261 results about "Morphological processing" patented technology

The invention discloses a machine vision-based intelligent detection method for steel galvanized surface defects, which comprises the following steps: S1, acquiring and preprocessing a steel galvanized surface image to obtain a standardized image; s2, constructing a specular reflection probability graph according to the brightness distribution and the gradient magnitude, and calculating a reflection intensity value; s3, calculating a structure tensor matrix, determining a main direction angle and an anisotropic consistency coefficient, and generating a direction feature matrix; s4, establishing a multi-scale direction adaptive phase kernel function, and performing phase modulation in a frequency domain by adopting an improved phase stretching transformation algorithm; s5, inverse Fourier transform is executed, and a phase response matrix is extracted; s6, performing weighted fusion to obtain a comprehensive phase response diagram; and S7, setting a threshold value according to the noise variance and the statistical characteristics, executing binarization and morphological processing, and outputting a defect region and boundary coordinates. According to the invention, high-precision identification and boundary positioning of steel galvanized surface defects are realized.

The invention provides a mountainous area tunnel crack intelligent identification and detection method based on deep learning, and relates to the technical field of deep learning, and the method comprises the steps: collecting multispectral image data, and carrying out the adaptive preprocessing to obtain an enhanced feature map; a double-flow network is combined with a space-channel cascade attention module to extract fusion features; constructing multi-scale feature representation through a feature association graph network; training the network by adopting a joint optimization target; and morphological processing and connectivity analysis are carried out to realize accurate identification and classification of the crack. According to the invention, the detection precision and the anti-interference capability of the tunnel crack in the complex environment are improved.

The invention discloses a power equipment defect intelligent identification method, system and device and a medium, and the method comprises the steps: collecting an original image of power equipment, and screening the original image of the power equipment to obtain a channel image; carrying out enhancement processing on the channel image and then calculating a gray scale difference value to obtain a gray scale image; converting the gray level image into a frequency spectrum image by adopting fast Fourier transform, and constructing a Gaussian filtering function to carry out convolution and inverse transformation on the frequency spectrum image to obtain a spatial domain image; and performing adaptive threshold segmentation on the spatial domain image, dividing the image into a defect area and a non-defect area, obtaining a segmented image, and performing morphological processing on the segmented image to obtain an electrical equipment defect identification result. According to the method, the problem of aliasing in traditional spatial domain processing is solved, the defect area is accurately extracted, short-time interference and real defects can be effectively distinguished, and the segmentation accuracy is improved.

The invention belongs to the technical field of medical image processing, and discloses a medical image diagnosis system based on image and text feature fusion, and the system comprises an image processing module which is responsible for carrying out the enhancement, segmentation and feature extraction of a medical image; the text analysis module is responsible for extracting key clinical information from the electronic medical record; the semantic alignment and feature fusion module is used for embedding image features and text features into a unified vector space to realize fusion of multi-modal data; the model training and optimizing module is responsible for training and optimizing a medical diagnosis system model; and the clinical application module is responsible for applying the trained medical diagnosis system model to an actual medical environment to assist doctors in disease diagnosis. According to the medical image diagnosis system based on image and text feature fusion, by combining image enhancement, morphological processing, NLP analysis and multi-modal feature fusion, the illness state of a patient can be understood more comprehensively, the diagnosis accuracy is improved, and a more reliable auxiliary decision making basis is provided for doctors.

The invention discloses a two-stage cascaded distributed optical fiber sound wave sensing vehicle trajectory reconstruction method and system, belongs to the technical field of intelligent traffic perception, and aims to solve the problem that geometric accuracy and topological integrity are difficult to consider under the conditions of low signal-to-noise ratio and complex road conditions in the conventional DAS vehicle trajectory reconstruction technology. The method comprises the following steps: in the first stage, carrying out nonlinear enhancement on original vibration data and converting the original vibration data into a two-dimensional space-time grayscale image, inputting the two-dimensional space-time grayscale image into a deep learning semantic segmentation network to generate a high-fidelity track mask, extracting a skeleton through morphological processing, and constructing an initial candidate topological graph; and in the second stage, multi-hop neighborhood kinematics constraint pruning is performed on the initial topological graph, false connection edges are eliminated, global optimization matching is performed by using a coupling cost function to repair fractures, then isolated points are recalled through local geometric scores, and finally a vehicle trajectory graph with complete topology is output. According to the method, geometric accuracy and topological integrity can be effectively considered in complex scenes such as speed change, lane change and multi-vehicle intersection, and the robustness of all-weather traffic flow monitoring is improved.

