164 results about "Bright field image" patented technology

The invention provides a metal product surface flaw detection method and system based on machine vision, and belongs to the technical field of machine vision. The method comprises the following steps: constructing a multi-modal image data set through bright field image acquisition, dark field image acquisition and three-dimensional point cloud data acquisition of the surface of a detected metal product, and registering the multi-modal image data set; based on the registration multi-modal image data of pixel-level alignment, pixel-level defect segmentation is carried out by adopting a double-flow diffusion Transform model, and a defect segmentation map is output; and on the basis of the defect segmentation image and the registered multi-modal image data, through segmentation image binaryzation and region extraction, multi-dimensional defect feature extraction and quantification, defect classification and report generation, a final defect detection report is output, and the metal product surface defect detection method is completed. The invention effectively solves the core pain points of low precision, poor robustness and insufficient generalization ability for unknown defects in metal surface flaw detection, and provides an automatic detection method with high reliability and high precision.

The invention provides a wafer mixed type defect detection method and system based on multi-modal deep learning. The method comprises the following steps: acquiring a multi-modal image of a wafer; wherein the multi-modal image comprises a bright field image, a dark field image and a differential interference comparison image; carrying out normalization processing on the multi-modal image; performing channel splicing on the normalized multi-modal image to obtain a multi-channel input image; detecting defect types, defect number, defect bounding box coordinates, first probability distribution of the defect types and second probability distribution of the defect number of the multi-channel input image based on a double-branch deep learning network; and carrying out weighted fusion on the first probability distribution and the second probability distribution to obtain a defect detection result, so that the defect detection precision can be effectively improved, the omission ratio is remarkably reduced, and meanwhile, the problem that a traditional single-mode detection method is insufficient in recognition capability under a complex background is solved.

The invention provides wafer detection equipment and a wafer detection method, which are used for solving the problem of how to improve the wafer detection efficiency. The wafer detection equipment comprises a double-sided detection unit, the double-sided detection unit comprises a scanning carrying platform, a front optical imaging module and a back optical imaging module, and the scanning carrying platform can linearly move along a first direction; the front optical imaging module and the back optical imaging module are respectively arranged on two opposite sides of the scanning stage and are respectively provided with a back bright field light source and a back dark field light source; when the scanning stage moves along the positive direction of the first direction, the front bright field light source and the back bright field light source are synchronously turned on, and the front optical imaging module and the back optical imaging module respectively generate bright field images of the front and back of the wafer; and when the scanning stage moves along the negative direction of the first direction, the front and back dark field light sources are synchronously turned on, and the front optical imaging module and the back optical imaging module respectively generate dark field images of the front and back of the wafer.

The invention relates to the technical field of biomedical detection, and discloses a non-invasive sperm cell death and viability detection method, which utilizes a bright field image and a fluorescence image to train a fluorescence-assisted bright field attention network, introduces a fluorescence attention guidance module to provide guidance for training of a Transformer encoder, and realizes non-invasive sperm cell death and viability detection through priori knowledge of the fluorescence image. By means of the method, the model can be quickly focused on key features displayed in the fluorescent highlight area, the situation that training resources are consumed on a large amount of irrelevant information is avoided, convergence is accelerated, the model training time is shortened, meanwhile, the detection precision is improved, and then a more accurate and efficient sperm cell death and activity detection model is obtained. And during actual detection, the sperm cell death and viability detection can be realized only by collecting a microscopic image, pre-processing the microscopic image and inputting the preprocessed microscopic image into the model without dyeing a sperm sample, so that the damage of a traditional detection method to sperms is avoided, the non-invasive detection of the sperm cell death and viability is realized, and the method is more suitable for clinical detection.

The invention relates to a glass flaw recognition method, system and equipment based on image enhancement and a medium. The method comprises the following steps: acquiring a multi-view image set of to-be-detected glass; respectively carrying out region-of-interest extraction on the vertical dark field image, the horizontal dark field image, the vertical bright field image and the horizontal bright field image to obtain multiple groups of image blocks; combining a plurality of image sub-blocks with the same position in each group of image blocks to obtain a standard image sub-block group; inputting the standard image sub-block group into a pre-trained glass flaw detection model to obtain a detection result and a classification probability corresponding to each image sub-block at the same position; and based on a preset channel fusion weight, performing weighted fusion on the detection results at the same position according to the corresponding classification probability to obtain a glass flaw recognition result at the corresponding position. According to the method, through region-of-interest extraction, channel separation feature extraction and fusion probability output under an independent view angle, the accuracy of glass flaw automatic identification is improved.

