60 results about "Visual computing" patented technology

The invention relates to the technical field of computer vision, computer aided design and artificial intelligence, and discloses a geometric feature recognition method based on a large language model, which comprises the steps of three-dimensional model natural linguistization, large language model feature reasoning, incremental learning and optimization, and feature verification and output. According to the geometric feature recognition method based on the large language model, three-dimensional geometric data are converted into structured text description, and a joint representation space of'geometric parameter-topological relation 'is constructed, so that the large language model can better understand the meaning of geometric features and the structure of parts; the method comprises the following steps: analyzing topological association among geometric features by utilizing long context understanding ability of a large language model, capturing dependency among the features through an attention mechanism, realizing hierarchical recognition of the features in a complex assembly, dynamically updating a geometric feature knowledge base of the large language model by continuously learning new geometric cases and engineering specifications, and realizing hierarchical recognition of the features in the complex assembly. Adaptive recognition of novel features is supported, and the model does not need to be retrained.

The invention discloses a concrete wall full-section deformation monitoring device and method, and relates to the technical field of engineering structure health monitoring, and the device comprises an integrated measurement unit, a passive stable characteristic target group and a processing unit. The integrated measuring unit is composed of an area array solid-state laser radar and a high-resolution binocular vision camera which are rigidly and fixedly connected, and is used for synchronously collecting three-dimensional point cloud and two-dimensional images of a wall and an environment. The target group is arranged on an independent stable object near the wall body; the processing unit identifies coordinates of the target group based on laser radar data, establishes a world coordinate system, calculates self pose drift of the measuring unit by periodically detecting position change of the target, and performs coordinate correction on a wall surface relative displacement field obtained by visual calculation by using the drift distance; and finally, outputting a full-section absolute deformation field under a world coordinate system. According to the invention, high-precision monitoring of full-field continuous deformation of the concrete wall is realized, and the reliability and comprehensiveness of structural health monitoring data are improved.

The proposed technique introduces embodiments of a computer-implemented method for interpreting image delineations as vector objects and topological extraction from segmentation by visual computational methods applied to sections (slices) of seismic volumes, in order to aid geological interpretation and sampling of parameters originating from the fracture network and its topology. Embodiments of a developed method integrates a software / application that allows the loading and generation of statistical data related to the fracture network while maintaining georeferencing and scale of the two-dimensional input data. In addition, a fracture segmentation method is shown that uses pyramid image smoothing (decomposition into hierarchical levels of resolution) in order to reduce the amount of details and aid the identification of main faults or fractures. The segmentation after this smoothing is based on adaptive thresholding segmentation.

The invention discloses a quick classification method and system for human scars through visual calculation, and relates to the technical field of artificial intelligence, and the method comprises the following steps: S1, scar image collection and preprocessing: obtaining a human scar region image and shooting parameters, and carrying out the noise removal, illumination correction and scar region segmentation of an original image; s2, multi-modal scar feature extraction: based on the segmented scar region, extracting color features, texture features, morphological features and depth features; s3, feature fusion and dimension reduction; s4, constructing and training a lightweight scar classification model; s5, outputting a classification result and dynamically optimizing the model; according to the rapid classification method and system for the human body scars through visual calculation, through multi-modal feature fusion and improvement of a lightweight model, the classification accuracy is better than that of a traditional manual classification and single feature machine learning method, subjective differences of doctors are eliminated, and the classification objectivity is guaranteed.

The invention provides a rapid dust removal system and method for engineering machinery welding, and the system comprises a rack, an air suction cover which is movably arranged on the rack and is used for capturing welding smoke dust; the dust removal purifier is arranged on the rack along with the suction hood and is used for purifying welding fume; the visual detection device comprises a camera and a visual calculation unit in communication connection with the camera, and the camera is arranged on the air suction cover and used for recognizing the welding point position in real time; the visual calculation unit is used for calculating the relative position of the welding point position and the suction hood and outputting a control signal; the servo driving mechanism is used for driving the dust removal purifier and the air suction cover to move along the rack, the servo driving mechanism is in communication connection with the visual inspection device, and the air suction cover is driven to move to the position over the welding point position according to the control signal; the dust removal efficiency can be improved, the energy consumption can be reduced, and the welding gun can be accurately tracked to collect smoke dust.

