221 results about "Image processing software" patented technology

Presented herein are systems and methods for the employment of machine learning models for image processing. A mobile application for client-side image processing and validation, which interacts with and leverages native image processing software of the client device, where the image processing software and the mobile application include any number of machine-learning models for identifying a document and attributes of the document for recognition and validation. This mobile application uses the image processing software from a client operating system to control the camera. The image processing software generates various types of information about a video frame and the document, and the mobile application invokes APIs or software libraries of the image processing software to access the information and validate the frame and document.

The invention relates to the technical field of substation defect detection, in particular to a substation defect detection method and system and a storage medium. The method comprises the following steps: acquiring a substation equipment image by adopting real-time image acquisition equipment, and establishing an equipment image database; performing denoising and edge detection through image processing software to generate preprocessed image data; detecting an abnormal area of the substation equipment through feature extraction and texture analysis, and training a defect recognition model; after real-time image information is input into the model, an abnormal defect area of the transformer substation is recognized, and fault risk gradient deterioration is estimated; calculating a risk index based on a pre-estimation result, and if the risk index exceeds a set threshold value, triggering an early warning function, providing defect types and positions, and suggesting repair measures; according to the invention, the transformer substation defect detection method is optimized, so that the transformer substation defect identification is more accurate.

Presented herein are systems and methods for the employment of machine learning models for image processing. A mobile application for client-side image processing and validation, which interacts with and leverages native image processing software of the client device, where the image processing software and the mobile application include any number of machine-learning models for identifying a document and attributes of the document for recognition and validation. This mobile application uses the image processing software from a client operating system to control the camera. The image processing software generates various types of information about a video frame and the document, and the mobile application invokes APIs or software libraries of the image processing software to access the information and validate the frame and document.

The invention relates to a micro-plastic polluted water area space distribution identification method based on remote sensing, particularly relates to the technical field of environment monitoring, and solves the problems that various characteristic data of micro-plastic are difficult to analyze through image processing software, and the characteristics are difficult to comprehensively construct a model to output a water area comprehensive pollution degree value. The method comprises the following steps: extracting 70% of sample data from a target water area, and preprocessing the sample data; screening out samples with micro-plastic abundance not smaller than the abundance average value of the micro-plastic in the target water area; extracting feature data from the remote sensing image, wherein the feature data comprises a total color adsorption capability degree, a total shape coverage capability degree and a total water turbidity degree; constructing a model by using the feature data to obtain a water area comprehensive pollution degree predicted value; spatial distribution of comprehensive pollution degree predicted values is generated through the GIS technology, and different colors are used for representing different pollution degrees.

The invention discloses a method for processing a printed file with a variable area, which comprises the following steps of: S1, establishing a database containing all variable filling patterns; s2, loading an image needing to be printed into image processing software, and establishing a coordinate system in the image; s3, inputting coordinates of the variable area according to the coordinate system, and setting the coordinate area as the variable area; s4, selecting a graphic file in the database, importing the graphic file into a printer, simultaneously importing an image into the printer for printing, reading printing data by the printer in rows, and for each row of data, calculating the position of the current row in the XY direction of the whole pattern according to a preset algorithm for printing operation; according to the method, the problem that a lot of pre-printing files need to be processed at present is solved, the pre-printing workload is greatly reduced, different printing requirements can be rapidly met by establishing the database and presetting the variable area, and it is guaranteed that graphs and images are printed at accurate positions through coordinate calculation and comparison.

Presented herein are systems and methods for the employment of machine learning models for image processing. A mobile application for client-side image processing and validation, which interacts with and leverages native image processing software of the client device, where the image processing software and the mobile application include any number of machine-learning models for identifying a document and attributes of the document for recognition and validation. This mobile application uses the image processing software from a client operating system to control the camera. The image processing software generates various types of information about a video frame and the document, and the mobile application invokes APIs or software libraries of the image processing software to access the information and validate the frame and document.

