295results about "Still image data retrieval" patented technology

A first image and a second image are accessed. The second image is generated by applying an augmented reality (AR) effect to the first image. The first image, the second image, and a prompt are provided to a visual-semantic machine learning model to obtain output describing at least one feature of the AR effect. A description of the AR effect is generated based on the output of the visual-semantic machine learning model. The description of the AR effect is stored in association with an identifier of the AR effect.

The invention provides a spartina alterniflora ecological environment damage value assessment method, medium and system, and belongs to the technical field of spartina alterniflora ecological environment damage assessment. A time-space damage analysis model based on a graph convolutional network is utilized to process a complex factor interaction relationship, a time weight factor correction model is established to quantify the dynamic change of a cumulative effect, and a biological inhibition factor effect model is combined to analyze the synergistic effect of multiple inhibition mechanisms such as local reed competition, natural enemy insect control, salt stress and the like. Accurate damage quantification and prevention and control strategy formulation are realized through the regional ecological environment damage value comprehensive evaluation model and the inhibition factor optimal configuration model, and the technical problem that the accuracy of ecological damage value evaluation is insufficient due to the fact that a multi-ecological factor interaction mechanism in the spartina alterniflora invasion process cannot be effectively analyzed in the prior art is solved.

The invention relates to the technical field of image processing, in particular to an artificial intelligence-assisted CT image quality enhancement and noise reduction method, which comprises the following steps of S1, acquiring low-dose CT scanning image data; s2, preprocessing the acquired low-dose CT image data; s3, extracting preliminary feature information in the CT image; s4, performing adaptive estimation on noise in the image; s5, inputting the noise estimation result and the initial feature information into a deep convolutional denoising network; s6, performing quality enhancement on the denoised image based on a generative adversarial network framework; s7, reconstructing the CT image after quality enhancement; and S8, performing medical applicability detection on the final image. Through the generative adversarial network, the convolutional neural network and the medical image discrimination model, denoising and quality enhancement of the low-dose CT image are realized, the CT image with a high signal-to-noise ratio and high structure fidelity is generated, the medical applicability of the CT image is ensured, and clinical diagnosis requirements are met.

A method and a system for managing sharing of a video in a collaboration session are disclosed. A sharing function obtains (B122) a synchronization time stamp of an audio frame of the collaboration session, wherein the audio frame is going to be used for synchronizing the shared video stream with the collaboration session. The sharing function determines, based on a video frame rate difference between the desired frame rate and a source frame rate of the video file, whether more, less or same number of video frames as in the video file are needed in the shared video stream. The sharing function iteratively retrieves (B140) video frames from the video file stored in the storage function (140). The sharing function provides (B160), by the sharing function (112), the shared video stream by sending said one or more generated video frames to the video communication service (110). The video communication service provides (B174), the shared video stream to the two client devices participating in the collaboration session.

The invention relates to the technical field of image processing, in particular to an anomaly detection method for a ballast bed cover plate of a railway track. Comprising the following steps: synchronously acquiring track area images from different angles by using at least two cameras, calculating a depth value of the ballast bed cover plate according to parallax of the same target track area in imaging of different cameras, and constructing a three-dimensional model of the ballast bed cover plate; in the high-speed running process of the train, real-time compensation processing is carried out on the image; a periodic action sequence including different illumination intensities, focal length adjustment and shooting frame rate changes is set, the duration of each action is set according to the train running speed and the camera shooting frequency, and multiple sets of track images are collected during the duration of each action. According to the railway track ballast bed cover plate anomaly detection method, the depth value of the ballast bed cover plate is obtained, the tilting anomaly of the cover plate is recognized, real-time compensation processing is carried out on the image, and the detection precision and efficiency are improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses an interaction intention-driven tool scheduling method, device and equipment and a medium. The method comprises the steps of obtaining to-be-processed interaction data of visual data or text data, executing cross-modal semantic analysis to generate a unified interaction intention, and sending the unified interaction intention to a server; detecting intention association strength between the matching tool and the context state data, selecting a context association mode based on the strength, determining a target dialogue node, scheduling the matching tool to perform sandbox execution to obtain a tool execution result, generating response content, and sending the response content to the server; and selectively updating the to-be-processed interaction data, the unified interaction intention, the response content, the tool execution result, the target dialogue node and the extracted service entity data to context state data. According to the method, the interaction accuracy is improved through cross-modal analysis and a switchable context association mechanism, tool decoupling is achieved in a sandbox execution mode, and the system adaptability and stability are enhanced.

