481 results about "Binary image" patented technology

The invention provides a stacked cell hierarchy judgment method based on contour concavity and convexity. The method comprises the following steps: performing contour extraction on a cell binary image; calculating a convex hull for the contour point set of each cell, and taking the convex hull as an ideal form reference of the cell in an unshielded state; detecting the convexity defect between the contour point set of each cell and the corresponding convex hull, and quantifying the characteristic parameter of each convexity defect; screening the convexity defects according to the characteristic parameters to determine significant recesses, marking cells with at least one significant recess as concave cells, and marking cells without significant recesses as convex cells; screening candidate cell pairs according to the spatial position relationship between the cells; according to the convex-concave attributes of the two cells in the candidate cell pair and the position relation between the coordinates of the center of mass and the remarkable concave area, the shielding relation between the two cells is judged; and constructing an attribute graph by taking each cell as a node and taking the occlusion relationship as an edge, and generating a cell stacking hierarchy sequence through topological sorting.

The invention discloses an intelligent artificial limb movement intention recognition method based on multi-modal information and a D-S evidence theory, and belongs to the technical field of artificial intelligence, mode recognition and intelligent artificial limb control. According to the method, firstly, electromyographic signals and kinematics signals of user limbs and three-dimensional point cloud information of the terrain where the user limbs are located are synchronously collected; filtering, segmenting and time domain / frequency domain feature extraction are carried out on the physiological signals, rotation correction and dimension reduction are carried out on the environment point cloud, and a binary image is generated; using a deep learning model to identify a preliminary motion mode from the physiological features and identify a terrain category from the environment image; and finally, inputting the two recognition results as evidence bodies into an improved D-S evidence theory model for decision-level fusion, and outputting a final comprehensive motion intention recognition result. According to the method, the human physiological signal and the external environment information are fused, so that the ambiguity and uncertainty of single information source identification are effectively solved, and the intention identification accuracy and adaptability of the intelligent artificial limb in different complex scenes are remarkably improved.

The invention relates to the technical field of measurement and detection, in particular to a test sieve calibration method based on machine vision. Comprising the following steps: acquiring a test screen image by a microscope, firstly performing graying processing on an original image, and converting a color image into a grayscale image; the method comprises the following steps: carrying out binarization processing on a grey-scale image, carrying out binarization on the image, setting a grey-scale value of a pixel point on the image to be 0 or 255, enabling the whole image to present an obvious visual effect which is only black and white, and better analyzing the shape and the contour of an object through binarization; and performing connected domain analysis on the binary image, finding a pixel point to which each sieve hole belongs, endowing each pixel point with a label through the connected domain analysis, and forming a connected domain by the pixel points with the same label value so as to realize segmentation of the region of interest. The method is applied to the measurement calibration work of the test sieve, the working efficiency of verification and calibration personnel can be greatly improved by using the method, and human resources are saved.

The invention relates to the technical field of atmospheric environment monitoring, and discloses a sand and dust path monitoring system, which is characterized by comprising a data acquisition module, an image processing module, a feature extraction module, a path fitting module, a path fusion module and a geographic mapping module which are sequentially cascaded, the data acquisition module is used for receiving remote sensing data of a stationary meteorological satellite and generating a multi-time sand and dust monitoring binary image; the image processing module performs edge detection and morphological operation on the sand and dust monitoring binary image to realize accurate segmentation of a sand and dust region; the feature extraction module comprises a minimum enclosing rectangle calculation unit and an edge analysis unit and is used for extracting four types of feature points of the sand and dust area, and the four types of feature points comprise the lower right corner, the upper right corner, the right side line and the lower side line. According to the invention, by fusing edge detection, morphological operation and multi-feature endpoint identification, precise segmentation of a complex dust region and stable capture of motion features are realized, and the problem that feature points are easy to drift in a traditional method is effectively solved.

