216 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 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.

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 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.

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.

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 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.

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 field of photoelectric management, in particular to an intelligent photoelectric management system and method based on big data, and the system comprises a target detection module, an image processing module, a mechanical tracking module, a disturbance compensation module and a visual processing module. The image processing module is used for suppressing a sub-peak value and carrying out boundary truncation, the mechanical tracking module is used for continuously tracking a target, the disturbance compensation module is used for compensating disturbance of a target point, and the visual processing module is used for outputting a non-delay motion equation. The performance of the photoelectric tracking system in executing a tracking task is improved, the mechanical disturbance effect is effectively inhibited, high-precision tracking control over a target object is achieved, the self-adaptive capacity of the photoelectric tracking system is improved, and the stability of target tracking and the accuracy of motion data analysis are ensured.

A system and method for assessing gaze stability and vestibular function using a mobile device are disclosed. The system includes a mobile application configured to execute gaze-stability testing protocols, a display module for presenting visual targets, a head-movement and eye-tracking module utilizing real-time video captured via the device's camera, a speech-recognition module for processing verbal responses, and a data-processing module to analyze head-movement and visual-acuity data. The system enables remote patient assessments and includes protocols such as static visual acuity, visual processing, and mobile gaze stabilization tests to evaluate metrics like peak head velocity and visual acuity. Results can be processed in real-time and can be securely transmitted to clinicians for remote evaluation. The disclosed system provides a cost-effective, user-friendly telehealth solution for vestibular function assessment, eliminating the need for specialized equipment or clinical visits.

An intelligent pressure control pneumoperitoneum system based on visual smoke removal and anesthesia linkage and a storage medium comprise a master control communication unit, an image acquisition and recognition unit, a smoke removal execution unit, a pressure detection and adjustment unit and an anesthesia linkage control unit. The main control module generates an instruction according to the signal; the image acquisition and identification unit comprises a camera and a visual processing module, the camera acquires an endoscope image, and the visual processing module processes the endoscope image to obtain a smoke level; the smoke removal execution unit comprises a smoke exhaust pump and an exhaust valve, the smoke exhaust pump controls the rotating speed, and the exhaust valve controls a switch; the pressure detecting and adjusting unit comprises a pressure sensor, a temperature module, a proportional valve and a pressure release valve, the pressure sensor detects pressure, the temperature module detects and controls temperature, the proportional valve controls air inlet flow, and the pressure release valve controls air outlet flow; the anesthesia linkage control unit obtains parameters of the anesthesia machine and transmits the parameters to the communication module.

The invention discloses a near-infrared convolutional image sensor structure design, and belongs to the technical field of infrared intelligent vision and convolutional neural network calculation. The circuit comprises a macro pixel unit and a corresponding reading circuit, wherein the macro pixel unit is composed of n * n sub-pixels. And each sub-pixel integrates an infrared photodiode and a static random access memory and is used for converting an optical signal into charges and storing a convolution kernel weight. The readout circuit adopts a convolution type metal wire interconnection structure, controls current of each sub-pixel to be reused according to weight and directionally flow in an analog domain, integrates the current through a double-path integrator, subtracts the current through a subtractor, and directly completes product accumulation operation of near-infrared image information and a convolution kernel on a pixel level, so that the readout circuit is more accurate and reliable. And outputting a voltage signal representing a convolution result. According to the design, perception and calculation integration is achieved, the problems of power consumption and delay caused by data carrying in a traditional architecture are solved, and the method is suitable for real-time edge intelligent visual processing under the near-infrared complex background.

A non-destructive method to predict the shelf life and maturity of perishable commodities using an intelligent vision system is presented here. The system includes a conventional camera and a vision processor (including shelf-life matrix, defect matrix and maturity matrix specific to each perishable commodity) which automatically determines ready for harvest condition, and the remaining shelf life of the perishable commodity.

The application provides a mechanical arm control method based on visual processing, comprising: acquiring original point cloud data of a target object, processing the original point cloud data by using a three-dimensional reconstruction algorithm to obtain spatial form and volume data of the target object; acquiring a terminal position of a mechanical arm, obtaining a spatial distance by calculating a spatial straight line distance between the terminal position and three-dimensional coordinates of a fragile part; if the spatial distance is less than or equal to a preset distance threshold, it is determined that a current stage is a close distance action stage, and the material attribute and surface roughness of the target object in the close distance action stage are acquired; the material attribute, the surface roughness and the volume data are processed by using a support vector machine to obtain a basic clamping strength; a final control instruction is acquired, and a terminal executor of the mechanical arm is driven to perform a grabbing action on the target object according to the final control instruction.

The invention relates to the technical field of augmented reality and computer vision processing, in particular to an augmented reality art display system, which comprises an environment perception processing center used for calling multi-dimensional environment sensing data, obtaining environment illumination features and scene semantic features, performing coupling processing on the environment illumination features and the scene semantic features, and obtaining a scene image; obtaining photometric semantic coupling data; when photometric semantic coupling data is generated, the imbalance prediction unit is used for obtaining an aesthetic imbalance index; the intention constraint unit is used for obtaining a conventional rendering signal or a reconstructed rendering signal; when a reconstruction rendering signal is generated, the reconstruction rendering unit is used for performing visual consistency synthesis on the obtained compensation model and a real video stream to obtain an augmented reality display image; according to the method, the technical problem caused by neglecting environment interference is effectively solved, and quantitative evaluation of environmental aesthetics quality is supported from a data bottom layer.

