330 results about "Vision processing" patented technology

The invention belongs to the technical field of artificial intelligence and computer vision, particularly relates to a visual processing edge adaptive decision-making method for resource and task perception, and aims to solve the problem of scheduling mismatch caused by resource dynamic change and task demand diversity in visual task processing in an edge computing environment. The method comprises the following steps: collecting multi-dimensional resource state data of edge nodes in real time to form a resource state vector with high time resolution; analyzing the visual task request, and constructing a quantifiable task feature vector; and establishing a resource-task association mapping model based on a dynamic weight distribution mechanism. The method also supports cross-edge domain collaborative decision, and processes a pipeline dynamic reconstruction and security isolation mechanism. According to the technical scheme, the fluctuation of the resource utilization rate is reduced to 15% or below, the average task processing delay is reduced to 60%, the scheduling satisfaction degree is improved by 40% or above, and the self-adaptability and the service quality guarantee capability of the edge vision system are remarkably enhanced.

The invention discloses a multi-modal fusion bank receipt intelligent processing system and method based on vision and NLP, and the method comprises the steps: receiving a bank receipt picture or a PDF document, and completing the text detection, direction correction and character recognition through a visual processing engine; a three-level receipt independent segmentation mechanism is applied, and independent receipt records are divided through spatial clustering analysis, semantic analysis, visual verification and cross-page association processing; jointly extracting text features and layout features of each receipt through a double-flow multi-modal fusion model, and fusing the text features and the layout features; field-level data extraction is executed through a field extraction engine, and financial data verification including account number, amount, date and abnormity quadruple verification is carried out; and generating structured JSON output to obtain a bank receipt processing result. According to the invention, the innovative five-layer processing architecture realizes high-precision analysis of the bank receipts through an independently researched and developed receipt independent segmentation engine, a vision-semantic fusion model and a financial data verification system.

The invention relates to a computer vision processing method and system for industrial defect real-time detection. The method comprises the following steps: extracting geometric features and textural features of predefined defect types, and generating a structured descriptor set; generating a synthetic defect image set based on the defect-free image set and the structured descriptor set; inputting the synthesized defect image set into a double-flow feature extraction network to obtain a fusion feature vector; generating a defect category threshold set based on the vector and the structured descriptor set; and inputting the to-be-detected image and the corresponding defect-free reference image into the double-flow feature extraction network, calculating defect probability distribution in combination with the structured descriptor set and the dynamic classifier, and outputting a defect category decision result based on the defect category threshold set. According to the method, the precision, robustness and adaptability of defect detection are improved by means of fusing the global features of the defect-free reference image and the local features of the defect image and expanding training samples by using the synthetic defect image set.

According to embodiments of the disclosure, a method, an apparatus, a device, and a storage medium for visual processing are provided. A method includes: converting a plurality of image blocks divided from visual data into a plurality of embedding representations respectively, where the visual data includes an image or a video; extracting, by using a first processing block in a trained visual encoder, first feature information from the plurality of embedding representations according to a first attention mechanism; extracting, by using a second processing block in the visual encoder, second feature information from the first feature information according to a second attention mechanism; and generating, by using a tokenizer in the visual encoder, an encoding representation corresponding to the visual data based on the second feature information. In this manner, the encoding efficiency can be improved, and better universality and scalability can be achieved.

The invention relates to the field of PCB detection, in particular to an AOI detection data processing system and method for a PCB, and the system comprises a visual processing module, a contour recognition module, a PCB alignment module, a region aggregation module and a defect detection module, the visual processing module is used for rasterizing each image pixel point, the contour recognition module is used for detecting a communication contour of the PCB, and the PCB alignment module is used for aligning the PCB alignment module. The PCB alignment module is used for aligning a PCB image with a standard image, the area aggregation module is used for identifying a defect view field and judging a qualified state, and the defect detection module is used for establishing a reinspection path and outputting a block defect rate. Production defects can be found in time, the probability of missing detection and false detection is reduced, the production quality of the PCB is improved, stable operation of an AOI system in a production environment is guaranteed, and the detection efficiency and accuracy are improved.

