873results about "Microscopic object acquisition" patented technology

The invention relates to the technical field of image recognition, in particular to an online real-time image recognition anti-counterfeiting verification method and system based on gold transaction. The method comprises the following steps: acquiring a micro texture image of the gold product; generating a unique digital fingerprint based on the physical unclonable feature of the microscopic texture image, and performing encryption in combination with a UTC timestamp to generate a dynamic encryption tag; extracting a feature descriptor of the micro texture image and performing texture comparison with a pre-stored database to obtain a texture matching degree; decrypting the dynamic encryption label to generate a dynamic code aging state; and according to the texture matching degree and the dynamic code aging state, performing anti-counterfeiting discrimination on the micro texture image of the gold product to obtain a genuine product discrimination result and a counterfeit discrimination result. According to the method, the micro texture image, the near infrared spectrum data, the dynamic encryption label and the block chain technology are combined, so that the accuracy, the safety and the efficiency of gold anti-counterfeiting are improved.

The invention discloses an aberration correction and image quality enhancement method for a laminated structure image, and the method comprises the steps: carrying out the deconvolution preprocessing of a to-be-detected marked image according to an aberration priori set, and obtaining an aberration-free image; meanwhile, combining label data to obtain a data set; feature extraction is carried out based on shallow convolution according to the data set, and global feature information is generated through activation function operation; enhancing the feature data by adopting a frequency domain feature and spatial domain feature fusion strategy; the enhanced feature map realizes initial aberration restoration through an aberration correction module; the corrected feature map is processed by a double-channel attention mechanism, and the global context modeling capability of the self-attention mechanism and the spatial perception characteristic of the position attention unit are fused in parallel; and the image resolution is improved through a super-resolution reconstruction module comprising a sub-pixel convolution layer. By adopting the technical scheme of the invention, the accuracy of overlay error detection is improved.

The invention provides a physical response data analysis method and system based on aircraft catalytic operation, and the method comprises the steps: obtaining three-dimensional cloud particle distribution data sets of a catalytic operation region before and after the catalytic operation through airborne cloud particle detection equipment, the three-dimensional cloud particle distribution data set comprises particle characteristic data before catalysis and particle characteristic data after catalysis; performing morphological feature extraction processing on the pre-catalysis particle feature data and the post-catalysis particle feature data by adopting a cloud particle AI image shape recognition algorithm to generate a pre-catalysis particle morphological difference feature set and a post-catalysis particle morphological difference feature set; constructing a dynamic physical response evaluation model, inputting the particle morphological difference feature set into the dynamic physical response evaluation model to execute multi-dimensional response correlation analysis, and generating a dynamic response feature set of the catalytic operation; and generating a catalytic strategy optimization instruction set according to the dynamic response feature set, and transmitting the catalytic strategy optimization instruction set to the airborne catalytic control system for catalytic parameter adjustment operation.

The invention relates to an infectious bacterium detection method and system based on double effects of a reflection spectrum and autofluorescence, and belongs to the field of biomedical detection. The method comprises the following steps: acquiring hyperspectral images of different samples under different light sources; performing data preprocessing on the collected sample hyperspectral image, realizing pixel-level alignment of the dual-effect image based on feature matching and image registration, and constructing a data set; the method comprises the following steps: constructing a wound infection bacterium detection model based on double effects of a reflection spectrum and autofluorescence, and comprising a double-branch feature extraction module which comprises a reflection branch and a fluorescence branch which are respectively used for extracting reflection hyperspectral image features and fluorescence hyperspectral image features; the redundant feature screening module screens the features extracted by the reflection branch and the fluorescence branch through a channel attention mechanism; and the feature fusion module adopts a cross-branch cross attention mechanism to fuse the features of the double branches. According to the method, learning is carried out from two physicochemical characteristics, and the effectiveness and adaptability of the technology are improved.

The invention belongs to the field of sealing element leakage image recognition, and particularly relates to a metal sealing element tiny leakage detection system and method based on infrared image recognition. A metal sealing element tiny leakage detection method based on infrared image recognition comprises the following steps that S10, the detection environment temperature is collected to serve as the background temperature, and the comparison temperature is determined according to the background temperature and the resolution ratio of an infrared camera; setting an air pressure threshold value, filling the closed space of the metal sealing element with steam of which the temperature is not lower than the contrast temperature, and monitoring the inflation air pressure of inflation to ensure that the inflation air pressure is not higher than the air pressure threshold value; and S20, acquiring image data from multiple angles by using infrared rays and ultrasonic waves, processing the acquired infrared image data, and identifying and marking a leakage range in the infrared image data. According to the scheme, the problem that the reconstruction precision and the calculation speed are unbalanced under the constraint of complex environmental noise and calculation resources in the prior art is solved, the leakage detection sensitivity and the positioning accuracy are improved, and the hardware dependence and the calculation amount are reduced.