The invention relates to a method for processing medical images of human tissue of an area of a patient's body and in particular of the breast by means of a medical imaging device (1) comprising a microwave probe array consisting of K>1 probes spaced apart from one another, the array comprising P>1 different configurations defining transmitting probes and receiving probes for one or more position(s) around the area, in which the transmitting probes are configured to transmit microwave signals so as to illuminate an area of the body and the receiving probes are configured to receive microwave signals after scattering and reflection in the area, the probes being capable, in a complementary manner, of being configured to transmit and receive simultaneously.

The invention belongs to the technical field of image processing, and particularly relates to an MBR flat membrane surface physical damage visual identification method and system, and the method comprises the steps: carrying out the low-pass filtering of a microscopic gray image, so as to melt a micropore background; calculating a texture variation index based on the outlier degree of the gray values of the neighborhood pixel points and the neighborhood gray dynamic contrast gain; determining a damage aggregation weight by using a space attenuation accumulation value, and eliminating isolated noise points; the texture variation index and the damage aggregation weight are fused through a self-adaptive gating function, and damage confidence is generated; and finally marking a connected damage region based on statistical threshold binarization and morphological processing. According to the method, dense micropore interference can be effectively inhibited, and the physical damage identification precision is improved.

The invention belongs to the technical field of three-dimensional measurement, and discloses a power transmission line icing volume measurement method, system and device, a medium and a program product, and the method comprises the steps: employing a binocular camera to shoot and correct an icing power transmission line view, and obtaining internal and external parameters, performing depth estimation on the power transmission line view based on the corrected power transmission line view and the internal and external parameters, training a conductor detection model and inputting the corrected power transmission line view to obtain a detection frame and a corresponding confidence coefficient, using the detection frame with the highest confidence coefficient as a prompt, inputting the prompt and the corrected power transmission line view into an SAM2 network for segmentation, and obtaining a segmentation result; de-noising and morphological processing are carried out on the masks of the ice-coated wires, and finally the ice-coated area of the power transmission line is calculated. The system, the equipment and the medium are used for implementing the method. The program product comprises a computer program for implementing the method; the method for measuring the icing volume of the power transmission line has the effects of stability, reliability, high efficiency, accuracy and high safety.

The invention provides an agricultural image background removal method, and relates to the technical field of agricultural image processing, and the method comprises the steps: carrying out the adaptive illumination condition judgment of a preprocessed agricultural image; calculating and selecting corresponding vegetation indexes according to the light condition judgment result, and calculating to generate a plurality of vegetation index feature maps; inputting the plurality of vegetation index feature maps into a machine learning model, learning the relative importance of each vegetation index through the machine learning model, and generating a comprehensive vegetation feature map; performing vegetation texture region judgment on the preprocessed agricultural image to obtain a target vegetation texture region judgment result; judging a foreground mask based on the comprehensive vegetation feature map and a target vegetation texture region judgment result; performing morphological processing on the foreground mask to obtain an optimized foreground mask; and according to the optimized foreground mask, removing a background from the agricultural image in the original RGB format to obtain a target vegetation image.

The invention relates to the technical field of image processing, in particular to an aluminum alloy die casting surface defect detection method based on machine vision. The method comprises the steps of obtaining a grayscale image of the surface of a die casting; constructing a Gaussian scale space of the grayscale image, and obtaining scale images under a plurality of scales; calculating a defect saliency index corresponding to each pixel point in the grayscale image based on the multi-scale image, and generating a defect saliency map; and performing threshold segmentation on the defect saliency map to obtain a segmented image, performing morphological processing identification on the segmented image, and determining the position of a surface defect area. According to the invention, missed detection and false detection in the surface detection process of the aluminum alloy die casting can be reduced.

