3102results about "Still image data indexing" patented technology

The invention discloses a transformer substation engineering construction drawing intelligent analysis and evaluation system based on image recognition, and relates to the technical field of transformer substation engineering construction informatization. The data processing center is in communication connection with a data acquisition layer, an image preprocessing layer, a feature extraction layer, an intelligent analysis layer, an evaluation decision-making layer and an application display layer, and all layers of architectures are in electric signal connection. According to the method, the improved convolutional neural network model is adopted, and an image preprocessing algorithm specially aiming at engineering drawing feature optimization is combined, so that the drawing recognition accuracy is remarkably improved compared with a traditional OCR technology, the manual proofreading workload is greatly reduced, a drawing element correlation analysis system based on a knowledge graph is constructed, and the engineering drawing recognition accuracy is improved. Key information such as equipment arrangement, line connection and size marking in the drawing can be automatically extracted and associated, and structural storage and intelligent retrieval of drawing content are achieved.

The invention provides a real-time map updating method and system based on multi-source geographic information data fusion. Wherein a three-dimensional model is constructed by integrating traffic flow data, remote sensing images and road network topology, the road traffic pressure is quantified, and a feature correlation index is established. And obtaining a vehicle displacement vector and a speed gradient in combination with laser point cloud and video monitoring data, generating a dynamic road feature data set, and establishing a mapping relationship with the index table. On the basis, a road state prediction model is constructed, and through correlation analysis of traffic pressure data and a real-time feature data set, deep correlation between vehicle motion features and road states is established. And dynamically adjusting a road network connection structure, synchronously updating mapping parameters and predicting model precision, and realizing real-time adaptive adjustment of the road network topology. According to the technical scheme provided by the invention, the real-time performance of map updating and the road condition prediction accuracy are remarkably improved, and the urban traffic congestion index can be reduced.

The invention relates to the technical field of civil engineering informatization and automation design, in particular to a parameterized construction drawing generation method based on a constraint solution algorithm, and the method comprises the steps: defining a construction drawing foundation structure and variable parameters through an engineering design model embedded with a parameterized template; a parameter constraint relation set is generated in combination with geometric constraints, size constraints and logic constraints input by a user, constraint rules are converted into intermediate expressions through a formula analyzer, a syntax tree is constructed, and an executable parameter association rule set is generated through semantic verification. The auditing module verifies the drawing compliance based on the engineering specification database and generates a correction suggestion; and realizing cross-system data linkage between the multi-terminal collaborative interface and external BIM model parameter mapping. According to the method, the problems of detail loss and specification deviation in three-dimensional model projection are effectively avoided, and the drawing generation efficiency and the multi-specialty collaborative robustness are improved.

The invention relates to the technical field of Otwei interactive maps, and discloses a geological data interaction method and system based on an Otwei interactive map. The method comprises the following steps: carrying out feature recognition and structural analysis on original geological data to obtain a standardized data packet, and establishing a geographic space reference conversion index table; performing feature extraction and classification on geological elements in the standardized data packet to obtain structured geological data; importing the structured geological data into an Ovoucher interactive map, and carrying out local registration through a mesh generation technology to obtain a visual geological element map layer; geological element drawing and attribute input are carried out, and an edited geological data set is obtained; and carrying out structure recombination and reverse coordinate conversion to obtain a standard format data file adaptive to the target geological information system. According to the method, accurate conversion of multi-source geological data between different coordinate systems and measuring scales is realized, and the problem of spatial dislocation during integration of different-source geological data in a traditional method is effectively solved.

The invention discloses an intelligent document analysis method and system, the method is executed by the intelligent document analysis system, and the method comprises the following steps: carrying out layout analysis on a PDF page by adopting a deep learning model; merging the block list from bottom to top by adopting a recursive algorithm; carrying out balance optimization on the binary tree structure; and outputting a result of the processed binary tree structure by adopting a preorder traversal mode. Layout analysis is carried out by adopting a deep learning model, various complex typesetting formats such as multi-column layout, image-text mixed typesetting, tables, lists and the like can be effectively identified and processed, and semantically continuous text blocks are ensured to keep continuity in a tree structure through a tree structure optimization module; a global semantic error correction module is added to carry out global document semantic representation learning and carry out adaptive adjustment and error correction on a preliminary structure, so that deep ambiguity is eliminated, logic errors are repaired, and the consistency of a final analysis result and human reading logic on the semantic level is maximized.

