40 results about "Filling-in" patented technology

The invention requests to protect a brain tumor segmentation network method in a missing mode based on mode perception, aims to solve the problem of missing modes in multi-mode MRI data, and belongs to the technical field of computer vision and medical image processing. The method comprises the following steps: step 1, providing a dynamic sampling encoder module which dynamically extracts available modal features and explicitly fills missing modal information through cross-modal feature mapping to enhance the robustness of a model; 2, a modal perception attention fusion module is designed, the module combines local and global attention mechanism adaptive fusion features, and the long-tail tumor region segmentation capability is further improved. And step 3, a two-stage training strategy is designed and comprises self-supervised modal reconstruction pre-training and supervised segmentation fine tuning, and the self-supervised modal reconstruction pre-training and the supervised segmentation fine tuning are supervised by a double-task loss function so as to optimize the network to adapt to various missing modal scenes.

The invention relates to the field of computer graphics and real-time rendering, and discloses a naked eye VR visual angle continuous roaming real-time tracking system, which comprises a multi-source viewpoint acquisition unit, a rendering feedback prediction unit connected with a depth buffer area and a template buffer area, and a viewport consistency arbitration unit. According to the method, motion vectors are purified according to object attributes of a template buffer area, a viewpoint is reversely deduced by means of data hierarchical solution of a depth buffer area, and a viewport consistency arbitration unit compulsively uses the reversely deduced viewpoint to take over rendering when the deviation of external sensing data exceeds a limit. The image visual residual inertia is used for filling the blank of external sensor data, and rendering collapse caused by sensor signal interruption or asynchronization is avoided.

The invention discloses a stable diffusion model lawn supplementing method based on hidden space feature editing. The method comprises the following steps: processing an input image by using a semantic segmentation network, and generating a binary mask of a lawn area; inputting the input image, the binary mask and the text prompt into a pre-trained neural network model; performing low-resolution lawn restoration, and outputting a low-resolution restoration image; the neural network model is a repair model which takes a Stable Diffusion XL network as a generation trunk and an Inpaintg-ControlNet network as a condition control network, and the like. The invention provides a lawn restoration method of a stable diffusion model based on hidden space feature editing, which can realize pixel-level accurate positioning and editing of a lawn area and effectively avoid any interference to a non-lawn area in an image.

The application relates to the technical field of defect detection, and discloses an intelligent identification and distribution visualization method and system for weld defects, which comprises the following steps: collecting a weld radiographic testing image, obtaining a smooth image after denoising and performing multi-scale feature extraction, and obtaining a weld segmentation image through semantic segmentation; adopting a convolutional neural network to classify different defect types, performing pixel labeling, and obtaining a defect labeling image; performing uniform grid division on the weld segmentation image, obtaining an optimized sub-region set after filtering boundary pixels; calculating the number proportion of defect pixels and total pixels in the sub-region, comparing the number proportion with a preset proportion threshold value, and completing classification after comparison, mapping to a two-dimensional matrix to obtain a density distribution matrix; aggregating proportion values along the length and width directions of the weld, and filling in missing data points to obtain a continuous proportion value sequence; and assigning corresponding colors to the proportion values, and obtaining a defect distribution heat map after rendering. The method can realize high-precision detection and visual presentation of weld defects.