The invention discloses a detection method for fertilizer production, and relates to the technical field of fertilizer detection.The detection method comprises the following steps that a near-infrared multispectral visual detection system is installed at the working section before packaging of a fertilizer production line, the positions of a camera and an illumination source are adjusted, and multispectral images of fertilizer particles are collected; sequentially carrying out dark current correction and flat field correction on the acquired image, eliminating noise and uneven illumination, and then carrying out median filtering to remove random noise; selecting a waveband image with the best contrast ratio, separating particles from the background through threshold segmentation, and positioning each independent particle after optimizing the boundary through morphological processing; and based on the multi-band reflectivity difference, analyzing the surface material of the particles pixel by pixel, and counting the proportion of the coating area of each particle to obtain the coating coverage rate of the single particle. By introducing the near-infrared multispectral visual detection technology and the coating uniformity quantitative analysis model, non-destructive and high-precision online detection of the coating coverage rate of the fertilizer can be realized.

The invention provides a maglev train bottom bolt looseness identification method based on deep learning. The method comprises the following steps: acquiring a bottom image of a maglev train by adopting imaging equipment, and carrying out bolt target detection on an image in a bottom image data set through an improved YOLOv8 network to obtain a bolt image; performing classification processing on the bolt image through a lightweight network RepViT fused with a lightweight ViT design principle; color space conversion, image binaryzation, morphological processing and connected domain screening processing are carried out on the bolt image to extract an anti-loosening line, and whether the vehicle bottom bolt in the bolt image is loosened or not is judged based on the bolt anti-loosening line according to loosening characteristics of different types of bolts under multiple visual angles. According to the method, the detection precision is improved by means of improving a target detection network structure, refining bolt loosening characteristics, providing adaptive detection methods for different types of bolts and the like; by designing a lightweight classification network mode, the calculation amount and time consumption are reduced, and the detection efficiency is improved.

The invention relates to a concrete structure crack detection method under a complex background based on transfer learning and a multi-stage image processing technology, and the method employs a combined process of image filtering, threshold segmentation, morphological processing and the like to extract cracks, guarantees the precise division of crack boundaries, and facilitates the accurate measurement. In quantitative analysis, the length and the width of the crack are measured in combination with geometric dimensions and physical transformation, and important data are provided for concrete crack evaluation. And finally, strictly verifying the proposed method by using a self-established data set. Experimental results show that the crack detection accuracy of TLGoogleNet is as high as 99.47%, and the error range of the length and width of the crack is always kept within about 5%. The results fully manifest high precision and strong robustness of the developed method.

The invention provides an urban water body efficient extraction method for a high-resolution satellite image, and belongs to the technical field of image processing, and the method comprises the following steps: 1, preprocessing satellite image data; 2, water body extraction based on index characteristics; step 3, water body extraction based on spectral characteristics; 4, water body extraction based on numerical characteristics; and 5, carrying out morphological processing on the water body binary image based on spatial characteristics. According to the method, when water body extraction is carried out on a high-resolution image with only four wave bands, compared with the method which only depends on calculation of a spectral index and setting of a threshold value, spectral characteristics of a real water body are comprehensively considered, and threshold values such as a spectral slope and a wave band value are set, so that the water body identification precision can be effectively improved; and the spatial distribution characteristics of the water body are further combined and morphological operation is carried out, so that the error of water body extraction caused by pixels such as building shadows in the urban complex environment can be greatly weakened.

The invention discloses a concrete column axial deformation identification method and system based on a visual image, and relates to the technical field of building structure monitoring, and the method comprises the steps: collecting a continuous image sequence of a concrete column from different sides; edge detection and morphological processing are carried out on the continuous image sequence, the overall contour of the cylinder is extracted, the axial center line of the cylinder is fitted, and global axial length change features are generated; executing adjacent frame tracking identification of the local evaluation center point, and establishing a first local deformation feature; performing local deformation feature extraction of the convolutional neural network, and establishing a second local deformation feature; and hierarchical fusion is carried out, and an axial deformation identification result is established. According to the invention, the technical problems of one-sided deformation information, insufficient monitoring precision and poor reliability caused by single feature extraction of the axial deformation of the concrete column in the prior art are solved, and the technical effects of realizing multi-source feature fusion identification of the axial deformation of the concrete column and improving the precision and reliability of deformation monitoring are achieved.

