445 results about "Color map" patented technology

The invention provides an underground long-distance pipeline three-dimensional mapping method and system based on three-dimensional Gaussian sputtering. The method comprises the steps that a color map, a depth map and IMU data are collected; pixel coordinate conversion is carried out to obtain point cloud coordinates; inputting the depth map, the color map and the IMU data into a pose estimation algorithm, and processing to obtain a predicted pose of the IMU data, visual pose estimation and visual pose estimation information; initializing three-dimensional Gaussian, performing back propagation through a three-dimensional Gaussian loss function, and optimizing to obtain an applicable three-dimensional model; on the basis of the initialized three-dimensional Gaussian, using a GS-SLAM algorithm to update the applicable three-dimensional model and visual pose estimation information, and using a three-dimensional Gaussian loss function to process to obtain an optimized camera pose; the initial camera pose is updated through Kalman filtering, and pipeline mapping operation is completed. The technical problems that the equipment memory is large, the labor cost and the time cost are high, errors are easy to accumulate, pose deviation is easy to occur, and the model is difficult to maintain relatively high accuracy are solved.

The invention discloses a sparse view angle-oriented three-dimensional Gaussian visual positioning method, which belongs to the technical field of positioning, is used for visual positioning, and comprises the following steps of: extracting global image features and local image features of a sparse image, and constructing a feature database; performing nearest neighbor search on the feature database and the RGB image to obtain a sparseness index, calculating a camera pose of a pseudo view angle, rendering a color image and a depth image under the pseudo view angle by using a training main model, extracting global features and local features and storing the global features and the local features into the feature database, constructing a 2D-3D corresponding relation in combination with a target color image, and performing dynamic inner point screening and pose solving. And obtaining a positioning result. According to the method, visual angle deficiency of a key area is intelligently completed, the balance and coverage of feature expression in a three-dimensional scene are effectively improved, and dynamic updating and mismatching elimination of a 2D-3D matching relation are realized, so that the positioning precision and the system stability are remarkably improved, and the method has higher robustness and actual availability under a sparse training condition.

Embodiments of the present disclosure provide a three-dimensional scene reconstruction method and apparatus, a device, a medium, and a program product. The three-dimensional scene reconstruction method comprises: acquiring a scene image collected for a three-dimensional scene; on the basis of the scene image, determining sparse point cloud data and camera parameter information corresponding to the scene image; performing model initialization on the basis of the sparse point cloud data to obtain a current three-dimensional data model; performing rendering on the basis of the camera parameter information and the current three-dimensional data model to obtain a current color map, a current depth map and a current normal map from the perspective of the scene image, and determining a pseudo normal map from the perspective of the scene image on the basis of the current depth map; and on the basis of the current color map, the current normal map, the pseudo normal map, and an actual color map, training the current three-dimensional data model to obtain a trained target three-dimensional data model. By means of the technical solution provided by the embodiments of the present disclosure, higher-quality automatic reconstruction of three-dimensional scenes can be achieved, reducing reconstruction costs, and improving the level of detail and rendering quality of scene models.

The invention discloses a fine controllable image stylization method based on a diffusion model. The method comprises the steps that training data is acquired, the training data is a group of sample data with aligned style attributes, and the sample data comprises a content image, a stroke image, a color image, text information and a target image; implicitly performing feature decoupling processing on the content image, the stroke image and the color image to obtain a content attribute feature, a stroke attribute feature and a color attribute feature; and fusing the content attribute feature, the stroke attribute feature, the color attribute feature and the text information through an adaptive gating mechanism, injecting the fused features into a controllable diffusion model, and combining the target image to obtain a prediction image. The technical problem that style transfer is difficult to finely and flexibly control due to the fact that various fine-grained style attributes in the reference image are not decoupled in the prior art is solved.

The invention relates to the field of robots, and discloses a robot ordering method and system, and the method comprises the steps: obtaining a depth map and a color map of a current scene; obtaining a detection frame of a target object, and cutting out three-dimensional point cloud data of the corresponding object from the depth map according to a detection result; reasoning by taking the three-dimensional point cloud data as input of a reasoning model, and predicting candidate grabbing poses; and according to the predicted candidate grabbing poses, trajectory planning and motion execution are carried out. Reasonable action information can be generated according to information provided by the visual language model in combination with specific task requirements, and therefore control over the mechanical arm is achieved.