The invention relates to the technical field of microscopic biological observation, in particular to a microscopic biological observation method based on a digital microscope. Firstly, original point cloud of a sample area on an objective table is obtained through structured light scanning, noise points are removed, point cloud density is optimized by adjusting scanning intensity for multiple times, high-precision and low-noise preprocessed point cloud is generated, then a reference plane is fitted in combination with the structured light point cloud, and obstacle and non-obstacle features are extracted. Detecting a second center coordinate of the bounding box by using a YOLO model, obtaining a third center coordinate through Hough circle detection, generating a standard coordinate by fusing multi-source coordinates, generating a global path based on a grid map and an RRT algorithm, predicting a track in real time, calculating a minimum Euclidean distance with an obstacle, dynamically inserting a B spline transit point to avoid a dangerous area, and ensuring a safe distance. Finally, collecting sample images in real time along the path and extracting microorganism characteristics; the technical problem that the efficiency is low when a traditional microscope is used for acquiring a microorganism image in a sample is solved.

The invention provides a composite fiber membrane aperture distribution prediction method based on deep learning, and relates to the technical field of distribution prediction.The method comprises the steps that multiple kinds of image data are collected and coded respectively, and a composite coding feature vector is generated through an attention mechanism; based on the composite coding feature vector, performing region segmentation on a fiber cross-linked region and a non-cross-linked region, and extracting and quantifying pore size distribution dominant features; performing associated coding by combining the pore size distribution dominant characteristics and spinning process parameters; and based on the associated codes, optimizing the depth prediction network, and adjusting the parameter weights of the associated codes by adopting an error correction mechanism. The pore size distribution of the composite fiber membrane can be accurately predicted, and powerful support is provided for material design optimization and production process control.

The invention includes systems and methods to combine acoustic sorting, high-throughput imaging technology and machine learning, such as convolutional neural networks (ConvNet) analysis, to analyze cells, pathogens, and other target particles from biological samples resolvable by high-throughput imaging microscopy, or other comparable instrument.

The invention relates to the technical field of medical image processing, in particular to an AI-based cervical cell image analysis auxiliary method and system, and the method comprises the steps: carrying out the multi-modal data collection, including cervical cell image data, clinical data and patient gene data, and carrying out the preprocessing of the collected cervical cell image data; constructing a cervix uteri cell image generative adversarial network to perform image enhancement and feature extraction on the cervix uteri cell image; fusing the extracted features with clinical data and patient gene data, and training a cervical cell classification model by using the fused feature data; and inputting a real cervical cell image into the trained cervical cell classification model, and outputting whether the cervical cells are abnormal or not and an analysis result of the abnormal type and degree. According to the method, multi-modal data acquisition is combined with an image enhancement and classification method based on a deep learning technology, so that the efficiency of cervical cell image analysis is improved, and the workload of doctors is reduced.

The invention provides a drug screening and curative effect evaluation method and system based on a glioma organ-like model, and relates to the technical field of biological material analys.The method includes the steps that a three-dimensional fluorescence image of the glioma organ-like model is collected, signal intensity analysis and compensation processing are carried out, a growth feature mapping model is constructed, and the three-dimensional fluorescence image of the glioma organ-like model is obtained; and phenotypic characteristics for representing the glioma organoid heterogeneity are obtained. Inputting the phenotypic characteristics into a drug response evaluation network, and calculating a drug sensitivity score; the drug molecular feature library and the drug collaborative analysis model are combined, the synergistic interaction index of different drug combinations is calculated, the optimal drug combination is screened, the drug administration dosage and the drug administration time sequence are determined in combination with phenotypic features, and finally an individualized treatment scheme is generated. According to the invention, the curative effect of the drug on the glioma organs can be effectively evaluated, and the optimal drug combination and administration scheme can be screened out, so that the accuracy and effectiveness of glioma treatment are improved.

