33 results about "Micrograph" patented technology

The present application relates to microlens three-dimensional imaging technical field, specifically relates to micrograph array method and three-dimensional dynamic imaging material with micrograph, aims at solving the problem that microlens three-dimensional imaging cannot simultaneously meet fixed visual angle stable imaging and variable visual angle dynamic response.The technical scheme main points are: including transparent substrate layer;Micrograph structure layer;Microlens structure layer, microlens structure layer is located on the side of transparent substrate layer away from micrograph structure layer;Vacuum coating layer, vacuum coating layer is arranged on the surface of microlens structure layer, microlens structure layer is matched with micrograph structure layer, so that three-dimensional dynamic imaging material presents integrated micrograph three-dimensional dynamic stereoscopic image that changes with observation angle in the visual angle range, micrograph structure of different visual angle frame rate is fused according to the viewing angle segmentation, and a kind of three-dimensional dynamic imaging film material is made, and the image gradually changes when micrograph is seen through vacuum coating layer reflection formed when viewing visual angle or deflecting material changes.

Provided herein is a method of imaging and analyzing plant microspores. The method comprises obtaining one or more two-dimensional micrographs of a microspore population, wherein the microspore population has not been labeled; sending the one or more two-dimensional micrographs to an image processing system; segmenting, via the image processing system, the one or more two-dimensional micrographs to distinguish individual microspores, thereby creating a segmented population of microspores; obtaining one or more z-stacks of the population of segmented microspores or the population of microspores to be segmented; and using one or more of pixel intensity information and intensity signal processing within the one or more z-stacks of segmented cells to find nuclear edges and nuclear geometry and determining whether cells are uninucleate or binucleate based on the nuclear edges and nuclear geometry. Also provided herein is a method of imaging and analyzing plant microspores comprising determining microspore viability.

The invention relates to a method for generating a three-dimensional structure of a porous material through image data processing and application of the method, and belongs to intersection of three-dimensional reconstruction, deep learning and material characterization. The problems that when the prior art is directly used for constructing the three-dimensional structure of the material, computing resource consumption is large, functions are split, simulation adaptability is poor, data dependence is high, and angle features are limited are solved. The invention relates to a method for generating a porous material three-dimensional structure through image data processing, which comprises the following steps of: 1, screening two-dimensional images to obtain images conforming to a signal-to-noise ratio and a contrast ratio as input microscopic image samples, and preprocessing the input microscopic images to generate a training sample set; 2, constructing a model of the algorithm; 3, training models, including discriminator model training and generator model training; and 4, inputting a sample image into the trained model, and obtaining a corresponding three-dimensional voxel structure based on the output three-dimensional probability voxel structure.

The application discloses a method for judging the sensitivity of a titanium alloy to sustained fatigue, which comprises the following steps: step (1): a mathematical model formula y=kx+b is simulated by using titanium alloy forging samples with different feather organization contents and a sustained fatigue sensitivity coefficient; in the formula, x represents the feather organization content, and y represents the sustained fatigue sensitivity coefficient; and step (2): the feather organization content of a titanium alloy forging sample to be detected is substituted into the mathematical model formula to obtain the result; the feather organization is a flaky cluster alpha phase in a net basket organization observed by a metallographic microscope, and the content of the feather organization is a ratio of an area of the feather organization to a total area of the net basket organization in a metallographic micrograph taken. The judging method can quantitatively judge the sustained fatigue sensitivity coefficient of the titanium alloy forging sample according to the microstructure characteristics of the titanium alloy forging sample in engineering, and the sustained fatigue value of the material does not need to be measured when mass-produced forgings or a process window is adjusted.

A method of automated control of a microscope in cryogenic electron microscopy (cryo-EM), wherein the microscope is configured to collect high-magnification micrographs of particles suspended in vitreous ice. Such particles are found in grid squares, and a square contains holes from which high-magnification micrographs are imaged. The method is carried out during an active data collection session, leveraging a pipeline that comprises a set of models. The pipeline evaluates a set of collection locations to determine whether to continue collection at a current grid / square or instead at a new grid / square. The evaluation is based on a set of one or more quality scores derived from one or more pretrained models and machine learning-based active learning. Based on the determination, control information is provided to automatically control the microscope to move to a next target for data collection.