1. Name of the product in this design: Image clearing dynamic graphical user interface image area of ​​electronic device. 2. Purpose of this design: An electronic device. 3. The key design feature of this product is its graphical user interface. 4. The image or photo that best illustrates the design points: Design 1 Interface Change State Diagram 3. 5. Design 1 is designated as the basic design. 6. Purpose of the graphical user interface: The overall purpose of the interface is to remove specific areas from the image based on user operations and dynamically fill in the removed areas; the local purpose of the interface is to display the different stages of the image cutout process through particle and light effects in the image area. 7. Human-computer interaction method of graphical user interface: In Design 1, the main view is the main interface displayed by the electronic device. When the user long-presses the area of ​​the image area in the main view that needs to be cleared, the interface will automatically jump to the interface change state diagram 1, interface change state diagram 2, interface change state diagram 3 and interface change state diagram 4 in sequence; among them, the gray area in the interface is used to display the image. In Design 2 and Design 3, the main view is the main interface displayed by the electronic device. When the user long-presses the area in the image area of ​​the interface that needs to be cleared in the main view, the interface will automatically jump to the interface change state diagram 1, interface change state diagram 2 and interface change state diagram 3 in sequence; the gray area in the interface is used to display the image. In Design 4, the main view is the main interface displayed by the electronic device. When the user long-presses the area in the image area of ​​the interface that needs to be cleared in the main view, the interface will automatically jump to the interface change state diagram 1 and interface change state diagram 2 in sequence; the gray area in the interface is used to display the image. 8. The areas in the diagram that are covered with a semi-transparent blue coating represent parts that do not require protection.

The present application relates to the technical field of marine engineering digital twin and three-dimensional visualization technology field, provide a kind of based on right triangle grid's dredging topography twin's generation method and system, comprising: obtaining regular grid elevation or sampling height data after pre-processing that have been unified to engineering plane coordinate system, determine the space range covered read space range and density parameter, based on space range and total row number and total column number of calculated grid;In the space covered, generate regularly arranged vertex, construct the topological structure of isosceles right triangle grid;With vertex as unit, several Chunk blocks are divided into global grid with fixed size, establish the triangular index mapping relationship of global row and Chunk block, the three vertices of isosceles right triangle and elevation or height data are filled in each Chunk block in parallel, generate local VBO, IBO and AABB;Batch rendering and coloring according to height color table are executed to all Chunk blocks, and the visualization of topography is realized.

The application provides a periodic image hole filling method and system, and relates to the technical field of digital image processing.A periodic image hole filling method comprises the following steps: preprocessing a periodic image based on a preset image processing model to obtain a hole region, a gray reference region and a gray statistical curve of the periodic image; performing smoothing processing on the gray statistical curve to obtain a period segmentation node; obtaining filling data based on the hole region and the period segmentation node; and filling holes of the periodic image according to the filling data.The periodic image hole filling method solves the technical problem that a sawtooth is generated in an edge excessive region after a periodic image hole is filled in the related art, which affects filling accuracy.

The application provides a non-spherical surface shape detection distortion correction method, device, equipment and medium, and relates to the optical technology field. The method determines the first CCD coordinates corresponding to each data point without distortion by operating the preset distortion model of the distortion parameter, determines the second CCD coordinates in the case of distortion, and then fills in each data point without distortion. The mapping relationship between the actual mirror and the CGH is used to establish the relationship between the actual mirror and the CCD coordinates by taking the CGH as a medium, so as to complete the correction of the imaging distortion and output more accurate non-spherical surface shape data. The above process does not need to set up physical markers on the actual mirror, avoids the influence of the physical markers on the high-precision optical element, simplifies the operation process of the non-spherical surface shape detection, and improves the distortion correction efficiency and accuracy.

The invention relates to an image multi-modal layered labeling method and a semi-automatic image data labeling method.The image multi-modal layered labeling method comprises the following steps that image-level labeling is established, at least one image-level selection box is established for each effective image so as to cover the whole effective image, the overall description of the effective image is filled in, and the image-level labels are obtained; if the current effective picture contains the key elements, marking that the current effective picture contains the key elements; independently labeling each key element in the effective picture by using a labeling box; performing visual information decoupling and text cleaning on the overall description of the effective picture, removing non-visual description, extracting and filling visual description, and extracting and filling subject nouns in the visual description; and filling in a logic process from the visual description derivation to the diagnosis conclusion. Non-visual description is eliminated, pure visual description is reserved, model illusion can be eliminated, and the robustness of the model is improved. The interpretability of the model is enhanced by filling in a logic process from visual description derivation to diagnosis conclusion.