The invention provides a cell growth dynamics prediction method and device based on a space-time image sequence, and belongs to the technical field of computer vision and deep learning. The method comprises the following steps: acquiring a multi-focal-plane space-time image sequence containing a cell complete growth process; preprocessing and normalizing images in the image sequence to obtain a final test image sequence; and inputting the test image sequence into a cell growth dynamics prediction model consisting of a multi-scale feature extraction module and a time sequence modeling module which are connected in sequence to obtain a cell growth stage identification result corresponding to each time point image in the sequence so as to realize cell growth dynamics prediction. The method can effectively solve the problems of poor adaptability, insufficient stability, high labeling cost and the like when a traditional model is used for processing the dynamic medical image sequence, and has remarkable advantages and application prospects in the aspect of dynamic prediction of the cell growth space-time image sequence.

The invention discloses a microscopic image scanning and parallel efficient splicing method based on a spanning tree, which considers the characteristic that the scanning visual angle of a microscope is distortionless and the characteristic that the corresponding relation between the physical scale and the pixel scale of a microscope platform is unchanged, and has the advantages of an image matching algorithm based on a template. Automatically dividing an overlapping region range for a template matching algorithm, fully considering the matching result credibility of the pictures on the surrounding adjacent sides, and calculating a global splicing point location by using a spanning tree algorithm; picture splicing types are divided through rows and columns, the problem of memory access conflicts is avoided, the computing power improvement brought by multiple threads is fully utilized, and the picture splicing efficiency is improved. According to the method, adjacent image matching, high-precision matching of global splicing point locations based on the spanning tree and a multi-thread high-speed image splicing technology are realized.

The invention discloses a sheep coccidiosis parasitic ovum accurate detection method based on deep learning, and the method comprises the following steps: automatically collecting a sheep manure sample image, carrying out the noise removal and enhancement of the image, and guaranteeing the image quality; the preprocessed image is input into an automatic sheep coccidiosis parasitic ovum detection model for training, the training process comprises image segmentation, feature extraction and accurate recognition, and a deep learning model is used for automatically recognizing sheep coccidiosis parasitic ovum; performing infection density analysis according to the number of sheep coccidiosis parasitic ova output by the improved deep learning model, and evaluating the infection risk of the sheep flock; the detection result is visually displayed, detailed analysis reports including the number, type distribution and infection density data of sheep coccidiosis parasitic ova are generated, hours or even longer time needed by a traditional method is shortened, and high accuracy of the detection result is ensured.

The present inventive concept relates to a method and a device for training a machine learning model to construct a digital color image depicting a sample. The method comprising: acquiring a training set of digital images of a training sample by: illuminating, by a plurality of white light emitting diodes, the training sample with a plurality of illumination patterns, and capturing, for each illumination pattern of the plurality of illumination patterns, a digital image of the training sample; receiving a ground truth comprising a high-resolution digital color image of the training sample, wherein a resolution of the high-resolution digital color image is relatively higher than a resolution of at least one digital image of the training set of digital images; and training the machine learning model to construct the digital color image depicting a sample using the training set of digital images and the ground truth. The present inventive concept further relates to a microscope system and a method for constructing a digital color image depicting a sample.

The invention discloses an intelligent management system and method for efficient utilization of straw feed resources, particularly relates to the technical field of production management, and is used for solving the problem that accurate recognition and dynamic management and control of local structure abnormal phenomena are lacked in the existing straw pretreatment process. Fiber component distribution information is generated through spectral component analysis, a multiple linear regression algorithm is combined to identify a local structure easy-curing risk area, microscopic image acquisition and feature extraction are used to identify a potential curing feature area, and a pre-processing risk adjustment area is generated based on the two areas. The abnormal degree of the risk area is evaluated by analyzing the deviation amplitude of fiber direction arrangement and the asymmetric coupling strength of interface interaction, and finally whether parameter optimization processing needs to be carried out or not is judged according to the evaluation result, so that dynamic monitoring and accurate regulation and control of the straw pretreatment process are achieved, and the straw pretreatment efficiency is improved. The stability of the pretreatment process and the utilization efficiency of straw feed resources are improved.

The invention discloses a portable sedimentary rock classification system and method based on deep learning, and relates to the technical field of rock identification and geological survey. The system comprises a portable microscopic image acquisition module, a cloud deep learning model and a mobile terminal module. A user shoots macroscopic or microscopic images of sedimentary rocks through a mobile phone, the macroscopic or microscopic images are uploaded to a cloud end and then subjected to feature extraction and classification through the optimized ResNet50 model, and a classification result is fed back to a user end in real time. The innovation points comprise: (1) adopting a macroscopic and microscopic data set training model to significantly improve the classification precision; (2) designing a portable microscopic module to realize rapid acquisition of microscopic images; (3) mobile terminal identification service; and (4) the ResNet50 model based on residual network optimization supports high-precision fine-grained classification (sandstone, conglomerate, siltstone and clay rock). Experiments show that the system classification accuracy reaches 99% or above, and compared with a traditional laboratory method, the efficiency is greatly improved. The method can be widely applied to geological exploration, teaching and scientific research and science popularization scenes.