The invention discloses a patterned wafer microstructure data acquisition system in a bright field environment and a data set construction method. The system comprises an adjustable light source assembly, a microscope imaging assembly and a three-axis electric displacement platform, the data set construction comprises the following steps: fixing a patterned wafer sample on a three-axis electric displacement platform, and setting illumination brightness through an adjustable light source assembly to obtain uniform bright field illumination; the three-axis electric displacement platform is controlled to move along a preset path, automatic focusing scanning in the Z-axis direction is executed at each point position, and an optimal focal plane image is generated; the collected images are classified after being cut and preprocessed, and normal samples and abnormal samples are distinguished; for the abnormal sample, generating a defect mask file through interactive labeling; and integrating images and masks to form a structured data set which can be directly used for algorithm research. According to the method, microscopic images with high depth of field and high contrast can be obtained, and a high-quality anomaly detection data set directly used for deep learning model training is constructed.

The embodiment of the application relates to a kind of processing method and device for optimizing electron micrograph quality, the method comprises: one complete model pre-training DDPM model is recorded as first pre-training model;And by the way of fusing multiple LoRA adapters on first pre-training model, obtain first fusion model;Collect multiple spherical aberration electron micrograph to form corresponding first training atlas;The parameter set L of first fusion model is fine-tuned based on first training atlas;After fine-tuning, corresponding electron micrograph quality optimization processing is carried out to any ordinary transmission electron micrograph based on the second denoising autoencoder of first fusion model, and corresponding optimized electron micrograph is obtained.Through the application, the image quality of ordinary transmission electron micrograph can be optimized to reach the quality level of spherical aberration electron micrograph, and the model fine-tuning efficiency can be improved, and the model use cost is reduced.

The application discloses a kind of over-fire cable protection multi-material path micrograph generation methods based on Mamba, it is related to bridge cable protection material microstructure intelligent characterization field after fire.The method obtains the micrograph of different materials in cable protection system under different over-fire temperatures and different electron microscope magnifications, and constructs material micrograph sample library;Classify, differentially pretreat and conditionally label material micrograph, form conditional sample set;Construct micro-morphology generation model based on Mamba, and use latent space condition generation training strategy and multi-path parameter optimization method to train;The micro-morphology generation model that completes verification is encapsulated as lightweight plug-in, and target micrograph generation and output are realized.The application can realize the controllable generation of multi-material micrograph under small sample condition, and improve the characterization ability of material specific micro-morphology feature.

The invention belongs to the technical field of computer image processing, and relates to a metal material phase content analysis method and system, a storage medium and equipment, and the method comprises the following steps: collecting a color metallographic micrograph of a sample of a metal material, and constructing a data set of a metal material microstructure by using the color metallographic micrograph; using a label labeling tool to label the data set of the microscopic structure of the metal material; the basic operation module is used for constructing a metal material phase content analysis model based on an environment and an interface provided by a deep learning framework; constructing a model based on an improved U-shaped convolution / self-attention codec algorithm; training a model based on an improved U-shaped convolution / self-attention codec algorithm by using a data set marked by a metal material microstructure; and calculating the phase content of the metal material. The method solves the problem that a traditional image processing method is not easy to analyze the phase content formed by a complex structure.

The invention discloses a marking method and system for a compact sandstone microscopic image, and the method comprises the steps: carrying out the edge enhancement of particles in the compact sandstone microscopic image through an image processing technology, and obtaining a first image; the sandstone microscopic image segmentation model is used to carry out particle pre-labeling processing on the first image to obtain a second image, and the second image comprises at least one piece of editable labeling information formed according to the particle identification result; and modifying the particle recognition result of the second image according to the editable annotation information. According to the method, the labeling efficiency can be remarkably improved, and the labeling fineness and accuracy are improved.