The present application relates to a kind of high light removal method and system of face image based on regional hierarchical processing, belong to digital image processing technical field.It includes the following steps: obtaining face image and generating face target area mask, image is converted from RGB color space to LAB color space in limited area, and overexposure and near overexposure area are divided based on brightness threshold value;Fill in repair is carried out to overexposure area using the statistical characteristics of near overexposure area on brightness and color difference channel;Further generate highlight area mask according to brightness distribution, calculate dynamic adjustment coefficient, and adaptively update brightness and color difference channel in highlight area, finally the repair result is converted back to RGB color space and output, effectively remove the high light of face and keep natural skin color.The present application can effectively retain face details and color consistency while suppressing overexposure highlight, improve the naturalness and robustness of the result of removing highlight.

The application discloses a kind of sea surface flow field inversion and reconstruction method based on stationary satellite high frequency image, belong to marine remote sensing technical field.It includes the following steps: satellite image is preprocessed and locally enhanced;Initial inversion is carried out using dense optical flow algorithm for single time phase flow field;The flow field data cube of space-time grid uniformization is constructed;Time series reconstruction is carried out using two-way Kalman filtering, through forward recursion, backward recursion and two-way fusion, using time series information to correct abnormal vector and fill in missing data;Finally, based on the physical consistency principle of seawater approximate incompressibility, the reconstructed flow field is subjected to iterative divergence constraint, and the non-physical disturbance is weakened.The application effectively solves the problem that the traditional optical remote sensing flow measurement method is easy to produce abnormal vector and poor spatiotemporal consistency under complex sea conditions such as cloud cover and weak texture by introducing time series correlation and physical constraint, significantly improves the accuracy, stability and physical reasonableness of sea surface flow field inversion.

This application relates to the field of robot vision inspection technology, and provides a vision-based intelligent robot control method and system. The method includes: acquiring original image data of multiple workpieces to be grasped within a material frame; determining information-deficient regions in the original image data based on the original image data; the image information includes brightness values ​​and texture features; filling in the information-deficient regions with pixels in the surrounding area of ​​the information-deficient regions in the original image data to obtain repaired image data; the width of the surrounding area is greater than a preset width threshold; generating and issuing a workpiece grasping command to the robot based on the repaired image data. This improves the accuracy of robot control when the robot grasps workpieces.

The invention provides a security inspection equipment imaging quality evaluation method and system, and the method comprises the steps: carrying out the three-stage preprocessing of Canny edge detection, Hough posture correction and polynomial distortion compensation on the X-ray images of a first test body and a second test body, and guaranteeing the regularity of the images; and then a plurality of function test areas are accurately segmented by using light YOLOv8n, and manual area finding is replaced. Then, a five-dimensional algorithm is started in parallel according to the national standard, and the pixel missing rate of a linear resolution area and a penetrating resolution area is measured by combining self-adaptive threshold value and curve fitting; the spatial resolution area uses RANSAC to supplement broken lines and count; a CNN residual high-frequency model is used for evaluating the definition of the penetrating power area, and contour features are superposed to calculate the integrity; organic matter equal four-resolution areas are converted into HSV, and the number of color blocks and the average saturation are calculated through K-means clustering; and converting the two resolution regions of the material into gray scale contrast difference. And in combination with preset parameters, the five-dimensional result is mapped into a quality judgment result, experts are not needed, human eye related errors are eliminated, and full-automatic efficient acceptance of the large hub security inspection equipment is realized.

This invention discloses an image processing method, system, device, and medium, relating to the field of image processing technology. The method includes the following steps: acquiring an image to be processed; extracting channel features from the odd-field and even-field data of the image to be processed, and weighting and fusing the odd-field and even-field data based on an adaptive channel weight allocation strategy to generate multi-scale features; extracting multi-level contextual information from the multi-scale features, analyzing the multi-level contextual information in conjunction with interpretability rules to obtain target object detection results, and dynamically adjusting image restoration parameters according to ambient light intensity, filling in missing areas using background color; semantically aligning the target object detection results with the filled image, and adjusting the brightness threshold of dark areas to generate the final image. This invention achieves accurate matching of cross-modal information in complex scenes and robust processing of complex lighting / occlusion scenes.

