201 results about "Microscopic image" patented technology

This invention relates to the field of fabric printing simulation optimization technology, specifically a method and system for nylon fabric printing simulation design optimization based on finite element analysis. The method includes: acquiring microscopic images and physical property parameters of the nylon fabric; constructing a three-dimensional microscopic finite element model reflecting the yarn interweaving structure; selecting a printing area on the model surface and defining ink droplet ejection and ink fluid property parameters; using these parameters as initial conditions, employing a fluid-structure coupling algorithm to simulate the dynamic process of ink droplet impact, wetting, and penetration, acquiring three-dimensional ink distribution data and fabric stress-strain response data; extracting printing quality characteristic indicators, comparing stress-strain data with fabric deformation thresholds, and evaluating the impact of printing on fabric structural integrity; and generating an optimization and adjustment scheme for printing process parameters based on the evaluation results. This method improves simulation accuracy, achieves simultaneous evaluation of printing quality and fabric structural integrity, and optimizes printing process design.

The present application relates to the chip manufacturing technical field, specifically for the photonic chip multilayer overlay precision monitoring and calibration system based on image recognition, including: image acquisition module gathers super-resolution microscopic image, photoetching machine motion parameter, photoetching cavity environmental variable and overlay alignment data;Overlay alignment digital twin module constructs photoetching machine and wafer digital twin, combines chip layout and process parameters, simulates multilayer overlay deviation, exposure distortion and interlayer coupling effect through strict coupling wave analysis and rigid body kinematics algorithm;Overlay deviation intelligent analysis hub adopts YOLO-Transformer model, identifies deviation mode and locates deviation source;Visual calibration strategy generation unit constructs three-dimensional photoetching virtual scene, renders alignment and exposure process and generates calibration strategy;Self-adaptive alignment calibration unit predicts deviation trend according to calibration data, dynamically adjusts photoetching machine parameter. Thus, the problems of calibration inefficiency, insufficient precision and the like in the prior art are solved.

This invention discloses a method, system, equipment, and medium for real-time detection of the overall performance of a mite remover, specifically relating to the field of performance testing technology. The method includes sealing and connecting the mite remover under test with a test sample chamber containing a built-in standard dust mite test sample block; continuously acquiring instantaneous input power and instantaneous wind speed through a power acquisition unit and a hot-wire anemometer, and calculating the instantaneous intake flow rate based on the cross-sectional area of ​​the duct; continuously acquiring a sequence of microscopic images of the upper surface of the fiber fabric layer within a set detection period; extracting individual dust mite displacement events using the inter-frame difference method, counting the total number of events, and calculating the average displacement velocity value; and using the average intake power, the total number of individual dust mite displacement events, and the average displacement velocity value as input variables, generating a comprehensive evaluation value of the overall machine's performance through a performance judgment model. This invention integrates power, flow rate, and the dynamic response parameters of live dust mites, achieving real-time and accurate quantitative evaluation of the overall performance of the mite remover.

This invention provides a method and system for lithium ore micro-image segmentation based on an improved Unet model, belonging to the field of machine vision and image segmentation. The method constructs a lithium ore micro-image dataset and acquires micro-images of lithium ore samples to be tested, performing image preprocessing; pixel-level annotation of the micro-image dataset is performed to generate corresponding mineral category mask images, which are then segmented and data augmented; a micro-image segmentation model is constructed and improved based on the Unet algorithm, constructing a cascaded structure of depthwise convolution and pointwise convolution in the downsampling path, embedding an anti-adhesion dynamic serpentine convolution module in the skip connections, introducing a channel-space dual-path adaptive attention mechanism module, and constructing a multi-scale feature aggregation module in the upsampling path; a mature model is obtained after training; the input micro-image to be tested yields a preliminary segmented image, which is then subjected to adaptive area filtering. This invention improves the segmentation accuracy and precision of lithium ore micro-images.

