257results about "Still image data retrieval" patented technology

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 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.

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.

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.

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.

A method of identifying a toilet seat comprising the steps of optically capturing an image of a toilet pan and processing the optically captured image to determine at least two characteristics from a group comprising: the distance between the fixing apertures of the toilet pan; the distance from the mid-point of the fixing apertures to the apex of the front of the toilet pan; the shape of the front edge of the toilet pan; and the diameter of the fixing apertures. A toilet seat database is interrogated to determine matching toilet seats that have the same characteristics as the determined characteristics and then at least one external stock database is interrogated to establish the location of stock of the matching toilet seat.

Segment identifying information is obtained for a portion of a transportation network, wherein the segment identifying information identifies a set of transportation network segments located within the portion of the transportation network. The segment identifying information and an image depicting the portion of a transportation network is processed with a machine-learned network identification model to obtain refinement information, wherein the refinement information is descriptive of one or more refinement updates for the segment identifying information. The segment identifying information is modified based on the one or more refinement updates.

The present invention belongs to the field of image recognition technology and relates to a method for extracting original structural information of a soil-rock mixture. The method comprises the following steps: capturing a color image of the soil-rock mixture to be processed, and preprocessing the color image file by grayscale processing and noise reduction to obtain a preprocessed image file; fine-tuning the preprocessed image file by restoring the edge shapes of the rocks in the image file and repairing the internal pores of the rocks to obtain an intermediate image file; and locally fine-tuning the intermediate image file by performing area filtering to eliminate non-rock areas in the intermediate image file and smoothing the edges of the rocks in the image to obtain a final image file. Using Matlab image processing technology, the microscopic features of the soil-rock mixture are quantified. The method effectively obtains original structural information of the soil-rock mixture, such as the maximum particle size, slenderness ratio, roughness, area difference ratio, angularity index, orientation, and stone content, without irreversibly damaging the soil-rock mixture.

The present invention discloses a method and system for automatically identifying the painting status of a vehicle body, comprising: obtaining a pre-production new vehicle body drawing number, determining whether it contains the original vehicle body drawing number corresponding to the pre-production, and determining whether the first final assembly parts plan corresponding to the original vehicle body drawing number contains the first part drawing number of the same color as the original vehicle model configuration; if the original vehicle body drawing number corresponding to the pre-production new vehicle body drawing number is not contained, or the first final assembly parts plan does not contain the first part drawing number, then the production plan containing the original vehicle body drawing number is sent to the automatic recognition and tracking system for painting vehicles; otherwise, the production plan containing the pre-production new vehicle body drawing number is sent to the automatic recognition and tracking system for painting vehicles, so that the automatic recognition and tracking system for painting vehicles tracks and automatically sprays the vehicle according to the received production plan. The use of the present invention can save labor costs and reduce the risk of accidental painting and economic losses in vehicle body spraying.

The application relates to the technical field of image recognition, and discloses a water surface floating object adaptive recognition and tracking method and system, which comprises the following steps: constructing an improved enhanced SSD model and fusing a Swin Transformer local window self-attention mechanism to process images layer by layer; detecting water surface floating objects by using the improved enhanced SSD model; inputting the historical coordinates of the water surface floating objects in the detection result into an LSTM to predict Kalman filtering parameters, ensuring that Kalman filtering dynamically optimally estimates the state of the water surface floating objects, and realizing water surface dynamic floating object target tracking. The application has the advantages of low operation complexity, fast floating object recognition, high precision, accurate dynamic tracking and monitoring, strong consistency of application precision in multiple scenes, and is suitable for dynamic water surface floating object monitoring in complex scenes.

The present disclosure provides computer-implemented methods, systems, and devices for responding to requests associated with an image. A computing system obtains, wherein the image depicts a first set of textual content. The computing system determines one or more characteristics of the first set of textual content. The computing system determines a response type from a plurality of response types based on the one or more characteristics. The computing system generates a model input, wherein the model input comprises data descriptive of the first set of textual content and a prompt associated with the response type. The computing system provides providing the model input as an input to a machine-learned language model. The computing system receives a second set of text as an output of the machine-learned language model as a result of the machine-learned language model processing the model input. The computing system provides the second set of text for display to a user, wherein the second set of textual content is associated with the response type.

Segment identifying information is obtained for a portion of a transportation network, wherein the segment identifying information identifies a set of transportation network segments located within the portion of the transportation network. The segment identifying information and an image depicting the portion of a transportation network is processed with a machine-learned network identification model to obtain refinement information, wherein the refinement information is descriptive of one or more refinement updates for the segment identifying information. The segment identifying information is modified based on the one or more refinement updates.

A computer-implemented method for managing a file is disclosed. The method comprises opening (E110) the file by means of a rendering function (610) to render at least a portion of the file as an image, and obtaining (E120) one or more event messages. For each event message of said one or more event messages, the method comprises obtaining (E130), from the rendering function (610), an image for each event message, wherein the image represents a rendered portion of the file, wherein the image is associated with a sequence indication, generating (E140) metadata based on the event message and the sequence indication, providing (E150) the metadata, and providing (E160) a video comprising a set of images, wherein the set of images comprises the image for said each event message, wherein the video is a representation of the file.

The invention provides a cloud layer segmentation method and device and electronic equipment, and the method comprises the steps: generating a threshold matrix according to a global brightness histogram and local comparison features of a target cloud picture; generating a strong light area segmentation threshold value and a weak light area segmentation threshold value according to the illumination intensity and the threshold value matrix; decomposing the target cloud picture into a plurality of hierarchies, and performing cloud layer segmentation on different hierarchies in the plurality of hierarchies by adopting a threshold matrix, a strong light region segmentation threshold and a weak light region segmentation threshold to obtain segmentation results corresponding to the different hierarchies; and fusing each segmentation result to obtain a cloud layer segmentation image. Based on the method, the accuracy and flexibility of cloud layer segmentation are effectively improved.