The embodiment of the invention provides a near-field MIMO radar data processing method based on multistage background suppression and target enhancement, and is applied to the technical field of radar signal processing. The method comprises the following steps: acquiring a real-time background template and a real-time image frame of a target scene; calculating to obtain differential image data corresponding to the real-time image frame based on the real-time background template and the real-time image frame; performing Gaussian filtering processing on the difference image data to obtain Gaussian filtering image data; performing morphological processing on the Gaussian filtering image data to obtain morphological image data; performing target enhancement processing on the morphological image data to obtain target enhanced image data corresponding to the real-time image frame; and outputting the target enhanced image data. The technical effect of improving the radar imaging precision is achieved.

The invention relates to the technical field of image processing, in particular to a gland structure irregularity quantitative analysis method and system based on a gastroscope image, and the method comprises the steps: carrying out the color correction and image contrast enhancement of an obtained original gastroscope image, and constructing a corresponding image pyramid; inputting the images in the image pyramid into an improved deep learning model for learning by adopting a multi-channel fusion input mode, and obtaining a gland binary mask after morphological processing; based on three dimensions of morphology, structural arrangement and complexity, quantitative features of a single gland structure are extracted from the gland binary mask; the extracted multi-dimensional quantitative features are trained through a gradient lifting decision tree model, an irregularity index is output, feature importance analysis is carried out, and the gland structure in the gastroscope image can be accurately recognized and quantized.

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 application discloses a kind of semi-structured orchard field ridge area detection method, this method is by picking robot along field ridge with stereo vision camera Real-time acquisition field ridge area image, utilize field ridge shadow area and the different of non-shadow area color and gradient field feature, combined with Gaussian mixture model can realize the detection of shadow area, again through the shadow color weighting compensation algorithm proposed in this paper to remove shadow area, finally through to image denoising filtering, by to image secondary segmentation, again after morphological processing, finally get complete field ridge area;Finally, through edge detection operator to the image after segmentation Edge detection, again to edge point optimization fitting, based on least squares method is carried out edge point fitting, obtains the edge line of field ridge area.This method can be under any illumination conditions 100% detection field ridge area.

The invention belongs to the technical field of image data processing, and particularly relates to a brake pad surface damage detection method and system based on machine vision, and the method comprises the steps: obtaining an image of the surface of a brake pad, and calculating the gradient vector of each pixel point; constructing a gradient interaction field based on the intensity of the gradient magnitude and the gradient direction angle, and obtaining the structural significance of each pixel point; path searching is carried out in the local main direction, and linear propagation potential energy of each pixel point is obtained by accumulating structural significance and direction consistency weights of the pixel points on the path; and adaptive threshold segmentation and morphological processing are carried out based on the linear propagation potential energy diagram, and accurate detection of the surface damage area of the brake pad is realized. According to the invention, the technical problems of strong noise interference on the surface of the brake pad and difficulty in detection of discontinuous micro crack characteristics are solved, and the robustness and accuracy of damage detection are improved.

The invention discloses a cervical cell segmentation method combining semantic condition diffusion and knowledge verification, which comprises the following steps: S1, constructing a cervical cancer diagnosis vertical field multi-modal large model, and performing fine tuning on an open-source multi-modal large model through a low-rank adaptation supervision fine tuning method to obtain the cervical cancer diagnosis vertical field multi-modal large model; s2, generating a semantic vector for guiding segmentation; s3, performing preliminary segmentation based on a conditional diffusion model, performing iterative denoising on the to-be-segmented image, and generating a preliminary cervical cell segmentation mask; and S4, performing segmentation post-processing based on domain knowledge: performing morphological processing on the preliminary segmentation mask to obtain a final cervical cell segmentation result. According to the method, large-scale text data can be fully utilized and converted into refined semantic information for guiding image segmentation, so that the challenge of scarcity of labeled samples is overcome, the performance, generalization ability and clinical applicability of a cervical cell abnormal region segmentation model are remarkably improved, and finally more accurate and more reliable cervical cell pathological diagnosis assistance is realized.

