a motion track, and a B spline is fitted to generate a track output cutting instruction. According to the method, multi-scale Gaussian filtering is combined with Sobel operator enhanced edge extraction continuous pixels, the fracture problem caused by a fixed threshold value is solved, vector space projection constructs a direction sequence to quantify angle deviation, dynamic window range judges fluctuation to trigger calibration, and a vector replacement strategy corrects an abnormal section control point direction. A smooth track is generated through B spline interpolation, mechanical vibration is reduced, and the flexible material cutting precision and the equipment cooperation efficiency are improved.

The invention provides an ore pulp liquid level prediction method and system based on flotation froth dynamic characteristics and a storage medium, and relates to the technical field of flotation process production detection.The method comprises the steps that under the working condition of a flotation machine, flotation froth images and ore pulp liquid levels are continuously collected; performing texture feature extraction on each flotation froth image to obtain texture features, and generating a texture distribution coefficient from the texture features associated with the ore pulp liquid level; using a Sobel operator to identify the marginal area of each flotation froth in the flotation froth image, and carrying out comprehensive analysis to generate a froth distribution index; analyzing two frames of adjacent flotation froth images to generate a flotation froth dynamic deviation degree; and constructing an ore pulp liquid level prediction model, and predicting the ore pulp liquid level by using the ore pulp liquid level prediction model. The technical problem that the ore pulp liquid level cannot be monitored in real time in the prior art is solved.

The invention discloses a ginkgo leaf extracting solution state real-time monitoring method based on image processing, and relates to the technical field of ginkgo leaf extracting solutions. The method comprises the following steps: constructing a multi-light-source imaging environment to shoot a ginkgo leaf extracting solution image; obtaining an extracting solution mask through a U-net segmentation model, and obtaining an extracting solution foreground image in combination with the ginkgo leaf extracting solution image; segmenting the extracting solution foreground image through an adaptive threshold method to obtain an extracting solution block graph, and optimizing through a watershed boundary optimization method to obtain an optimized extracting solution block graph; a block boundary probability value is calculated through a boundary probability model with double distance changes, a block gradient magnitude is calculated through a Sobel operator, block boundary confidence is obtained by combining the block boundary probability value and the block gradient magnitude, and block boundary pixels are determined; and collecting a boundary gradient feature vector sequence of the block boundary pixels, inputting the boundary gradient feature vector sequence into the extracting solution evolution trend model, outputting to obtain an oxidation risk index, and performing early warning if the oxidation risk index is greater than a preset threshold value.

The invention relates to the technical field of three-dimensional modeling, in particular to a three-dimensional modeling system and method based on a laser radar, and the system comprises a point cloud boundary extraction module, a boundary error analysis module, a self-adaptive cutting repair module and a stitching structure optimization module. According to the method, three-dimensional coordinates, normal vectors, reflection intensity, distances and indexes of boundary points are called layer by layer, a Sobel operator and a Canny algorithm are combined to cooperatively recognize a boundary and an error region, then local region repair is completed based on least square plane fitting and spatial residual analysis, a tension vector is analyzed through dynamic planning, and stitching path adjustment is executed. On the cooperative basis of multi-type point cloud features, a spatial topological structure and boundary continuity dynamic modeling, the problems of local damage, connection errors and instable stitching paths of point cloud boundaries in complex scenes are solved, and the integrity of boundary structures, the coherence of stitching transition and the rationality of spatial geometric expression are remarkably improved.

The invention discloses a geotechnical engineering slope deformation monitoring method and system. The method comprises the following steps: acquiring multi-modal image data, enhancing the weak deformation area contrast of the multi-modal image data by using a CLAHE algorithm, extracting gradient edge information through a Sobel operator, and filtering laser radar point cloud noise based on VMD; capturing a bidirectional dependency relationship of a time sequence based on a BiLSTM bidirectional long short-term memory network, and dynamically allocating weights of different time steps and spatial positions through an attention mechanism; optimizing hyper-parameters of the hybrid prediction model by using a GJO Golden Litsea Optimization algorithm to obtain a target hybrid prediction model; and inputting the feature image data into the target hybrid prediction model for prediction, generating a risk probability thermodynamic diagram, and positioning the position of the potential slip crack surface of the slope according to the risk probability thermodynamic diagram. And reliability and accuracy of geotechnical engineering slope image data analysis are improved.