This invention discloses a live-cell analysis method and system based on a large model and formal verification. The method includes context injection and intent reasoning, receiving natural language and analyzing it using a large language model to infer the required algorithm modules and output candidate configuration files. Formal logical verification involves reading the candidate configuration files and converting them into standardized logical constraint code, which is then verified by a solver. Conflict self-healing and iteration are implemented: if the solver verification fails, the conflict core is identified, and the connection method of the candidate configuration file or algorithm module is modified according to an arbitration strategy, while updating the logical constraint code until the solver verification passes. An analysis pipeline is instantiated and constructed: if the solver verification passes, an executable analysis pipeline is built based on the final determined candidate configuration files for live-cell analysis. Analysis code is generated using a large language model and logically verified using formal methods to analyze live-cell microscopic images.

The present application belongs to the technical field of intelligent prediction of geotechnical engineering parameters, and discloses a dynamic multi-modal loess physical and mechanical parameter prediction method based on physical constraints: obtaining and preprocessing the microscopic images and multi-modal data such as penetration resistance of loess samples, and extracting image and numerical features respectively; fusing multi-image features through self-attention and cross-attention guided by numerical features, and then generating joint deep representation through a dynamic gated multi-expert module, and outputting parameter prediction values by a shared multi-layer perception prediction head; adopting a composite loss function based on physical constraints to train the model in a phased strategy, and after deployment, the model is used for new sample prediction. The present application realizes deep adaptive fusion of multi-source information and controllable embedding of physical laws, and improves the precision, generalization ability and physical rationality of loess physical and mechanical parameter prediction.

This application relates to the field of microscopic image analysis technology and provides a method and related products for detecting plant stomatal aperture in exploratory experiments. The method includes: acquiring leaf epidermal images under different light intensities; segmenting the preprocessed leaf epidermal images; extracting multiple stomatal regions from the segmented leaf epidermal images; calculating stomatal aperture indices for each stomatal region under different light intensities; recording stomatal aperture data from each field of view; removing outliers from the stomatal aperture data to obtain an effective stomatal set; statistically calculating the average and standard deviation of the stomatal aperture data of the effective stomatal set under the same light intensity as representative values ​​for the corresponding light intensities; obtaining fitting results based on the light intensities under various experimental conditions and the representative values ​​for the corresponding light intensities; and generating exploratory experimental conclusions and reports based on the fitting results. This application enables a lightweight quantitative investigation of the relationship between stomatal opening and closing and environmental factors.

The application discloses a multi-focus microscopic image fusion method, system and program based on depth map correction. The method comprises the following steps in sequence: image sequence acquisition, texture extraction, definition evaluation, depth map generation, confidence map generation, depth correction and color image reconstruction. According to the definition curve, the depth map and the confidence map are determined, the unstable points are determined by using the confidence map, the depth data of the non-texture and weak-texture areas are corrected by HSL color space guided filtering, and thus the phenomenon that the depth map is wrong in focus plane judgment due to the non-texture and weak-texture areas of the image source is avoided.

The application discloses an animal cell adaptive tracking method and system based on deep learning, and belongs to the technical field of cell image analysis; a YOLOv10 deep learning model is combined with a Btrack adaptive tracking algorithm, animal cell samples are first dyed and microimage collection is carried out, a cell detection model is trained after image preprocessing, data labeling and augmentation, the model is converted into an ONNX format to realize efficient inference; then, Kalman filtering prediction and three-round Hungarian algorithm matching are carried out to complete cell adaptive tracking, the problems of cell occlusion, division, overlap and loss of tracking can be effectively handled, and stable and continuous cell motion trajectories are generated; the application can be deployed without GPU, has strong generalization ability, high detection and tracking precision, is suitable for scenes such as microecological research, clinical sample testing, drug effect evaluation and cell behavior analysis, and can significantly improve the automation level and stability of animal cell detection, counting and dynamic tracking.

The present disclosure provides example embodiments for automatically or semi-automatically classifying cells in microscopic images of biological samples. These embodiments include methods of selecting training sets for the development of a classifier model. The disclosed selection embodiments can allow for retraining of a classifier model using training examples that have undergone the same or similar culture conditions as a target sample. These selection embodiments can reduce the human effort required to specify the training examples. The disclosed embodiments also include classifying individual cells based on metrics determined for the cells using phase contrast imaging and out-of-focus brightfield imaging. These metrics can include size, shape, texture, and intensity-based metrics. These metrics are determined based on segmentation of the underlying imagery. In some embodiments, the segmentation is based on phase contrast imaging and / or out-of-focus brightfield imaging of the biological sample.