The invention relates to the technical field of image segmentation, in particular to a steel microscopic image ferrite and pearlite grain boundary segmentation method. The invention provides a segmentation method based on deep learning, and aims to solve the problems of insufficient grain boundary segmentation precision, serious noise interference and fuzzy boundary in a steel microscopic image in an existing image segmentation method. The method comprises three main steps of image acquisition and preprocessing, U-Net model construction and training, and post-processing of a segmentation result. Specifically, the image quality is improved and the training data set is expanded through the image preprocessing step and in combination with the technologies of targeted denoising, contrast enhancement, data enhancement and the like. In the model training stage, composite loss of cross entropy and a Dice loss function is adopted, and the problem of imbalance between the grain boundary and the background is solved. And in the post-processing stage, fine grain boundary deletion and noise residue are repaired by using morphological operation and an eight-neighborhood completion algorithm, and the integrity and connectivity of the grain boundary are ensured.

The invention discloses a pathological section full-automatic quality detection method and system based on an OCT technology, and relates to the technical field of pathological detection. The system comprises an OCT imaging module, a sample bearing and positioning module, a data processing module and a quality evaluation module, according to the method, automatic flattening and positioning of a pathological section are achieved through a sample bearing and positioning module, three-dimensional scanning is conducted on the pathological section through an OCT imaging module to obtain an original interference signal, signal noise reduction, wave number resampling, Fourier transform and other processing are completed through a data processing module, and a three-dimensional microscopic image is constructed. And finally, the quality evaluation module automatically identifies slice defects, quantifies defect levels and outputs a quality detection result. According to the invention, full-process automation of pathological section quality detection is realized, manual intervention is not needed, the detection precision can reach a microscopic scale, the problems of low efficiency, high omission ratio and dependence on experience judgment in traditional manual sampling inspection are effectively solved, and an objective and efficient technical scheme is provided for pathological section quality control.

This invention discloses a method and system for dislocation defect identification based on image recognition and deep learning networks, belonging to the fields of image recognition and machine learning. The method first calibrates the dimensions of a high-resolution electron micrograph, identifies atomic coordinates using a deep learning object detection model, and calculates the local atomic environment vector for each atom. Second, using an atomic model dataset covering various materials, dislocation types, and deformation states, a deep learning network is trained capable of predicting the distance between atoms and dislocations based on the local environment vector. Finally, the image is meshed, and the neighboring atomic information of each mesh node is extracted. Combined with the dislocation distance information output by the trained deep learning network, unsupervised clustering is used to identify the dislocation location. This method achieves automated and high-precision identification of dislocation defects in face-centered cubic materials, while also enabling dynamic characterization of crystal defect deformation behavior.

The invention provides a shale permeability analysis method considering multi-media distribution based on an electron microscope image, and relates to the technical field of oil and gas development, and the method comprises the following steps: based on a shale organic matter electron microscope image and a shale inorganic matter electron microscope image, carrying out statistics on pore sizes and distribution rules of organic matters and inorganic matters to obtain a statistical result; considering the micro-scale effect, and respectively calculating the gas permeability in the organic matter and the gas permeability in the inorganic matter according to the statistical result; performing micro-crack identification on the shale multi-medium electron microscope image, and calculating the gas permeability in the micro-crack through the opening degree of the micro-crack; and substituting the gas permeability in the organic matter, the gas permeability in the inorganic matter and the gas permeability in the microfractures into the shale core scale model to solve a shale gas flow mathematical model, and calculating to obtain the shale permeability. According to the method, the microscale effect in shale multiple media is considered, and the problem that the gas microcosmic flow mechanism cannot be comprehensively considered in shale pressure pulse experiment permeability calculation is solved.

The invention discloses an activity evaluation method and system for the whole stem cell culture process, and belongs to the technical field of stem cell culture.The technical scheme includes the steps that stem cell microscopic images are periodically collected through a microscopic imaging system, and the stem cell microscopic images covering the convergence degree interval of 0-100% are obtained and preprocessed; carrying out weak supervision segmentation on the preprocessed microscopic image to obtain a morphological segmentation image; performing recursive multilayer decomposition on the preprocessed microscopic image through two-dimensional discrete wavelet transform to obtain a texture feature map; inputting the segmentation image and the texture feature image into a convolutional neural network for convolution calculation, and then performing flattening to obtain a morphological feature vector and a texture feature vector; splicing the morphological feature vector and the texture feature vector to obtain a fused feature vector; and inputting the fusion feature vector into an evaluation network to output an activity score. The method and the system have the beneficial effect that the activity evaluation method and the system for the whole stem cell culture process are provided.

