25 results about "Morphology Method" patented technology

The invention relates to a long water tank mobile surface flow field measurement method, which comprises the following steps: utilizing a river channel local scouring experiment model, the model is a water tank, and the middle part of the water tank is provided with a vertical support rod for simulating a building component; a constant-speed motion traction camera device is built above a water tank, so that a mobile camera moves at a constant speed at a certain inclination angle, meanwhile, water flow in the water tank is shot, and a water flow surface flow field is obtained through calculation according to a shooting result. The method comprises the following steps: performing perspective transformation processing on an obtained image to obtain a continuous front view image, identifying checkerboard control points to position each frame of corrected image, and cutting a tracer particle image by using coordinates of intelligent identification side wall control points. After surface tracer particles are extracted through a morphological method, flow field splicing is carried out on instantaneous flow fields at different moments to obtain a surface flow field in a large range, the problems that traditional fixed flow field measurement is low in precision and small in range are effectively solved, and an effective technical means is provided for hydraulic structure research and water body monitoring.

The application provides a remote sensing image building extraction and segmentation result regularization method, which comprises the following steps: acquiring a remote sensing image, wherein the full-color spatial resolution of the remote sensing image is greater than 1 m; cutting the image picture to 512*512 as input data; extracting a building probability map in the image picture by using an improved FCN based on DeepLabV3+; and regularizing the probability map by using a trained generative adversarial network, wherein the trained generative adversarial network comprises a generator and a discriminator, and the generator comprises an encoder and a decoder; the full convolutional neural network is trained by using the generative adversarial method, is used for post-processing of a semantic segmentation result of a building in a remote sensing image, and is used for regularizing the semantic segmentation result; compared with a traditional morphological method, the influence of errors in semantic segmentation on subsequent regularization results is reduced, so that regular building contours are generated.

The application discloses a kind of weak supervision learning-based hair defect image detection methods, the method includes the following steps: build detection device, obtain the image of the silk ingot product to be detected, using sliding window cuts out all possible hair image sample, using segmentation model to infer one by one to image sample, obtain sample segmentation graph;Sample segmentation graph is spliced, and the overall segmentation graph after processing integration is handled using morphological method;Hair block in overall segmentation graph and its area, bounding box information are counted;Integrate segmentation graph, the hair information existing on single silk ingot is counted, and the hair defect information of silk ingot is output.The application introduces weak supervision learning paradigm, overcome the performance bottleneck caused by the poor consistency of artificial label under supervised learning paradigm, improve the accuracy of silk ingot hair defect detection, while saving the cost required for manual annotation.

This specification discloses a method, apparatus, and system for processing three-dimensional tooth images. The method includes: acquiring a 3D original image containing teeth; identifying tooth voxel regions from the 3D original image; selecting crown feature voxels from the tooth voxel regions based on the grayscale information of the crown; determining a crown mask based on the crown feature voxels using morphological methods; and segmenting each tooth from the tooth voxel regions according to the mask to obtain a tooth segmentation result. This method uses morphological methods to determine the crown mask to be compatible with the actual dentition, resulting in high accuracy when segmenting each tooth from the tooth voxel regions according to the mask. This solution is highly adaptable to cases of malocclusion or tooth defects. This solution can segment individual teeth from three-dimensional tooth images fully automatically or with minimal manual assistance, achieving high segmentation efficiency and accuracy.

The application discloses a kidney stone load evaluation method and device based on CT plain scan images, and relates to the technical field of medical image processing. The method comprises the following steps: automatically analyzing and recording semantic coordinates of each lesion area, constructing a structured data set, and constructing a local feature space for the lesion. The semantic boundary of the suspected kidney stone in the CT plain scan image sequence is used to judge the lesion morphology by exploring the corresponding semantic features in the local feature space through a morphological method. A density space matrix is constructed by using the semantic features of the lesion, an optimal semantic density is calculated in the density space matrix by designing a search strategy corresponding to the lesion morphology, and the number of kidney stones is obtained by using an unsupervised clustering algorithm. According to the analysis results of the morphology and number of the kidney stones, the automatic volume measurement of the kidney stones is realized by using a graphics method. The application can realize the full-automatic and accurate measurement of the morphology, number and volume of the kidney stones in the CT plain scan image sequence.