The invention relates to the technical field of computer aided design and visual computing, and provides a visual communication design evaluation system and method based on data analysis. The method comprises the following steps: acquiring original design data, and extracting a spatial layout feature matrix, a color distribution feature vector and a semantic content feature set; calling a design specification knowledge base based on the design purpose information; performing simulated attention distribution analysis and design compliance analysis to generate corresponding features; constructing a transmission efficiency prediction model to generate a comprehensive transmission efficiency score and an initial design evaluation report; performing interference effect joint analysis to obtain a comprehensive interference effect quantitative evaluation result; and performing efficiency optimization correction on the initial design evaluation report based on the result, and outputting a final design evaluation report integrating spatial layout and visual attribute optimization suggestions. According to the method, the interference effect is accurately quantified through conjoint analysis of spatial layout and semantic attributes, and the accuracy and reliability of design conveying efficiency evaluation are improved.

Presented are apparatus, systems and methods for creating tuned color emissions, from lighting and displays, that can be electronically controlled to select a desirable spectrum of wavelengths safer for human vision, for optimal color reproduction, for energy / brightness efficiency, and more. Apparatus including light emitting chips, materials, package design, electronic control devices and circuits, lights, light-fixtures, display panels, visual computing devices and systems, are disclosed. An embodiment is described which is capable of operating in modes, where eye-safe colors are rendered with minimal harmful wavelengths, as well as at least one mode of operation favoring color rendering, and brightness configurations. An embodiment is operable to deliver a paper-like black-on-white viewing experience, in both night-time and day-time operating modes, with reduced high-energy blue-wavelength light spectra. In one embodiment, the light-emitter, controller, display and system are operable to switch between these modes of operation.

The invention provides a multi-modal data real-time analysis and feedback method and system, and the method comprises the steps: collecting a video frame and an audio frame, reading a count value of the same monotonic timer when the collection of the video frame and the audio frame is completed, and generating an audio and video sequence; calculating the behavior popularity of each region in each time slice based on the sequence, and generating a region set for multi-modal event analysis in combination with a preset threshold and a quantity upper limit; scheduling the corresponding video sub-blocks to a visual computing power unit for target positioning and action classification, and executing voice activity detection and keyword category judgment on the time-aligned audio clips at the same time; visual and audio results are fused, and a structured event sequence is generated according to time slice and region compression; and constructing a classroom teaching chain through the sequence, identifying a key event, and finally generating a teaching event description. According to the invention, under the condition of domestic chip combination, cost, time delay, multi-modal consistency and data security controllability are considered, and real-time perception and feedback of teaching behaviors are effectively supported.

The invention relates to the technical field of crossing of computer vision, computer graphics and digital fashion, and discloses a three-dimensional digital clothing stylization method, which comprises the following steps of: firstly, constructing a double-encoder color mapping network to realize color alignment of a clothing image and a style image; the problem that training data is difficult to obtain is solved through a self-supervision strategy; then extracting depth features of the multi-view garment image and the style image by using a pre-trained VGG network, and embedding the style features into a garment feature space by means of an edge enhancement nearest neighbor feature matching algorithm; secondly, introducing an attention-guided edge enhancement feature extraction network, extracting edge features, and performing alignment optimization; finally, a stylized three-dimensional digital clothing image which has high style fidelity and clear structure details and supports rendering at any viewing angle is generated, the problems of color distortion, fuzzy details, inconsistent styles and the like in the prior art are effectively solved, and the method is suitable for the fields of virtual fitting, digital fashion design and the like.