The application provides a microscopic imaging detection device for micro-hole inner wall layer identification, comprising a microscopic imaging unit, a displacement driving unit, an image processing unit and a light path adjusting unit; the light path adjusting unit comprises a light source or an optical fiber, an aperture, a lens and a light splitting structure, which is used for guiding the illumination light into the imaging light path of the microscopic imaging unit and into the micro-hole along the optical axis. The image processing unit is used for determining the plate surface position according to the plate surface image and calculating the micro-hole aperture, controlling the light path adjusting unit to adjust the illumination light path parameters according to the micro-hole aperture, controlling the displacement driving unit to change the relative position step by step along the micro-hole depth direction, and determining the copper layer starting position, ending position and thickness according to the annular feature and depth position information in the different depth images. The application can improve the copper layer identification accuracy and stability in the micro-hole, and provides the depth basis for secondary drilling or hole expansion.

This invention discloses a microscopic denoising method based on multi-expert judgment, relating to the field of computer image enhancement technology. The method includes: acquiring original microscopic image data; preprocessing the image data; calculating initial expert weights through an expert weight threshold discrimination network; inputting the preprocessed data into multiple expert denoising networks for denoising; calculating result weights based on the denoising results and performing weighted fusion; and generating a denoised microscopic image. By introducing a multi-expert structure and threshold discrimination mechanism, this invention can adaptively denoise under various observation objects and imaging conditions, significantly improving the generalization ability of the denoising method. This invention, through a multi-expert mechanism, overcomes the shortcomings of current denoising methods, such as weak generalization and applicability limited to specific observation sample types or imaging conditions.

This application discloses an illumination rotation module arranged in a structured light super-resolution illumination microscopic imaging optical system. It includes a rotatable galvanometer with two parallel and opposite mirror surfaces arranged relative to a rotation axis intersecting the principal optical axis. M mirror groups, each comprising at least three mirrors, are arranged around the galvanometer. When the galvanometer is at an angular position about the rotation axis, linearly polarized light incident on the illumination rotation module is reflected sequentially through one mirror surface of the galvanometer, each mirror in a mirror group corresponding to the angular position, and the other mirror surface of the galvanometer. This causes the propagation direction and deflection direction of the linearly polarized light to rotate synchronously before exiting the illumination rotation module. This application also discloses a structured light super-resolution illumination microscopic imaging optical system.

The present application relates to the technical field of early cancer risk assessment of digestive tract, and particularly relates to a confocal endoscope early cancer risk assessment method of digestive tract based on artificial intelligence. The method acquires macroscopic images and microscopic images of the digestive tract and a reference position of the confocal probe in the macroscopic images in real time; according to the differences in gray scale and reference position distribution between the macroscopic images at the current moment and the previous adjacent moment and the differences in shape and position distribution of the cell regions in the microscopic images at the current moment and the previous adjacent moment, a comprehensive relative displacement vector of the confocal probe at the current moment is acquired; based on the reference position at the previous adjacent moment of the current moment and the comprehensive relative displacement vector, a corrected reference position of the confocal probe in the macroscopic images at the current moment is acquired. The present application acquires the corrected reference position of the confocal probe in the macroscopic images in real time, and effectively improves the accuracy of the early cancer lesion site positioning of the digestive tract.

The application provides a touch screen function sheet silver paste laser bevel processing device, belonging to the touch screen processing technical field. The quality detection assembly, wherein the confocal microscopic technique, provides the three-dimensional surface imaging ability of nanometer level resolution, can monitor the processing of the silver paste edge bevel in real time, the three-dimensional adjustable support supports the probe to adjust in the X / Y / Z axis direction, cooperates with the nanometer level displacement detection ability of the miniature confocal microscope, can calibrate the bevel angle of the laser processing head and the function sheet in real time, thereby ensuring the accuracy of the bevel cutting, avoiding the short circuit of the silver paste line caused by the angle error, the high light reflection smoke dust is easy to produce during the laser processing, the air curtain isolation device forms a barrier through the annular airflow, can blow away the particles generated by the molten silver paste in real time, ensures the definition of the microscopic image, and the ceramic sleeve keeps the structure stable under the high temperature environment generated by the laser processing, prevents the deviation or failure of the miniature confocal microscope lens caused by thermal deformation.