The embodiment of the invention provides a deposition configuration depicting method and device, a storage medium, a processor and a program product, and belongs to the technical field of oil exploration and development. The method comprises the following steps: acquiring a sedimentary configuration mode in a target work area obtained based on logging data, lithology data and a seismic data volume; based on the lithology data and the logging data, determining low-frequency, intermediate-frequency and high-frequency single-frequency bodies of each sedimentary layer in the sedimentary configuration mode from the seismic data body, and obtaining an RGB frequency division attribute fusion image of the target work area; extracting a configuration boundary skeleton graph from the RGB frequency division attribute fusion graph by adopting a morphological method; and depicting boundary features of each of the plurality of configuration units in the target work area and contact features of different configuration units in the plurality of configuration units based on the extracted configuration boundary skeleton so as to complete deposition configuration depiction in the target work area. According to the method, the limitation of low-resolution seismic data can be overcome, and the description of the configuration unit in the exploration target work area, especially the description of the river channel configuration unit, is completed.

The invention discloses a method for identifying the purity of a coconut strain. The method comprises the following steps: collecting DNA of a target sample; constructing a DNA library of the target sample, and performing whole genome re-sequencing; filtering data in the DNA library to obtain an impurity-removed DNA sequence; comparing the DNA sequence subjected to impurity removal with a reference genome, carrying out variation detection, generating SNP and INDEL marker files, then carrying out genetic typing, and constructing a variety identification model; comparing and analyzing whole genome SNPs of the two groups, and screening specific sites capable of identifying and distinguishing the two groups in the genome; and inputting the DNA of the target sample into the variety identification model, detecting the SNP specific site of the gene, and judging the variety result to realize the identification of the purity of the coconut variety. The identification accuracy of the identification method can reach 99% and is far higher than that of a traditional morphological method, rapid identification can be conducted in the seedling stage, and the identification period is greatly shortened.

The present disclosure provides a method for processing a cell image, an electronic device, and a storage medium. The method includes: obtaining, based on a morphological method, a target cell nucleus map according to an initial image segmentation result of a target cell image, where the target cell image is an image corresponding to at least one cell; obtaining, based on the morphological method, a target cell region contour map according to the target cell nucleus map and the initial image segmentation result; obtaining, based on the morphological method, a target cell contour map according to the target cell region contour map and the target cell nucleus map; and obtaining a target image segmentation result according to the target cell contour map and the target cell nucleus map.

The invention discloses a transmission line component corrosion defect detection method, which comprises the following steps of: acquiring images of a transmission line at different angles and under different illumination in a real scene, and preprocessing and marking to obtain a transmission line component corrosion defect image with clear image and uniform illumination and a marking file of the transmission line component corrosion defect image; dividing the image and the annotation file into a training set and a test set; the enhanced training set is input into the improved PP-LiteSeg network for training, and an optimal network is obtained; inputting the test set into the optimal network to obtain a prediction result; and processing the prediction result by using a morphological method, and generating a prediction result image, thereby completing fine segmentation of the corrosion defect of the power transmission line component. According to the method, the problem of low segmentation precision caused by complex background interference and component surface illumination change in transmission line component identification corrosion defect detection is solved.

This application discloses a method for searching for missing map regions by a robot, comprising: Step A, the robot uses a first image morphology method to search for at least one set of target boundary points from a pre-constructed map; Step B, based on the number of points, candidate boundary point sets are determined one by one; Step C, the robot uses a second image morphology method to extract the map region to be explored from the pre-constructed map; Step D, the robot sets a region expansion starting point from the map region to be explored, and then uses a filling algorithm to process the map region to be explored starting from the region expansion starting point to obtain the region expanded by the filling algorithm; Step E, based on the positional relationship between the region expanded by the filling algorithm and the robot's current position, and the positional relationship between the neighborhood of the node expanded by the filling algorithm and the candidate boundary point set, missing map regions are searched so that the robot can fill in the missing map regions.