The invention discloses a safe and rapid storage method and system for image data of an imaging department, and relates to the technical field of medical image data processing. The method comprises the following steps: 1, designing a distributed storage system, and selecting and configuring storage nodes; 2, selecting and updating an encryption algorithm, and implementing file-level and block-level encryption; 3, realizing accurate data deduplication by utilizing a machine learning technology, and selecting a proper compression algorithm; 4, constructing an index system, and establishing a data warehouse and a data lake by using big data analysis and machine learning technologies; 5, establishing an image data backup and recovery mechanism; and 6, constructing a data integrity checking and monitoring system. According to the invention, by adopting the technologies of distributed storage, encryption, data deduplication, compression, big data analysis, machine learning, backup and recovery, data integrity verification, monitoring and early warning and the like, efficient storage and safety protection of image data are realized.

The invention discloses an industrial visual detection system based on multi-modal fusion, which belongs to the technical field of industrial data acquisition, processing and processing, and comprises a multi-modal data acquisition and calibration module, a scene image capture module, a visual information synthesis module, an algorithm optimization and training module and a multi-modal image output module, the multiple modules are integrated into the industrial visual detection system for multi-modal data acquisition, processing and fusion, high-quality original data are provided for subsequent image analysis, images of industrial scenes can be automatically captured under different illumination conditions, an original image database containing various illumination and scene conditions is constructed, and the visual detection system has the advantages of being simple in structure, convenient to operate and high in practicability. The system can operate stably, high-precision analysis and processing of the image data set in the industrial scene are realized, the corresponding fusion image is generated, the industrial visual inspection report is generated based on the fusion image, and the adaptability and reliability of the industrial visual inspection system are improved.

The invention provides an industrial visual anomaly recognition method, system and device based on multi-modal feedback and a medium, and belongs to the technical field of industrial visual detection.The method comprises the steps that a production line video stream is collected in real time, an ROI is automatically segmented, a picture sequence is generated, features of a set dimension are extracted, and the features of the set dimension are extracted; comparing a preset dynamic distance threshold with a feature library, and preliminarily identifying an abnormal image; a cue word of a detection requirement is constructed, the cue word and abnormal related information are input into the multi-modal large model, and a determined abnormal image is output; using the determined abnormal image to construct a data set to train a target detection model, and adjusting the learning rate in the training process; and inputting pictures captured in an industrial scene into the deep learning model, the multi-modal large model and the target detection model in sequence to obtain a recognition result, feeding back the recognition result to a user for confirmation, adding the recognition result to the data set, and optimizing training of the target detection model. According to the invention, accurate identification of abnormal images in industrial production is realized, and the identification efficiency is high.

A method is provided for generating a dynamic image object (DIO), comprising: generating a synthetic image from a first image and a second image; storing the synthetic image within a DIO; and generating a display image based on the DIO and a viewing parameter.

The invention discloses a transformer equipment oil leakage monitoring method and system based on image recognition, relates to the technical field of image processing, and effectively eliminates static background interference by constructing a standardized image sequence S (t), performing gray gradient analysis and pixel feature extraction, recognizing candidate oil spot areas and constructing a disturbance active score Sx. The edge structure and the morphological stability coefficient of the dynamic oil stain suspected area are further extracted, and the credible level quantitative judgment of the dynamic oil stain suspected area is realized by integrating the characteristic indexes and the morphological stability coefficient, so that the dynamic identification capability and the anti-interference capability are realized; the oil leakage identification accuracy and the system automation risk response capability in a complex environment are improved, false alarm and missing alarm are effectively avoided, and the operation safety of equipment is guaranteed.

Techniques are generally described for query evaluation for image retrieval and image generation. In various examples, a first encoded representation of first natural language input data may be generated. An image retrieval process may be selected from among the image retrieval process and an image generation process based at least in part on the first encoded representation of the first natural language input data. A second natural language encoder may generate a second encoded representation of the first natural language input data. The second encoded representation may be used to determine first image data stored in a first data repository. The first image data may be sent for output on a display of a first computing device.

This disclosure provides novel methods and systems for indexing embedding vectors representing disjoint classes at above-billion scale for fast high-recall retrieval. The disclosed methods and systems solved above-billion scale image (e.g., face image) search problems by building a new vector database, the new Nearest Neighbor Database (NNDB) based on a novel architecture for indexing vectors.

The invention discloses a defect file and image number association method based on multi-format analysis, and relates to the technical field of semiconductor detection and data analysis, and the method comprises the following steps: constructing a multi-format rule base, receiving batch Klarf files and corresponding defect image folders, and extracting image numbers in defect image file names; reading field names of a preset number of Defect data of the Klarf file, matching the field names with the multi-format rule base, and selecting an analysis strategy according to a matching result by adopting a self-adaptive analysis algorithm; the analyzed Defect data are preprocessed, the association confidence coefficient is calculated through a three-layer verification algorithm, and an association result table is generated; according to the invention, automation, standardization and high reliability of semiconductor defect detection data processing are realized.