The application belongs to the field of artificial intelligence, computer vision and computer graphics, and discloses a gaze point prediction method and system for VR large space immersive tour, which comprises the following steps: acquiring an eye image transmitted by a near-eye camera; constructing a gaze point prediction network model; inputting the eye image into the gaze point prediction network model to output a predicted line-of-sight direction. The application provides a method for accurately predicting a gaze point in real time, and through the method of eliminating 80% irrelevant pixels in the input image and the method of outputting a prediction value by a multi-level neural network, the calculation complexity is reduced in multiple dimensions, thereby reducing the system delay and improving the rendering quality.

Presented are apparatus, systems and methods for creating tuned color emissions, from lighting and displays, that can be electronically controlled to select a desirable spectrum of wavelengths safer for human vision, for optimal color reproduction, for energy / brightness efficiency, and more. Apparatus including light emitting chips, materials, package design, electronic control devices and circuits, lights, light-fixtures, display panels, visual computing devices and systems, are disclosed. An embodiment is described which is capable of operating in modes, where eye-safe colors are rendered with minimal harmful wavelengths, as well as at least one mode of operation favoring color rendering, and brightness configurations. An embodiment is operable to deliver a paper-like black-on-white viewing experience, in both night-time and day-time operating modes, with reduced high-energy blue-wavelength light spectra. In one embodiment, the light-emitter, controller, display and system are operable to switch between these modes of operation.

The invention discloses a dynamic scene image domain adaptation system and method based on four-dimensional Gaussian sputtering and a computer storage medium, and relates to the field of computer vision and computer graphics, and the method comprises the steps: obtaining dynamic images captured at different time points from a plurality of visual angles; generating a four-dimensional Gaussian model based on the dynamic image; decomposing the four-dimensional Gaussian model into a conditional three-dimensional Gaussian model and an edge one-dimensional time component; extracting a target domain embedding vector; based on the extracted target domain embedding vector and the embedding vector of the three-dimensional Gaussian model, performing affine transformation on each Gaussian point, and mapping the Gaussian representation to the distribution of the target domain; and the multi-view consistency is maintained by predicting the corresponding relationship between different training views. According to the dynamic scene image domain adaptation method based on four-dimensional Gaussian sputtering provided by the embodiment of the invention, the four-dimensional Gaussian is decomposed into the conditional three-dimensional Gaussian and the one-dimensional Gaussian based on time distribution by utilizing extension, so that the multi-view visual consistency can be ensured.

The invention discloses a soil cation exchange capacity detection method based on color developing solution image recognition, and relates to the technical field of soil physicochemical property detection. Comprising the following steps: acquiring a supernatant image after a soil sample of to-be-detected soil reacts with a methylene blue solution under a multi-light-source condition; zero-sample semantic segmentation is carried out on color blocks in the supernatant image to extract position information of different color developing areas in the color blocks, and color partitions are obtained; performing color correction on the color subareas to eliminate color deviation of the color subareas under different light source conditions to obtain RGB parameters of the methylene blue circular color development area; and inputting the RGB parameters into an extreme gradient lifting algorithm so as to fit a numerical relationship between the color concentration of the color developing area and the soil cation exchange capacity, thereby obtaining the soil cation exchange capacity of the soil to be detected. According to the method, a complex chemical detection process is converted into a visual calculation task, and an artificial chemical process depending on multiple ion exchange, washing and titration is simplified.

This invention discloses a method and system for synchronous monitoring of bridge structural displacement based on wireless intelligent vision, belonging to the field of bridge health monitoring technology. The method involves acquiring images of the bridge monitoring area and performing format standardization processing; synchronizing the clock and frame acquisition timing of multiple monitoring nodes, achieving microsecond-level synchronization through linear drift compensation, hardware-triggered synchronization, and frame buffering technology; calculating sub-pixel displacement data using either target-independent or improved marker tracking modes, the former based on frequency domain phase correlation analysis, and the latter using a constrained attitude decomposition algorithm to improve accuracy; performing localized visual calculations on the displacement data and outputting displacement time-series data; and wirelessly transmitting the data to achieve real-time visualization and data interaction with downstream systems. The system includes modules for image acquisition, node synchronization, displacement calculation, local calculation, wireless transmission, and data interaction. This invention achieves non-contact, high-precision displacement monitoring, improves multi-node synchronization and data real-time performance, and is adaptable to various inspection scenarios.