The invention discloses a cold region concrete freeze-thaw damage detection method and system based on machine vision, and the method comprises the steps: obtaining an RGB image and three-dimensional depth information of the surface of a concrete structure through a high-resolution depth camera carried by an unmanned plane, and carrying out the detection of the freeze-thaw damage of the concrete in combination with the technologies of image preprocessing, large model segmentation, three-dimensional contour reconstruction and the like. Damage features such as aggregate exposure and matrix peeling of concrete under the action of freeze-thaw cycle can be comprehensively reflected, a multi-dimensional parameter system is established to realize visual expression of a damage state, an SAM model and a morphological method are adopted to identify an aggregate area, a depth image is matched to construct a real geometric contour, and the accuracy of the aggregate area is improved. The surface degradation process is more accurately described from two-dimensional and three-dimensional angles, and meanwhile, the freeze-thaw training times are intelligently judged by means of a machine learning model, so that the analysis efficiency and objectivity are improved.

The invention belongs to the technical field of hydroelectric generating set monitoring, and particularly provides a hydroelectric generating set axis trajectory recognition method based on multidimensional features and CFAWOA-SVM, which comprises the following steps: firstly, preprocessing an axis trajectory image by adopting a morphological method, then extracting contour, moment and geometric feature sets of an axis trajectory sample, and combining into a multidimensional feature set; then feature selection and dimension reduction are realized by adopting an RF-Fisher method, and finally, a trained CFAWOA-SVM model is adopted as a classifier to identify an axis trajectory mode. The hydroelectric generating set axis track identification method provided by the invention has relatively high identification precision on the axis track of the hydroelectric generating set, and also has relatively high identification efficiency and relatively good robustness.

Systems and methods provide an antialiasing engine and its related functions that distribute the computational effort and image reconstruction functions to achieve higher visual quality using morphological methods and operations. For example, an antialiasing engine detects one or more edges within a first frame and generates an edge image indicating a respective pixel's proximity to the foreground or the background based on detection of the one or more edges within the first frame. The antialiasing engine then generates an analytical edge by tracing the edge image and encodes the analytical edge into a depth frame within a video stream. The video stream is then transmitted to a client device where the analytical edge is responsively decoded by a client-side antialiasing engine and sampled to generate a final composition integrating the first frame into a second frame using the analytical edge, where the second frame is generated by the client device.

The present application relates to a kind of tower tilt online monitoring method based on remote sensing satellite optical image, comprising: step 1: obtain remote sensing satellite optical image, and carry out segmentation, obtain the image of tower shadow area;Step 2: with morphological method, the image of tower shadow area is enhanced and segmented, adopt a kind of adaptive variable structure element morphological edge detection operator, by Top-Hat transformation and Bottom-Hat transformation, remote sensing satellite optical image is high processing, then again using Canny operator carries out edge detection;Step 3: using Hough straight line detection algorithm extracts the straight line of tower shadow edge in tower shadow image, calculates the midpoint of the coordinates of contour line, obtains the geometric center line of contour.The step 4: establish the geometric relationship model of tower tilt and tower shadow;Step 5: based on step 4, calculate the inclination angle, according to inclination angle, judge tower tilt situation, and output result.

The present invention relates to extracting the rotation speed of cells in an ODEP field based on an area change algorithm. The method comprises: binarizing an RGB image acquired by a CCD camera to extract cell morphological features; identifying and selecting cells by calculating connected domains in the binary image; calculating the area of ​​the selected cells using morphological methods; calculating the area of ​​all frames of the selected cells to obtain a cell area change curve; smoothing the change curve using convolution calculations to remove noise from the curve; and obtaining the cell rotation speed using the periodic changes shown on the processed cell change curve according to rotation theory using the values ​​of every two adjacent peaks and valleys. The method can accurately and automatically obtain the rotation speed of a cell of interest from a video containing various motions and cells; extract the complete morphology of the cell; and quickly obtain the motion trajectory of the cell of interest, thereby performing a comprehensive analysis of the cell. The method has applications in biomedicine, bioengineering, and other related fields.