This invention relates to a comprehensive diagnostic method for aeration tank operation based on continuous image recognition, comprising: acquiring water samples from the aeration tank; continuously acquiring multiple microscopic images of the samples within a fixed observation area, and obtaining a sequence of sample images after preprocessing; dividing the sequence into moving and stationary regions based on pixel differences between the sample images, identifying the trajectory of an object and calculating its length; extracting images of the moving object and extracting its contour and texture features; determining the microbial species of the moving object based on its contour, texture, and trajectory length; analyzing the morphological characteristics of flocs in the stationary region, and comprehensively diagnosing the aeration tank's operational status in conjunction with the microbial species. This invention can accurately determine the dominant microbial species in the aeration tank. Based on this, and combined with the characteristics of the flocs, through the coupling of multiple factors and various feature values, it can directly reflect the sludge activity and treatment effect in the aeration tank, improving the accuracy of operational status assessment.

This invention proposes an image fusion method and system based on physical priors and a continuous depth field, aiming to solve the problems of mosaic artifacts and insufficient clarity caused by discrete layer selection in existing pathological microscopic Z-stack image fusion. The key technical points are: after sequentially performing adaptive background suppression, frequency domain focusing feature extraction, and edge-preserving energy smoothing on the Z-stack image sequence, a continuous depth field is obtained through sub-pixel level interpolation fitting; finally, a high-resolution pathological image with full depth is output based on weighted fusion of the continuous depth field. This invention can be applied to biomedical pathological imaging scenarios, effectively improving the quality of microscopic image fusion, eliminating inter-slice artifacts, and providing high-fidelity full depth image data for pathological analysis.

This application provides a method, apparatus, device, storage medium, and program product for testing electrode tortuosity, relating to the field of lithium-ion batteries. The method includes: acquiring an electrode to be tested; processing the surface and cross-section of the electrode using a differentiated sample preparation process to form a conductive layer; imaging the surface and cross-section of the processed electrode using a scanning electron microscope to obtain a two-dimensional microscopic image; and performing feature parameter analysis on the two-dimensional microscopic image based on a differential effective medium to obtain the tortuosity of the electrode. This method overcomes the limitation of traditional electrochemical impedance spectroscopy, which only provides macroscopic average values, and solves the problem of difficulty in characterizing the heterogeneity of local tortuosity in complex structures such as thick electrodes and nanoparticle systems. It also avoids the high cost and long cycle of three-dimensional imaging methods, significantly improving the scientific nature of electrode design and the targeted optimization of battery performance.

This invention relates to the field of biomedical image processing technology, and discloses a method and system for quantitative analysis of hypothalamic immunofluorescence images. The method includes: performing spectral unmixing on multispectral fluorescence microscopy images based on a nonnegative matrix factorization algorithm to obtain a clean signal distribution map; using Gaussian Laplace filtering and watershed transform to achieve cell detection and segmentation; performing affine and B-spline registration between slice images and standard brain atlases to generate regions of interest masks for neural nuclei; using a local background adaptive correction strategy to perform fluorescence quantification and positive determination; and calculating Pearson correlation coefficient and Manders overlap coefficient to achieve colocalization analysis. The system includes a spectral unmixing module, a cell detection and segmentation module, an atlas registration and region recognition module, a fluorescence intensity quantification module, and a colocalization analysis and statistical output module.