The invention discloses a dynamic self-adaptive edge server visual task processing method and system, and belongs to the technical field of visual task processing, and the method comprises the steps: constructing an edge load model and an energy risk model, outputting an edge load rate and an energy risk coefficient, and generating a local execution confidence coefficient based on the edge load rate and the energy risk coefficient; generating a cloud execution confidence coefficient in combination with the network index; quantizing the data quality and the environment state through the data evaluation model and the data acquisition environment model; fusing the data timeliness factors to construct a local-data adaptation degree and a cloud-data adaptation degree; local / cloud execution is decided according to the decision model, and the resolution is optimized based on the execution adaptation degree. According to the method, resource dynamic adaptation and task quality optimization are realized, and the edge visual processing efficiency is remarkably improved.

The invention discloses an aviation transient electromagnetic pod coil attitude correction method and system, and belongs to the field of aviation geophysical exploration, and the system comprises a surface scanning industrial camera module which is used for obtaining the image information of a suspension coil in real time; the industrial-grade integrated navigation module is used for outputting inertial navigation data; the time synchronization and data acquisition module is used for establishing a time unification mechanism; the visual processing module is used for extracting the image processed by the time unification mechanism to obtain visual features; the inertial navigation resolving module is used for obtaining an inertial navigation state of a coil attitude angle through angular velocity integration; the data fusion and attitude estimation module is used for performing fusion calculation by combining the visual features and the inertial navigation state to obtain a fusion result; and the projection area and magnetic moment calculation module is used for calculating the ground projection area and the effective emission magnetic moment component of the coil according to the fusion result. According to the method, the system complexity and the electromagnetic interference risk caused by multi-inertial navigation layout are reduced, and the detection precision and stability of the aviation transient electromagnetic data are improved.

The invention discloses a three-dimensional wave liquid level visual intelligent reconstruction evaluation system, and belongs to the field of ocean engineering monitoring. The system comprises an image acquisition module, an image stereoscopic vision processing module, an attention-enhanced reconstruction neural network module, a camera attitude evaluation module, a visualization and output module and a hydrodynamic parameter analysis module. An image is collected through a fixed baseline binocular camera system, after preprocessing, a neural network fused with a multi-scale attention mechanism is utilized to reconstruct a three-dimensional wave structure, coordinate system conversion is achieved in combination with self-supervised attitude evaluation, and finally hydrodynamic parameters such as significant wave height and a three-dimensional velocity field are extracted and visualized. The system does not need explicit calibration and large-scale data, has high automation, real-time performance and strong environmental adaptability, can be deployed on various platforms, and significantly improves the precision and engineering applicability of non-contact wave observation.

In some embodiments, a display system comprising a head-mountable, augmented reality display is configured to perform a neurological analysis and to provide a perception aid based on an environmental trigger associated with the neurological condition. Performing the neurological analysis may include determining a reaction to a stimulus by receiving data from the one or more inwardly-directed sensors; and identifying a neurological condition associated with the reaction. In some embodiments, the perception aid may include a reminder, an alert, or virtual content that changes a property, e.g. a color, of a real object. The augmented reality display may be configured to display virtual content by outputting light with variable wavefront divergence, and to provide an accommodation-vergence mismatch of less than 0.5 diopters, including less than 0.25 diopters.

The present disclosure relates to scalable encoding and decoding of pictures. In particular, a picture is processed by one or more network layers of a trained module to obtain base layer features. Then, enhancement layer features are obtained, e.g. by a trained network processing in sample domain. The base layer features are for use in computer vision processing. The base layer features together with enhancement layer features are for use in picture reconstruction, e.g. for human vision. The base layer features and the enhancement layer features are coded in a respective base layer bitstream and an enhancement layer bitstream. Accordingly, a scalable coding is provided which supports computer vision processing and / or picture reconstruction.