The invention relates to the technical field of die cutting machines, and particularly discloses a die cutting defect detection method and system, and the method comprises the steps: obtaining a bright field image and a polarization image of a to-be-detected film; correcting, registering and integrating the bright field image and the polarization image to obtain multi-channel image input information; utilizing a pre-trained defect positioning model to analyze and obtain a defect area according to the multi-channel image input information, and positioning coordinates of the defect area; extracting defect characteristic information in the defect area; associating the defect characteristic information with production parameter information of the to-be-detected film, and storing the defect characteristic information in a defect database; analyzing the defect information in the defect database regularly or in real time to generate an analysis result; according to the method, a complete defect detection, analysis and feedback closed loop is formed through multi-modal imaging, image processing, model positioning, feature extraction and data association and analysis, and the accuracy and robustness of defect detection are improved.

The invention provides a defect detection method and device and electronic equipment, and belongs to the technical field of automatic optical detection.The defect detection method comprises the steps that a bright field image and a dark field image of a to-be-detected area of a wafer are obtained; feature extraction is carried out on the bright field image and the dark field image, and multi-feature spatial data is constructed; performing context analysis on the multi-feature space data, and determining context attribute information corresponding to each local area in the to-be-detected area of the wafer; according to the context attribute information, self-adaptive fusion weights of all the local areas are generated, and the self-adaptive fusion weights comprise weight components corresponding to the bright field image and the dark field image respectively; and according to the adaptive fusion weight, carrying out fusion processing on the bright field image and the dark field image to generate a defect enhanced image. Complementary information in bright and dark field multi-mode image data can be deeply mined, rough background noise can be inhibited, and feature expression of weak defects can be remarkably enhanced.

Methods, systems, and devices for enumerating the number of cells present in each event for cell-secretion based sorting applications are described herein. The cell secretion applications use carrier to encapsulate cells and collect the biomolecules they secrete. The carriers are then sorted using flow based particle sorting. The total number of cells present in a carrier is very important for several applications. A convolutional neural network is used to count the number of cells present from a brightfield image, and outputs the information to be used as part of the sort logic.

The invention discloses a variable polyadenylation dynamic analysis method and device. The method comprises the following steps: acquiring initial space transcriptome data and an initial space bright field image; performing first preprocessing on the initial space transcriptome data to obtain target space transcriptome data; performing second preprocessing on the initial space bright field image to obtain a target space bright field image; performing data analysis and integration processing to obtain spatial variable polyadenylation data, spatial gene expression data and spatial cell distribution dynamic data; constructing a gene expression regulation network; generating an integrated analysis result; performing space-time analysis to obtain a space-time analysis result; performing trajectory analysis to obtain a trajectory analysis result; performing disease process analysis to obtain a disease process analysis result; and generating a variable poly-adenosine acidification dynamic analysis result. According to the invention, dynamic analysis of variable polyadenylation is realized, the accuracy is improved, and multi-angle biological understanding is provided. The method can be applied to the technical field of gene analysis.

The invention relates to the technical field of detection, in particular to a high-throughput bacterial colony detection sensor based on multi-mode optics and spectrum fusion and a detection method thereof.The method comprises the steps that data of a bright field image, a multi-channel fluorescence image and a near infrared spectrum of a bacterial colony to be detected are collected, and metadata in the collection process is recorded synchronously; preprocessing the data, and extracting multi-dimensional features of bacterial colonies from the preprocessed data; performing fusion processing on the multi-dimensional features in combination with space-time sequence information; identifying and classifying bacterial colonies according to the fused multi-dimensional features; and generating a detection report containing traceability information according to a result after identification and classification. Through multi-modal synchronous data acquisition, targeted data preprocessing, multi-dimensional feature fusion and standardized recognition and classification, high-throughput accurate detection of bacterial colonies is realized, manual intervention is reduced, and reliability and traceability are improved.