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 beam damage assessment method and system fusing crack images and multi-scale features, and relates to the technical field of structural health monitoring. The method comprises the following steps: firstly, obtaining a binary image based on an original crack image of a concrete beam body; then, explicit features representing the macroscopic geometrical morphology of the crack are extracted from the binarized image, and implicit features representing the microscopic complexity and heterogeneity of the crack based on the multi-fractal theory are extracted from the binarized image; secondly, fusing the two types of features to form a fused feature space; and finally, training a machine learning model based on the feature space, establishing a nonlinear mapping relation between the machine learning model and a concrete beam damage factor, and realizing automatic and quantitative evaluation of a damage state by using the model. According to the method, dominant and recessive multi-scale features are fused, the defects that a traditional method depends on artificial experience and feature extraction is one-sided are overcome, the damage state can be represented more comprehensively and accurately, and the objectivity, accuracy and efficiency of evaluation are remarkably improved.

The invention relates to the technical field of defect detection, in particular to a defect detection method and system based on a microwave frequency crystal resonator, and the method comprises the steps: forming a microwave probe through a microwave frequency crystal resonator array, placing a to-be-detected material below the microwave probe, and setting a scanning area and scanning step lengths in two directions on the surface of the material; controlling the probe to perform point-by-point scanning according to step length, and collecting a plurality of reflection coefficients of each sampling point to form an echo signal matrix; performing singular value decomposition on the matrix, applying an adjustable coefficient to a first singular value and a second singular value, and adaptively selecting an optimal coefficient according to imaging quality to reconstruct an echo signal; performing two-dimensional Fourier transform on the reconstructed matrix, constructing a spatial filter in combination with probe spacing and defect depth to perform phase compensation, performing two-dimensional Fourier transform to obtain a defect image, and performing threshold segmentation on the defect image to obtain a binary image only containing defects; and analyzing the connected regions to obtain the gravity center position of each defect and the defect area determined by the pixel number and the single pixel area. The problems of serious clutter interference, insufficient defect imaging contrast ratio, difficulty in quantitative evaluation of defect size and the like in the existing microwave detection can be solved.

The invention relates to a pavement crack intelligent detection method, system and device based on image recognition and a medium. The method comprises the steps of obtaining an original pavement image, and performing preprocessing according to global brightness distribution of the original pavement image to obtain an image after illumination equalization; inputting the illumination equalization image into a pre-trained convolutional neural network to extract multi-scale texture features, and generating a multi-scale texture feature map; mapping the feature map into a crack confidence map through a segmentation network; performing threshold segmentation based on the confidence map to obtain a crack binary image; performing sub-pixel-level edge refinement on the binary image to obtain a sub-pixel-level crack edge set; and based on the edge set, crack width and length quantization data are calculated through geometric parameters, and a detection result is output. By adopting the method, local shielding interference can be effectively resisted, a scene with dramatic illumination change is adapted, sub-pixel-level precise positioning and quantification of the crack edge are realized, the recognition capability and detection continuity of the fine crack are improved, and the reliability and generalization capability in a complex road environment are enhanced.

The embodiment of the invention provides an actor recognition method, system and device based on ViT gait analysis and a medium, and belongs to the technical field of gait recognition. The method comprises the following steps: acquiring behavior video data of a target object; performing background subtraction processing on the behavior video data to obtain a binary image of the gait of the target object; inputting the binary image into a feature analysis model, wherein feature analysis is constructed and formed based on Transform; performing attitude feature extraction through a double-path parallel multi-head attention module of the feature analysis model, fitting a feature weight of the attitude feature through a feature average fusion module, and outputting an embedded feature vector; and comparing the embedded feature vector with a preset feature vector, and identifying the identity of the target object according to a comparison result. The embodiment of the invention aims to improve the accuracy of performing actor identity recognition through gaits.