The invention relates to the technical field of medical image analysis, computer vision and deep learning, in particular to a texture perception and multi-scale feature adaptive fusion hip joint osteophyte detection method. According to the method, firstly, a bone texture feature extraction module is used for carrying out feature extraction and enhancement on a hip joint X-ray image, a parallel double-branch structure is adopted, gradient and space structure features are extracted through a Sobel operator and pooling operation, and feature maps are fused; then, the fused features are input into a double-backbone network, the first backbone network extracts local fine textures and long-range dependence features by combining convolution, self-attention and a gating mechanism, and the second backbone network extracts multi-scale semantic features through hierarchical grouping and depth separable convolution; and double-trunk output is dynamically weighted and fused through an adaptive fusion module, features are optimized through a multi-scale semantic fusion module, and finally the position and confidence of osteophyte are output.

The invention discloses a cascade multi-scale convolution and modal enhancement brain tumor segmentation method based on a Mama framework, and relates to the technical field of brain tumors. According to the method, an MCME-UNet model integrated with an MCMS module is provided, through a hierarchical feature extraction mechanism and a multi-modal feature fusion strategy optimized by an MEM module, the segmentation precision is remarkably improved while the calculation efficiency is kept, and particularly, an EEM module innovatively applies Sobel operator to be connected with residual errors, so that the tumor boundary continuity and the segmentation accuracy are synchronously improved; moreover, a novel training normal form of a focus Tversky loss function is introduced, so that the problem of class imbalance is effectively solved, the sensitivity of the model to a small tumor region is enhanced, a smoother segmentation boundary can be generated, and the stability and accuracy of a segmentation result are greatly improved.

The invention provides a gear keyway width measuring method applying machine vision, and relates to the technical field of gear detection. The method comprises the steps of image acquisition, preprocessing, boundary extraction, contour separation, central point calculation, pixel equivalent calculation, symmetric line slope calculation and keyway width calculation. The method specifically comprises the steps of extracting a boundary through a Sobel operator, separating a contour based on a connected domain area, calculating a central point through an arithmetic average method, determining a pixel equivalent in combination with the actual size of an inner hole, achieving symmetric line slope calculation and point classification through iterative fitting and Hough conversion, and finally completing width measurement. According to the invention, a non-contact machine vision technology is adopted to replace traditional contact measurement, so that the problems of large manual operation error, low automation level and insufficient online detection capability are solved, high-precision and automatic measurement of the width of the gear keyway is realized, the measurement efficiency and the result reliability are improved, and the production cost is reduced. The method is suitable for a rapid detection scene in gear batch production.

The invention discloses a wide-angle image acquisition and processing system for underwater target identification, which relates to the technical field of image processing and target detection, and comprises an image acquisition and preprocessing module for acquiring an underwater image, removing initial noise through a preset filter, processing an enhanced grayscale image by adopting image smooth combination, and obtaining an underwater image; the pixel density gradient calculation module is used for calculating a multi-direction pixel density gradient by adopting a horizontal direction convolution kernel and a vertical direction convolution kernel of a Sobel operator according to the enhanced grayscale image, and obtaining a gradient intensity graph through gradient amplitude calculation; according to the wide-angle image acquisition and processing system for underwater target recognition, through multi-level feature fusion and classification positioning, the detection precision and positioning accuracy of an abnormal region in an underwater complex environment are remarkably improved, and efficient technical support is provided for underwater target recognition.

The invention discloses an image classification method based on a Transform neural network, belongs to the field of image classification, relates to a Transform neural network technology, and comprises the following steps: obtaining an input image, and respectively calculating a gradient amplitude component of each pixel in the input image by using a Sobel operator; calculating a gradient variation value of the input image according to the gradient amplitude component, and performing dynamic self-adaptive partitioning on the input image according to the gradient variation value; flattening the blocks into a vector sequence, expanding a linear projection layer weight according to a pre-trained ViT model, and linearly projecting the vector sequence into a d-dimensional embedded vector through a linear projection layer; adding a position code to the embedded vector and generating a feature vector; according to the type of the dynamic adaptive block, processing the feature vector by using a multi-head attention mechanism or a cross-scale cross attention mechanism of Transform, and outputting category probability distribution of the input image; according to the invention, the efficiency and precision of image classification can be balanced.