The application relates to a kind of complex fusion network training system and method based on blockchain, including target visual computing network and target feature chain;Target visual computing network is based on public data set and adaptive loss function, and pre-training is carried out for different task types to the target frame in the target monitoring video collected, and target feature information related to the current specific task is calculated;Target feature chain is used to store the target feature information after encryption.The application can process the identification of multiple tasks of pedestrians and vehicles in parallel, while introducing blockchain technology to effectively guarantee the security and non-tamperability of data, greatly improving the usability and practicality of the system, achieving the effect of enhancing system robustness, promoting multi-source data security fusion and improving complex scene perception ability.

The present application belongs to the field of augmented reality, computer vision, computer graphics, and relates to a kind of garment three-dimensional reconstruction method based on geometric prior and generated image auxiliary, comprising: obtaining image, inputting image into trained garment three-dimensional reconstruction model, and obtaining detailed three-dimensional garment grid;Garment three-dimensional reconstruction model includes: multi-modal prior information extraction module, garment generation network and geometry enhancement module;The present application extracts prior information that can reflect geometric structure from synthetic image, i.e.semantic segmentation map and surface normal map, which is jointly trained with real data set, and combined with real data set to construct garment statistical model, and according to garment statistical model, principal component coefficient is restored to garment grid, to make up for the problem of insufficient three-dimensional supervision signal, without relying on additional three-dimensional scanning grid data, improve the representation ability of model to complex garment shape, thereby improve the precision and generalization performance of three-dimensional garment reconstruction.

The invention discloses a ship-end sailing berthing and departing active safety intelligent prevention and control system, which comprises a ship visual perception module, navigation equipment, a video storage module, a visual computing server, a terminal host, a network switch, a serial port server and a wireless module, and is characterized in that the ship visual perception module comprises a berthing visual perception module and a sailing visual perception module; the acquisition module is used for acquiring berthing environment video data and navigation video data of a ship; the wireless module is used for transmitting the berthing environment video data and the navigation video data to the management platform in real time; the system can provide the functions of situation awareness, sailing collision prevention and bridge collision prevention early warning in the sailing process, provide ship side images, berthing and leaving data and early warning information for a driver in the ship berthing process, reduce the berthing blind area of the driver, provide berthing safety guarantee and reduce the collision risk of the ship in the berthing and leaving period to the maximum extent.

The invention belongs to the field of artificial intelligence, computer vision and computer graphics, and discloses a VR real-time rendering method and system for low-bit-rate cloud rendering plug flow, and the method comprises the steps: obtaining a rendering frame image; a lightweight super-resolution model is constructed; inputting the rendered frame image into the lightweight super-resolution model to obtain a reconstructed 4K-resolution image; and performing real-time rendering and display on the VR equipment based on the reconstructed 4K-resolution image. According to the invention, high bandwidth pressure caused by direct transmission of the 4K video is avoided, and the hardware limitation that low configuration equipment cannot carry out complex rendering tasks is overcome. Through the combination of a scene customization model and video coding, high-quality and low-delay visual experience under limited resources is realized, and the method is suitable for various application scenes with relatively high requirements on image quality and real-time performance, such as large-scale cloud simulation, VR tourism and intelligent power grid monitoring.

The present application relates to a dance posture and movement recognition and interaction method and system based on visual computing, comprising the following steps: upon receiving a recognition start command, obtaining a student's dance movement information and dance music rhythm information through a camera device; extracting the student's posture profile information at several preset moments from the dance movement information based on the dance music rhythm information; comparing the extracted posture profile information with pre-stored standard posture profile information and outputting the comparison results; based on the comparison results, screening out posture profile information with a degree of overlap with the standard posture profile information below a preset threshold, extracting the standard posture profile information corresponding to the posture profile information, generating a movement correction data packet; and sending the movement correction data packet to an interactive terminal. The present application has the effect of accurately determining whether a student's dance posture movement is standard and providing timely feedback to the student.