Systems and methods are provided for increasing a quality of images generated by a computed tomography (CT) system. In one example, an initial assessment of contrast timing and flow through different anatomical regions of a patient is performed, and based on the initial assessment, different visualization schemes are applied to the different anatomical regions of a reconstructed image, where each visualization is optimized for assessing a different anatomical region. In particular, color maps (e.g., heat maps and / or probability maps) and / or color overlays based on material decomposition information may be superimposed on contrast-optimized images, where the color maps accentuate a contrast between diseased tissues and healthy tissues. An automated report may be generated including a first visualization based on a first scan, and a second visualization based on a second, earlier scan, to show a progression of a disease of the patient.

Systems and methods are provided for increasing a quality of images generated by a computed tomography (CT) system. In one example, an initial assessment of contrast timing and flow through different anatomical regions of a patient is performed, and based on the initial assessment, different visualization schemes are applied to the different anatomical regions of a reconstructed image, where each visualization is optimized for assessing a different anatomical region. The visualizations may increase a contrast between diseased tissues and non-diseased tissues of the different anatomical regions, and may include color maps (e.g., heat maps and / or probability maps) and / or color overlays based on spectral decomposition information. The different visualizations may be combined into a single 2D image, where each anatomical region (e.g., organ, bone, etc.) is displayed in high contrast, and where aspects of various anatomical regions may be highlighted or colorized to visualize specific information.

A three-dimensional temperature gradient field visualization method comprises the following steps: after analyzing a part model and storing a point-surface relationship and temperature information, calculating a data point temperature gradient and carrying out point cloud, grid and entity model visualization; after creating temperature field color mapping and calculating a temperature field contour line, representing a temperature gradient field based on a point icon, representing a temperature gradient field based on a streamline and representing a temperature gradient field based on texture; through design of a data visualization core algorithm and model building, visualization of three-dimensional data of various vector fields for an unstructured grid model is realized.

The invention discloses a full-field non-contact health monitoring method for a large-scale structure. The method mainly comprises the following steps: S1, dividing monitoring sub-regions; dividing the to-be-monitored area into a plurality of to-be-monitored sub-areas; arranging a plurality of distributed channel discrete speckle interferometers to respectively and synchronously image and monitor each to-be-detected sub-region; s2, acquiring a speckle interferogram and a dual-channel color map; the monitored object is deformed; respectively collecting speckle interferograms and dual-channel color maps before and after deformation of the to-be-detected sub-region; s3, processing a speckle interferogram; processing the speckle interferograms before and after deformation, extracting a phase fringe pattern reflecting defect information of the to-be-detected sub-region, and determining a defect form; s4, splicing the monitoring data; splicing the two-color channel images shot by the sub-regions to be detected; and performing geometric transformation and fusion on the phase change information by using the two-color channel image splicing parameters obtained before and after splicing to generate full-field monitoring data, and obtaining the defect position and damage evolution of the monitored object.

The invention relates to a blood flow quantitative analysis method and device based on an indocyanine green contrast video. The method comprises the steps that video data of indocyanine green fluorescence contrast are collected and preprocessed; extracting dynamic change characteristics of fluorescence intensity of the blood vessel region in the time sequence by adopting an optical flow model based on brightness change; introducing a weighted cross-correlation optical flow model, and extracting dynamic information of all blood vessel regions from each video frame; based on the optical flow vector and fluorescence intensity information of the blood vessel region, blood perfusion parameters are calculated, and a global blood perfusion pseudo-color graph is generated; and selecting a region of interest on the blood perfusion pseudo-color graph to obtain the change condition of the statistical fluorescence brightness value along with time in the region of interest, and drawing a time-brightness curve for presenting the dynamic change trend of the blood flow. Compared with the prior art, the global blood perfusion graph can be generated in real time, and the dynamic change of blood perfusion of all blood vessels can be visually displayed through the time-brightness curve.

The invention belongs to the technical field of non-intrusive underwater collection, and particularly discloses a liquid concentration and underwater terrain collection method and system based on Kinect, and the method comprises the following steps: obtaining water surface point cloud data and underwater terrain point cloud data based on a Kinect camera; the method comprises the following steps of: fitting a relation curve of a gray value and liquid concentration by preparing mixed liquid with different sediment volume concentrations; importing the water surface point cloud data into point cloud visualization software for processing to obtain an RGB value of the measured liquid, and obtaining the liquid concentration of the measured liquid through a relation curve of a gray value and the liquid concentration; correcting the obtained underwater topography point cloud data to obtain corrected point cloud data of the underwater topography; and importing the corrected point cloud data into point cloud visualization software to carry out underwater terrain three-dimensional reconstruction. According to the invention, one-to-one correspondence color maps and depth maps are obtained based on the Kinect camera, the liquid level liquid concentration can be effectively collected, and meanwhile, three-dimensional reconstruction of the underwater terrain can be quickly realized.