The present application relates to a kind of article volume measurement methods of preventing personnel interference.The article volume measurement method includes S1, obtaining article image, removing background information in image, and carrying out binary processing;S2, find the outline of article, judge whether the outline of article extends to the boundary of field of view, if yes, turn into step S4, if no, turn into step S3;S3, carry out the volume measurement of article, turn into step S7;S4, by morphological method, remove irregular part in the outline of article, if can remove, turn into step S5, if can not remove, turn into step S6;S5, according to the outline of article of removing irregular part, carry out the volume measurement of article, give first-level warning, turn into step S7;S6, give second-level warning, turn into step S7;S7, end.The article volume measurement method of preventing personnel interference proposed in the present application can effectively improve the efficiency of article volume measurement.

Systems and methods provide an antialiasing engine and its related functions that distribute the computational effort and image reconstruction functions to achieve higher visual quality using morphological methods and operations. For example, an antialiasing engine detects one or more edges within a first frame and generates an edge image indicating a respective pixel's proximity to the foreground or the background based on detection of the one or more edges within the first frame. The antialiasing engine then generates an analytical edge by tracing the edge image and encodes the analytical edge into a depth frame within a video stream. The video stream is then transmitted to a client device where the analytical edge is responsively decoded by a client-side antialiasing engine and sampled to generate a final composition integrating the first frame into a second frame using the analytical edge, where the second frame is generated by the client device.

The invention discloses a big data identification method for periodic weighting of a coal face, and belongs to the technical field of coal mine pressure analysis. The method comprises the steps that S1, support resistance data are cleaned based on pressure gradient characteristics, and process noise is eliminated; s2, constructing a nonlinear propulsion model, and realizing accurate mapping from time domain data to a space propulsion degree; s3, the working face is divided into regions and boxes, the group energy of each region is calculated, and a multi-dimensional energy sequence is constructed; s4, extracting a static load baseline by adopting an anti-noise trend separation method based on quantiles, separating load disturbance characteristics, and enhancing by using a morphological method; and S5, constructing an adaptive threshold based on the robust statistics, and performing consistency verification in combination with the geological prior periodic step pitch to realize automatic and accurate identification of the periodic weighting. According to the method, the problems of incomplete data cleaning, distortion of space-time mapping, weak anti-noise capability, dependence on empirical thresholds and the like of a traditional method are solved, and the accuracy, the robustness and the automation level of weighting identification are remarkably improved.

The application discloses a kind of periodic weighting big data identification methods of coal mining face, belong to coal mine strata pressure analysis technical field.The method includes: S1, based on pressure gradient feature cleaning support resistance data, eliminate process noise;S2, construct nonlinear propulsion model, realize the accurate mapping of time domain data to space propulsion degree;S3, working face is divided and is boxed, calculates each regional group energy, constructs multidimensional energy sequence;S4, using the anti-noise trend separation method based on quantile extracts static load baseline, separates load disturbance characteristics, and is enhanced using morphological method;S5, based on robust statistics, construct adaptive threshold, combine geological priori period step distance and carry out consistency check, realize the automatic, accurate identification of periodic weighting.The application solves the problems of traditional method, such as incomplete data cleaning, space-time mapping distortion, weak noise resistance and identification relying on empirical threshold, significantly improves the accuracy, robustness and automation level of weighting identification.

The invention relates to the technical field of bridge damage detection, in particular to a lightweight concrete bridge apparent damage identification research method. The method comprises the following steps: constructing a lightweight segmentation network model, wherein the lightweight segmentation network model comprises an encoder and a decoder; inputting a bridge image into the lightweight segmentation network model, and performing feature extraction on the input bridge image by the encoder to generate shallow features and deep features; the deep features are input to the ASPP module; the ASPP module comprises five parallel branches, each branch is connected with an ECA attention mechanism, and the ECA attention mechanism is used for fusing multi-scale features; the decoder splices the shallow-layer features and the deep-layer features output by the ASPP module, the shallow-layer features and the deep-layer features are recovered to the original image size through up-sampling, and a segmented damaged image is output; and processing the segmented damaged image based on a morphological method, and extracting feature parameters of the damage. According to the invention, the accuracy of bridge damage detection results is improved.