The application relates to the field of machine vision and image recognition, and discloses a high-precision edge extraction method based on image automatic processing, which comprises the following steps: a standardized image system is constructed, and an initial standardized wood template image is acquired through the standardized image system; the initial standardized wood template image is subjected to multimodal fusion pretreatment; an Otsu algorithm is used to determine a high threshold value and a low threshold value, an initial edge image and an edge gradient image are generated after edge detection of a binary image; then, the initial edge image is input into a pre-trained lightweight adaptive U-Net model, and an edge probability image is output; the edge probability image is sequentially subjected to morphological opening operation denoising processing, B-spline curve smoothing fitting processing and coordinate system calibration processing, and wood template edge coordinates are obtained; the average error of the wood template edge coordinates and standard template edge coordinates is calculated. The application realizes accurate conversion of wood template edge from an image to actual physical coordinates.

The present application relates to an image processing method, and a computer program product, a device and a storage medium. The method comprises: acquiring a first image file and a second image file, wherein the first image file is a binary image file, and the second image file is a folder image file; verifying the first image file and the second image file; and refreshing the successfully verified first image file to a first partition of a preset logical flash, and refreshing the successfully verified second image file to a second partition of the preset logical flash, wherein the first partition of the preset logical flash is a flash partition, and the second partition of the preset logical flash is an embedded memory partition. The method can improve the efficiency of system updating and maintenance.

The invention relates to a method and device for detecting a stripe lap joint or intersection phenomenon in an image, and belongs to the technical field of image processing, and the method comprises the steps: carrying out the preprocessing of a to-be-detected image, and extracting a binary image of foreground stripes; carrying out refining processing on the binary image to obtain a stripe skeleton image with a single-pixel width; calculating the global average spacing of stripes in the image; traversing pixel points in the stripe skeleton image, and based on eight neighborhood features of the pixel points, preliminarily judging and marking potential lap joints or intersection points as a suspected defect point set; and for each suspected lap joint or intersection point in the suspected defect point set, calculating the maximum connection depth of the suspected lap joint or intersection point, and comparing and verifying the maximum connection depth with a threshold value set based on a global average distance to determine a real lap joint or intersection defect. According to the invention, the problem of high false alarm rate of stripe defect detection in a complex industrial image is solved, and the accuracy and reliability of detection are remarkably improved.

The embodiment of the invention provides an electric precipitator polar line surface defect identification method and system, and belongs to the technical field of electric precipitation. The method comprises the following steps: acquiring an original digital color image of the polar line in a corona discharge state; generating a grayscale image based on the original digital color image, and constructing a binary image for representing a brightness area according to a preset threshold; performing median filtering and mean filtering in sequence based on the binary image to construct a de-noised binary image, and performing Canny edge detection on the de-noised binary image to generate a halo edge image of a burr tip point; and based on the halo edge image, corona discharge positions and brightness information of two sharp points of each barb are extracted, and a polar line surface defect identification result is output according to a preset normal distribution mode. According to the scheme, the corona discharge characteristics can be stably extracted in complex imaging environments such as high dust and weak light, so that the polar line surface defects can be reliably identified.

The application discloses an OCR-based medical document intelligent identification method and system, relates to the technical field of document identification, and comprises the following steps: collecting a medical document image through a mobile terminal, generating a binary image based on the collected image through a U-Net combined with multi-scale feature fusion and an attention mechanism, and performing clipping; based on the clipped binary image, extracting text information through an OCR model, and extracting structured fields according to the layout rules and geometric distribution of the medical document; and performing deterministic rule judgment and risk assessment on the structured data. The application reduces the image calculation complexity through standard gray scale processing, improves the text region feature extraction accuracy through the U-Net structure combined with the CBAM attention mechanism, realizes effective fusion of multi-scale features and noise suppression in combination with the Attention Gate, realizes text direction correction in combination with the Hough transform, and improves the text detection robustness and recognition accuracy.