The invention discloses a camera-free pose three-dimensional scene reconstruction method based on 3DGS, and the method comprises the steps: firstly obtaining an ordered picture sequence shot by a camera and a depth map of the ordered picture sequence, constructing a picture, and pre-training a feature encoder for a data set; then initializing a camera pose of a first frame of picture in the ordered picture sequence and an empty training picture set, recovering point cloud information and initializing a 3D Gaussian model; randomly selecting a color image and a depth image of the image from the training image set as supervision information to iteratively optimize the 3D Gaussian model; iteratively optimizing the camera pose of the next frame of picture in the ordered picture sequence according to the optimized 3D Gaussian model; and finally, according to a picture sequence in the ordered picture sequence, alternately iteratively optimizing the 3D Gaussian model and optimizing the camera pose of a new frame of picture by adopting a progressive training mode to realize three-dimensional scene reconstruction. According to the method, three-dimensional reconstruction with higher quality can be realized, the quality of a picture synthesized by a new view angle is improved, and the error of camera pose estimation is reduced.

Embodiments of the present application relate to the technical field of image recognition, and disclose a color band resistor color extraction method and apparatus, a device, and a medium, which solve the technical problem in the prior art of poor precision of color band resistor color detection. The color band resistor color extraction method comprises: acquiring an RGB color image of a target color band resistor; acquiring an LAB color image on the basis of the RGB color image; performing transformation processing on an LAB value corresponding to each color type of the color band resistor to acquire a color extraction threshold; and performing color extraction processing on the LAB color image on the basis of the color extraction threshold. The method of the present application effectively improves the precision of color band resistor color detection.

The invention relates to the field of image processing, in particular to a low-illumination image enhancement method and system based on an HVI color space. Carrying out HVI color space conversion on the original RGB image to obtain an intensity graph and a horizontal and vertical color graph; performing feature extraction on the intensity image and the horizontal and vertical color image to obtain an intensity image feature and a horizontal and vertical color image feature with the same dimension; inputting the intensity graph feature and the horizontal and vertical color graph feature into a pre-constructed double-branch U-Net network to obtain an HV branch feature graph, and cascading the I branch feature graph and the HV branch feature graph to obtain an enhanced HVI feature graph; and carrying out inverse transformation processing on the enhanced HVI feature map to obtain an enhanced image. According to the dual-branch U-Net network provided by the invention, through parallel processing of the I branch and the HV branch, decoupling enhancement of brightness and color features is realized, scene information can be supplemented at each branch, and the visual effect of an enhanced image is further improved.

Embodiments of the present disclosure provide techniques for colorizing visual content using artificial intelligence models. An example method generally includes receiving an image and an input prompt specifying a colorization to apply to the image. Based on an encoded version of the image and a textual description of the image input into a machine learning model, one or more color maps associated with the specified colorization to apply to the image are generated. A colorized version of the image is generated by a generative artificial intelligence model based on combining a grayscale version of the image and the one or more color maps, and the colorized version of the image is output.

The invention relates to the technical field of image analysis, in particular to a spooling color tube detection method and system based on image analysis, and the method comprises the following steps: carrying out the statistics of color component intensity distribution based on a captured bobbin yarn conveying track image of a spooling machine, comparing the difference with a standard illumination color image, and carrying out the proportion adjustment of color components; and obtaining an illumination color correction image. In the invention, by separating and adjusting three color components of red, green and blue in the image, eliminating the influence of illumination conditions, and by calculating the pixel brightness gradient value and correcting the shadow area, the shadow interference in the image is reduced, the track background and the yarn area are accurately distinguished, and the color deviation and uniformity of the ring bobbin are accurately evaluated; and the color deviation tolerance threshold is dynamically adjusted according to the statistical result, so that the flexibility and accuracy of color anomaly detection are improved, and the recognition efficiency and the quality control capability of color anomaly are improved.

The invention relates to the field of digital printing equipment, and discloses a multi-modal data fusion digital printing equipment color dynamic calibration system which comprises a data acquisition and preprocessing module, a tensor modeling and optimization module, a causal reasoning module, a feedback control module, a cross-equipment mapping module and a self-diagnosis module. The method comprises the following steps that multi-modal data of equipment and environment are collected in real time, intelligent analysis is carried out, and control parameters are dynamically adjusted, so that the problem that a traditional color management method lacks real-time feedback and dynamic adaptive capacity is solved, and the stability and accuracy of the color calibration process are ensured. According to the method, color, image, environment and equipment operation data are collected, a multi-modal tensor is constructed, sparse feature compression is carried out, color deviation reasoning and adjustment quantity generation are realized in combination with causal structure learning, and a self-diagnosis mechanism and closed-loop control are assisted, so that real-time monitoring of the equipment operation state and dynamic adjustment of the color state are realized.