The invention relates to the technical field of optical imaging calculation, and particularly discloses a method for correcting intensity diffraction tomography image surface intensity. Under the condition of LED array illumination, an edge variance value is calculated by using image edge light and shade transition features, and images influenced by the vignetting effect are screened and removed in combination with a threshold condition. And performing AND operation on the position of the lamp bead of which the recorded image is the bright field image and the position of the lamp bead of which the image is influenced by the vignetting effect to obtain the position of the lamp bead of which the exposure time needs to be corrected. According to a linear relation between image plane intensity and camera exposure time, intensity linear coefficient items and constant items at different lamp bead positions are obtained by recording images of two different exposure times under illumination of each lamp bead, and then the exposure time of lamp beads at other positions is calculated according to the image plane intensity and the exposure time during illumination of a central lamp bead. Finally, image plane intensity correction under illumination of different lamp beads is realized, and the reconstruction quality of intensity diffraction tomography is effectively improved.

The invention discloses a blue glass detection method and system, and the method comprises the steps: configuring dynamic adaptive illumination based on the defect type and stress distribution of blue glass; generating a bright field light source, a dark field light source and a polarization field light source based on dynamic adaptive illumination, and performing exposure to obtain three light source images; synchronously acquiring bright-field light source, dark-field light source and polarization-field light source images to obtain a fused image; separating the bright field image, the dark field image and the polarization field image based on the fused image, extracting defects in the bright field and the dark field, and extracting stress distribution of the polarization field; and taking the bright field image, the dark field image and the polarization field image as input, constructing a defect-stress model, and outputting a defect type and a stress feature level. Through multi-light-source fusion (bright field, dark field and polarization) and model construction, efficient and accurate detection of defect types and stress distribution is realized.

The invention belongs to the technical field of single cell imaging and analysis, and provides an unmarked single cell two-dimensional light scattering imaging modal amplification method and system. The method comprises the following steps: acquiring a two-dimensional light scattering image of an unmarked single cell, and preprocessing the two-dimensional light scattering image; according to the proposed fusion condition adversarial loss LcGAN, L1 distance loss LL1 and a new imaging modal amplification model of perception loss # imgabs0 # based on a VGG network, generating a corresponding single-cell bright field image and a fluorescence image by using the processed light scattering image, and realizing amplification of a single-cell multi-modal image; multi-modal fusion and classification identification are carried out on the single cell two-dimensional light scattering image, the corresponding bright field image and the fluorescence image, so that the cancer single cell analysis accuracy is improved; the method can generate the corresponding single-cell bright field image and fluorescence image according to the unmarked single-cell two-dimensional light scattering image, is especially suitable for diagnosis and classification of cancer cells, and facilitates promotion of biomedical research and clinical application.

The invention relates to the technical field of material analysis, in particular to a light microscope-field emission electron microscope combined shale rock slice analysis and identification method, which comprises the following steps of: firstly, combining a brightness distribution condition represented by a bright field image of a shale rock slice and a birefringence deviation condition represented by a cross polarization image from an optical angle; determining a mineral discrimination index representing an atomic number agent; applying offsets of different sizes based on the distribution condition of mineral discrimination indexes and the surface fluctuation condition of shale rock slices, and determining a position offset vector by combining gray scale gradient distribution on the basis; and finally, determining the cross-scale positioning and more accurate electron microscope objective table coordinates corresponding to each pixel point in combination with a position offset vector in a pixel coordinate conversion process based on an image physical distance scale and rotation translation processing, so that the cross-scale positioning accuracy of the optical microscope and the field emission scanning electron microscope is higher. The reliability of analyzing and identifying the shale rock slice is obviously improved.