The invention discloses a reversible information hiding method and system for enhancing image smoothness, and relates to the technical field of information security, and the system comprises a preprocessing module, an information embedding module and an optimization recovery module. According to the method, the Sobel operator is utilized to calculate the image gradient map, the region is divided based on the threshold T, the smooth region and the texture region can be accurately distinguished, an accurate basis is provided for differential processing of different regions, region characteristics are fitted, large-size blocks and sub-blocks of the smooth region are divided to facilitate subsequent refinement processing based on the gradient mean value, and the processing efficiency is improved. The small-size blocks of the texture area are beneficial to operation aiming at texture features, the rationality and effectiveness of overall processing are improved, meanwhile, the pixel mean value in the large-size blocks of the smooth area is calculated, the pixel and mean value difference value is recorded, reversible low-pass filtering is carried out, and the reversibility of the image in the whole information hiding and recovering process is ensured.

The invention discloses a dead pixel detection method and system combining spatial domain analysis and time domain analysis, and belongs to the technical field of image processing. The method comprises the following steps: an airspace analysis stage: extracting edge information through a Sobel operator; carrying out image binaryzation, filling potential holes by using morphological closed operation, and strengthening dead pixel areas; identifying all candidate dead pixel regions by adopting a connected domain labeling algorithm; in the time domain analysis stage, the spatial position and area characteristics of candidate dead pixel areas are combined, the candidate dead pixel area corresponding relation between cross frames is established, the continuity of the same candidate dead pixel area on a time axis is judged, and only two assumptions are met, the time domain analysis is completed. And finally judging the candidate dead pixel regions with the areas and the contrast ratios of the dead pixel regions exceeding the set threshold values as stable dead pixels and outputting the stable dead pixels. By analyzing the stability of the dead pixels in the time sequence, abnormal dead pixels continuously existing in multiple frames can be effectively recognized, and particularly, the method is more stable and accurate when detecting small-area and low-contrast dead pixels.

The invention relates to the technical field of pupil boundary detection, in particular to a multi-algorithm fusion pupil boundary detection method and system, and the method comprises the following steps: obtaining an eye image through an infrared camera, converting the eye image into a grey-scale map, optimizing the grey-scale map, carrying out the median filtering noise reduction, maintaining the edge information, and determining the pupil boundary through a Sobel operator and Hough transform. And the pupil center displacement is analyzed, the motion consistency is evaluated, the pupil position is corrected, and the pupil positioning accuracy is optimized. According to the method, the image preprocessing process is optimized by applying infrared camera shooting and image processing algorithms, accurate detection, median filtering and local contrast enhancement of pupil boundaries are achieved through multi-algorithm fusion, and particularly noise and edge information in the images are optimized; according to the method, the edge detection precision and the image definition are effectively improved, the pupil boundary is accurately mapped through combined use of the Sobel operator and Hough transform, and the misjudgment rate is greatly reduced.

The invention discloses a video saliency target detection method based on attention and graph collaborative enhancement. The method comprises the steps of video frame preprocessing, network model establishment and video saliency detection. A collaborative attention graph network is adopted, a collaborative attention graph module AGM and a space edge attention module SEM are respectively used for independently extracting space-time clues and space edge features, inter-frame attention is combined with a space-time graph convolutional network GCN, and then motion information between video frames is captured while a receptive field is expanded to capture a long-distance dependency relationship. The space-time diagram convolutional network GCN connects the features from time to space branches, can collaboratively fuse cross-modal features, and considers global and local space-time features at the same time. The SEM enhances high-level information by extracting spatial and edge features from low-level data using a sobel operator and a spatial attention module. According to the method, the most interested target content of human eyes in a video can be effectively extracted, more attention is paid to the salient region, and background interference is inhibited.

The invention belongs to the technical field of image processing, and discloses an SLM printing material defect prediction method and system based on a visual algorithm, and the method comprises the steps: synchronously collecting material image data, material parameter data and mechanical motion data in an SLM printing process; performing non-overlapping image block segmentation on the material image data, and extracting gradient information of each non-overlapping image block through a Sobel operator; based on a local search strategy, on the basis of gradient information, candidate blocks with the most similar gradient change among non-overlapped image blocks are screened by adopting a gradient difference minimization similarity matching function, and a three-dimensional image block data set is constructed; performing three-dimensional transformation processing on the three-dimensional image block data set, designing a weighting function based on human visual perception characteristics, dynamically generating an adaptive threshold, and performing differential denoising processing on the three-dimensional image block data set to obtain an image feature data set; and the material loss and the production cost can be reduced.