The invention provides a pallet pose recognition method for an unmanned forklift, and the method comprises the steps: firstly obtaining depth point cloud data, and converting the depth point cloud data into 2D image data; then constructing a pallet coarse positioning model, processing the 2D image data through the pallet coarse positioning model, obtaining 2D bounding box data, and obtaining a pallet index matrix through the 2D bounding box data; then acquiring a plate instance point cloud based on the plate index matrix, and acquiring a left support leg point cloud, a middle support leg point cloud and a right support leg point cloud through a classification cutting strategy and the plate instance point cloud; according to the method, the 3D depth point cloud is converted into the 2D binary image, a complex 3D point cloud recognition problem is converted into a more efficient and mature 2D image target detection problem, and the template matching problem of a pure point cloud scheme is replaced by a lightweight neural network, so that the optimal balance between the speed and the precision is realized.

The present application relates to the technical field of image data processing, and more particularly to a forging surface defect detection method and system based on machine vision. The method comprises: acquiring a grayscale image of a forging surface to be detected; determining a boundary saliency weight; determining a path consistency weight; screening the boundary saliency weight and the path consistency weight to obtain a final weight; performing local adaptive threshold segmentation on the final feature map to obtain a binary image, and based on the connected regions in the binary image, performing defect recognition on the forging surface to be detected. By constructing the boundary saliency weight and the path consistency weight, the present application is respectively used for accurately positioning the defect edge and verifying the structural continuity, effectively suppresses the texture interference and reduces false alarms; by adaptive Gabor filtering, the response blind area problem of the traditional method is solved, and finally the two weights are fused to modulate the filtering response, thereby improving the accuracy of the forging surface defect detection.

The disclosure provides a kind of recognition positioning method and system of colloidal gold particles in cryo-EM image, the scheme includes: obtaining the electron microscope image to be identified, and carries out corresponding pretreatment;Wavelet transform is handled to the image after pretreatment based on Atrous algorithm, obtains corresponding wavelet image;For the wavelet image obtained, in turn, binaryzation processing, noise point removal and clustering operation are carried out, obtain the corresponding several clusters of colloidal gold particles in binary image;From the several clusters, the cluster satisfying the preset constraint is screened out, and the average template is obtained based on the obtained cluster satisfying the preset constraint;Template matching is carried out using the average template and the several clusters respectively, and the cluster whose matching result is lower than the preset threshold is filtered out, and the remaining cluster is used as the candidate colloidal gold particle target;The position and size estimation of the candidate colloidal gold particle target obtained are obtained by corresponding the original image.

The application discloses a method for quantitatively analyzing the infection degree of white spots in the spleen of Pseudosciaena crocea based on machine learning, and relates to a threshold segmentation technology of machine learning. 1) Data acquisition: dissect the Pseudosciaena crocea infected with internal visceral white spot disease, use a high-definition camera to shoot the spleen, and acquire pictures of the white spots in the spleen to be quantitatively analyzed; 2) creation of an interactive segmentation GUI interface: used for reading the pictures of the white spots in the spleen of the Pseudosciaena crocea and automatically converting the pictures into binary images; 3) running of a main program: the main program is written by a python code and is used for the GUI interface to display the selected pictures on the interactive segmentation GUI interface, so that the pictures are provided for contrast segmentation in the next step; 4) picture processing: selecting the pictures, drawing a frame to select a region to be segmented, adjusting a segmentation threshold to acquire a relatively accurate segmentation result, and realizing visual interactive segmentation of the white spots in the spleen; and 5) quantitative analysis: realizing the quantitative analysis of the infection degree of the white spots in the spleen of the Pseudosciaena crocea by the proportion of the white spots in the spleen.

The invention provides a rock mass fracture network medium reconstruction method and system based on image tracking, and belongs to the technical field of fracture network reconstruction, and the method comprises the steps: obtaining a to-be-reconstructed fracture network, and carrying out the preprocessing of the to-be-reconstructed fracture network, and obtaining a binary image; skeletonizing the binary image to obtain a single-pixel wide fissure main ridge; identifying end points and intersections of the fracture skeleton based on a neighborhood connection relation, and performing constrained graph tracking by taking the end points and the intersections as graph nodes, taking a connection skeleton sequence between the end points and the intersections as graph edges and taking the end points as starting points to obtain an ordered vertex coordinate sequence of each fracture broken line; and performing multi-point coordinate calibration on the ordered vertex coordinate sequence, mapping the ordered vertex coordinate sequence to a target two-dimensional coordinate system, generating a polyline command text file based on the mapped coordinates, and reconstructing the fracture network medium by using the polyline command text file. The method is oriented to data flow of heterogeneous software, a special interface is not needed, batch processing is supported, and the efficiency and accuracy of cross-platform modeling are improved.