The invention discloses a multi-page screen display data automatic traversal method and system based on visual computing, mainly solves the problems that manual information input in a screen display is easy to make mistakes and low in efficiency, and overcomes the defect that the existing optical character recognition technology is difficult to competent to screen display complex scenes. The method comprises the following steps: firstly, preprocessing collected multi-page screen display optical character image data, and improving the recognizability of optical characters in an image through a character information enhancement method; then, analyzing the highlighted character image set by using a combined scanning method, obtaining a specific region with optical character sub-images, constructing a character sub-image set, and further extracting sub-image information and obtaining a character set; finally, a text proofreading and correcting algorithm is used for proofreading and correcting the extracted character information, the recognition accuracy and the output result quality are improved, more complete and smooth character information can be output, multi-page screen display data can be processed in real time, and the method is suitable for real scenes where characters need to be extracted.

The invention provides an industrial part assembling device based on visual identification and a method thereof, and belongs to the technical field of industrial automation. According to the industrial part assembling device based on visual identification, a structure supporting base, a human-computer interaction interface module and a visual computing host are arranged in an automatic assembling station in a layered mode, and a rotary working assembly and an assembling mechanical arm are arranged on an assembling reference platform. The modularized assembling carrier is provided with an annular indexing positioning rotary disc, a servo steering actuator and a visual sensing module are integrated on the central bearing base, and the side edge error compensation mechanism is provided with a deviation rectifying mechanical arm. During working, part images are collected in real time through the visual sensing module, deviation data are generated through analysis of the visual computing host, the error compensation mechanism drives the deviation correction mechanical arm to conduct fine adjustment compensation, and the assembling mechanical arm completes final assembling. According to the device, visual guidance is matched with servo compensation to execute double-closed-loop control, the assembly precision can be remarkably improved, and the device is suitable for automatic assembly scenes of high-precision and various parts.

The present application relates to navigation control technical field, especially to a kind of unmanned aerial vehicle navigation method and system based on vision and reinforcement learning, method includes: obtaining lagged first time visual image, real-time second time depth image and the continuous sensing data between them;Through the preset distillation vision network extraction global semantic features and with positive shadow image matching, the visual positioning anchor point of first time is solved;Inertial compensation positioning point of current time is obtained by using sensing data to calculate displacement and attitude change in time window, and mapping visual positioning anchor point to second time;Tensor splicing is carried out to inertial compensation positioning point and depth image to build reinforcement learning state space, and control instruction is generated by inputting strategy network;Through inertial compensation mechanism, the time-space deviation caused by visual calculation delay is eliminated, the flight trajectory oscillation problem caused by different step of multi-source heterogeneous data is solved, and the high robustness autonomous navigation and smooth control of unmanned aerial vehicle in dynamic environment are realized.

The application provides a construction safety visual calculation and resource scheduling method, system and electronic equipment, which comprises the following steps: deploying and initializing the calculation and resource scheduling system; the edge end collects video streams in real time and performs preprocessing, light-weight model inference and local alarm; the edge end performs three-level screening on the video data, intelligent compression of the region of interest (ROI) and end-to-end encryption before uploading to the cloud; a multi-objective optimization decision model is constructed based on FAHP, and the allocation strategy of the task in the edge end and the cloud is dynamically adjusted according to the real-time collected network state parameters, computing power state parameters and event characteristic parameters; the cloud adopts a high-precision model for inference, links a knowledge base to generate a rectification scheme, and realizes full-process closed-loop management; an incremental data set is constructed to fine-tune the light-weight model and the high-precision model. The scheme provided by the application can balance the contradiction between the real-time alarm demand of the construction site and the high-precision calculation demand of the complex model.