The invention discloses a code nail stamping defect detection method and system based on machine vision, and belongs to the technical field of industrial automation quality control. The method comprises the following steps: acquiring multi-modal visual data such as a two-dimensional bright field image, a two-dimensional dark field image and three-dimensional contour data of a to-be-detected code nail; preprocessing the multi-modal visual data to obtain bright field, dark field and three-dimensional image data adaptive to a neural network model; inputting the three kinds of image data into a preset neural network model for defect identification processing, and outputting a defect segmentation mask and defect category information; and finally, performing quantitative analysis on the defects based on the output defect information, and judging whether the product is qualified or not according to an engineering specification threshold value. According to the method, multi-dimensional and complementary visual information is fused, and a specially designed neural network model is adopted for analysis, so that the problem of insufficient detection capability of tiny, low-contrast and three-dimensional geometric defects in the prior art can be effectively solved.

A system and method for performing virtual birefringence imaging and virtual staining (e.g., Congo red) of label-free human tissue is disclosed to show that a single trained neural network can rapidly transform autofluorescence images of label-free tissue into brightfield and polarized microscopy images, matching their histochemically-stained versions. Blind testing with quantitative metrics and pathologist evaluations on cardiac tissue showed that the virtually stained polarization and brightfield images highlight amyloid patterns in a consistent manner, mitigating challenges due to variations in chemical staining quality and manual imaging processes in the clinical workflow.

The invention discloses a Hu sheep primary hepatocyte separation effect intelligent evaluation system based on image recognition, and belongs to the technical field of biomedical engineering and intelligent detection. The physical mapping relation between the non-uniform illumination light field and the three-dimensional curved surface form and the tension state of the optical soft spherical shell is established; and the data field generation module is used for projecting serialized structured light based on the physical mapping relation, collecting a bright field image sequence generated by the structured light, synchronously collecting a fluorescence image, carrying out physical field association on modulation information carried by the bright field image sequence and topological information of the fluorescence image, and generating a high-dimensional native response data field. According to the method, the contact ridge line of the high-density adhesion Hu sheep primary hepatocytes can be accurately identified by constructing the optical soft spherical shell model and coupling mapping of the light field and the physical state of the cells, the problems of under-segmentation and over-segmentation are effectively avoided, and the extracted morphological parameters such as the area and the perimeter of the cells better fit the reality.

The invention provides a medium manganese steel oxygen vacancy defect identification method and device. The method comprises the following steps: obtaining a first oxygen vacancy defect area based on a measurement result of a positron annihilation lifetime spectrum and a Doppler broadening spectrum on a target sample block; aiming at each ultra-thin sample, observing the ultra-thin sample through a high-resolution transmission electron microscope, and collecting a bright field image and a dark field image of the ultra-thin sample; obtaining a second oxygen vacancy defect area based on the bright field image, the dark field image and the first oxygen vacancy defect area; performing electron energy loss spectrum analysis on the second oxygen vacancy defect area, and determining a third oxygen vacancy defect area and corresponding oxygen vacancy defect concentration in the ultrathin sample; and determining each target oxygen vacancy defect region and oxygen vacancy defect concentration of the target medium manganese steel sample according to the third oxygen vacancy defect region and oxygen vacancy defect concentration in each ultrathin sample. According to the invention, information such as position, number, size and distribution pattern of oxygen vacancy defects can be accurately obtained.

The invention discloses a wafer surface defect detection system and method based on a high-speed line scanning camera, and belongs to the technical field of wafer defect detection, and the system comprises an illumination system which is used for providing bright field illumination and illumination with an adjustable illumination angle, and forming a line light spot with an adjustable size on the surface of a wafer through a line light spot generation device; the wafer scanning assembly comprises a carrying table used for carrying a wafer and a displacement control assembly used for driving the carrying table to move; the imaging system comprises a bright field imaging assembly; the high-speed line scanning camera is used for carrying out high-speed scanning imaging on the signals collected by the imaging system and balancing the signal-to-noise ratio, the dynamic range and the detection resolution through a programmable pixel merging function; and the control system is used for coordinating and controlling all system components and realizing parameter optimization and online self-adaptive calibration. According to the invention, high-resolution and high-sensitivity wafer surface micro-nano defect detection can be realized while high detection speed is ensured.