The invention discloses a blasting vibration monitoring method and system based on vision and vibration sensors, and relates to the technical field of blasting vibration monitoring. An aerial vision monitoring device is arranged at a set height right above a blasting point, and a vibration sensor array is radially arranged around the blasting point; and the visual monitoring device and the vibration sensor array are in wireless communication connection with the blasting vibration analysis module based on the computer. Omnibearing monitoring of the blasting process is achieved by combining the aerial vision monitoring device and the ground vibration sensor array, ground vibration data can be captured, and the scattering condition of fragments generated by explosion can be observed. An image feature recognition algorithm is used for analyzing an image captured by a high-speed camera, and visual evidence is provided for understanding the explosion effect. Complex vibration data and visual feature changes can be converted into charts and schematic diagrams which are easy to understand through image processing software and a data visualization tool, and non-professionals can conveniently and rapidly grasp key information.

The present invention relates to the technical field of production of package process forms, and in particular to a system and method for detecting production of package process forms, comprising an image acquisition module; an image processing and analysis module, the image processing and analysis module is used to receive package process form image data transmitted by the image acquisition module, and perform noise reduction and enhancement processing on the image data, and then analyze the processed data to determine whether the package process form has quality problems; a dimension measurement module; and a result display and alarm module. The present invention uses a high-resolution camera and image processing software to quickly and accurately detect the appearance of package products through the cooperation of the image acquisition module, the image processing and analysis module, the dimension measurement module and the result display and alarm module, and can automatically identify shape deviations, surface flaws, color differences, pattern defects and other problems, thereby improving detection efficiency and accuracy and reducing the influence of human factors.

The present invention relates to a non-destructive evaluation method for internal pores of an additive manufacturing titanium alloy test bar. The reconstructed image is subjected to filtering and noise reduction processing by Avizo image processing software, and then interactive threshold segmentation is performed to obtain the test bar contour image, and the gray-scale image of the sample with closed internal holes is obtained; the gray-scale image is subtracted from the test bar contour image to obtain a gray-scale image containing only holes; by setting a gray-scale threshold greater than the standard deviation of the gray-scale of the sample material in the image, gray-scale difference operation is performed on the gray-scale image containing only holes to remove noise, and a binary image is obtained. The present invention proposes a method for detecting the internal porosity of an additive manufacturing titanium alloy test bar by using micro-focus industrial CT, which overcomes the disadvantage that conventional ray detection cannot detect the internal micro-defects of an additive manufacturing titanium alloy test bar, detects and counts the geometric characteristics of the pores, and realizes the non-destructive evaluation of the internal micro-defects of an additive manufacturing titanium alloy test bar.

Presented herein are systems and methods for the employment of machine learning models for image processing. A mobile application for client-side image processing and validation, which interacts with and leverages native image processing software of the client device, where the image processing software and the mobile application include any number of machine-learning models for identifying a document and attributes of the document for recognition and validation. This mobile application uses the image processing software from a client operating system to control the camera. The image processing software generates various types of information about a video frame and the document, and the mobile application invokes APIs or software libraries of the image processing software to access the information and validate the frame and document.

The invention belongs to the technical field of hydrogen sulfide detection, and discloses a batch quantification method for bacterial endogenous hydrogen sulfide yield. Materials (qualitative filter paper, a 96-well plate and a lead acetate solution) used in the method are cheap commercially available consumables, a used gel imaging system is also a laboratory basic instrument, and Fili imageJ is widely used free image processing software. Therefore, the method has the advantages of low cost, simplicity in operation, capability of quickly quantifying in batches and the like. Besides, membrane covering of the qualitative filter paper enables each pore of the 96-pore plate to be relatively closed, and meanwhile, different from a color developing agent directly added into the bacterial liquid, the measuring position is located above the bacterial liquid, so that accurate quantification is achieved, and growth and metabolism of bacteria are not affected at the same time. The method not only can realize accurate quantification, but also can detect the yield of hydrogen sulfide of different bacteria in batches, and is convenient to popularize and apply.

The invention belongs to the technical field of visual inspection, and particularly relates to an industrial defective product detection method and defective product detection device.The industrial defective product detection method comprises the steps of S1, image collection, S2, image preprocessing, after image collection, image preprocessing to improve image quality, S3, key features in images are extracted through a computer vision algorithm, and S4, defect recognition and classification. S5, decision making and judgment: classifying the products through a processing system according to the identified defects or abnormal characteristics, and judging whether the products are defective products, S6, outputting results, judging whether the products are qualified or unqualified, and finally outputting qualified or unqualified judgment results, and the system is connected with a production line, automatically sorts the defective products or prompts an operator to carry out manual intervention. According to an image sensor, a light source, image processing software and an analysis algorithm, the product appearance and defects are recognized and classified, and through the image obtaining and processing technology, whether the product meets the design standard or not is detected in real time, and the problems of surface defects, shape inconformity, size out-of-tolerance and the like are recognized.