A particle size distribution estimation device includes: captured image acquisition processor circuitry configured to acquire a captured image that is an image generated by imaging a target region including a plurality of granular particles at least partially overlapping each other; a binary image generator configured to perform, on the captured image, binarizing that converts a value corresponding to each of a plurality of pixels constituting the captured image into a first value or a second value based on a threshold value, and thus to generate a binary image; a corrected binary image generator configured to perform, on the binary image, opening including eroding and dilating, the eroding and the dilating being filtering using a kernel, and thus to generate a corrected binary image.

The invention provides a surface roughness quantification method with a hidden surface and a readable storage medium. The method comprises the following steps: acquiring an initial binary image; wherein the initial binary image comprises a surface profile curve with a hidden surface; fitting according to the surface profile curve to obtain a theoretical datum line; obtaining a reference binary image and an actual binary image corresponding to the reference binary image according to the theoretical reference line and the surface profile curve; the theoretical information entropy is obtained through calculation according to the reference binary image, the actual information entropy is obtained according to the actual binary image, the relative information entropy of the actual information entropy relative to the theoretical information entropy is obtained with the theoretical information entropy as the reference, and the relative information entropy is used for representing the surface roughness with the hidden surface. According to the method, the surface roughness of the hidden surface is quantified through the relative information entropy, and the surface roughness of different parts can be quantitatively evaluated by comparing the relative information entropy of the different parts.

A method for extracting key contour points of Chinese characters based on key skeleton points and stroke width belongs to the field of computer and Chinese character vectorization, which comprises the following steps: inputting a single Chinese character picture, performing pretreatment to generate a binary image; extracting the skeleton of the Chinese character by using a skeleton extraction algorithm; further extracting key skeleton points of the Chinese character; obtaining the stroke width at each key skeleton point of the Chinese character; obtaining the contour points of the Chinese character by using a contour extraction algorithm; sequentially traversing all contour points of the Chinese character, calculating the distance from the current contour point to the nearest key skeleton point, calculating the distance threshold of the current nearest key skeleton point, judging whether the current contour point is a key contour point by comparing the distance from the current contour point to the nearest key skeleton point with the distance threshold, and recording the current contour point as a key contour point if the distance is less than the distance threshold. The application effectively improves the quality of Chinese character vectorization, maintains all detail features, effectively removes noise contour points and redundant contour points, and reduces the storage space size of the vectorized Chinese characters.

The application discloses a contour determination method and device of a BIM model, a terminal and a storage medium, and the method comprises the following steps: acquiring a BIM model to be calculated, and acquiring model parameters of the BIM model to be calculated; importing the BIM model to be calculated into a pre-set graphics engine; creating a virtual camera in the graphics engine; rendering the BIM model to be calculated by using the virtual camera in combination with the model parameters of the BIM model to be calculated, so as to obtain a binary image of the BIM model to be calculated; and performing edge calculation and vectorization processing on the binary image of the BIM model to be calculated, so as to obtain a two-dimensional contour of the BIM model to be calculated. According to the scheme, the contour of the BIM model is determined by using the image recognition method in the case that the volume of the BIM model is large, and the calculation difficulty can be reduced.

An image processing apparatus according to one embodiment of this application may include: an image acquisition unit for acquiring an image of a plurality of active materials; an image segmentation unit for segmenting a plurality of active materials included in the image of active materials to obtain a segmented image; and an image generation unit for inputting the image of active materials into an artificial intelligence model to generate at least one of the following: a binary image in which the active materials and cracks on the surface of the active materials are separated, a pore binary image representing the pore characteristics of the active materials based on the segmented image, or a combination thereof.