The invention discloses a map database-based land type map spot storage and analysis method and system. The method comprises the following steps of: acquiring and processing data; storing data; monitoring pattern spot type distribution analysis; performing annual change investigation pattern spot and land type change analysis; and reading and displaying data. According to the method, spatial analysis is carried out on land type change monitoring pattern spots and annual change investigation pattern spots, and a method based on a pattern database is designed to store analysis results; the incidence relation and important attributes of the change monitoring pattern spots and the annual change pattern spots can be expressed on a visual platform, and the monitoring type distribution condition and the annual change survey of the change conditions of different land types in a certain space range are analyzed; according to the storage and analysis method, the internal relation between the change monitoring pattern spots and the annual change pattern spots is visually presented, the effectiveness of change monitoring pattern spot analysis is improved, the data value of the monitoring pattern spots is deeply mined, natural resource data analysis and application are facilitated, and planning and decision making of related departments of the natural resource industry are supported.

The invention relates to the technical field of quality evaluation, and particularly discloses a rubber and plastic sealing element quality evaluation method and system based on big data analysis, and the method comprises the steps: training a normal appearance model based on an unsupervised model architecture through constructing and dynamically managing a dynamic baseline library only containing good rubber and plastic sealing element images; learning the internal characteristic mode of the image of the good rubber and plastic sealing element; furthermore, in a quality evaluation stage, a to-be-detected rubber and plastic sealing element image is input into the trained normal appearance model, the normal appearance model carries out image reconstruction on the to-be-detected rubber and plastic sealing element image based on a good product image internal characteristic mode learned by the normal appearance model, and an image reconstruction error is calculated to serve as an abnormal score of the detected image. Therefore, the defect evaluation of the rubber and plastic sealing element is realized. The method not only improves the detection precision and efficiency, but also reduces the collection and labeling cost of the training data, and can meet the detection requirements of novel or accidental defects at the same time.

The invention discloses a CAD application system design scheme data management method and system, and relates to the computer cartographic technology, and the method comprises the steps: in a target design scheme creation stage, separately creating design model data and design view data of a target design scheme, and copying to obtain intermediate design model data and intermediate design view data; a design scheme data editing stage: loading part of design model data associated with editing data, executing editing, and storing edited update data to the intermediate design model data and the intermediate design view data after editing; and synchronizing the intermediate design model data and the intermediate design view data to the design model data and the design view data of the target design scheme in a design data storage and submission stage. According to the method, through the data middle layer, high consistency and integrity of design scheme data can be well guaranteed through two-stage submission.

A first image and a second image are accessed by a computer system. The second image is generated by applying an augmented reality (AR) effect to the first image. The first image, the second image, and a prompt are provided, by the computer system, to a visual-semantic machine learning model to obtain output describing at least one feature of the AR effect. A description of the AR effect is generated by the computer system based on the output of the visual-semantic machine learning model. The description of the AR effect is stored by the computer system in association with an identifier of the AR effect.

The invention relates to an optimal face image extraction method and device based on a multi-face video, and the method comprises the steps: obtaining a multi-face video, and carrying out the detection of video frames in the multi-face video, and obtaining a detection result; detecting a first face feature vector in the detection result and a preset face database, and determining whether a new face is detected in the video frame; when a new face is detected in the video frame, extracting an optimal face image corresponding to the new face according to the confidence coefficient and the key points in the detection result; the optimal face image is updated to a face database; the key points are used for indicating shielding of key parts in the human face. According to the method and the device, the problems that the real-time requirement is not met and the method and the device are not suitable for real-time video analysis due to the fact that a large amount of iterative computation is needed in the prior art are solved, the identification requirement of processing a new person in real time is realized, and the adaptability is enhanced.