The application discloses a kind of by image processing software calculation metal material EBSD grain size method and device, it is related to the measurement technical field of physics. Including: by electron backscattering diffraction device, the metal material to be measured is carried out electron backscattering diffraction EBSD analysis, and analysis data is obtained;By the supporting software of electron backscattering diffraction device, analysis data is handled, and orientation imaging micrograph is exported;According to orientation imaging micrograph, the numerical value A of measurement target Area is obtained m And the numerical value P of measurement target Perimeter m ;According to numerical value P m And numerical value A m , the average equivalent area effective grain size of metal material to be measured is calculated. When the effective grain size of metal material is obtained using the application, a relatively large EBSD step can be set, the time of detection is reduced under the premise of ensuring the accuracy of the results, and the average grain size obtained is more accurate than other methods.

The present disclosure relates to an automatic measurement method for electron micrograph images obtained in a semiconductor process. The method comprises: obtaining an electron micrograph image to be analyzed as a first image; obtaining a region selected by a user in the first image as a first region, and obtaining information of a manual measurement operation performed by the user in the first region; obtaining a value of a specified image feature of the first region, and selecting one from a plurality of preset region matching algorithms as a first algorithm based on the specified image feature value; obtaining a value of a specified edge feature related to the manual measurement operation information, and selecting one from a plurality of preset edge recognition algorithms as a second algorithm based on the specified edge feature value; and recognizing at least one second region from the first image by using the first algorithm, recognizing an edge to be measured in the second region by using the second algorithm, and performing automatic measurement by using the edge.

The present application relates to the technical field of image processing, and particularly relates to a kind of extraction method and system of microscopic image profile of algae.The method comprises the following steps: obtaining multi-view image stream of algae cell under microscope, and carrying out frame-level scheduling and semantic splicing, to obtain multi-angle fusion image unit;Topology profile tensor graph is constructed to multi-angle fusion image unit;Multi-scale positive and negative profile comparison training is carried out to topology profile tensor graph, to obtain profile embedding spectrum;Holographic algae sample matching back-propagation is executed to profile embedding spectrum, to obtain candidate matching spectrum set;Edge feature contribution degree is analyzed according to candidate matching spectrum set, and edge feature contribution degree is dynamically fused with multi-angle fusion image unit, to generate edge saliency heat map;Independent algae sample is screened using edge saliency heat map;Data modeling is carried out to independent algae sample, and data modeling result is synchronized to algae sample library.The present application is helpful to improve image profile extraction precision and efficiency.

The utility model discloses a micro-optical imaging system and an anti-counterfeiting product. The micro-optical imaging system comprises a light-transmitting spacing layer; the micro-imaging structure array comprises a plurality of micro-imaging structures and is arranged on one side of the light-transmitting spacing layer; the miniature image-text array comprises a plurality of miniature images and texts and is arranged on the other side opposite to the light-transmitting spacing layer; wherein the micro imaging structure array is used for amplifying and imaging the miniature image-text array to form amplified images and texts, and the arrangement density of the miniature images and texts in at least part of the area of the miniature image-text array is different from that in other areas, so that the brightness of at least two amplified images and texts is different; and / or the brightness of at least two areas of the amplified image-text is different. The magnification imaging of the miniature image-text is realized through the micro imaging structure array, so that the details of the miniature image-text can be clearly displayed. The brightness of the at least two amplified images and texts is different and / or the brightness of the at least two areas of the amplified images and texts is different, so that the brightness of the amplified images and texts is distinguished.