The invention relates to a binding material single particle local spatial evolution analysis method, and belongs to the technical field of civil engineering binding material microstructure characterization. The method comprises the following steps: acquiring back scattering (BSE) and characteristic X-ray energy spectrum (EDS) images of a sample; selecting a target gel particle based on the BSE image and intercepting a local area; identifying the reaction edge of the target gel particle; performing equal-width stepped strip division inwards and outwards by taking the reaction boundary as a starting point, and storing the mask; carrying out image operation on adjacent masks to extract strips, and carrying out statistical analysis on gray feature and element feature changes of each strip; according to the method, surrounding phase interference is avoided through local analysis, whole-process analysis is realized by adopting unified image processing software, a programming basis is not needed, the technical threshold is reduced, the operation process is simplified, the uniformity of strip division and the reliability of an analysis result are ensured, and the method is suitable for large-scale popularization and application. And a simple, convenient and effective technical means is provided for revealing the reaction mechanism and quantitative reaction activity of the cementing material.

The invention provides an interaction method for image processing and a corresponding image processing method. The method comprises the following steps: firstly, receiving a stroke which is added on a displayed first image by a user through an operable control and is used for representing a local area needing to be changed, and then displaying a prompt word which aims at the stroke and is used for representing a modification target of the local area. A second image generated according to the stroke, the prompt word and the first image is displayed, other parts, except for the stroke coverage range, of the second image are similar to the first image, and the content represented by the modification target is correspondingly generated at the position where the stroke is located in the second image. A user can draw the content which wants to be creatively modified on the first image by adding strokes on the image, and the device can recognize the strokes of the user and generate the content needed by the user. In this way, even if a user is not familiar with image processing software, efficient creative processing can be performed on the image through simple operation.

The invention discloses a full-automatic electrochemical luminescence detector based on a photodiode, and relates to an electrochemical luminescence detector, the full-automatic electrochemical luminescence detector comprises a shell, the shell is internally provided with an optical signal acquisition module, a small packaging circuit board, an ECL chip, a man-machine interaction module and a chip inlet and outlet device; a light current detection unit, an electrochemical reaction excitation unit and a microcontroller are arranged in the small packaging circuit board; wherein a test strip sample pad and a reaction area are arranged in the ECL chip. The photodiode used in the invention can make a quick response to the change of light intensity, which is far faster than the frame capturing speed of the camera, and the demand and complexity of subsequent data processing are reduced, so that the system is easier to realize automation. Compared with a camera, the photodiode is lower in hardware cost, does not need to use complex image processing software, and is suitable for household use. The detector is compact in structure, small and exquisite in appearance and convenient to use in a handheld mode and carry about.

The invention provides a tunnel secondary lining visual construction method, and belongs to the technical field of tunnel lining construction. A visualization device is adopted to conduct visualization detection in the tunnel second lining construction process, the visualization device comprises a probe, a display device and a connecting line used for connecting the probe and the display device, and image processing software used for processing pictures and images is arranged in the display device. Comprising the following steps: installing a visualization device; performing visual construction on the tunnel secondary lining; pouring of a tunnel second lining is started, and the three probes conduct simultaneous observation from bottom to top according to the distribution mode of the three probes and the left side and the right side of the concrete pouring reaching height; the display carries out real-time recording and timing according to pictures returned by the probe in real time so as to gradually record the concrete pouring condition and plumpness; establishing an observation analysis database used as a later secondary lining concrete construction standard; and obtaining an observation analysis database according to an observation analysis result, and using the observation analysis database as a subsequent secondary lining concrete construction standard database.