The invention relates to the technical field of computer vision processing, in particular to an automatic defect positioning method based on a BIM virtual image. The method comprises the following steps: establishing a high-rise building model, and making a data set integrating an illumination condition and a full view angle; calculating camera parameters; performing cross-modal image retrieval: performing targeted optimization on the basis of a classical ResNet architecture, and constructing a backbone network structure suitable for a building image retrieval task; initializing position attitude estimation based on matching; and correcting the camera position posture. According to the method, a defect fixed frame based on a building BIM virtual image is provided, a building image data set BIM-Vision based on Revit is constructed, rich visual angles and illumination condition setting are achieved, accurate camera position postures and 3D labels between beam columns are provided, high-quality basic data support is provided for building visual research, and the method has the advantages of being high in practicability and high in practicability. And during inspection, accurate positioning of defect positions and component association can be completed only by shooting a field image, so that the field operation process is greatly simplified.

The invention discloses a hand-eye cooperative robot control system and method based on dynamic operator arrangement, an upper computer planning layer operates a master control computer to periodically trigger task scheduling, a joint controller of a lower computer execution layer receives an instruction through a redundant bus to perform servo control, hand-eye camera data triggers a visual assembly line through an interrupt event, and a visual assembly line is controlled through a control interface. According to the method, pressure is calculated through visual processing, motion planning and joint control in a load sharing mode, a double-buffering mechanism is adopted to enable current frame visual processing and previous frame motion control to be executed in an overlapping mode, real-time scheduling and parallel computing of tasks are completed, hot data are cached in a memory database, cold data are archived to an HDFS and migrated through an LRU strategy, and the real-time scheduling and parallel computing of the tasks are completed. Data type conversion is automatically derived and performed based on a feature type registry, and conditional branch execution is triggered according to real-time sensor data. According to the control system, cross-hardware plug and play, algorithm flow configurability and data flow real-time sharing are achieved, and the requirement for improving the efficiency of complex operation tasks is met.

The invention discloses a video synthesis method and system, and relates to the technical field of audio and video processing. A video synthesis system comprises a video source acquisition and processing module, a cross-modal semantic understanding module, an attention tensor generation module, a hierarchical progressive fusion module and a quality evaluation and optimization module. According to the method, the spatial-temporal joint features are extracted through the three-dimensional convolutional network, and the audio-visual cross-modal attention mechanism is constructed, so that the dynamic association strength of the audio event and the video content can be quantified, and the main body space mask can be generated, and therefore, the traditional isolated visual processing can be expanded into sound and picture semantic linkage understanding; in this way, deep guidance of multi-modal information on the synthesis process is achieved, and the synthesis effect of the video synthesis method and system is improved.

The invention provides a workpiece surface target merging and partitioning method, system and device, and relates to the technical field of image processing, and the workpiece surface target merging and partitioning method comprises the steps: obtaining a surface image of a target region of a target maintenance workpiece; performing graph sampling and visual processing on the surface image to generate a binary image of the target area, and analyzing the binary image to obtain a plurality of connected domains of the target area; performing convex hull generation according to the connected domains to obtain a convex hull corresponding to each connected domain; generating a rotation enclosing rectangle set according to the leading edge vector of each convex hull; and through a variant ant colony optimization algorithm, carrying out global optimization on the rotating enclosing rectangle set, and generating a combined partition of the target area. According to the invention, through accurate capturing of the original image and subsequent layer-by-layer optimization, the redundancy of the laser maintenance path is reduced, and the operation cost is reduced; through construction and global optimization of a high-adaptation form carrier, precise coverage of a complex form target is realized, and the maintenance precision is improved.

In some embodiments, a display system comprising a head-mountable, augmented reality display is configured to perform a neurological analysis and to provide a perception aid based on an environmental trigger associated with the neurological condition. Performing the neurological analysis may include determining a reaction to a stimulus by receiving data from the one or more inwardly-directed sensors; and identifying a neurological condition associated with the reaction. In some embodiments, the perception aid may include a reminder, an alert, or virtual content that changes a property, e.g. a color, of a real object. The augmented reality display may be configured to display virtual content by outputting light with variable wavefront divergence, and to provide an accommodation-vergence mismatch of less than 0.5 diopters, including less than 0.25 diopters.