The invention provides a similar picture retrieval method, which comprises the following steps of: constructing a hierarchical semantic-visual representation framework, and decomposing query and images into three levels, namely a core concept, attribute modification and relation description; a context-aware professional term understanding system is achieved, the professional field background and purpose of query are analyzed, context feature vectors are generated, and term ambiguity is eliminated; establishing a semantic-vision bidirectional mapping and optimization framework, realizing forward mapping from semantics to vision and reverse mapping from vision to semantics, and generating an optimization mapping matrix and a candidate set through iterative optimization; and performing multi-modal similarity calculation, converting the context feature vector into a hierarchical weight vector, fusing the hierarchical weight vector with the multi-level similarity into a comprehensive similarity score, and sorting and outputting a retrieval result set according to the comprehensive similarity score. According to the method, the accuracy of similar picture retrieval in the professional field is improved.

The invention discloses an archive management system based on artificial intelligence, and the system comprises a data preprocessing module which is used for obtaining and preprocessing original archive data, a missing region detection module which is used for carrying out the missing region detection and feature extraction of the preprocessed archive data, and generating missing region description information; the global search optimization module is used for carrying out global search by adopting a variable-scale Levy flight algorithm based on the missing region description information; the local optimization module is used for carrying out detail optimization on a local missing region by utilizing an improved grey wolf optimization algorithm based on the global search candidate data; the data fusion and consistency matching module is used for fusing the locally optimized filling data and the preprocessed archive data; and the filling quality evaluation and optimization module is used for performing error feedback evaluation on the filled archive data and finally filing the corrected archive data. According to the invention, the overall consistency and rationality of archive filling in the archive management process are improved.

The invention relates to a lightweight and privacy protection cross-modal retrieval method in an Internet of Things environment. The method comprises the steps that S1, a data owner generates and sends a secret key to a data user; s2, training the cross-modal feature extraction model; s3, the data owner inputs data to be uploaded into the trained model, and the obtained data features are converted into binary vectors; s4, encrypting the data obtained in the S3, generating a quadtree index, encrypting data to be uploaded, and sending the data and the quadtree index to a cloud server; s5, the data owner generates and sends AAI and digital signature information to the data user; s6, the data user converts the query data into binary vectors, encrypts the binary vectors and sends the binary vectors to the cloud server; s7, the cloud server generates a query result and integrity verification certification information and sends the query result and the integrity verification certification information to the data user; and S8, the data user verifies the query result, and when the query result is correct, the cloud server sends the encrypted data to the data user. According to the method, the complexity of a model for extracting data features is reduced while privacy protection is carried out on the data.

The invention provides a visualization system capable of synchronously displaying multiple types of data according to time. Comprising a distributed service cluster composed of a micro-service, a user management micro-service, a data transmission micro-service, a data storage management micro-service, a data extraction micro-service, a data visualization display micro-service, a data visualization template management micro-service and a resource monitoring micro-service; the micro-services are packaged in a containerized manner and interact by adopting a lightweight communication protocol, and automatic deployment and dynamic expansion are realized through a container arrangement tool; the system is provided with a basic parameter set with customizable micro-service configuration and a graphical configuration interface which are used for cooperatively generating a system operation environment configuration strategy. The user management micro-service establishes a multi-dimensional authority system based on a role access control model, and records an operation log to realize audit tracking; the data transmission micro-service integrates a plurality of heterogeneous data access modes, carries out transmission process verification and carries out secure channel transmission through the service gateway.