The invention relates to a light effect animation data generation method and apparatus, a device and a medium. The method comprises the steps of obtaining a target image for constructing light effect animation data; according to the color values of the pixels in the target image, constructing a palette containing a fixed number of reference colors, and determining attribution probability distribution corresponding to each reference color mapped to the palette by each pixel; corresponding to the parity of each pixel in the plane space position of the target image, selecting a reference color according to the attribution probability distribution so as to update the color value of the pixel; and packaging the target image with the updated color value into the light effect animation data. According to the method, the light effect animation data is generated by utilizing the attribution probability and odd-even spatial modulation, and the smooth pixelated light effect can be realized when the atmosphere lamp is played.

The invention relates to a depth map super-resolution method based on double-edge consistency constraint, belongs to the field of image quality enhancement, and solves the problem that the generalization ability is reduced due to the fact that the double-edge consistency constraint is not considered and feature fusion of a color map and a depth map is insufficient in an existing depth map super-resolution method. The method comprises the following steps: firstly, converting a depth map super-resolution problem into a three-task optimization model of double-edge consistency constraint, and secondly, according to an alternating direction multiplier method theory, converting the model into an iterative optimization process of color map edge updating, depth map edge updating, depth map updating and augmented Lagrange multiplier updating. Converting the optimization process into an expansion network, embedding a learnable module into an expansion model of an original pure expression to realize high-throughput transmission so as to enhance the expression capability, and further providing a multi-cross attention compensation fusion module; the module realizes approximation of a near-end operator by aggregating a color map edge, a depth map edge, cross-stage features and auxiliary information.

The invention relates to the technical field of laser measuring instruments, and provides a plug-in type handheld laser scanner and method based on light spot fine positioning, and the method is characterized in that a light spot scanning plug-in is accessed through a standardized interface of an equipment main body, and the light spot scanning plug-in supports a transverse arrangement mode and a longitudinal arrangement mode; acquiring centimeter-level coarse positioning information by using the equipment main body; according to the arrangement mode of the light spot scanning plug-in, controlling the light spot scanning plug-in to carry out light spot scanning according to a corresponding scanning strategy; in the light spot scanning process, depth data and a color image of the target object are synchronously collected; performing fusion processing on the centimeter-level coarse positioning information, the submillimeter-level fine measurement data, the depth data and the color image, and constructing point cloud data of the target object; and the detail precision and the texture effect of the point cloud data are optimized based on the depth data and the color image, so that the problem of target loss caused by contradiction between efficiency and precision of a traditional laser measuring instrument can be solved.

Embodiments of the present disclosure relate to classifying the color of objects in digital images. This disclosure relates to a color classification system that accurately classifies objects in digital images based on color. Specifically, in one or more embodiments, the color classification system utilizes a multidimensional color space and one or more color maps to match objects to colors. In practice, the color classification system can utilize one or more color similarity regions generated in the multidimensional color space to accurately and efficiently detect the color of an object.

The invention is suitable for the technical field of aircraft obstacle avoidance, and particularly relates to an aircraft obstacle avoidance control method and system, and the method comprises the steps: delimiting a flight region of an aircraft, collecting the stereoscopic optical remote sensing data of the flight region, drawing an obstacle recognition graph, marking the position of an obstacle, and configuring the color of the obstacle. Wherein one obstacle at least corresponds to one color, calculating the height of each obstacle, and marking the height in the obstacle identification graph; and segmenting the height into a plurality of layers, and creating branch nodes and leaf nodes. By constructing the obstacle avoidance tree, the color data of all obstacles can be visually displayed, and the color image can be quickly verified, so that the flight safety is greatly improved while the normal flight of the aircraft is ensured, the suspicious obstacles on the flight path can be verified by extracting the RGB values, the flight path planning is optimized, and the flight path planning efficiency is improved. And the flight safety is further improved.

The invention relates to the technical field of computers, and discloses a digital twinning-based power grid monitoring data visual interaction method and system, and the method comprises the steps: carrying out the real-time monitoring of the operation state of a target power grid, and collecting the operation data of the target power grid; mapping the operation data to a digital twin power grid model of the target power grid to carry out anomaly diagnosis, and obtaining a power grid diagnosis result of the target power grid; based on the power grid diagnosis result, through a fault tree model of a target power grid and a fault tree logic relationship, determining occurrence states of each intermediate event and each bottom event, and obtaining an abnormality diagnosis result; and displaying abnormal equipment and a fault area in a display interface of the digital twin power grid model with different colors, icons and flickering effects based on the abnormal diagnosis result, and viewing detailed information of the abnormal equipment and the fault area through click operation. According to the invention, the abnormal source can be rapidly and accurately positioned, and the power grid operation stability is improved.