The present application relates to image multimodal layered labeling method and semi-automatic image data labeling method, wherein the image multimodal layered labeling method comprises the following steps: establishing picture level labeling, establishing at least one picture level bounding box for each effective picture to cover the whole effective picture, and filling in the overall description of the effective picture, if the current effective picture contains key elements, indicating that the key elements are contained; using the labeling box to independently label each key element in the effective picture; decoupling the visual information of the overall description of the effective picture and cleaning the text, removing the non-visual description, extracting the visual description and filling in, and extracting the subject noun in the visual description and filling in; and filling in the logical process from the visual description to the diagnostic conclusion. Eliminate non-visual description, keep pure visual description, help to eliminate model illusion, improve model robustness. By filling in the logical process from the visual description to the diagnostic conclusion, the model interpretability is enhanced.

This invention provides a projection-based method and system for compressing and transmitting LiDAR point clouds, belonging to the field of LiDAR data processing technology. The method includes: receiving and initializing raw 3D point cloud data from a LiDAR system; mapping the point cloud data in a spherical coordinate system to 2D image pixel coordinates; generating a dual-output depth image and a pseudo-color visualization image; filling in void regions using a distance-weighted interpolation algorithm; and compressing the pseudo-color image using JPEG and pushing it via the RTSP protocol. This invention, through spherical projection dimensionality reduction, a dual-output mechanism, and distance-weighted interpolation with horizontal surround characteristics, reduces the transmission bandwidth from 5.22MB / s to 39KB / s while ensuring the integrity of spatial information, achieving low-latency, high-compression-ratio, and cross-platform compatible real-time transmission of LiDAR point clouds.

The present application relates to computer graphics and real-time rendering field, disclose a kind of naked-eye VR view angle continuous roaming real-time tracking system, comprising: multi-source viewpoint acquisition unit, rendering feedback prediction unit and viewport consistency arbitration unit connected with depth buffer and template buffer, the present system is through rendering feedback prediction unit, according to the object attribute purification motion vector of template buffer data hierarchical solution to deduce viewpoint, viewport consistency arbitration unit is when external sensing data deviation overrun, forcedly use deduced viewpoint to take over rendering, the present application constructs independent hot backup and abnormal takeover mechanism based on rendering pipeline data, uses picture visual residual inertia to fill in external sensor data blank, avoid rendering collapse caused by sensor signal interruption or asynchronization.

A first detection unit (121) performs object detection on a target image (191) to calculate a detection result (192). A processing unit (130) fills in a target bounding box in the target image for each target bounding box to obtain a filled image group (193). A second detection unit (122) performs the object detection on each filled image to obtain a detection result group (194). A first removal unit (141) performs a first removal process on a detection result set to obtain a first result set (195). A second removal unit (142) performs a second removal process on the first result set to obtain a second result set (196) as an object detection result (197).

This invention provides a method and system for dense 3D reconstruction by fusing imaging radar point cloud and visual image data, belonging to the field of 3D reconstruction technology. First, imaging radar point cloud data and visual image data are simultaneously acquired to form a dual-source data set. Next, a pixel-level depth correlation is established between the two, defining the pixel depth range. Then, visual texture detail information is extracted to fill in sparse areas of the imaging radar point cloud, forming intermediate dense point cloud data. Next, visual texture color information is mapped to the intermediate point cloud, and the spatial point depth positions are adjusted to generate dense point cloud data with coordinated depth and texture. Finally, all spatial point information is extracted to restore the target scene shape and texture, forming a color dense 3D reconstruction model. This invention effectively integrates the advantages of both types of data to achieve high-precision dense 3D reconstruction.

Systems and methods for identifying a personalized prior within a latent vector space of a generative model based on a set of images of a given subject. In some examples, the technology can also include using the personalized prior to constrain the input to the generative model to the latent vector space associated with the given subject, such that when the model is tasked with editing an image of the subject (e.g., performing inpainting to fill in masked regions, increasing resolution, or deblurring the image), the identifying features of the subject will be reflected in the image produced by the model.