The invention discloses a micrograph code collecting and uploading device and a self-checking method. The method comprises the following steps: 1) detecting the quality of images collected by all cameras in each set period in the running process of the device; (2) if the image quality detection result in the step (1) comprises that the image is fuzzy, the two-dimensional code recognition is abnormal, and the difference between the image brightness and the standard exceeds a threshold value, when one of the conditions occurs, the system sends an alarm signal and a shutdown signal to an equipment control card of the acquisition and uploading equipment; and step 3) after the equipment receives the alarm and shutdown signals, controlling the equipment to stop slowly, then sending a reverse signal, after the equipment receives the signal, controlling the paper feeding platform to reverse, stopping at the position of the image where the problem is detected according to the detected image, and after the problem is eliminated by a worker, restarting acquisition by the equipment. The method is used for image acquisition of the micrograph code anti-counterfeit label, and the definition of the acquired image and the operation stability of acquisition equipment are improved.

The present application relates to a kind of metallographic defect intelligent detection system and method based on improved YOLOv11, by integrating metallographic image acquisition subsystem, rotating frame labeling and enhancement subsystem, improved neural network processing subsystem, realize the full process automation from metal material micrograph image acquisition to defect accurate identification;By introducing the rotating target detection mechanism adaptation inclined defect morphology, design progressive data enhancement strategy to strengthen small target feature learning, using replacement feature extraction module and embedding attention mechanism optimize YOLOv11 network structure, finally, while guaranteeing industrial real-time, improve metallographic defect detection precision, meet the high-precision quality inspection demand.The present application not only solves the key technical bottlenecks such as the difficulty of positioning in the existing metallographic defect detection, the difficulty of detecting small targets, model deployment, weak anti-interference ability, etc., but also realizes the efficient and automated metallographic defect detection of industrial grade, has wide application prospect and popularization value.

The invention discloses a multicolor FDM printing real-time visual pose correction method and system, and the method comprises the steps: collecting a microscopic image of the surface of a printing layer in real time through a miniature CMOS image sensor integrated in the side direction of a printing head, and transmitting the microscopic image to an FPGA processing unit; performing multi-scale pyramid feature matching processing on the microscopic image in the FPGA processing unit, and extracting the sub-pixel-level position deviation between the current printing contour and the target model; based on the sub-pixel-level position deviation, calculating a dynamic compensation vector required by each axis through a kinematics inverse solution module, and generating a nozzle pose correction parameter; and the motion trail of the printing head is adjusted in real time according to the position and posture correction parameters of the spraying head, meanwhile, visual collection and compensation execution are synchronously triggered through color switching signals, and closed-loop control over the multi-color printing boundary precision is achieved. By utilizing the embodiment of the invention, the problem of boundary dislocation of multi-color printing can be solved, and the appearance precision and quality consistency of finished products are improved.

The invention discloses an accurate grafting method based on vascular bundle configuration, and the method comprises the steps: firstly obtaining a microscopic image of a rootstock section, and recognizing the vascular bundle configuration features of the rootstock section; acquiring a scion section microscopic image, and identifying vascular bundle configuration characteristics of the scion section; overlapping the microscopic image of the section of the rootstock and the microscopic image of the section of the scion, and pre-matching vascular bundle configurations of the scion and the rootstock by adopting an image analysis technology by taking maximization of the overlapping area of vascular bundle configuration characteristics of the rootstock and the scion as a target; after the pre-matching is completed, marking pose characterization data in the microscopic images of the sections of the scion and the rootstock at the moment; the marked pose characterization data is subjected to coordinate change and is converted into a coordinate system of a grafting device, so that the grafting device is guided to adjust the pose of the scion and / or the stock and complete the butt joint of the scion and the stock, the vascular bundle overlapping area of the scion and the stock is maximum, the vascular bundles are accurately aligned, the contact area is maximized, and the grafting quality is improved. The grafting effect is improved.