The invention discloses a graph-driven intelligent medical image retrieval method and system, and relates to the field of image retrieval, and the method comprises the steps: firstly, coding a text query input by a user through constructing a medical knowledge graph, querying to generate a graph-driven enhanced embedded vector containing an advanced medical concept, and querying a related graph concept set; then, performing preliminary vector similarity retrieval by using the enhanced vector, and quickly screening out Top-K preliminary candidate images; and finally, on the basis of querying a related map concept set, carrying out map concept secondary sorting on the preliminary candidate images, and accurately matching deep medical semantics of the images by calculating a concept coincidence degree. The mechanism of two-stage retrieval and map deep fusion ensures that the retrieval result is not only similar in vision, but also highly related in medical concept, so that the accuracy and intelligent level of medical image retrieval are remarkably improved.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for embedding perturbation data via a machine learning model and filtering, aligning, and aggregating the embeddings to generate a genome-wide perturbation database for real-time generation of perturbation heatmaps. In particular, in one or more embodiments, the disclosed systems can receive a plurality of perturbation images portraying cells from a plurality of wells corresponding to a plurality of cell perturbations. Further, the systems can generate, utilizing a machine learning model, a plurality of well-level image embeddings from the plurality of perturbation images. Moreover, the systems can align, utilizing an alignment model, the plurality of well-level image embeddings to generate aligned well-level image embeddings. Additionally, the systems can aggregate, according to perturbations of one or more perturbation experiments, the well-level image embeddings to generate perturbation-level image embeddings. Furthermore, the systems can generate perturbation comparisons utilizing the perturbation-level image embeddings.

The invention provides a food freshness evaluation method and system based on image processing. The method comprises the following steps: acquiring a multi-view image of food, transmitting the image to a central processing center, setting personalized image acquisition parameters, and forming a food image database; based on the food image database, performing high-precision dynamic monitoring on color and texture changes of the food in different time periods, and generating a preliminary evaluation result; according to the preliminary evaluation result, combining food types, storage conditions and historical sales data, performing prediction modeling on the freshness of the food to obtain a final freshness evaluation report; and utilizing the freshness evaluation report to adjust an inventory strategy in real time, optimizing a food distribution plan, and generating an optimized management process. According to the technical scheme provided by the invention, the accuracy of food freshness evaluation is improved.

The invention discloses a multi-dimensional holographic image real-time rendering method and system based on dynamic light field modulation, and relates to the technical field of holographic display, the method is applied to terminal equipment and a server, the terminal equipment determines a space coordinate range based on quantum dot super-structure surface characteristics by obtaining related parameters of a target holographic scene, and the real-time rendering of a holographic image is realized. A quantum dot excitation sequence is generated to regulate and control the micro-nano structure of the super-structure surface, a joint coding model is constructed, a target light field modulation instruction set is generated through optimization, corresponding multi-dimensional light field data is requested from a server, and a holographic scene is generated through processing, fusion and rendering; the server receives the request, calls original holographic data, constructs a coding model and a compression model, and sends multi-dimensional light field data subjected to entropy compression to the terminal through a multi-channel transmission protocol; according to the method, efficient real-time rendering of the multi-dimensional holographic image is achieved, the rendering efficiency and quality are improved, and the method is suitable for scenes with high requirements for real-time performance of the holographic image.

A method, computer program product, and computing system for generating a plurality of chunks for a plurality of text portions of a document, wherein the document includes the plurality of text portions and a plurality of images. Each chunk is indexed using a word embedding. Each of the plurality of images is indexed based upon, at least in part, a position of a respective image relative to a corresponding chunk. An image placeholder is generated for each of the plurality of images. A plurality of image-enhanced embeddings is generated by inserting the image placeholder for each of the plurality of images into a respective word embedding for the corresponding chunk. The plurality of image-enhanced embeddings are provided for processing a query using a generative artificial intelligence (AI) model.

The invention relates to the technical field of image retrieval, in particular to an image retrieval method based on big data mining, which comprises the following steps: collecting images and metadata, screening metadata fields, determining a candidate image set and obtaining a metadata screening result, according to the method, in the image retrieval process, image metadata screening, geographic information clustering, visual feature fusion, index construction and matching strategy optimization are combined, and the retrieval accuracy and response efficiency are improved. In the metadata screening link, data is screened by using information such as time, places and equipment types, so that interference of irrelevant data is reduced, and a candidate image set is more targeted. Geographic information and timestamps are combined for classification, and clustering analysis is performed by utilizing a geographic information system, so that spatial distribution information plays a greater role in a retrieval process, and a more accurate result can be obtained for query of a specific region.

Described is a system for identity fraud detection by receiving an image of a user for an identity verification process of the user; generating a vector embedding for a face within the image using a machine learning model, comparing the vector embedding with at least two embedding repositories to determine a likelihood of fraud by: comparing the vector embedding with a first index of embeddings, the first index being updated at a first time interval; comparing the vector embedding with a second index of embeddings, the second index being updated at a second time interval, the first index and second index having at least a subset of the same points in the multidimensional space; and determining a characteristic of potential fraud based on the comparison of the vector embeddings with the first index and second index; and outputting an identity verification result based on the likelihood of fraud.