The invention discloses a mouth scanning method and mouth scanning instrument based on pose information and point cloud data fusion, and belongs to the field of medical instruments, and the method comprises the following steps: S1, selecting facial feature points which are fixed relative to a position relation between an upper jaw alveolar ridge and a lower jaw alveolar ridge as facial marking points for marking; s2, calculating the spatial positions of a mouth scanning sensor and an attitude sensor fixed on the mouth scanning instrument relative to the facial mark point; s3, obtaining multi-angle point cloud data; s4, obtaining full-port point cloud data; s5, obtaining an intraoral scanning model; s6, reconstructing an edentulous jaw soft and hard tissue three-dimensional digital model; and S7, calculating the soft tissue thickness of an edentulous region in the edentulous soft and hard tissue three-dimensional digital model by using image processing software. According to the oral scanning method and the oral scanning instrument based on the pose information and the point cloud data fusion, errors occurring in the splicing process of the oral scanning instrument only depending on the point cloud data can be remarkably reduced or avoided, and the precision and the efficiency of edentulous jaw digital impression are improved.

The invention relates to the technical field of underwater sonar detection, and discloses a region segmentation method and system for underwater target sonar image detection, and the system comprises an image collection module, an algorithm optimization module and a verification evaluation module. According to the region segmentation method and system for underwater target sonar image detection, an image acquisition module is connected with a side-scan sonar to acquire sonar image data of all regions, then image processing software is utilized to perform data enhancement on a sonar data set, an experimental data set is constructed, an algorithm optimization module sets a YOLOv9 algorithm model as a real-time target detection framework, and a real-time target detection result is obtained. A BoTNet network and a SimAm attention mechanism are introduced, optimization and improvement are carried out, feature information in an experiment data set is extracted, the feature capturing capacity is high, and the verification and evaluation module calculates the average precision mean value and score of each algorithm model according to the experiment data set and carries out calculation according to the average precision mean value and score of each algorithm model. The detection precision of the improved YOLOv9 algorithm model is compared with the detection precision of the YOLOv3 algorithm model, the detection precision of the improved YOLOv5 algorithm model, the detection precision of the improved YOLOv7 algorithm model and the detection precision of the improved YOLOv9 algorithm model are compared with the detection precision of the improved YOLOv9 algorithm model.

The invention relates to the technical field of image acquisition, and provides a microscopic image acquisition system based on a smart phone, and the system comprises an access control module which is used for carrying out the adaptive access of the smart phone and a microscopic photographing interface or a microscope eyepiece at the top of a microscope, so as to achieve the collection of an image through a microscope light path through the smart phone, and obtaining a target image; the adjustment control module is used for adjusting and controlling the process of collecting the target image by the smart phone; the interaction control module is used for interacting the smart phone with a computer so as to display a target image on an electronic large screen such as a computer display; and the processing and analyzing module is used for processing and analyzing the target image based on designed image processing software. According to the invention, the intelligent mobile phone is used, the microscope is combined to collect and store the microscopic image, and the microscopic image is projected to the display screen to be displayed, edited and analyzed, so that the microscopic image can be flexibly, efficiently and conveniently operated, processed and applied, and the image analysis and application efficiency is improved.

The invention discloses a plasmon metasurface chip biological image detection system integrated with digital microfluidics, and belongs to the field of microfluidics and metasurface biological image sensing. Comprising a digital micro-fluidic chip which is provided with a driving electrode array, a dielectric layer and a hydrophobic layer and is used for constructing a liquid path and controlling liquid drops; the plasmon metasurface chip is combined with the digital micro-fluidic chip to form a detection site, and biomolecules are detected by utilizing a plasmon resonance effect; the light source is used for exciting plasmon resonance; the CMOS image sensor is used for capturing an image at the detection site; and the control circuit is used for controlling the electrode of the digital micro-fluidic chip and realizing the construction of a liquid path and the control of liquid drops. A biomolecule solution to be detected can be accurately guided to a detection site of the plasmon metasurface chip, plasmon resonance is excited by using a light source, and an image is captured through the CMOS image sensor. And analyzing the image intensity change through image processing software to obtain a detection result of the biomolecules.