The present invention relates to the technical field of tire detection, and in particular to a method and system for detecting fine cracks on the tire surface based on tactile sensing and predicting the crack propagation direction. This method combines the use of a three-dimensional displacement stage and a three-axis force sensor to precisely control the sensor to scan the tire surface and collect the force data in the X, Y, and Z directions. Through image processing techniques, including Canny edge detection and morphological operations, the crack area is extracted; further, the force gradient is calculated, and the Sobel operator is used to obtain the force change direction in the crack area. Combining with the Hough transform technology to extract the main crack direction, thereby predicting the crack propagation direction. By judging whether the crack propagation enters a preset dangerous area, the system automatically issues a warning signal to remind the operator to take necessary maintenance measures. This technology can detect fine cracks with high precision and predict their propagation trend, effectively improving the tire safety management level and reducing the safety hazards and losses caused by cracks.

The invention relates to the technical field of medical examination, in particular to a method for calculating the definition of a leukocyte microscopic image based on deep learning, which comprises the following steps: preparing and expanding a data set of the microscopic leukocyte image, outputting a clear image and a blurred image, and enabling each image to have a corresponding definition evaluation index; performing multi-feature fusion definition evaluation on the microscopic leukocyte image based on comprehensive evaluation calculation of a Sobel operator, a Laplacian operator and information entropy; accessing multi-feature fusion definition evaluation into a deep learning model, and building a definition evaluation model; the definition evaluation model is trained, and in the training process, parameters of the definition evaluation model are adjusted by optimizing a loss function; the trained definition evaluation model is deployed in an automatic microscope system, and the problems that definition evaluation is carried out on the whole view, the definition evaluation algorithm is limited, and cell nucleus and cytoplasm characteristics are not distinguished in the prior art are solved.

The invention relates to the technical field of image processing, in particular to a lens edge detection method and system based on image generation, and the method comprises the following steps: collecting a lens image through an industrial camera, carrying out the weighted graying, extracting an edge coordinate through a Laplace operator, constructing an edge pixel coordinate set, and carrying out the image processing; screening an edge validity marking result by combining a gray difference threshold, calculating a phase difference between a gradient direction and light source incidence based on a Sobel operator, extracting pixels in a consistent direction, carrying out spatial clustering, generating a lens edge pixel cluster, and fitting a continuous curve to construct a complete lens edge contour. Noise and reflection interference are restrained through pixel neighborhood gray difference, effective pixels are screened in combination with a gradient direction and light source incident phase relation, edge direction consistency is enhanced, pixels are clustered according to spatial continuity and gradient intensity, contours are continuously fitted based on pixel cluster distribution, and lens edge integrity and geometric consistency under complex illumination are improved.

The invention discloses a workpiece edge weak defect position detection method and system, and relates to the field of industrial visual inspection, and the method comprises the steps: carrying out the initial edge position detection of a workpiece image through employing a Sobel operator, and determining the initial edge position of the workpiece image; determining the gradient magnitude of each center pixel point in the range of the initial edge position based on the initial edge position, and determining an edge sub-pixel point as a target edge position of the workpiece image by adopting a centroid algorithm; performing closed area division on each edge sub-pixel point in the target edge position by adopting a DBSCAN clustering method to obtain a plurality of closed areas, calculating the distance between the center coordinate point of the current closed area and each edge sub-pixel point, and judging the workpiece feature form corresponding to the current closed area according to each distance calculation result, according to the workpiece feature form, the workpiece edge weak defect position is detected based on each edge sub-pixel point, and the accuracy of workpiece edge weak defect position detection is improved.

The invention discloses a corn seed environment adaptability cultivation method based on edge calculation, which comprises the following steps: step 1, acquiring environment factor data of a corn seed sowing area, and preprocessing the environment factor data; 2, extracting the edge contour of the corn seedling by adopting Canny edge detection and a Sobel operator based on the RGB image; 3, constructing a multi-source heterogeneous data sample set according to time and space coordinate alignment; 4, constructing a causal structure diagram by adopting an improved NOTEARS algorithm; 5, carrying out comprehensive sorting to obtain a key factor path; step 6, inputting the multi-dimensional environment factor vector of the target area to the starting node of the key factor path to obtain a predicted phenotypic feature vector; and 7, comparing the actual phenotypic feature vector with the predicted phenotypic feature vector, and counting a prediction error. According to the method, causal modeling and image analysis are fused, and the breeding efficiency and the accuracy of regional suitability judgment are improved.