Systems and methods are disclosed for creating, editing, sharing and distributing high-quality, media-rich web-based communications. The communications are created in a layered fashion that integrates user-selected text, colors, background patterns, images, sound, music, video, or other media. The systems and methods are used to generate, edit, broadcast, and track electronic presentations, brochures, advertisements (such as banner advertisements on highly trafficked media websites), announcements, and interactive web pages, without the need for the user to understand complex programming languages.

The invention discloses a tunnel wide and thick earthquake region argillaceous filling fracture zone lining shedding detection system, which comprises an acquisition module used for scanning the surface of a tunnel based on multiple groups of laser radar arrays to acquire point cloud data; the reconstruction module is used for reconstructing the point cloud data based on a point cloud density adaptive algorithm to generate a reconstructed topological graph of the tunnel wide and thick seismic region; and the detection module is used for analyzing the argillaceous filling fracture zone in the reconstructed topological graph, and identifying a lining falling area and generating a grading early warning report according to the change of the lining surface morphology of the argillaceous filling fracture zone. Through accurate lining falling-off detection, potential safety hazards can be found in time, measures are taken for repairing, and the safety of the tunnel is greatly improved.

The present invention relates to the field of image data processing technology, and specifically discloses a lightweight 3D medical image real-time reasoning method and system based on edge computing, including the construction of a C++ framework model, anisotropy detection and processing, separation of low-resolution axis resampling, 3D resampling, determining the sliding window step size and generating a weight map, using a lightweight student model for prediction, sliding window reasoning, image post-processing, performing size adjustment, voxel connectivity and integrity filling on the predicted segmentation map obtained by reasoning, and outputting the final global segmentation map. The present invention uses a lightweight student model to replace the traditional nnUNet, which requires 5 model reasonings, reducing the number of reasonings by 80% and significantly shortening the single reasoning time; at the same time, C++ language reconstruction is used to achieve CUDA parallelization, and through GPU acceleration of data block extraction, mirror transformation and weighted aggregation operators, the performance of the entire process is improved by more than 10 times compared with Python implementation.

The present invention discloses an image denoising and adaptive enhancement method based on pixel discretization and illumination guidance, which relates to the field of image processing technology. The method comprises: downsampling and denoising an input image, and obtaining a preliminary denoised image through deconvolution, fuzzy pixel discretization, and residual continuity processing; estimating the illumination component from the preliminary denoised image using an illumination estimation network; calculating a reflected image based on the estimated illumination component and the preliminary denoised image and according to image enhancement theory; splicing the reflected image and the illumination component in the channel dimension based on a dynamic splicing mechanism to obtain a spliced ​​image; denoising and enhancing the spliced ​​image using a reflection denoising network, with the preservation of image detail and brightness information as the criterion, to output a denoised and enhanced image. The present invention achieves high-quality restoration of low-light images, improving the intelligence level of power inspection systems.

The invention relates to a wallpaper surface defect detection method, which comprises the following steps of: acquiring a wallpaper surface image by using a line-scan digital camera, correcting the image, establishing an average background model, and performing reflection compensation based on the average background model; s200, the image after reflection compensation is separated into R, G and B color channels, a gray level co-occurrence matrix (GLCM) of each channel is calculated to extract contrast features, the dynamic weight of each sub-block is calculated, a weighted RGB image is obtained, then the RGB image is converted into an LAB color space, an L channel is extracted, and # imgabs0 # is obtained; fusing the weighted RGB image and the L channel; carrying out three-layer decomposition by using a Daubechies8 wavelet basis, and carrying out reconstruction to obtain a background image; and carrying out dual-channel Ostu segmentation on the defect candidate image, carrying out decision fusion after segmenting the two channels, and carrying out noise removal and non-defect region filtering to obtain a defect region.