The application relates to a method and device for setting interval segments, equipment, a medium and a program product, in particular to the technical field of human-computer interaction. The method comprises the following steps: when a user interface involves a threshold setting task, a visual number axis is generated and displayed; in response to node threshold input of a user on the visual number axis, a node is added to the visual number axis, the node is used for dividing the visual number axis into multiple segment intervals, and the multiple segment intervals do not intersect each other. The embodiment of the application is used for solving the problem of intersecting segment intervals when a threshold is manually filled in, a visual number axis is generated on a user interface, and when a user sets a node threshold, the node is correspondingly displayed on the visual number axis, so that the setting of the node threshold is more specific and intuitive, the node intervals are prevented from intersecting each other, the accuracy and rationality of interval setting are improved, and the use experience of a user is improved.

The present disclosure discloses a background image generation method and device, an image fusion method and device, an electronic device and a readable medium. The method comprises: performing instance segmentation on each of at least two target images to obtain a background segmentation map corresponding to each target image without instances; for each target image, filling in the area where the removed instances are located in the target image according to the background segmentation map of a set image to obtain a filling result of the target image, wherein the set image comprises at least two target images different from the target image; and generating a background image according to the filling results of the target images. The above technical solution uses the background segmentation map of the set image to fill in the area where the removed instances of each target image are located, and generates a background image by comprehensively considering the filling results of the target images, fully reuses the features of the background in the target images, makes the segmentation between instances and background smoother, and thus generates a high-quality background image.

The present application relates to the field of pattern recognition and image processing based on multi-modal sensor data fusion, especially to a wine grape vineyard intelligent picking management system based on multi-modal data fusion, comprising a perception device group, a computing platform and an execution unit; the computing platform is built-in with a perception module, a defect mapping module, a curvature inversion module, a manifold segmentation module and a constraint addressing module. The system synchronously collects color images and point cloud data, extracts optical space bias vectors through a defect nesting model and reversely calculates physical surface normals to fill in missing coordinates and reconstruct closed fruit grain three-dimensional geometric curves. The manifold segmentation module extracts a three-dimensional topological skeleton of the fruit stem, the constraint addressing module locates the morphological bifurcation structure and outputs picking point coordinate parameters, guiding the execution unit to complete the cutting work. The present application effectively solves the positioning deviation problem caused by inconsistent multi-modal perception under strong light.

The invention provides a training data acquisition device for an AI multiphase flow detection system. Comprising an integral frame used for supporting and fixing, a transparent container, a movement module used for controlling movement of a drum assembly, the drum assembly used for simulating dispersion phases at different speeds, an image acquisition module used for acquiring multiphase flow image information, and a data processing module used for identifying and processing the dispersion phases in the multiphase flow image information. According to the invention, different drum radiuses are matched with different motor rotating speeds, and simultaneous movement of a dispersed phase in the horizontal direction and the vertical direction can be simulated according to the movement speed of the simulated dispersed phase at any rotating speed; different liquids can be contained in the transparent container and can also be measured and corrected, so that the influence of different physicochemical properties of the liquids on a measurement result is avoided; the identification result obtained through detection can be marked and stored as a data set for training of an AI intelligent multiphase flow detection system.

This invention discloses a method for filling holes in 3D videos based on depth image edge detection, primarily addressing the hole problem that arises when mapping virtual viewpoints within a field of view using depth information from multiple reference viewpoints. The method involves edge detection extraction of the depth maps of each fusion image before virtual viewpoint image fusion, identifying edge and non-edge regions. Then, in non-edge regions, distances before and after mapping are used to fill holes. Subsequently, the filled images are fused using a voting fusion mechanism, followed by fusion of complementary mapping results for virtual viewpoints at each angle within the field of view. After virtual viewpoint image fusion, pixel data from adjacent virtual viewpoints or reference viewpoints are used to fill holes in the current virtual viewpoint. This invention effectively improves the efficiency of hole filling in virtual views by combining edge detection processing before image fusion with post-fusion filling.