A system includes a plurality of satellites orbiting around a celestial body in a plurality of orbital planes. Each satellite includes an imaging device having a field of view (FOV) to capture an image of a sky that includes celestial image features of resident space object (RSO), stars, and / or planets. The satellite processor is configured to input the image data for each image into a pre-processing software pipeline to generate for each image a contrast-enhanced image data replica of each image, and an enhanced image data replica of each image, to receive from an output of a known-unknown RSO split data processing pipeline, a determination that a candidate RSO is a known RSO stored in an RSO catalog, or an unknown RSO, and to assign to the candidate RSO based on the determination, an RSO ID of the known RSO listed in the RSO catalog, or a new RSO ID.

The invention discloses a customs intelligent specimen cabinet monitoring system, relates to the technical field of specimen cabinet monitoring, and aims to solve the problems that the monitoring strength of an existing specimen cabinet is reduced, and abnormality checking cannot be carried out in time. According to the invention, the most suitable transmission channel is flexibly selected through different transmission requirements and environmental conditions, the adaptability and flexibility of the system are enhanced, the feature extraction of the environmental monitoring data covers the monitoring parameters of each sensor, and the comprehensive monitoring of environmental factors is ensured. The monitoring of real-time video data provides intuitive visual information, so that workers can intuitively know the real-time condition in the specimen cabinet, manual intervention is reduced through automatic detection, recognition and alarming, the automation and intelligence level of the monitoring system is improved, and the monitoring efficiency is improved through automatic data classification, visual display and anomaly analysis. And the intelligent level of the customs intelligent specimen cabinet monitoring system is improved.

The invention discloses a construction quality defect detection method and system based on image processing, and belongs to the technical field of material surface defect nondestructive testing, and the method comprises the steps: obtaining original image data of a construction area and preset building information model data, generating a material feature map and a reflection feature map, and carrying out the denoising and illumination compensation of the original image data, and acquiring enhanced image data, delimiting a key detection area to generate a dynamic mask, executing defect identification by adopting a preset high-precision convolution kernel to generate an initial defect distribution diagram, and outputting a final defect detection result by combining defect correlation correction parameters generated by matching the initial defect distribution diagram with the defect evolution database. According to the method, a double-flow convolutional network is adopted to decouple material attributes and illumination interference, a dynamic mask is combined to focus a high-risk area, and a defect evolution database is integrated to realize mechanical simulation analysis, so that the defects in the germination period can be accurately captured in a complex construction environment, the extension trend can be predicted, and the engineering safety management and control level can be remarkably improved.

The invention relates to the technical field of image retrieval, and discloses a multi-modal image retrieval method, system and device, a medium and a program product, the method comprises the following steps: obtaining a to-be-retrieved image and / or character description information of the image, inputting the to-be-retrieved image and / or the character description information of the image into a pre-constructed multi-modal image retrieval model, and taking a feature vector output by the model as a query vector; performing similarity calculation on the query vector and a feature vector corresponding to each image in a preset image library, and taking the image of which the similarity is greater than a preset threshold as a matched image; and displaying the matched images as retrieval results according to the sequence of the similarity from high to low. The multi-mode image retrieval method can provide retrieval modes of searching images by text, searching images by images and searching images by image-text mixing, realizes multi-mode image retrieval capability, provides ideas of attribute learning and sorting learning, realizes image retrieval of information of other dimensions except categories, adapts to the characteristic of complexity of an image retrieval task, and improves the image retrieval efficiency. And the convenience, accuracy and robustness of image retrieval are improved.

The invention discloses a method for generating a three-dimensional building GLTF file based on index compression. The method comprises the following steps: S1, collecting three-dimensional coordinate data of a building; s2, grouping and classifying the data according to floors to form a classification index structure based on a hash table; s3, selecting a reference model and recording Mesh data and vertex coordinates of the reference model; s4, calculating a translation matrix T of the polyhedral structure, and performing coordinate transformation; s5, generating Mesh data of other polyhedrons based on index compression, and reducing redundant surface data by adopting a topology preserving surface optimization algorithm; s6, independently processing the geometric data of each floor, and generating corresponding Mesh data and Node nodes; and S7, combining all floor data to generate a complete GLTF file, and performing compression by using a dynamic Huffman coding algorithm. Through the improved index compression and dynamic Huffman coding technology, the file size is remarkably reduced, the transmission efficiency is improved, and the geometric and topological integrity of the model is ensured.

In other embodiments, a method to perform image-based searching may include receiving a group of images from a content owner; creating an index using the group of images; receiving a further image from a mobile computing device of a user; determining a correlation between the further image and the index; and / or directing the mobile computing device of the user to content of the content owner, responsive to the determining of the correlation.