The invention provides a photovoltaic module shadow recognition and power generation optimization method based on deep learning, and the method comprises the steps: carrying out the shadow edge detection through employing an improved Sobel operator in combination with the illumination intensity and temperature data of the surface of a photovoltaic module, and optimizing the shadow feature extraction through a local weighting correction factor; combining the extracted shadow feature data with electrical parameters, temperature and other information to form a photovoltaic feature data set, training a deep learning model to predict the theoretical maximum output power and actual output power of the photovoltaic module, and optimizing an MPPT control strategy; in addition, the mode of combining static voltage correction and dynamic voltage adjustment is adopted, and it is ensured that the photovoltaic system can still keep the optimal power generation performance under the complex shadow condition. According to the method, the shadow recognition precision is improved, and meanwhile, the power generation efficiency and reliability of a photovoltaic system are effectively improved.

The invention discloses a road underground defect AI intelligent identification method based on a three-dimensional radar image, and the method comprises the steps: setting electromagnetic wave parameters of ground penetrating radar equipment, and carrying out the multi-view image collection; the gray gradient change rate of the C-Scan image is calculated through a Sobel operator, the road state is identified, and a road disease area corresponding to the abnormal color plaque is extracted; constructing an advancing direction road defect type identification model, and identifying the road defect type of the road disease area; constructing a depth direction road defect type identification model, and identifying the road defect type of the road disease area; and according to the road defect type identification accuracy of the model, performing model optimization in combination with a preset accuracy threshold. According to the method, the problems of single parameter setting, high image interference misjudgment rate, insufficient defect identification precision and the like in traditional ground penetrating radar detection are effectively solved, and high-precision, multi-dimensional and intelligent identification of road underground structure diseases is realized.

The invention discloses a part machining surface defect visual detection method and system based on image enhancement and a binarization algorithm, and belongs to the field of machine manufacturing. The problem of how to realize high-precision and low-false-detection defect detection by optimizing an image processing flow without relying on big data training is solved, and meanwhile, the requirements of an industrial scene on real-time performance and deployment cost are met. The method comprises the following steps: inputting a part surface image and a mask image, and carrying out corrosion operation to generate a safety detection area; carrying out bottom cap transformation by adopting longitudinal and transverse morphological nucleuses, and separating scratches and noise; extracting transverse and longitudinal gradients by using a Sobel operator, and generating a multidirectional gradient feature map; performing dynamic threshold segmentation to generate a binary image, and connecting fracture scratches through closed operation; and screening longitudinal and transverse scratch contours according to the length-width ratio, carrying out distance transformation and minimum inscribed circle radius calculation, and judging the region with the radius smaller than a threshold value as a real defect. The method is mainly applied to the defect detection field.

The invention discloses a long-distance binocular camera calibration optimization method based on multistage feature enhancement, and the method comprises the steps: carrying out the processing of a condition that a long-distance calibration plate has a highlight region and is fuzzy in angular points, employing a bilateral filter to suppress the reflection of a highlight mirror surface, reducing the reflection of light, and maintaining the definition of an image; the checkerboard texture is enhanced by adopting a CLAHE algorithm in combination with blocking processing and a contrast gain threshold value; a Sobel operator and a Harris corner response function are fused, gradient direction distribution characteristics of a pixel neighborhood are extracted through the Sobel operator, weighted fusion is carried out on the gradient direction distribution characteristics and the Harris corner response function, and the response intensity and specificity of a corner area are remarkably enhanced. A three-level image preprocessing framework including reflection suppression, contrast enhancement and corner enhancement is constructed, the problem of feature extraction of a traditional method under a complex illumination condition is solved, the corner area detection capability is enhanced, the problems of specular reflection noise, low contrast and corner blur are effectively solved, and the calibration precision is improved.

The embodiment of the invention provides a steel rail corrugation recognition method and system and a storage medium, and belongs to the technical field of railway track detection. The steel rail corrugation identification method comprises the following steps: sending preprocessed steel rail corrugation true value image data into an image segmentation identification model to extract global semantic features and local texture features; performing channel averaging on the output of the intermediate feature extraction stage, generating edge features through a Sobel operator, and fusing the global semantic features, the local texture features and the edge features to obtain a segmented mask graph; and inputting the ROI image segments cut based on the segmentation mask image and the features extracted in the intermediate feature extraction stage and used for target detection into a target detection network together, executing regression calculation based on bounding box overlapping degree optimization loss, and outputting a rail corrugation detection result. According to the scheme of the invention, through multi-scale feature extraction and channel space attention fusion, the positioning precision and segmentation robustness of the rail corrugation region are effectively improved.