The application discloses a kind of photoelectric performance prediction method and system based on random crack template, the method is first directly modeled simulation by format conversion to micrograph, obtains electromagnetic shielding performance simulation result;Then according to aperture unit area size, long axis size and short axis size, carry out graphic data iterative fitting, and according to the parameters of the metal mesh transparent conductive film to be prepared, carry out full-wave electromagnetic simulation, obtain electromagnetic shielding performance simulation result;Finally, the average value of the two simulation results is obtained by solving to predict the electromagnetic shielding performance.The method can quickly predict the light transmission and electromagnetic shielding performance of the metal mesh transparent conductive film prepared based on the pattern and size distribution of the random crack template, greatly improve the efficiency of the metal mesh transparent conductive film with target photoelectric performance, and fully exert the high cost performance advantage of the metal mesh transparent conductive film with excellent performance prepared based on the random crack template method.

This invention discloses a method for measuring the spatial distribution of tobacco shreds based on three-dimensional imaging using X-ray microscopy. The method involves pre-selecting or labeling different types of mixed tobacco shreds, taking X-ray micrographs of the finished cigarette or the mixed tobacco shreds from different angles, and then overlaying these images to obtain the spatial distribution of the mixed or rolled tobacco shreds. The distribution is then compared with a random distribution model to determine whether the different types of tobacco shreds are evenly mixed. This invention's detection method is non-destructive and can be used to detect the effects on finished cigarette products.

A method of automated control of a microscope in cryogenic electron microscopy (cryo-EM), wherein the microscope is configured to collect high-magnification micrographs of particles suspended in vitreous ice. Such particles are found in grid squares, and a square contains holes from which high-magnification micrographs are imaged. The method is carried out during an active data collection session, leveraging a pipeline that comprises a set of models. The pipeline evaluates a set of collection locations to determine whether to continue collection at a current grid / square or instead at a new grid / square. The evaluation is based on a set of one or more quality scores derived from one or more pretrained models and machine learning-based active learning. Based on the determination, control information is provided to automatically control the microscope to move to a next target for data collection.

The present application relates to a kind of statistical camellia pollen germination rate and germination length method and system, method includes: S1, collection camellia pollen and in vitro culture, obtain the micrograph of camellia pollen tube, the micrograph is divided into training image and to be measured image;S2, different marking mode is handled to training image, obtain the detection label for statistics germination rate and the segmentation label for statistics germination length;S3, using the training image after marking, detection label and segmentation label training obtain the artificial intelligence model for detection and segmentation;S4, to be measured image is input in the artificial intelligence model for detection and segmentation, obtain pollen grain count result and germination pollen tube true length;S5, according to pollen grain count result and germination pollen tube true length, determine and statistics camellia pollen germination rate and germination length.Solve the image processing tedious, workload after collecting camellia pollen tube germination and other technical problems.

The invention provides a security element. The security element includes: a base; the sampling unit array layer is arranged on one side of the base body, the sampling unit array layer comprises a plurality of sampling units which are arranged in a periodic array mode, and the period p1 of the sampling unit array layer is smaller than 100 micrometers; the micro image-text layer is arranged on the other side of the base body, a three-dimensional pattern can be observed through the anti-fake element, and the three-dimensional pattern is in rotational symmetry; patterns observed in at least two projection directions on one side of the three-dimensional pattern are the same, so that an angle between the at least two projection directions is a repetition angle of the three-dimensional pattern, and the repetition angle of the three-dimensional pattern is matched with the period p1 of the sampling element array layer. The problems that an anti-fake element in the prior art is large in production difficulty and low in concealment are solved.

The invention discloses a dislocation defect identification method and system based on image identification and a deep learning network, and belongs to the field of image identification and machine learning. The method comprises the following steps: firstly, carrying out size calibration on a high-resolution electron microscopic image, identifying atomic coordinates through a deep learning target detection model, and calculating a local atomic environment vector of each atom; secondly, an atomic model data set covering multiple materials, dislocation types and deformation states is utilized to train a deep learning network capable of predicting the distance between atoms and dislocation according to local environment vectors; and finally, carrying out grid division on the image, extracting adjacent atom information of grid nodes, and recognizing a dislocation position through unsupervised clustering in combination with dislocation distance information output by the trained deep learning network. According to the method, automatic and high-precision identification of the dislocation defect in the face-centered cubic material is realized, and meanwhile, dynamic characterization of the crystal defect deformation behavior can be realized.