The invention relates to the field of intelligent detection, and provides a slub yarn fabric process detection method and system based on computer vision, and the method comprises the steps: firstly obtaining a plurality of local grayscale images of a slub woven fabric through a digital image collection device, and then carrying out the feature-based global analysis of the images to generate a complete slub yarn fabric image; the method comprises the following steps of: firstly, acquiring an image of the slub yarn fabric, marking and positioning slubs in the image through image processing software, connecting adjacent slubs, recording position coordinates of a starting point and an ending point of each connected slub, and finally detecting key process parameters of the slub yarn fabric by using a specific slub parameter calculation method according to the coordinate data. Therefore, the method can better adapt to different fabric types and complex environmental conditions in detection, and is beneficial to improving the precision and reliability of bamboo joint parameter detection.

The invention discloses a dual-mode immunochromatography detection system based on smart phone deep learning, and the system comprises a smart phone which is provided with a camera and a detection APP, and is disposed on an adaptive hardware platform; the self-adaptive hardware platform is provided with a stretchable support, an optical path module, an imaging macro lens and other structures. The stretchable bracket utilizes a micro slide rail to adjust the position of the mobile phone; the light path module is used for exciting an imaging signal of the immunochromatography chip and guiding the imaging signal to the imaging lens; the imaging macro lens is used for amplifying a to-be-detected area of the chip and guiding the to-be-detected area to the smart phone camera; and the bimodal immunochromatography chip is respectively configured to be a distance type fluorescence chip and a traditional colorimetric chip. The detection system does not need an extra large detection device and complex image processing software, the detection process is remarkably simplified, the data size needed by deep learning model training is reduced, computing resources are optimized, convenience and intelligence of detection are achieved, and expandability and maintainability of the system are remarkably improved.

The invention provides a method for in-situ measurement of an internal stress state of an ultrathin film surface by using RHEED, and relates to the technical field of condensed state substance structure detection, and the method comprises the following steps: epitaxially growing an ultrathin film on a substrate by using film growth equipment; irradiating the ultrathin film by using an RHEED electron beam to obtain a diffraction pattern, and obtaining characteristic parameters of the diffraction pattern; obtaining the lattice constant of the ultrathin film through the characteristic parameters of the diffraction pattern; based on the lattice constant of the ultrathin film, the stress type and strain borne by the ultrathin film are determined. According to the method, the distance between diffraction fringes in a diffraction pattern is accurately extracted from a simple RHEED image, the lattice constant from the diffraction pattern is calculated, the stress state of the ultrathin film is accurately judged by comparing lattice constants in a stress-free state, existing equipment does not need to be greatly modified, and the method is simple and convenient to operate. The method can be realized only by using the existing RHEED electron beam and image processing software.

The invention discloses an embroidery positioning printing method based on machine vision, electronic equipment and a computer readable storage medium, and relates to the technical field of embroidery printing, basic data of a camera and a printer are obtained, the camera is connected to an upper computer, and if the camera is used for the first time, a calibration process is executed to complete starting point binding and distortion correction of the camera and the printer; selecting template mapping positioning or template deformation mapping positioning according to matched material characteristics, intercepting a printing area image as a template, and extracting feature points for positioning; positioning the logo image and the template map, and adjusting a local form into a logo template by using image processing software; finally, the logo template and material position information are transmitted into a program and mapped to a blank TIF image, the blank TIF image is transmitted into a printer to complete positioning printing after PRN processing, the steps of calibration parameter calculation, stretching and compression scale factor calculation, rotation angle adjustment and the like are involved, coordinate optimization, color calibration and anti-aliasing processing are further conducted on the mapped image, and accurate embroidery positioning printing can be achieved.

The invention provides a design method for checking dislocation between layers of a multilayer circuit board, which comprises the following steps of: designing an intelligent positioning identifier at a board corner of each layer, capturing an SPM high-definition image, transmitting the image to a central control system through a high-speed network, designing special image processing software to automatically identify the SPM, and extracting concentric circle and rectangular bar code information; the method comprises the steps of calculating relative position deviation between layers, preprocessing original image data, converting the original image data into structured offset data, establishing a real-time data analysis platform, generating a detailed interlayer offset report, setting a threshold value alarm mechanism, providing a visual interface to display the state and historical trend of a production line, and obtaining a real-time data analysis result based on real-time data analysis. A machine learning algorithm is adopted to predict an offset trend, subsequent process parameters are optimized, interlayer offset in a final finished product is reduced, a dynamic process control system is introduced, operation parameters of production equipment are automatically adjusted according to real-time feedback, and production stability and precision are ensured.