The invention belongs to the field of computer vision, computer graphics and robots, and relates to a three-dimensional digital human and object interactive motion synthesis method and system based on a diffusion model. The method comprises the following steps: acquiring three types of feature vectors: a three-dimensional grid shape of an object, a frame-by-frame motion sequence of the object and a body type feature vector of a digital human; performing conditional feature coding on the obtained three types of feature vectors to obtain conditional vectors; on the basis of the diffusion model, predicting noiseless estimation of the current time step by using a denoising device in a condition vector iteration manner, and optimizing the generated interactive motion sequence of the three-dimensional digital human and the object according to a generation guide strategy; and obtaining a three-dimensional grid of the human body based on the interactive motion sequence of the three-dimensional digital human and the object, and obtaining an interactive motion synthesis result of the three-dimensional digital human and the object through three-dimensional modeling software. According to the invention, a three-dimensional digital human and object interactive motion sequence with strong sense of reality can be synthesized, and the synthesized three-dimensional digital human has the motion of the trunk and the two hands at the same time.

The application discloses a scene gray image recovery system and method based on a dynamic visual sensor, and belongs to the field of computer vision and computational imaging. The application gradually opens the light of an image acquisition system by an incident light intensity control component, acquires the brightness change events on the sensor plane in the process by the dynamic visual sensor, obtains a time mapping gray image based on the brightness change events and the change relationship of brightness with the acquisition time, obtains an event number mapping gray image according to the mapping relationship of the brightness change events, the gray scale and the event number, and obtains the scene gray image according to the time mapping gray image and the event number mapping gray image. The application enables the dynamic visual sensor which can only output the brightness change discrete information to also output the gray image. Compared with the prior art, the application is compatible with static scenes and has better recovery effect.

The invention discloses a logistics package supply station multi-split control method and system based on vision and cloud collaboration, and the method comprises the steps: independently collecting package bar codes and image information by each package supply station, and transmitting task data to a server after local verification; the server performs priority ranking and resource allocation on the tasks according to a dynamic scoring mechanism; reasoning the batch processing tensor by using a detection model, and outputting information of packages in all the images; performing multi-dimensional rule filtering on an original result output by the detection model, and starting a processing flow for a triggered abnormal condition; and the packet supply station verifies and executes the control instruction, and feeds back confirmation information to the server after completing the packet loading action. Through a multi-split intensive processing mode, the visual computing units originally needing to be independently deployed on each packet supply platform are concentrated to the server for unified processing, the hardware cost is greatly reduced, meanwhile, only the packet supply platforms need to be added during expansion, and computing resources do not need to be repeatedly configured.

The application discloses a kind of based on edge visual computing's table data row and column self-adapting alignment method and system, the method includes creating table video stream self-adapting extractor, real-time intercepts video frame containing table data, identifies target table position information, extracts table image to be detected;For target table image, construct the self-adapting alignment model based on visual computing, splice multiple sets of table profile of video frame, align table row and column data;Based on table row and column data, construct deep reinforcement learning model, fine adjustment table structured information;Based on table structure information, customization identification and processing cell text data;According to table structure and text data, carry out structured encoding and storage.The application realizes the automatic identification, storage and alignment of table data in video stream, improves the acquisition efficiency and accuracy of table data, greatly saves manpower cost.

The invention relates to an image classification method and device based on a side suppression attention mechanism and electronic equipment, and the method comprises the steps: constructing an image classification model based on the side suppression attention mechanism, and carrying out the training through employing a neuromorphic data set, and obtaining a trained image classification model based on the side suppression attention mechanism; inputting a to-be-processed neuromorphic image into the trained image classification model based on the lateral inhibition attention mechanism to obtain an image classification result; according to the method, secondary features and background information are actively inhibited, so that the network is more focused on a key visual mode, and the feature identification capability and robustness of the model are improved under the condition that the parameter quantity is not excessively increased; according to the method, the effectiveness and generalization ability of a side suppression attention mechanism in an image processing task provide a new thought and method support for a low-power-consumption and high-efficiency bionic vision calculation model.