The invention relates to the technical field of oil development, and discloses a method, a device and equipment for identifying an alkali lake deposited tight oil reservoir structure. According to the method, the lightweight convolutional neural network model is constructed and the model is trained, so that the filling quality of the blank zone of the electric imaging image is improved, the characteristics of different stratum structures on the electric imaging image are enhanced, the accuracy and the recognition speed of the alkali lake deposition tight oil reservoir structure recognition are improved, and the recognition efficiency is improved. And a foundation is laid for research on lithology identification, reservoir evaluation and stratigraphic contrast of the alkali lake sedimentary tight oil reservoir. The method comprises the following steps: converting micro-resistivity imaging logging data into a pseudo-color image; filling a blank strip in the micro-resistivity imaging logging image; obtaining a training sample set; constructing a lightweight convolutional neural network model and carrying out model training; identifying an alkali lake deposition tight oil reservoir structure; and the identification result is imported into logging software for visual display, and the identification result is obtained.

This invention relates to the fields of image processing and industrial inspection, and provides a quantum-level image reconstruction and dynamic spectral restoration method, comprising: acquiring an original row scan image obtained from a single camera scan, containing information on various exposure pixels arranged in an interleaved column pattern; separating the original row scan image into various sparse exposure images based on a preset pixel arrangement rule; using the original row scan image or a preliminary interpolated image as a guide image, performing guided filtering on the various sparse exposure images to fill in missing pixels, generating various spatially aligned and uniformly reconstructed images with different exposures; and performing weighted fusion on the various reconstructed images with different exposures to generate a fused image, wherein the weights are adaptively determined based on the brightness information of the pixel positions. This invention avoids temporal misalignment and registration errors from multi-frame acquisition, improves acquisition efficiency, and effectively suppresses artifacts and enhances details in dark areas through guided filtering and adaptive fusion.

The invention discloses a human traffic dynamic visualization interactive display interface generation method and system, relates to the technical field of data visualization, and is used for solving the problem that macroscopic situations and microscopic details cannot be considered in large-scale human traffic monitoring. Firstly, trajectory vector features are analyzed, an interaction entropy field reflecting the disorder degree of microcosmic motion is constructed, and global continuous data are generated; then, extracting features by using Gaussian difference convolution, and constructing a visual saliency weight matrix; probability sampling and weighted Thiessen polygon subdivision are executed based on the weight, and a space geometric constraint type self-adaptive viewport topology grid is generated; and finally, the grids are used as projection containers to execute differential rendering, microscopic trajectory dynamic reconstruction at a small viewport and pressure color gradation filling generation at a large viewport, so that fusion interactive display of multi-granularity data is realized, and the decision efficiency of public safety monitoring is improved.

Methods and systems for using images that represent time-series data to forecast corresponding images depicting future values of the time-series data are provided. The method includes: receiving a set of time-series data; converting the set of time-series data into a partial first image that includes a blank region to which future data to be included in the first set of time-series data corresponds; and performing an inpainting operation with respect to the partial first image by generating pixels for filling in the blank region in order to produce an augmented version of the first image. A machine learning algorithm that is trained by using historical time-series data may be used to perform the inpainting operation.

The invention belongs to the technical field of panoramic look-around, and particularly relates to a vehicle panoramic look-around and anti-collision early warning integrated system, which designs a scene adaptive weighted fusion algorithm, builds a multi-modal fusion network containing scene recognition branches, and combines an attention mechanism to realize feature-level efficient fusion. A GAN and geometric constraint mixed scheme is adopted to construct a double-branch network, and distortion-free and full-view correction of the fisheye image is realized; providing a semantic and feature dual-drive splicing strategy, separating and multiplexing a dynamic target, correcting illumination, and complementing splicing dead angles; an IMU and visual fusion anti-shake system is constructed, and anti-shake and image restoration are realized through coarse compensation, fine correction and deblurring algorithms; and an edge computing deployment architecture is established, and a lightweight technology and hardware multiplexing are adopted, so that the computing power demand is reduced, and the resource utilization rate is improved.