The present application relates to the target ISAR image evaluation method based on signal processing and image change, belongs to ISAR image processing field. In view of the ISAR image imaging fuzzy under the condition of target posture change on ISAR image, feature extraction is difficult, leading to the problem of target threat assessment difficulty, a kind of target ISAR image evaluation method based on signal processing and image change is presented. Including: based on the rearrangement and elimination method of micro-motion to realize the de-micro-motion imaging of space micro-motion target;The ISAR image is carried out morphological processing open operation, using Canny operator carries out edge detection processing, carries out Hough transformation and carries out straight line detection, obtains the size and boundary of target;The echo model of compound micro-motion target of combined micro-motion is established, and the micro-motion period is estimated using the method based on the phase difference signal aggregation degree, realizes the de-micro-motion imaging of space target and micro-motion target feature extraction and solves the problem of target threat assessment difficulty.

The application provides an endoscopic imaging system field mask obtaining method and device, wherein the endoscopic imaging system field mask obtaining method comprises the following steps: firstly, imaging a target object to obtain a target image; then, determining the positions of feature pixel points in the target image according to the pixel values of the target image; then, forming a binary image according to the positions of the feature pixel points; finally, performing morphological processing on the binary image to obtain an endoscopic imaging system field mask. Through the endoscopic imaging system field mask obtaining method, the circular window region of the fiber bundle end face can be accurately identified, and the endoscopic imaging system field mask can be obtained at a high speed. The obtained endoscopic imaging system field mask has a good shielding effect on the region outside the circular window of the fiber bundle end face, and is convenient for diagnosing the body tissue lesion condition of a subject.

The invention discloses a target hierarchical enhancement reconstruction method and reconstruction system based on a polarization camera array. The method comprises the following steps: acquiring a depth parallax model of the polarization camera array; collecting a multi-view target original view in multiple polarization directions; according to the target original view, performing occlusion layering based on a depth parallax model to obtain occlusion layering images of different depths; performing adaptive threshold segmentation, morphological processing and central viewpoint conversion on the shielding layered image of any depth to obtain a shielding mask of any viewpoint, further obtaining a shielding mask corresponding to a target layer, performing pixel-by-pixel reconstruction, and obtaining a calculation imaging result of the target layer after shielding is removed; and obtaining a final target enhanced reconstructed image through polarization state fusion. The image contrast and the layered sensing capability of the system under the shielding condition are improved, a higher-precision polarization imaging result can be generated, and the method is used for layered reconstruction of any object layer.

The invention discloses a CBCT (Cone Beam Computed Tomography) projection metal track segmentation and artifact removal method of sparse labeling fine-tuning SAM (Sparse Assisted Modulation), which comprises the following steps: firstly, collecting actual scene data, constructing a data set with lower cost by adopting a sparse labeling mode for SAM fine tuning, and containing metal objects in all shapes as far as possible when collecting the data; then, performing fine adjustment on the SAM, taking the spliced projection frames with different contrast ratios as input during fine adjustment, and taking Mask obtained by image domain threshold segmentation as Prompt; then, directly applying the fine-tuned SAM to actual projection data to predict an accurate projection domain metal Mask angle by angle; then, morphological processing is carried out on the predicted projection domain metal Mask, and triangulation interpolation is carried out on the original projection by using the processed Mask; and finally, carrying out Padding on the interpolated projection to suppress truncation artifacts, and reconstructing by adopting a weighted FDK algorithm to suppress incomplete sampling artifacts to obtain a final metal artifact removed image for subsequent application.