A computer-implemented method, performed by a system, for creating a video representation of a file is disclosed. The method comprises receiving, by the client device a request to start a synchronization addon with a file, whose content is requested to be shared by a user of the client device, wherein synchronization of data, collaborated on in the collaboration session, is performed by a synchronization framework used by the synchronization addon, and receiving, by the recording function, a message, originating from the client device instructing the central server to capture image frames of the content displayed by an instance of the synchronization addon. The method further comprises listening, by the recording function, to event messages from the synchronization addon executing in the recording function, and determining, by the recording function, a respective event time stamp for each respective event message of the event messages to obtain metadata associated with the content.

A method for generating a training gallery for visual AI detection of defective products of a junction box relates to the field of photovoltaic technology, and uses an image processing algorithm to generate a defective product effect picture according to extracted features, each defect can have a plurality of different degrees and forms, and the visual AI detection of the defective products of the junction box can be realized by combining different defect features and iterative optimization. Various defective product effect pictures are generated, and it is ensured that the generated defective product effect picture library can cover various defect conditions possibly occurring in actual production. And integrating the generated defective product effect pictures into a library for subsequent visual AI model training. The defective product effect picture generated through image processing is used for replacing defective products in actual production, manpower, material resources and time cost are saved, meanwhile, the generated defective product effect picture library provides rich defect types and forms, and the robustness of the model and the convergence of the AI model are improved.

To provide a technique capable of, in an image recognition system that recognizes shapes included in an image, determining a success or a failure of individual prediction results for those shapes even if a plurality of shapes appear in the image.SOLUTION: An image recognition system according to the present invention calculates an importance degree of feature quantities for each target shape recognized in an image and for each type of the feature quantities, and determines a success or a failure of a recognition result by comparing, for each target shape, the importance degree with a statistic amount for the type of the feature quantity.SELECTED DRAWING: Figure 2

The present invention proposes a SAR ship target detection method based on masked network fusion of image features, and the steps are as follows: generating an adaptive SAR image ship semantic segmentation label by using the brightness gradient difference between the ship target and the background; constructing a masked feature fusion sub-network and a masked feature fusion target detection network; iteratively training the target detection network by using the constructed loss function; and obtaining the target bounding box coordinates and confidence of the test samples. The present invention combines the target detection and segmentation tasks even when the data set lacks masked labels by generating an adaptive SAR image ship masked label; constructs a masked feature fusion sub-network to highlight the ship target features, suppress the background information, and improve the target detection accuracy; and designs a loss function for the network to solve the problems of unbalanced foreground and background of the image and unstable training process.

A system and method of identifying objects is provided. In one aspect, the system and method includes a hand-held device with a display, camera and processor. As the camera captures images and displays them on the display, the processor compares the information retrieved in connection with one image with information retrieved in connection with subsequent images. The processor uses the result of such comparison to determine the object that is likely to be of greatest interest to the user. The display simultaneously displays the images the images as they are captured, the location of the object in an image, and information retrieved for the object.

The present application relates to a defect detection method and storage medium. The present application first obtains multiple time-series images of a target object, performs defect detection on the multiple time-series images, and obtains a first candidate defect type of the target object. Then, a target detection mode that matches the first candidate defect type is selected from a plurality of pre-set detection modes to verify the first candidate defect type. Finally, the first candidate defect type that passes the verification is determined as the first defect type of the target object. In this way, customized training can be performed for different targets and different production scenarios to meet the needs of complex scenarios and improve the accuracy and adaptability of defect detection on targets.

The invention relates to a contrast-agent-free angiography generation system and method based on mask guidance, a parameter optimization device is used for performing parameter optimization on a conversion device by utilizing a loss function capable of highlighting a contrast agent shadow, and the capability of capturing complex image details of the conversion device is improved. Then, the Gaussian noise image is fused into a contrast-agent-free computed tomography image through a conversion device, computed tomography angiography is obtained through a fusion mode of feature sampling and gradual denoising, and it is guaranteed that the obtained computed tomography angiography has enough consistency and sense of reality; and the sharpness and fidelity of the generated vascular structure are improved.