The self-adaptive welding device comprises a robot, a robot controller, an industrial personal computer, a visual assembly and a welding gun, a visual processing module, a welding seam contour recognition module, a welding seam precision adjustment module and a track generation module are arranged in the industrial personal computer, and the visual assembly is used for conducting multi-angle shooting on a workpiece; the visual processing module is used for identifying geometrical characteristics of a main pipe and a branch pipe of the workpiece; the weld contour recognition module is used for recognizing a weld contour; the welding seam precision adjusting module is used for refining the precise position of the welding seam point at the junction of the main pipe and the branch pipe through a calibration method; the track generation module is used for generating a welding track; and the robot controller receives the welding track and controls the welding gun to move according to the welding track to execute welding operation. According to the method, the reasonable robot welding posture is automatically matched, the track continuity and the welding process stability are improved, through automatic track planning and execution, it is ensured that the quality of each welding seam is stable, and manual operation errors are reduced.

The invention provides an automatic testing method and system for AI vision, and is applied to the field of software UI automatic testing. According to the method, mobile terminal UI automatic test requirements are processed based on a visual processing model and a large language model, a JSON format test case is generated, and a YAML format test case for system execution and an XMIND format test case for test personnel to check and edit are generated after conversion and integrity check; the test equipment information is processed based on Appium service and WebDriver, and equipment interface screenshot information is obtained; processing the equipment interface screenshot and the element description information based on a visual processing model to generate element relative coordinates, and generating an operation result through coordinate conversion and operation processing; performing screenshot verification processing on the operation result to generate a test result record; and processing the test result record, the test case in the YAML format and the test case in the XMIND format to generate test report information.

The invention provides a wiring diagram identification method based on a neural network and computer vision processing, and relates to the technical field of graph model identification, and the method comprises the following steps: inputting a power grid plant station wiring diagram, the wiring diagram format specifically comprising BMP, JPG and PNG; uniformly scaling the wiring diagram to a uniform pixel resolution, processing the power grid plant station wiring diagram, scaling, denoising, detecting circuit breakers and other primitives, correcting the direction, identifying characters, complementing to generate a structured record, reconstructing a wiring topology, associating the primitives, the characters and the topology through multi-modal matching, and finally obtaining the power grid plant station wiring diagram. And finally, a CIM file conforming to IEC61970 is mapped, automatic extraction and standardized conversion of wiring diagram information are achieved, the rotating frame detection and direction restoration technology is adopted, primitives rotating at any angle are accurately recognized, the direction of the primitives is unified, the problem of angle loss of traditional horizontal frame detection is effectively solved, and the detection efficiency is improved. The omission ratio and the repetition rate of dense or multi-scale primitives are reduced, and the robustness of primitive detection is improved.

The invention relates to the technical field of photoelectric sensing and integrated circuits, and particularly provides a reconfigurable convolution weight loading and reading system of a sensing and computing integrated visual chip, which comprises a reconfigurable convolution weight loading system, a sensing and computing integrated pixel array, a column merging reading system and a time sequence control module, the reconfigurable convolution weight loading system is used for configuring a multi-scale convolution kernel, converting a digital weight into an analog voltage signal, and loading the analog voltage signal to the sensing and computing integrated pixel array by adopting a reconfigurable convolution weight loading mechanism based on a 13 * 13 weight loading array; the sensing and computing integrated pixel array is used for carrying out pixel-level visual information sensing and parallel convolution calculation on the analog voltage signals, the column merging reading system is used for reading a parallel convolution calculation result of the sensing and computing integrated pixel array, and the time sequence control module is used for providing time sequence control signals. According to the method and the device, multi-scale convolution kernel configuration is realized, convolution results of different convolution kernel sizes and step lengths are read, and diversified visual processing requirements are met.

The invention discloses a computer vision processing system based on machine learning, and the system comprises a dynamic topology management module which constructs and maintains a dynamic knowledge graph of a camera network in real time through a graph neural network technology, and carries out the topology self-adaption; the intelligent search module is used for dynamically selecting an optimal monitoring node for query based on a reinforcement learning technology; the multi-modal detection center is used for integrating multi-modal data, carrying out flame / smoke detection by combining an OfficientDet-D7 model with an optical flow method, integrating living body detection in a face recognition model trained in an MS1M-ArcFace data set based on an ArcFace framework to resist attacks, and carrying out multi-target tracking and cross-camera re-recognition by utilizing a DeepSORT algorithm; the distributed early warning system is used for triggering graded early warning through a dynamic threshold engine and carrying out context-aware false alarm suppression and key alarm priority response; the data persistence layer is used for storing structured data by adopting MongoDB fragmentation clusters and storing original video streams by adopting MinIO object storage; and the edge calculation node is used for locally executing lightweight calculation.