This application provides a method, apparatus, computer device, and storage medium for generating pet visual data. The method includes receiving a data generation request, which includes visual cue text and reference visual data of a target pet; invoking a pre-trained generative model based on the data generation request, wherein the generative model includes a data fusion sub-model and a visual processing sub-model; invoking the data fusion sub-model to perform data fusion based on the visual cue text and the reference visual data to obtain a visual latent vector; and invoking the visual processing sub-model to generate visual data based on the visual latent vector to obtain target visual data corresponding to the visual cue text, wherein the target visual data includes an image and / or a video of the target pet. This method can improve the consistency of pet visual data generation.

The embodiment of the present specification provides a query task processing method, a document question and answer and an information processing method based on a processing model, wherein the query task processing method is applied to the technical field of computers and includes: obtaining document query information of a target query task; screening a target text block from a plurality of text blocks included in a target document according to the document query information; processing the document query information and the target text block by using a text processing model and a visual processing model to obtain a document query result of the target query task. The accuracy of the query task processing is improved by text block analysis of the target document to locate the target text block corresponding to the document query information from the target document. While the text processing model is used to understand the text content of the target text block, the visual processing model is used to analyze the non-text elements contained in the target text block, which provides more comprehensive query task processing services and further improves the comprehensiveness and accuracy of the document query result.

The invention discloses a robot intelligent welding unit and method based on 3D visual guidance, and the unit comprises an industrial robot arm, a welding gun, welding equipment, at least one 3D visual sensor and a visual processing controller. A three-dimensional reconstruction module, a self-adaptive path planning module, a real-time feedback control module and a safety monitoring module are integrated in the visual processing controller; a workpiece is scanned through the 3D vision sensor, a three-dimensional model is reconstructed, a welding seam is automatically recognized through the self-adaptive path planning module, an optimized welding path with embedded technological parameters is generated, and in the welding process, the real-time feedback control module dynamically corrects the robot track and adjusts the welding parameters based on a prediction-correction strategy, so that the welding quality is improved. And meanwhile, the safety monitoring module utilizes real-time point cloud to construct a virtual fence for collision protection, so that the whole-process intelligence from pre-welding sensing and autonomous planning to real-time correction and safety monitoring in welding is realized, and the welding precision, the process adaptability and the operation safety are greatly improved.

A vision transformer (ViT) is a deep learning model that performs one or more vision processing tasks. ViTs may be modified to include a global task that clusters images with the same concept together to produce semantically consistent relational representations, as well as a local task that guides the ViT to discover object-centric semantic correspondence across images. A database of concepts and associated features may be created and used to train the global and local tasks, which may then enable the ViT to perform visual relational reasoning faster, without supervision, and outside of a synthetic domain.

This invention discloses a sensor device and control method for high-precision tracking of laser lines. The device includes a base plate, an omnidirectional rotation adjustment mechanism, and a photosensitive unit. The photosensitive unit includes a cylindrical shell, a semi-transparent diffuser element, and a photosensitive sensor. The cylindrical shell consists of a front cylinder and a rear cylinder, forming a dark environment chamber inside. The semi-transparent diffuser element is located inside the dark environment chamber. A light inlet is provided at the front end of the front cylinder, and the area S of the light inlet and the axial depth L of the front cylinder satisfy 30 ≤ S / L ≤ 80. The control method includes: acquiring a laser line pattern; performing dimensionality reduction and downsampling processing on the pattern to extract the center coordinates of the laser line; identifying unilateral shadows and vertical offsets based on the center coordinates, and calculating the compensation amount in conjunction with the axial depth L; and driving the omnidirectional rotation adjustment mechanism to adjust the attitude of the photosensitive unit to achieve real-time tracking. This invention achieves mechanical attenuation of ambient light through the dark environment chamber, combined with visual processing and geometric compensation algorithms, to achieve high-precision real-time positioning in strong light environments.

The exophthalmos measuring method and system of the present application simplify the complexity of computer vision processing by first converting a three-dimensional face file into a two-dimensional map. Then the eye region is extracted to remove the redundant part of the picture and improve the accuracy. Then the eye contour is extracted, the pupil is detected after the orbit is detected, and the coordinates of the temporal orbital edge and the pupil edge on the two-dimensional map are extracted. Then map back to the three-dimensional photo, matrix the three-dimensional spherical coordinate equation, and use the least square method to fit the spherical center coordinates and the eyeball radius length, so as to calculate the exophthalmos distance. The present application uses convenient and non-contact three-dimensional face images as input, and realizes the automatic and accurate measurement of exophthalmos by means of artificial intelligence, computer vision and mathematical calculation based on three-dimensional coordinates.