The invention discloses a vortex spinning yarn defect detection method and system based on computer vision, and relates to the technical field of textile industry quality control and image recognition, and the method comprises the steps: obtaining a bright field image sequence and a dark field image sequence, and collecting a yarn position signal and an air pressure pulsation signal of a spindle vortex field; performing track modeling and inter-frame displacement compensation on the bright field image sequence to generate a yarn structured displacement image; constructing an airflow fiber-carrying feature map; performing spatial registration and fusion on the yarn structured displacement image and the airflow fiber-carrying feature map to obtain a dual-channel feature map; generating a defect cause directivity judgment instruction; marking a defect position based on defect information of the defect cause directivity judgment instruction, and generating a process parameter adjustment suggestion signal; and constructing a defect-process feedback sample set. The air vortex spinning yarn defect detection system effectively solves the key problems that an existing air vortex spinning yarn defect detection system is single in detection dimension, defect causes cannot be distinguished, and real-time process closed-loop feedback is lacked.

The invention discloses a biased binocular visual odometer method and device based on strawberry cuttlefish bionics. The biased binocular visual odometer method comprises the following steps: synchronously acquiring a bright field image and a dark field image collected by a biased binocular camera and inertial data of an inertial measurement unit; constructing a perceptual attention weight grid according to the bright field image and the dark field image, and performing brightness normalization processing on the bright field image and the dark field image; extracting lateral visual features based on the normalized image and the perceptual attention weight grid; and fusing the biased visual features with the inertial data, and solving through nonlinear optimization to obtain a carrier pose estimation result. According to the invention, the dynamic range of the binocular vision system is expanded, and the imaging and sensing capabilities of the system in an HDR scene are improved. And meanwhile, the positioning precision and robustness of the binocular visual odometer in an HDR scene are improved.

A first bright field reticle and a second bright field reticle are utilized for a double exposure EUV photolithography process in which exposure areas of the first and second bright field reticles overlap. The first and second reticles each include, respectively, a substrate, a reflective multilayer on the substrate, a main pattern of absorption material on the reflective multilayer, a black border area, and an additional absorption area of the absorption material between the black border and the main pattern.

Methods for distinguishing particles in a fluid sample are disclosed. In one embodiment, the method includes acquiring a background SIMI image and a background brightfield image of a membrane filter while the membrane filter is free of a fluid sample, introducing a fluid sample onto the membrane filter, acquiring a SIMI image and a brightfield image of filtered particles resting on the membrane filter, distinguishing between the filtered particulates and the membrane filter based on the background SIMI image, generating a particle mask based on the SIMI image, and detecting beads via the particle mask. Methods for distinguishing particulates include distinguishing between viable and non-viable cell populations, distinguishing between cellular and non-cellular particulates, distinguishing between biological and non-biological particulates, distinguishing between first and second protein types, determining stability of monoclonal antibody drugs, identifying beads in a cell therapy, and detecting bacteria.

The application relates to a kind of mitochondria-based single cell feature extraction and analysis method and system, comprising: obtaining the multiple modal images such as bright field image, nucleus fluorescent image and mitochondria fluorescent image of single cell;Image preprocessing is carried out to the three modal images obtained;For different structures such as mitochondria, morphological and texture features are extracted, and feature analysis is carried out;Further, through the fusion of mitochondria and machine learning technology, the automatic classification of cells is realized.The application is used for the classification of human cervical epithelial cells (H8) and cervical cancer cells (HeLa), and the machine learning analysis of morphological features and texture features shows the potential of mitochondria in the classification of cervical cells.The application has strong applicability, can be combined with machine learning and other analysis methods, and can be applied to various biological cells, has universality, and is easy to popularize.

The present invention relates to the field of cloud and machine vision technology, specifically a high-precision multi-light field contact lens edge defect detection method and device based on a Cartesian coordinate system, which obtains a multi-light field original image of a printed contact lens immersed in water in a glass bowl, pre-processes the multi-light field original image to obtain a bright field image, a dark field image, and a pre-processed image; performs glass bowl detection on the pre-processed image, and extracts the geometric features of the glass bowl when a glass bowl exists in the pre-processed image; extracts contour features of the printed contact lens in the glass bowl area based on the geometric features of the glass bowl; and then performs rough inspection and Cartesian coordinate system edge defect detection based on the above-extracted images and features. This application further utilizes the contact lens images captured by a high-resolution camera, performs automated identification on the images, and determines whether the corresponding products are normal or not, so as to reduce labor costs, save time, and improve production yield.