The invention discloses a method for discriminating chicken breast with a white line and wood symbiosis defect based on a hyperspectral microscopic imaging technology, which comprises the following steps: acquiring normal meat, white line meat, wood meat and white line and wood symbiosis chicken breast samples, and cutting the samples into microsections; building a hyperspectral microscopic imaging system, and collecting and correcting microscopic spectral data; extracting a region of interest by using an image processing algorithm; performing defect grade labeling on each region of interest; extracting texture features of the microscopic hyperspectral image of each region of interest; selecting the characteristic wavelength of the spectral data in each region of interest; the obtained spectral data under the characteristic wavelength of each region of interest is fused with image texture features to serve as the input of a model, the defect category and defect grade score of the corresponding region of interest serve as the output of the model, and a Transform model is established and trained; compared with the existing visual and palpation methods, the method provided by the invention has the advantages of high accuracy and high efficiency.

Means for rigorous quantitative assessment of sperm motility are presented, based on a number of movement and morphology parameters measured using various image processing methods. The quantitative assessment thereby achieved is objective and multidimensional, allowing for detailed and repeatable assessment of samples. Deep learning methods are also disclosed for detection and classification of sperm cells (or other types of cells or particles) using neural networks, adapted to work both in good and in poor imaging condition, such as low magnification and resolution. Specific methods for both supervised and unsupervised deep learning approaches are delineated. In a non-limiting disclosure, the methods are particularly adapted to deal with cases of azoospermia, where there is a very low number of sperm cells (most of which do not swim) and a lot of debris in the imaged field of view.

The invention relates to the technical field of robot control and autonomous navigation, in particular to a bronchoscope robot autonomous control method based on deep reinforcement learning. Autonomous navigation of the bronchoscope robot is performed through a deep reinforcement learning training agent, pose data and picture data are acquired in real time by using an electromagnetic navigation technology and an endoscope camera, then data feature extraction and multi-modal data fusion are performed, the data are input to a reinforcement learning agent, and motion output is obtained through a deep network. And finally, three-degree-of-freedom control of the robot is realized. The method has a wide application prospect, and is particularly suitable for the fields of medical robot navigation, artificial intelligence operation assistance and the like. Position data are acquired through electromagnetic navigation, image data are acquired through an endoscope camera, and observation input of deep reinforcement learning is constructed by using a processing method of feature extraction and attention mechanism fusion.

Methods, devices, apparatus, and systems for three-dimensional (3D) contoured scanning photoacoustic imaging and / or deep learning virtual staining.

The present invention provides an automatic semen analysis method based on multi-frame information fusion with occlusion awareness. The present invention first proposes the combination of an edge-sensitive U-Net model and an occlusion-aware tracker based on joint probabilistic data association to achieve accurate and stable sperm detection and tracking. Thanks to the pixel-level contour segmentation and the overlap inference based on multi-frame target contours of the present invention, the present invention can accurately distinguish two or more spermatozoa that are close but not overlapping, and provide accurate head positioning; can accurately predict and judge the occurrence, progress, and end of target overlap; and can effectively match spermatozoa before and after overlap to ensure the consistency of tracking. The present invention can be conveniently integrated into existing computer-aided sperm analysis systems to provide semen analysis data with better accuracy and robustness.

The invention relates to the technical field of medical information systems, in particular to a blood smear abnormal cell intelligent recognition system and method based on deep learning. The multi-stage convolutional neural network module is in data connection with the image acquisition module, and the abnormal cell bank module is in data connection with the multi-stage convolutional neural network module; the knowledge graph module is in data connection with the abnormal cell bank module, and the blood index calculation module is in data connection with the multistage convolutional neural network module and the abnormal cell bank module and is used for calculating various blood indexes; generating a graphical display result of the blood indexes; the result output module is in data connection with the knowledge graph module and the blood index calculation module; according to the blood disease diagnosis method and system, the whole process from image acquisition to diagnosis report generation can be automatically completed, detailed analysis results and diagnosis suggestions can be provided for doctors, and the efficiency and accuracy of blood disease diagnosis are greatly improved.

A fluorescence microscopy inspection system includes light sources able to emit light that causes a specimen to fluoresce and light that does not cause a specimen to fluoresce. The emitted light is directed through one or more filters and objective channels towards a specimen. A ring of lights projects light at the specimen at an oblique angle through a darkfield channel. One of the filters may modify the light to match a predetermined bandgap energy associated with the specimen and another filter may filter wavelengths of light reflected from the specimen and to a camera. The camera may produce an image from the received light and specimen classification and feature analysis may be performed on the image.