The invention relates to the technical field of computer vision processing, in particular to an online education video behavior analysis and early warning system fusing facial expression and eye movement parameters, which comprises a video data acquisition and preprocessing module, a video data processing module, a video data processing module and a video data processing module, and extracting a video frame sequence, a face key point, a head posture parameter and an eye region image. According to the invention, video data of students are collected through a camera, blink detection and original frequency calculation are carried out in sequence, a posture and image quality joint evaluation module is introduced innovatively, and multi-dimensional quality scoring is carried out on collection conditions. And performing quality correction on the blink frequency based on the score, and eliminating the individual difference influence of the user in combination with an individual baseline modeling and self-adaptive correction module. And finally, performing time sequence integration and behavior analysis on the effective data to realize accurate early warning of the fatigue state of the student. And the problem of analysis distortion caused by image quality fluctuation and individual difference is effectively solved.

The application relates to the technical field of underwater robot vision processing, and discloses a GAN-based underwater robot image real-time repairing and target identification method. The method acquires an original image sequence obtained by an optical sensor of an underwater robot, extracts degradation features of each frame of image, including scattering noise distribution, color distortion parameters and motion blur intensity. The original image sequence is input into a network, and based on the degradation features, a repaired image is generated frame by frame, and local structure consistency features and global semantic coherence features of the repaired image are extracted. A cascaded classifier is used for multi-scale target detection of the repaired image, local structure features and global semantic features are combined, and a target boundary box and a category label are output. According to the spatial distribution density of the target boundary box, the priority of the repaired area is dynamically adjusted, and the synchronous output of the repaired result and the target identification result is realized. The method can effectively improve underwater image quality and target identification precision.

The application discloses a psychological general measurement paper answer sheet recognition method and system based on visual processing. The method comprises the following steps: acquiring a target image and correcting it; positioning a specific region in the corrected target image; performing first image processing on the positioned identity identification region to extract identity identification information, including region binary processing and grid division, and completing grid and identity identification information mapping; counting the number of non-zero pixels of the grid to extract the identity identification information; performing second image processing on each question region to extract the answers of each question, including dynamically positioning the region of a single question and binary processing; performing morphological operation on the region of a single question, querying the contours of all options and mapping them to corresponding question numbers and options; acquiring the blackening confidence of each option, comparing the blackening confidence with each other and comparing the blackening confidence with a blackening confidence threshold, and determining the answers of the questions. The application can improve the flexibility and accuracy of psychological general measurement paper answer sheet recognition.

The invention relates to the technical field of computer vision, and provides a synchronous positioning and mapping method for a robot under a corn field canopy, and the method comprises the steps: obtaining and preprocessing an environment image, IMU data and encoder data, and obtaining visual data, inter-frame motion increment and motion data; updating a world coordinate system state value based on the inter-frame motion increment; performing visual SFM processing on the basis of the visual data to obtain an image acquisition posture and a road sign point position; performing visual inertia combination to obtain an InEKF initial state; performing InEKF fusion based on the state value of the world coordinate system, the motion data and the initial state of the InEKF to construct a self-sensing odometer; and performing nonlinear optimization processing based on the visual data, the inter-frame motion increment, the motion data and the self-sensing speedometer to obtain a pose estimation result and a map point cloud. According to the method, feature matching, edge feature supplementation and IMU-encoder sensor fusion technologies are utilized, and high-robustness and high-precision robot positioning and map construction adaptive to the whole corn growth cycle are achieved.

The invention provides a forestry pest recognition method and device based on Improved-YOLOv11 and a storage medium, and relates to the technical field of computer vision processing. According to the method, cross-geographic-area and multi-insect-state forestry pest images are collected and preprocessed, and a high-quality data set is constructed; on the basis of YOLOv11, a novel activation function, a dynamic loss function and a multi-scale feature fusion module are introduced, an Improved-YOLOv11 initial model is built, and the Improved-YOLOv11 initial model is used for recognition after being trained. According to the method, full-process automation is achieved, the problems that traditional model feature extraction is weak in pertinence, poor in multi-scale adaptation and low in regression precision are solved in a targeted mode, the types and the positions of the pests can be accurately output without manual intervention, the recognition efficiency is greatly improved, the cost is reduced, and rapid data support is provided for forestry pest control.

The invention discloses an uncertainty visual perception enhancement method and system based on a local contour, and relates to the technical field of machine vision automation. Comprising the following steps: acquiring a local contour image sequence of a target workpiece in real time, performing visual processing, extracting features, matching the features with pre-stored reference contour data, and resolving and generating a state uncertainty vector of quantitative perception reliability based on a matching residual error and feature stability; a follow-up motion path of the acquisition equipment is planned online, and an active sensing path for optimizing contour information integrity is generated; taking the state uncertainty vector and the active perception path as joint adjustment parameters, driving a pre-trained visual target dynamic model, generating a predicted pose and predicting a visual evolution trend; and finally, comprehensively predicting the pose, the evolution trend, the state uncertainty and the active path, and generating and outputting contour perception enhanced data. According to the method, the reliable estimation of the target pose and the active improvement of the sensing quality are realized under local and dynamic observation conditions.