A method and apparatus for early warning of power equipment faults based on infrared image area expansion is disclosed. The method comprises the following steps: acquiring an infrared thermal image sequence of power equipment in a substation using an infrared camera; preprocessing the infrared thermal image sequence using morphological methods to solve for the difference image of the infrared thermal image sequence; performing edge detection on the two-dimensional matrix of the difference image using the Sobel operator to generate a new two-dimensional pixel matrix; scanning the new two-dimensional pixel matrix line by line to obtain a target region; extracting the endpoints and vertices of multiple contour curves from the target region; connecting the multiple contour curves based on the endpoint set and vertex set to obtain a closed curve; and solving for the area of ​​the closed curve; and realizing early warning or alarm of power equipment faults based on the change in the area of ​​the closed curve corresponding to the difference image. This invention reduces the probability of faults being obscured and thus advances the fault detection time.

The present application relates to a kind of structural light projection's luggage three-dimensional version shape degree of fit real-time detection method, to solve the problem of insufficient adaptability to complex geometric boundary identification and process tolerance in traditional fit detection.The core scheme includes: collecting and analyzing the three-dimensional point cloud of luggage surface, calculating the principal curvature direction and change rate to generate curvature distribution map, extracting process boundary by first derivative and morphological method, realizing process constraint mapping by combining knowledge base area semantics, generating point-by-point fit degree weight field by fusing curvature and semantic weight, then combining with measured gap data to obtain weighted deviation, and implementing enhanced correction in boundary narrowband.Region normalization statistics are output, and quantitative fit score is output.Finally, it is automatically compared with tolerance threshold, and the defect precision alarm is realized.The method improves the accuracy of boundary detail identification and the intelligent level of fit detection, adapts to different types of luggage and complex process rules, and realizes efficient and scalable fit quality control.

The application discloses a Chinese environment mathematical formula extraction and recognition method based on a Transformer. The method comprises the following steps: inputting a picture containing a mathematical formula into a mathematical formula mask extraction module in a Chinese environment, and extracting a mask containing the mathematical formula, wherein only the mathematical formula part is reserved and the text part at other positions is removed in the mask; inputting the mask into a mathematical formula picture extraction module, and further repairing and perfecting the mathematical formula position in the mask through a morphological method, and generating a mathematical formula picture according to the repaired mask; inputting the mask into a conversion display module, and obtaining the Latex of the formula through the module, and generating a mathematical formula picture through Katex to display and compare. The method improves the accuracy of formula extraction and formula recognition based on the Transformer technology. Unlike the prior art, the application can automatically infer the formula region of the input picture, and is more convenient to use.

These disclosures relate to a method for detecting and removing personnel interference while measuring volume of an object. An image of an object is obtained, a background information of the image is removed, and the image is converted into a binary image. An outline of the object is detected from the binary image. A determination is made whether the outline extends towards a boundary of the field of view. If the outline of the object does not extend to the boundary of the field of view, the volume of the object is measured. Otherwise, an extending part is removed from the outline using a morphological method, if removable. If the extending part is removed, the volume of the object is measured based on an outline of the object obtained after the extended part is removed, and a class 1 warning is issued. Otherwise, a class 2 warning is issued and the measurement is ended. This approach improves efficiency of the object volume measurement.

This application discloses an image processing method, apparatus, and electronic device, comprising: acquiring a set of discrete contours to be processed in a target image; determining pairs of contours that are adjacent to each other in the set of discrete contours based on preset distance conditions; grouping and aggregating directly or indirectly adjacent contours in the set of discrete contours according to the pairs of adjacent contours to form at least one contour connected group; determining the aggregation boundary region corresponding to each contour connected group; and performing subsequent image processing on the target image based on the aggregation boundary region. This application significantly improves the accuracy and robustness of keyboard character segmentation and localization, laying a solid foundation for subsequent character detection, and avoiding the problem of erroneous adhesion of adjacent characters caused by the fixed kernel size in traditional morphological methods.