The present invention discloses a method, system, equipment and medium for online detection of wear of a receiving shoe skateboard. By controlling the timing of different cameras and light sources to control the bright and dark fields of the receiving shoe skateboard, dark field images of the receiving shoe skateboard are collected for automatic detection of skateboard wear, and bright field images of the receiving shoe skateboard are collected for automatic detection of skateboard status. This not only improves detection accuracy and detection stability, but also has relatively low cost and is suitable for large-scale promotion and application.

According to the high-throughput drug screening method and system based on large-view-field imaging and machine learning, data acquisition of high-throughput organs or tumor balls is achieved through a large-view-field biological imaging system, the data acquisition process is accelerated, and the integrity of original image information is guaranteed; a two-stage image processing means that a lightweight target detection model is connected with a semantic segmentation model is adopted, so that the calculation parameter quantity is reduced, heterogeneity interference is removed, and the image information integrity is ensured while the accurate acquisition of the individual image contour of the organoid or the tumor ball is realized; for organoid image acquisition, a Z-axis scanning strategy is combined with a screening strategy based on IOU and a definition function, so that information acquisition integrity and subsequent organoid activity prediction precision are ensured; in combination with a machine learning model based on an ensemble learning principle for a regression task, the activity prediction of the bright field image of the organoid or tumor sphere is realized, and the influence of a traditional method on the activity of a culture is avoided.

This invention relates to the field of materials analysis technology, specifically to a method for analyzing and identifying shale thin sections using a combination of optical microscopy and field emission scanning electron microscopy. First, from an optical perspective, the method combines the brightness distribution of bright-field images of shale thin sections with the birefringence deviation of cross-polarized images to determine mineral discrimination indices representing atomic number proxies. Then, based on the distribution of the mineral discrimination indices and the surface undulations of the shale thin sections, different biases are applied, and the positional bias vector is determined by combining this with the gray-level gradient distribution. Finally, based on the physical distance scale of the image and the pixel coordinate transformation process involving rotation and translation, the positional bias vector is used to determine the electron microscope stage coordinates corresponding to each pixel point for more accurate cross-scale positioning. This improves the accuracy of cross-scale positioning using optical microscopy and field emission scanning electron microscopy, significantly enhancing the reliability of shale thin section analysis and identification.

The invention relates to the technical field of heat dissipation substrate detection and correction, in particular to a multi-spectral detection and automatic correction system and method for the flatness of a heat dissipation substrate, and the method comprises the steps that a multi-spectral acquisition module emits a plurality of light beams with different wavelengths to the detected heat dissipation substrate; collecting multispectral image data and generating a multidimensional image data set comprising an RGB image, a multispectral image, a laser image, a bright field image and a dark field image; the temperature field construction module receives the multi-dimensional image data set, calculates the spectral reflectivity of each point on the surface and constructs temperature field distribution; the flatness evaluation module receives the temperature field distribution, performs homogenization processing, calculates flatness offset and judges whether the flatness offset exceeds a preset threshold value or not; when the flatness offset exceeds a threshold value, the automatic correction module determines correction force distribution based on thermal-structural coupling analysis and applies correction force by selecting at least one mode of an electric field, a magnetic field, pressure or mechanical adjustment, automatic correction of the flatness is achieved, and the detection precision and reliability are remarkably improved.

The application discloses a flat field correction method, a flat field correction system and a computer readable storage medium thereof. The flat field correction method comprises the following steps: providing a camera to be corrected and a blank wafer; collecting a dark field image of the blank wafer; collecting a bright field image of the blank wafer; and performing flat field correction calculation processing of the camera to be corrected according to the dark field image and the bright field image of the blank wafer. In the technical scheme, the blank wafer is used as a target object for flat field correction, the blank wafer is in a dust-free environment during production, is not easy to be polluted, and has extremely high consistency of wafer surface gray scale, so that the problems of difficulty in guaranteeing sufficient size and uniformity of a flat field target in the prior art can be solved.