In some embodiments, a method for classifying elements of a blood sample is provided, the method including: digitally staining an image of the blood sample using a trained machine learning model to generate a digitally stained image; extracting one or more intermediate features generated by the trained machine learning model during the digital staining of the image; providing the one or more extracted intermediate features to a trained multi-class classifier; and employing the trained multi-class classifier to classify at least one element in the blood sample based on the one or more extracted intermediate features. Many other embodiments are also provided.

The invention belongs to the technical field of biological sample detection, and particularly relates to a microscanning and classifying all-in-one machine for a cell pathology dyeing sealing sheet and a dyeing method of the microscanning and classifying all-in-one machine for the cell pathology dyeing sealing sheet. Comprising a machine body, a top feeding assembly, a dyeing assembly, a lifting and transferring assembly, a glue dripping assembly, a piece sealing assembly, a microscope scanning assembly and a stacking and storing assembly. According to the method, isochronous standardized dyeing is adopted, all sample slide plates are further arranged before each cycle, the dyeing time of each step is equal accurate time, large-flux standardized dyeing can be performed, and a standardized dyeing method which is high in flux, high in speed, good in dyeing effect and accurate in time is formed. And meanwhile, micro-scanning is integrated into a whole to form a full-automatic assembly line with a small-space large-flux function and full feeding and discharging convenience.

Described herein are systems, methods, and programming for analyzing and classifying digital pathology images agnostic to sample processing techniques used to prepare the digital pathology images. In some embodiments, image data including a first image set and a second image set may be obtained. The first and second image sets may be processed using a first and second slide preparation machine, respectively. A first augmented view set and a second augmented view set may be generated based on augmentations applied to the first and second image sets. For each image, a first vision transformer to may be trained to: generate a first representation of an augmented view of the first augmented view set, and enhance a similarity between the first representation and a second representation of an augmented view of the second augmented view set. The second representation may be generated via a second vision transformer.

The invention relates to the field of bioinformatics and computational biology, in particular to a method for realizing accurate alignment of biological slices by utilizing a spatial graph network and a cell communication network based on the biological slices of a graph attention network. The method mainly comprises the following steps: (1) acquiring a high-resolution biological slice image and spatial transcriptome data of a target tissue or brain region (such as DLPFC), and carrying out standardization processing on the high-resolution biological slice image and spatial transcriptome data; (2) constructing a space diagram network based on cell space distribution, and constructing a cell communication network by using a ligand-receptor database; (3) carrying out feature fusion and deep learning training by adopting GAT, extracting high-dimensional features, and designing a comprehensive loss function fusing a mean square error and triple loss based on MNN at the same time; and (4) globally optimizing rotation, translation and X-axis overturning parameters through a differential evolution algorithm, realizing precise alignment of the slices, and evaluating an alignment effect by a consistency index.

The invention relates to the technical field of image processing, and discloses a three-dimensional neural signal extraction method based on a four-dimensional light field, and the method comprises the following steps: S1, obtaining time sequence multi-view in-vivo neural data D under the four-dimensional light field; s2, analyzing neuron distribution according to the time sequence multi-view in-vivo neural data D, and obtaining multi-view data representing neuron positions; s3, reconstructing the multi-view data based on PSF to obtain three-dimensional space position data of neuron distribution; s4, performing neuron segmentation based on the three-dimensional space position data to obtain a segmentation mask body of each neuron; s5, performing full-frame-rate frame-by-frame reconstruction on the time sequence multi-view living neural data D based on PSF to obtain a time sequence three-dimensional body stack, and obtaining a neuron activity matrix of each neuron in the time sequence three-dimensional body stack according to the segmentation mask body; according to the invention, accurate capture and segmentation of neurons can be realized from the neural data of the four-dimensional light field, so that extraction of neuron activities is realized more accurately.

A method for generating a training dataset for determining grain boundaries and minerals in a thin section of a rock sample, includes receiving the thin section of the rock sample, generating optical images of the thin section with an optical tool, generating mineral phase images of the thin section with an electron microscopy tool, computing first and second pseudo-images based on different features extracted from the optical images, generating the training dataset based on (1) the optical images, (2) the mineral phase images, and (3) the pseudo-images, and training a single deep neural network, DNN, based on the training dataset to simultaneously determine a mineral type and grain boundaries in the thin section of the rock sample.