This invention provides a method for processing live single-cell extraction based on deformation trends, comprising the following steps: Step S1: Controlling a microfluidic chip to immerse itself in a culture medium containing adherent cells while maintaining a preset gap, controlling the injection flow rate of the injection channel and the aspiration flow rate of the aspiration channel to form a microjet acting on the cells; Step S2: Controlling an image processing module to receive and process cell morphology images on the culture substrate acquired by a microscopic imaging module, and calculating deformation parameters based on the comparison between the cell area in the current frame image and the initial cell area; Step S3: Comparing the deformation parameters with critical deformation parameters obtained through experimental calibration, and when the deformation parameters are greater than the critical deformation parameters, controlling a fluid drive module to reduce the injection flow rate and / or the aspiration flow rate; or, calculating a comprehensive damage risk index based on the deformation parameters, and when it is greater than 1, reducing the injection flow rate and / or the aspiration flow rate.

The application discloses a kind of microstructure ultra-precision machining in-situ measurement and error compensation method and system, it is related to ultra-precision machining and measurement technical field, comprising: using the optical microscopic imaging module integrated on machine tool, in-situ obtain the low-resolution microscopic image of the microstructure surface of workpiece clamping state under processing, and the pixel size calibration of optical microscopic imaging module is carried out;Low-resolution microscopic image is input into the super-resolution reconstruction network based on local implicit image function framework and is carried out super-resolution reconstruction, and high-resolution reconstruction image is generated according to the set arbitrary magnification;Pixel-level edge detection and sub-pixel fitting are carried out again, and the actual measurement size of microstructure key geometry is calculated;Based on the deviation of design target size and actual measurement size, compensation amount is calculated, and the compensation amount is converted into tool path correction amount under the same clamping reference and is implemented in-machine reprocessing, and geometric error closed-loop compensation is realized.The application can improve the efficiency and size consistency of ultra-precision machining.

This invention provides a method for determining the stacked cell hierarchy based on contour completion and gradient quantization. The method involves extracting the visible contour point set of each cell from a cell microscopic image; performing ellipse fitting on the visible contour point set of each cell to obtain the fitted ellipse parameters for each cell, including center coordinates, major axis length, minor axis length, and rotation angle; calculating a global gradient magnitude map on the cell microscopic image; filtering candidate cell pairs based on the fitted ellipse parameters, including overlapping cell pairs and adjacent cell pairs; for each cell pair in the candidate cell pairs, extracting the contour points of each cell within the overlapping region, obtaining the gradient values ​​corresponding to the contour points from the global gradient magnitude map, and calculating the average gradient value for each; determining the hierarchy relationship between the two cells by comparing the average gradient values; constructing an attribute graph using each cell as a node and the hierarchy relationship as an edge; performing topological sorting on the attribute graph; and outputting a global stacked hierarchy sequence.

The application discloses a reflection type super-large depth of field super-resolution wide-field optical microscopic system and method, and the system comprises a sample displacement module, a ring-shaped focusing light beam generation module, a light beam scanning module, a super-large depth of field super-resolution wide-field optical microscopic imaging module, an optical microscopic imaging module and a computer. Through the advantages of the ring-shaped focusing light beam illumination, the high focusing efficiency of the traditional lens and the high scanning speed of the light beam, efficient and fast super-long focal depth super-resolution focusing scanning illumination is realized, and then the traditional optical microscopic system can be used to realize fast, super-large depth of field super-resolution wide-field optical microscopic imaging. The application can be applied to non-labeled super-resolution microscopic fast imaging of biological samples, and can also be applied to industrial related super-resolution microscopic detection, real-time imaging of micro-operation and other fields.

The application provides a general single-stage sperm morphology image segmentation method and system, which comprises the following steps: obtaining a stained or unstained sperm microscopic image; inputting the sperm microscopic image into a single-stage segmentation network model, outputting a domain robust shared feature map through multi-view transformation and adaptive fusion; after multi-scale feature extraction and aggregation, a prediction head is used to output a semantic segmentation and / or instance-aware component segmentation result in parallel; tail skeleton features are extracted and topological optimization is performed based on a continuity constraint loss to maintain tail connectivity. The application enhances the cross-domain generalization capability by structure-appearance decoupling, and uses topological-aware learning to constrain the tail continuity, thereby solving the problems of weak generalization between stained and unstained images and tail segmentation fracture in high-density overlapping scenes in the prior art, and improving the clinical applicability and segmentation integrity.