The invention discloses a high-precision photoelectric imaging monitoring system and method based on image processing, and belongs to the technical field of artificial intelligence. The method comprises the following steps: acquiring a default shooting angle and light source configuration of a precise instrument of a corresponding model from a database, performing image acquisition on a precise instrument sample by using photoelectric imaging equipment, and importing the acquired image into image processing software for image preprocessing; identifying the contour of the precise instrument sample through edge detection, and detecting the shape feature of the precise instrument sample through transformation; segmenting a flaw area and a normal area of the precise instrument to be detected by using an image segmentation algorithm, and carrying out real-time flaw detection and classification by using a YOLO algorithm; light source configuration is optimized, the effect of each light source configuration is evaluated, and the optimal light source configuration is obtained; training a convolutional neural network, and determining an optimal shooting angle; under the optimal shooting angle and the optimal light source configuration, verification shooting is carried out on the to-be-tested precise instrument, the imaging quality is automatically evaluated, and feedback is provided.

The invention discloses an OpenMC-based BNCT dose full-process automatic calculation output method, and particularly relates to the technical field of boron neutron capture therapy dose calculation, and the method comprises the steps: reading a DICOM-CT image, automatically completing HU value segmentation, material assignment and voxel grid modeling, and generating an OpenMC geometry and material input card; setting a neutron / photon source, an operation mode and a counting grid, and executing Monte Carlo transport calculation; an ICRP flux-dose conversion coefficient library is built in, and dose calculation is automatically completed; the dose field is written into an Rt dose file, and the Rt dose file can be directly imported into mature medical image processing software for visual evaluation and plan optimization. According to the method, end-to-end automation from the CT image to the clinical available dose file is achieved, manual intervention is not needed, the BNCT dose calculation efficiency, precision and standardization level are remarkably improved, and a reliable tool is provided for BNCT dose simulation research.

Presented herein are systems and methods for the employment of machine learning models for image processing. A mobile application for client-side image processing and validation, which interacts with and leverages native image processing software of the client device, where the image processing software and the mobile application include any number of machine-learning models for identifying a document and attributes of the document for recognition and validation. This mobile application uses the image processing software from a client operating system to control the camera. The image processing software generates various types of information about a video frame and the document, and the mobile application invokes APIs or software libraries of the image processing software to access the information and validate the frame and document.

The application relates to a scanning electron microscope measurement method for the thickness of a metal compound layer, which comprises the following steps: obtaining a cross section containing the compound layer by wire cutting, and polishing after inlaying; observing under a scanning electron microscope, selecting a copper-tin alloy compound layer to be measured, selecting a backscattering electron probe to obtain a composition contrast image, adjusting the image contrast so that the measured object and the matrix have obvious differences; selecting a magnification of 2500 times matching the thickness of the metal compound layer, obtaining a high-definition backscattering electron image at a scanning speed of 28 seconds / frame; and quantitatively and finely measuring the thickness of the metal compound layer by using ImageJ software. The advantages of the application are as follows: the metal compound layer is extracted by using the ImageJ image processing software, the area is calculated to obtain the average thickness of the film thickness measurement method, not only the accurate alloy layer thickness information is obtained, but also the standard deviation value of the measured object is obtained to evaluate the uniformity of the measured object; the measurement method improves the accuracy and can be applied to detection and analysis practices.

This invention provides a method for determining the carbon dioxide huff and puff mechanism in fractured reservoirs, belonging to the technical field of determining the carbon dioxide huff and puff mechanism in fractured reservoirs. This invention establishes a unified digital core by selecting outcrop cores to create cast thin sections and employing multi-scale fusion reconstruction technology. Topological connectivity is evaluated based on a seepage topological invariant fracture network connectivity evaluation algorithm. During well shut-in, a diffusion-convection hybrid encoding / decoding model is used to predict migration in real time, and the model learning rate is adjusted by a dynamic learning rate adjustment function. After well shut-in, a feedforward-feedback composite control strategy is used to gradually reduce back pressure to complete the production stage. The experiment ends after cleaning the chip with anhydrous ethanol. Finally, the degree of crude oil recovery is statistically analyzed using image processing software. This invention solves the technical problem of not being able to predict the migration process in fractures and matrix of fractured reservoirs in real time during well shut-in.