The present invention provides a system of trademark surveillance system with image understanding for multi-country and the method thereof, which is implemented by providing a user-operated user electronic device. The user electronic device comprises a second processor and a second network interface controller. A server comprises a second application program. The second processor is connected to the server through the second network interface controller and executes the second application program to perform trademark monitoring, particularly to monitor trademark images by inputting a text description.

The present invention discloses a spatial vision positioning method based on the design of a two-color rectangular target. The method includes: designing a two-color rectangular target pattern; performing recognition processing on the two-color rectangular target pattern by the contour corner point ellipse extraction method to obtain the target rectangular corner points in the two-color rectangular target pattern; based on the target rectangular corner points in the two-color rectangular target pattern, performing global numbering positioning processing by the sorting corner point numbering method to obtain the spatial positioning of the two-color rectangular target. The present invention can meet the requirements of spatial positioning and long-distance positioning with the help of the target in a dark environment through a simple target pattern composed of two-color target rectangles. As a spatial vision positioning method based on the design of a two-color rectangular target, the present invention can be widely applied to the field of machine vision technology.

This application provides a training method for an image processing model, an electronic device, and a computer storage medium. The training method for the image processing model includes: obtaining a sample image group, the sample image group including a first image and a second image with the same image content; dividing the first image into regions according to image features and corresponding preset region division parameters using an image processing model to obtain at least two types of regions to be blurred; constructing blur degree data corresponding to each type of region to be blurred based on image features and corresponding blur parameters, thereby obtaining blur degree data corresponding to the first image; deblurring the second image using the image processing model based on the blur degree data corresponding to the first image to obtain a deblurred image; calculating a loss value based on the first image and the deblurred image; and adjusting the image processing model based on the loss value to deblur the image using the adjusted image processing model.

A semiconductor device includes: a processor configured to perform a rendering operation on an image frame to obtain rendering data and write the obtained rendering data to a memory device; and a display controller configured to perform a read operation on the memory device to which the rendering data is written to obtain the image data. The semiconductor device also includes a micro-timing circuit configured to transmit a start signal to the display controller based on the execution level of the rendering operation. The display controller is further configured to initiate a read operation based on the transmitted start signal.

The present invention discloses a lining loss detection system for mud-filled, crushed zones in thick, seismic-affected tunnels. The system comprises an acquisition module for scanning the tunnel surface using multiple laser radar arrays to acquire point cloud data; a reconstruction module for reconstructing the point cloud data using a point cloud density adaptive algorithm to generate a reconstructed topological map of the thick, seismic-affected tunnel zone; and a detection module for analyzing the mud-filled, crushed zones in the reconstructed topological map. By analyzing changes in the lining surface morphology of the mud-filled, crushed zones, the system identifies lining loss areas and generates a graded early warning report. Accurate lining loss detection can promptly identify potential safety hazards and enable measures to repair them, significantly improving tunnel safety.

The invention discloses an underground space resource utilization suitability evaluation system. The system comprises a space database, ArcGIS software, an analytic hierarchy process module, a comprehensive evaluation model, a fuzzy comprehensive evaluation model and a visualization tool. According to the invention, the landform, landform and geological data of the underground space are stored through the spatial database, the ArcGIS software extracts and processes the data in the spatial database to facilitate later calculation, the analytic hierarchy process module calculates the weight value of the data, and the comprehensive evaluation model is used for comprehensively evaluating the underground space data, so that the underground space data can be comprehensively evaluated. The fuzzy comprehensive evaluation model is used for performing single-factor evaluation on the underground space data, so that single evaluation can be performed on each factor influencing the suitability of the underground space, comprehensive and comprehensive evaluation can be performed on each factor influencing the suitability of the underground space, the suitability of the underground space can be evaluated, and the applicability of the underground space is improved. Therefore, objective and reasonable underground space resource endowment and potential conditions are obtained, and an underground space resource comprehensive development and utilization feasibility evaluation report is formed through a visual tool and is convenient for personnel to check.