The invention relates to the field of visual processing, and discloses an AI visual identification system based on a neural network, which comprises an image acquisition and preprocessing subsystem, an image identification subsystem, a multi-target tracking subsystem, a target tracking cooperation subsystem, a model optimization subsystem, a cloud server and a user terminal, the multi-target tracking subsystem comprises a self-adaptive sampling module, a sampling area optimization module, a streaming frame tracking processing module and a multi-task collaborative optimization module; the target tracking collaboration subsystem comprises a multi-source data collaboration module, a scene tracking adaptation module, a dynamic-visual fusion tracking module and a system monitoring emergency module; the model optimization subsystem comprises a data optimization expansion module, a model feature optimization module, an algorithm fair management and control module and a system monitoring feedback module. Technical innovation is carried out from multiple levels of data processing, model optimization, multi-target tracking, target tracking and the like, and finally, the balance between real-time performance and accuracy is realized.

In embodiments, a method for configuring an intelligent agent to do a task based on spatial-temporal magnetic imaging data of the brain of a worker is disclosed. The method includes generating a brain region parameter indicating an active neocortex region associated with visual processing during performance of the task based on the spatial-temporal magnetic imaging data. The method further includes selecting a convolutional neural network (CNN) component type in response to a match between the brain region parameter and an associated CNN component type. The method includes configuring the intelligent agent based on the selected CNN component and a neocortical processing flow parameter derived using the spatial-temporal magnetic imaging data, wherein the intelligent agent is configured to process image data using the CNN component and provide an output of the CNN to another AI component via a data connection created based on the neocortical processing flow parameter.

Effective Retrieval-Augmented Generation (RAG) pipelines face significant challenges when processing domain-specific technical documents that have diverse content types like text, figures, equations, and tables. To address this challenge, a context-oriented RAG system can be implemented for various domain-specific applications. The RAG system can include a lightweight, two-stage architecture to facilitate contextual understanding: a content analysis and enrichment pipeline for structured metadata extraction and a query processing pipeline for context-aware retrieval. In some cases, tabular data is processed using a dual-stream approach: semantically via text and visually via screenshots. The embedding vectors and the metadata can be stored in a visual data management system. The RAG system, utilizing the visual data management system, can answer questions and precisely retrieve technical information in a way that can preserve structural relationships and semantic connections across different modalities.

The invention discloses a difficulty perception-based passive domain adaptive fundus image segmentation method and device, and belongs to the technical field of computer vision processing, and the method comprises the steps: obtaining a target domain fundus image, and carrying out the preprocessing of the target domain fundus image; initializing a teacher model and two student models; performing weak enhancement processing on the preprocessed image, inputting the processed image into a teacher model, obtaining a prediction probability, calculating an entropy value of each sample, and dividing the samples into an easy sample set and a difficult sample set according to the entropy values; strong enhancement processing is carried out on the easy sample set and the difficult sample set, and then the easy sample set and the difficult sample set are respectively input into student models for training; external information is injected into the two student models respectively, and the training process is optimized; after each iteration period is finished, the parameters of the teacher model are updated based on the parameters of the two student models until the teacher model converges; and inputting a to-be-segmented eye fundus image into the converged teacher model to obtain a segmentation result of the eye fundus image. According to the invention, high-precision segmentation of the optic cup and optic disc of the eye fundus image is realized.

The invention belongs to the technical field of image processing, and particularly relates to a film space position prediction method and system based on machine vision, and the method comprises the steps: collecting a film operation image, extracting a transverse projection value of an edge sub-pixel point, and synchronously obtaining physical field data such as linear speed, mechanical wear and tension; a dynamic drift index is determined by using differential operation, and a steady-state characteristic index is solved by combining fluid dynamics and a physical constant; the spatial displacement of the film in a visual processing hysteresis stage is accurately predicted by calculating the processing time consumption of a visual system based on dynamic drift, steady-state characteristics and a dynamic second-order compensation item, so that a transverse prediction projection value is obtained, and the transverse prediction projection value is further mapped back to a physical spatial position. According to the method, the phase lag problem of visual detection under the high-speed working condition is effectively solved, and the prediction precision and the operation stability of closed-loop control are improved.