The application discloses a kind of nematode body bending behavior identification and counting method and related equipment, the method comprises: obtaining nematode video, the original gray image is obtained by frame processing to nematode video, and binary image is obtained according to the original gray image of nematode;According to the original gray image of nematode, the numerical coordinate regression algorithm based on convolutional neural network obtains head coordinate and tail coordinate;According to the binary image of the nematode, the feature point extraction algorithm based on curvature calculates the peak point on the center line of nematode;The maximum distance of the line between the pharynx and tail of nematode is calculated, and the number of nematode body bending is calculated according to the change of maximum distance of each frame.The numerical coordinate regression algorithm based on convolutional neural network is used for the coordinate identification of nematode head and tail, the accuracy is improved, and the number of nematode body bending is calculated more simply and quickly by extracting feature points.

The application discloses an elevator left-over detection method and system, and the method comprises the following steps: collecting front and rear images; extracting a front feature map from the front image and a rear feature map from the rear image; calculating the cosine similarity of the front feature map and the rear feature map; generating a binary image based on the cosine similarity; and judging whether there is a left-over based on the binary image. Thus, the images before and after taking the elevator are collected to extract features, and whether there is a left-over is judged by the similarity between the two images, instead of only identifying a single image, so that the influence of environmental factors is effectively excluded, and the identification accuracy is significantly improved.

A computer-implemented method (200, 300) for generating a contour of a patient or an organ of the patient, the method comprising: obtaining (201) a three-dimensional binary image of the patient or the organ of the patient, wherein the binary image comprises a plurality of voxels having one of a first value and a second value; processing (202) the binary image to determine one or more connected volumes, wherein each connected volume consists of a plurality of voxels having the first value that are located close enough to each other to satisfy a connectedness criterion; identifying (203) a largest connected volume among the one or more connected volumes, wherein the largest connected volume comprises a largest number of voxels; removing (204) from the binary image all connected volumes among the one or more connected volumes except the largest connected volume to obtain an updated binary image; and performing (205) a contouring algorithm on the updated binary image to obtain the contour of the patient or the organ of the patient.

This invention discloses a method for predicting the water content of the cathode flow channel in a visualized cathode fuel cell device, belonging to the field of fuel cell visualization. The method includes an anode unit, a cathode unit, and an MEA (Mechanical and Electrical Assembly). The cathode unit has a transparent end plate with a hollow cavity. Circulating hot water is introduced to eliminate the temperature difference between the two sides of the end plate, preventing fogging of the viewing window. Images of the cathode flow channel with water content and images of the dry flow channel are acquired. A difference image is obtained through difference calculation, and then a binary image is obtained through adaptive binarization. The water coverage rate is calculated using a mask image as a quantitative representation of the water content. A convolutional neural network is constructed, and the model is trained using the water coverage rate as input. The Sobol sensitivity analysis method is used to optimize the hyperparameters of the learning rate, dropout rate, and batch size. The optimized CNN model is used to predict the water content in the cathode flow channel in real time, providing a basis for flood warning. This invention achieves non-invasive quantitative prediction of the water content in the cathode flow channel, improving prediction accuracy and robustness.

The application discloses a boride identification method, system and related device, and relates to the field of alloys.The method comprises the following steps: obtaining a metallographic image of a titanium-aluminum alloy; converting the metallographic image into a first image represented by color in terms of hue, saturation and brightness; determining a saturation threshold and a brightness threshold; dividing each pixel point in the first image into a boride category and a background category according to the saturation threshold and the brightness threshold; obtaining a binary image of the first image according to the classification results of the pixel points; identifying at least one target region composed of multiple target pixel points in the binary image by using a continuous region identification algorithm; and determining the target region as a boride region.The application screens the pixel points by using saturation and brightness, so that the possibility of selecting a shallow corrosion structure is reduced; after a suspected boride region is selected, the continuous region identification algorithm is used to identify and screen according to the shape characteristics of the boride. Therefore, the application can effectively improve the identification accuracy of the boride.