The invention provides a PRPD atlas processing method and related equipment, and the method comprises the steps: obtaining an original atlas corresponding to an atlas processing instruction in response to the atlas processing instruction; performing content extraction on the original atlas to obtain atlas content in the original atlas; adjusting the size of the map content of the original map to obtain a standard size map; background information in the standard size map is eliminated, and a target map is obtained; rGB data of all target pixel points in the target map are converted into gray values, a pseudo-color map is generated according to the gray values obtained through conversion by means of a preset pseudo-color mapping formula, and the target pixel points are pixel points not smaller than a preset gray threshold value. By applying the method provided by the embodiment of the invention, the background information in the atlas can be efficiently eliminated.

The embodiment of the invention discloses a shielding removal method and device based on a nerve radiation field, electronic equipment and a medium. A specific embodiment of the method comprises the following steps: acquiring scene data of each view angle to obtain a scene image set; generating a depth image set and a prediction color map set according to the scene image set; generating a first view angle mask according to the scene image corresponding to the first view angle; generating each second view angle mask based on the first view angle mask and the depth image set; generating non-occlusion region supervision information based on the multi-view data and the prediction color map set; generating occlusion area supervision information based on the multi-view data and the depth image set; according to the non-shielding area supervision information and the shielding area supervision information, training to obtain a shielding-free three-dimensional model; and based on the de-occlusion three-dimensional model, obtaining a de-occlusion scene image of the target visual angle. According to the embodiment, the de-occlusion image is generated through the multi-view clue and the neural radiation field model, and the quality of the de-occlusion image is improved.

Methods and devices are provided to encode and decode a data stream carrying data representative of a three-dimensional scene, the data stream comprising color pictures packed in a color image; depth pictures packed in a depth image; and a set of patch data items comprising de-projection data; data for retrieving a color picture in the color image and geometry data. Two types of geometry data are possible. The first type of data describes how to retrieve a depth picture in the depth image. The second type of data comprises an identifier of a parametric function and a list of parameter values for the identified parametric function.

A method, apparatus, non-transitory computer readable medium, and system for data generation include obtaining a text prompt describing an object within a scene and generating, using a text-to-mask generation model and based on the text prompt, a color map corresponding to the scene. The color map indicates a region corresponding to the object from the text prompt. An image segmentation mask is generated based on the color map. The image segmentation mask comprises a plurality of regions corresponding to a plurality of image elements in the scene including the region corresponding to the object from the text prompt.

A method for determining a position of joining partners for a joining process includes providing the joining partners in a joining position, capturing a real image of the positioned joining partners, generating a false color image based on the real image, and determining, based on the false color image, at least one item of information about the position of at least one of the joining partners.

The invention provides a motion feature prediction method, a task execution device, an electronic device and a medium, the motion feature prediction method comprises the following steps: obtaining an intermediate feature output by an image processing model, the intermediate feature being obtained by processing input data by the image processing model, the input data comprising a color image feature, and the color image feature being a color image; the color image features are used for representing color images collected in the process that the task execution equipment executes the target task, and the intermediate features are used for representing information in the color image features; the middle features, the depth image features and the noise motion features are input into a motion model, predicted motion features output by the motion model are obtained, the predicted motion features are used for representing the motion trail of the task execution equipment in the future time period, and the depth image features correspond to the color image features in time. According to the technical scheme, the accuracy of motion feature prediction can be improved.

The invention discloses a three-dimensional scene reconstruction method for obtaining real three-dimensional data based on simple shooting, and the method comprises the steps: 1, carrying out the surrounding video shooting of a target object through a camera of a cellphone, and obtaining the video data containing the target object; 2, decomposing the video data into a plurality of image sequences according to frames, and optimizing the image sequences to generate an original image sequence; 3, performing streaming segmentation on the original image set frame by frame to obtain a mask sequence of the target object, and obtaining a foreground image sequence according to the mask sequence; step 4, performing camera pose estimation on the original image set; step 5, inputting the foreground image sequence and corresponding camera internal and external parameters into a 2DGS model to generate an initial three-dimensional scene; and step 6, performing fine processing on the generated three-dimensional scene, generating a color map and a depth map in the 2D three-dimensional scene for mapping a voxel network, and extracting a contour surface on the voxel network to obtain a final three-dimensional scene.