The application relates to the technical field of robot vision control, and discloses a robot air attitude real-time correction method based on vision prediction. The method collects a vision data sequence of a robot air attitude, calculates attitude prediction parameters at each time point through a vision processing algorithm, and generates an initial data set; subsequently, adjacent attitude points are analyzed through clustering based on a space-time proximity relationship, consistent sections and conflict sections of motion modes are identified, and a partitioned labeling result is formed. Then, the motion speed and time sequence data of the attitude points in the consistent sections are extracted, the fluctuation characteristics are analyzed in combination with the attitude angle change amount, the motion influence strength under the influence of external environment interference is evaluated, and motion influence superposition evaluation data is generated. Finally, the attitude deviation points in the evaluation data whose response values exceed a reference value and are located in the conflict sections are detected, a deviation point set is constructed, the points needing correction are marked according to the detailed attitude information of the deviation points, real-time attitude correction instructions are generated and output.

The embodiment of the invention provides a visual editing system based on a LabVIEW platform, and the system comprises a project management module which is used for carrying out the creation, deletion, selection and loading operation of a visual algorithm project, and an image collection and display module which is used for displaying an intermediate result and a final result of an image in a processing process. The visual operator module is used for providing various visual operators for a user to call in the form of a graphical component, a bottom visual processing function of the LabVIEW platform is integrated in the graphical component, and the algorithm step module is used for receiving a selection instruction of the user for the visual operators from the visual operator module. According to the method, a user selects a plurality of visual operators, an editable visual algorithm process formed by the plurality of visual operators is recorded according to the order of the selection instruction, the system separates the visual algorithm process constructed by the user from a program source code and stores the visual algorithm process as a structured configuration file, and field personnel can directly debug, store and call an algorithm without modifying codes. And the maintainability and the flexibility of the system are obviously improved.

A computer vision process processing method and a computer vision process processing system are provided. The computer vision process processing method includes: obtaining a first image; and correspondingly determining one or more computer vision process events according to the first image and one or more computer vision automation steps. The one or more computer vision automation steps are each completed by using one or more computer vision automation step simulation components.

The invention provides a low-turbine shaft nut tightening method and a related device. According to the method, a camera is used for collecting nut-shaft head end face image data, and the image data are transmitted to a nut tightening angle real-time measurement module by means of a visual processing technology. The module comprises a feature extraction sub-module, an error fusion sub-module and an included angle calculation sub-module, a nut and shaft head locking groove boundary equation is extracted in sequence, a center line equation is screened based on a theoretical circle center value, and then the minimum angle difference of the nut and the shaft head locking groove is obtained. According to the minimum angle difference, the nut is automatically tightened through the tightening shaft to complete assembling, and meanwhile result visualization is achieved. The problems of invisibility, difficulty in alignment, low efficiency and the like in the assembly process are solved, automatic visual detection and automatic assembly in an invisible space are achieved, workers can conveniently check the assembly condition in real time, assembly automation integration is promoted, and the method has important significance in the field of low-turbine shaft nut assembly.

The invention relates to the technical field of intelligent decision making, can be applied to business system platforms of financial science and technology, medical health and the like, and discloses an interactive visual processing method, device, equipment and medium for texts and images. Obtaining question and answer image data of different groups; generating a plurality of answer tracks corresponding to the question and answer image data of different groups by using a visual language model, and combining the plurality of answer tracks of different groups into an answer track group; calculating a reward function value of each answer track in the answer track group; optimizing the visual language model according to the reward function value to obtain an optimized visual language model; and obtaining a to-be-analyzed question text and image data in the target question and answer scene, and performing interactive processing on the to-be-analyzed question text and image data by using the optimized visual language model to obtain an interactive processing answer in the target question and answer scene. And the visual processing accuracy is improved.