The invention discloses a crack sub-pixel precision edge detection method based on an improved Canny operator. The crack sub-pixel precision edge detection method comprises the steps of obtaining a crack image corresponding to a structural body surface crack; the crack image is preprocessed; carrying out gradient calculation on the preprocessed crack image through an improved Sobel operator to obtain a gradient image containing a gradient magnitude and a gradient direction; performing non-maximum suppression on the gradient image, adaptively obtaining an optimal threshold by adopting an improved maximum between-class variance method, judging a crack edge according to the optimal threshold, and further obtaining a coarse edge pixel-level image; processing edge information in the coarse edge pixel-level image by using a Zernike orthogonal moment to obtain coordinates of sub-pixel edge points; and connecting all the sub-pixel edge points, and outputting a crack detection result image. On the basis, the crack edge detection precision is improved to a sub-pixel level.

The invention relates to a lightweight traffic road semantic segmentation method based on multi-scale feature fusion, and belongs to the field of intelligent driving. The method solves the problems that precision and speed are difficult to balance and small target recognition accuracy is low in real-time application of a traditional semantic segmentation method. According to the technical scheme, context information is extracted by adopting a Vision Transform branch, space information is extracted by adopting a CNN branch, a model is optimized through feature fusion and alignment loss during training, and only the CNN branch is used during reasoning; sobel operator edge detection, a multi-scale feature fusion module and a feature distribution simulation module are integrated. The method has the technical effects that high-precision semantic segmentation is realized, the small target recognition capability is remarkably improved, and meanwhile, the real-time processing speed is ensured.

The invention relates to a reconstructed image anti-aliasing processing method based on an adaptive sampling rate, belongs to the technical field of CT (Computed Tomography) reconstructed image processing, and solves the problem that the existing anti-aliasing processing method for a CT reconstructed image is poor in effect. The anti-aliasing processing method for the reconstructed image comprises the following steps: preprocessing the reconstructed image to obtain a to-be-processed image; performing edge extraction on the to-be-processed image based on a preset gradient threshold and a preset Sobel operator, and segmenting the to-be-processed image into a to-be-replaced image region and a reserved image region; determining an adaptive sampling rate based on the horizontal direction edge pixel connection number and the vertical direction edge pixel connection number of all sawtooth vertex pixels in the to-be-processed image; determining a pixel value of each pixel in the to-be-replaced image region based on the adaptive sampling rate and the to-be-processed image, and obtaining a replaced image region; and combining the replaced image area and the reserved image area to obtain an image after anti-aliasing processing. And the anti-aliasing effect of the CT reconstructed image is improved.

The invention relates to an edge-enhanced multi-exposure space target three-dimensional reconstruction method, device and equipment, and the method comprises the steps: randomly selecting an optical image from data sets with different exposure levels as a group of to-be-reconstructed data, and employing a 3DGS method to carry out the reconstruction of the to-be-reconstructed data under the randomly initialized three-dimensional Gaussian distribution, reconstructing the space target according to the group of data to obtain three-dimensional Gaussian data, carrying out two-dimensional projection according to different poses of the space target in the data to be reconstructed to obtain a plurality of corresponding rendering images, and respectively extracting the plurality of rendering images and high-frequency feature images of the corresponding optical images by adopting a Sobel operator; the method comprises the following steps: constructing a three-dimensional Gaussian distribution model, calculating to obtain a structure sensing gradient loss function, optimizing the three-dimensional Gaussian distribution in combination with a multi-exposure joint optimization strategy, and performing three-dimensional reconstruction based on the finally optimized three-dimensional Gaussian distribution so as to overcome the problem of uneven illumination of a space target image and obtain an accurate three-dimensional reconstruction model of a space target.

The invention relates to the technical field of image analysis, and discloses an image segmentation method and system for calculus removal, and the method comprises the steps: carrying out the real-time image collection of a target part based on a pre-deployed calculus image collection device, obtaining an initial calculus image, carrying out the preprocessing of the initial calculus image, and obtaining an initial calculus image; obtaining a preprocessed initial stone image; inputting the preprocessed initial stone image into a deep learning model based on a convolutional neural network to obtain a preliminary segmentation result; optimizing the preliminary segmentation result by adopting a Sobel operator in combination with closed operation to obtain an optimized segmentation result; a connected domain analysis method is adopted for the optimized segmentation result, and a complete stone area image is obtained. According to the invention, the problem of inaccurate segmentation results caused by the influence of noise on seed point selection in the prior art can be solved.