The present application relates to a hyperspectral remote sensing image radiation normalization method, in particular to a hyperspectral remote sensing image relative radiation normalization method based on pseudo-invariant region pairs, which solves the technical problems of low spatial matching accuracy and spectral feature deviation of the existing relative radiation normalization method based on pixels. The present application adopts superpixel segmentation, morphological processing and superpixel guided method to screen pseudo-invariant regions, then adopts a self-correction model based on spatial-spectral constraints to adaptively learn the relative radiation normalization of the high light remote sensing spectral image to be corrected, adopts a quantitative method, which can not only fully utilize the spatial-spectral features of the hyperspectral remote sensing image, but also adaptively correct the radiation intensity of the hyperspectral remote sensing image in different time phases, improve the consistency and quantitative inversion accuracy between time sequences, thereby improve the accuracy and consistency of the relative radiation normalization, and is suitable for the radiation normalization of high spatial resolution, complex ground object scene hyperspectral remote sensing images.

The application provides a kind of multi-level consistency constraint UAV inspection image difference detection method and system, wherein the method comprises: obtaining the same inspection target before and after time sequence image, after format conversion, scale regularization and local feature matching, the time sequence image after mapping to the time sequence coordinate system before registration is reliable;Through pixel difference, threshold segmentation, morphological processing and connected domain analysis to generate candidate area, pixel difference is only used as a candidate basis, and the candidate area is subjected to contour extraction and polygon approximation;Again, according to the area-perimeter compactness, the number of vertices and the main axis ratio, the geometrically stable region is screened, and the gradient amplitude variation rate and the gradient direction consistency are combined to confirm the effective difference region, and the statistical results such as polygon contour, number, area and vertex coordinates are output;The application can suppress the false difference caused by light, shadow and texture disturbance, and improve the stability, interpretability and engineering applicability of real structure change detection.

This application discloses a method, apparatus, and storage medium for defect extraction from display screen images, aimed at reducing the decrease in defect detection accuracy caused by shadows. The method involves: acquiring a display screen image and determining the target region; performing image segmentation accuracy analysis on the display screen image; correcting the display screen image; generating four dynamic array coordinate arrays for the target region and performing shadow removal processing based on these arrays to generate a shadow-free display screen image, where the four dynamic array coordinate arrays are the final row and column arrays of the individual segmented image regions; performing interpolation calculations on the pixels in the corner regions of the shadow-free display screen image; constructing an adaptive spatial filter kernel matrix and performing convolution enhancement operations between the adaptive spatial filter kernel matrix and the shadow-free display screen image; performing threshold control processing on the shadow-free display image and integrating the defect region through grayscale morphological processing; and extracting and analyzing defect features from the shadow-free display image.

The invention discloses an endoscope video frame specular reflection highlight detection method and system, and belongs to the field of endoscope optical detection. The method comprises the following steps: firstly, converting an endoscope video frame into a grey-scale map; a Sobel operator is utilized to extract edge gradient to generate a binary image gradient mask, and meanwhile, a binary image highlight mask is generated by obtaining an area with the brightness obviously higher than the average level of a grey-scale map; dividing the binary image gradient mask and the binary image highlight mask into image blocks, adaptively adjusting the sizes of the blocks according to the highlight ratio of the image blocks so as to fuse the binary image gradient mask and the binary image highlight mask, and performing morphological processing and region screening to obtain a final binary image highlight mask of the grey-scale image; and carrying out time domain compensation on the final binary image highlight mask to obtain a specular reflection highlight detection result of the endoscope video frame. The accuracy, integrity and robustness of specular reflection highlight detection of the endoscope video frame are effectively improved.

The present application relates to the technical field of intelligent detection and automation, and discloses a kind of surface cleaning device's jet flow nozzle fault on-line detection method.Just determine the pixel cleaning area of each nozzle in image;Collect historical images before and after strip cleaning and construct difference map based on color difference, establish surface cleaning quality detection model in combination with the highest and lowest original score, and set qualified standard;Real-time acquisition of images before and after cleaning input model score, identify unqualified images according to qualified standard and mark unqualified area;Semantic segmentation and morphological processing are carried out on the marked area, the contour boundary is extracted, and the faulty nozzle is located in combination with the pixel cleaning area.The method solves the problem of difficult positioning of nozzle fault in existing high-pressure abrasive water jet surface cleaning device.