The application provides a microplastic species identification method based on concept constraint multi-task instance segmentation, comprising: performing instance-level labeling and concept label labeling on a microscopic image containing microplastic particles to obtain an input sample; constructing a concept constraint multi-task instance segmentation network with microplastic particle instance segmentation and polymer category identification as a main task and prediction of a concept label as a secondary task; the network processes the output result of the main task according to the output result of the secondary task to obtain a single-concept additional feature area, avoids missing detection, generates a minimum value result according to the output result of the secondary task and the output result of the main task, and further obtains a final identification result; training the network; and inputting a to-be-identified microscopic image into the trained network to obtain the final identification result. The method of the application completes microplastic particle instance segmentation and species identification under the condition of explicitly learning and utilizing the concept attributes of microplastics.

Method and device for processing multidimensional microscopy data for interface positioning between chemically homogeneous areas of a material sample. This method includes acquiring at least one microscopy image of said sample forming an input data block, and the steps of: - normalizing (42) the input data block to obtain a normalized data block, including adjusting the contrast of the input data block; - segmenting (43) the normalized data block to determine a first data block representative of a first homogeneous area and a second data block representative of a second homogeneous area, such that the point-by-point sum of the values ​​of the first data block and the second data block is equal to the same value;- calculation (50) of a standard deviation data block between said first and second data blocks, then thresholding (52) of the standard deviation data block to obtain the chemical interface between the first homogeneous zone and the second homogeneous zone. Figure for the abstract: Figure 2;

The present application relates to the technical field of image enhancement, in particular to a contrast enhancement method for nylon thread surface microscopic image, comprising the following steps: obtaining a nylon thread surface microscopic gray image, calculating the horizontal and vertical gradient values of the pixel points of the nylon thread surface microscopic gray image, constructing a pixel-level structure tensor matrix, performing eigenvalue decomposition operation on the pixel-level structure tensor matrix, and generating a nylon thread texture coherence distribution factor. In the present application, the nylon thread roughness gain mapping points are multiplied by the detail component and reconstructed with the background component to output a high-contrast enhanced image of the nylon thread surface, so that the contrast enhancement is concentrated on the real texture details and the risk of pseudo-texture caused by background gray drift and excessive enhancement is reduced, while the texture direction consistency enhancement, local roughness difference enhancement, and visual readability improvement after the separation of details and background are realized.

The present application relates to the technical field of image processing, and discloses a light staining sperm microscopic image light-weight style consistency preprocessing method and system, comprising: acquiring a light staining sperm microscopic image, performing color normalization on the light staining sperm microscopic image according to a deep staining sperm microscopic image, performing local contrast enhancement on the normalized light staining sperm microscopic image according to local contrast of a sperm head, a tail and a boundary region, performing brightness correction on the enhanced light staining sperm microscopic image according to the definition of the sperm head, the middle section and the tail structure and the background stability, and obtaining a light staining sperm microscopic image after light-weight style consistency preprocessing. The present application can solve the problems of staining difference, uneven brightness and boundary blur through image preprocessing without increasing the number of samples and additional training burden, improve the overall quality of the light staining sperm microscopic image, and improve the detection performance of the subsequent model.

This invention discloses a method and system for quantitative structured light microscopy image reconstruction based on self-supervised learning, belonging to the field of super-resolution fluorescence microscopy imaging. The method includes: acquiring an initial SIM super-resolution image of the observed sample and a corresponding experimental wide-field image; inputting both into a physics-driven self-supervised neural network model, which learns super-resolution structural features and intensity difference features through a dual-branch architecture, and constructs a joint loss function based on the SIM imaging physical convolution model for constraint, achieving self-supervised training without requiring super-resolution true intensity labels, ultimately reconstructing a super-resolution SIM image with accurate intensity quantification and high structural fidelity. This invention uses the wide-field image as a physical intensity reference standard and utilizes its true linear intensity constraint network optimization to effectively solve the problems of intensity distortion and structural artifacts in existing SIM reconstructions, significantly improving the quantitative reliability and structural authenticity of the image.