65 results about "Color model" patented technology

This invention provides a method for detecting adhesive buildup on the surface of a running track, comprising: acquiring an HSV image corresponding to the running track to be tested; performing a first image processing on the HSV image to obtain a first intermediate image identifying a feature region; the feature region consisting of undifferentiated adhesive buildup areas and interference areas; performing a second image processing on the HSV image to obtain a second intermediate image identifying interference areas; and determining the target information of the adhesive buildup on the surface of the running track to be tested based on the first and second intermediate images. A running track surface adhesive buildup detection device, a computer device, and a storage medium are also provided. Using the HSV color model, and through relevant image processing on the HSV image, the target information of the adhesive buildup on the surface of the running track to be tested is finally determined; compared with manual visual inspection, this method can greatly improve detection efficiency, reduce labor costs, and accurately determine the target information such as the area of ​​the adhesive buildup on the surface.

One variation of a method for modeling a human body includes: driving a sensor block along a path above a platform occupied by a user and recording a sequence of depth images and color images, via the sensor block, at each capture position in a sequence of capture positions along the path; compiling the set of depth images into a low-density 3D model of the user and a high-density 3D model of the user; extracting a set of color patches, from the sequence of color images, corresponding to discrete regions on a surface of the low-density 3D model of the user; projecting the set of color patches onto the surface of the low-density 3D model to generate a 3D color model of the user; and extracting a value of a dimension, projected from the low-density 3D color model onto the high-density 3D model, from the high-density 3D model.

According to various embodiments, there is provided an operation method of an electronic device including: acquiring a plurality of images of a user, captured by an RGB camera of the electronic device, acquiring a specific area on a specific body part of the user from the plurality of images, generating a plurality of first data associated with an RGB color model on the specific area associated with the plurality of images, generating a plurality of second data associated with a YCrCb color model based on the plurality of first data, generating first time-series data associated with a green channel based on the plurality of first data; generating third time-series data based on combining the first time-series data and the second time-series data; and estimating a pulse of the user based on conversion of the third time-series data into a frequency domain.

The invention relates to the technical field of remote sensing observation, and discloses an image color uniformizing model training method and device and an image color uniformizing method and device, and the method comprises the steps: constructing a training sample library and a verification sample library for an initial image in an image set; the training sample library is used for training the multiple different initial models, and multiple initial uniform color models are obtained; combining the initial uniform color models in pairs to obtain a plurality of candidate uniform color combination models; samples in the verification sample library are input into the candidate uniform color combination models, output results of the candidate uniform color combination models are obtained, one initial uniform color model in the combination models obtains a first uniform color image, the other initial uniform color model obtains a second uniform color image, and the output results are the second uniform color image; and evaluating and selecting an output result of each candidate uniform color combination model. According to the invention, the problem that color uniformity between images at different positions cannot be processed because reference images at the same position need to be subjected to model training is solved.

The invention relates to a three-dimensional part color recovery method and device, equipment, a medium and a product. The method comprises the steps of obtaining an initial model file corresponding to a target part; recursive analysis processing is conducted on the initial model file, a topological shape set and a color set are obtained, and the color set comprises colors corresponding to all topological shapes in the topological shape set; obtaining a target assembly structure corresponding to the target part, and constructing a target shape combination based on the topological shape set and the target assembly structure; and performing color mapping processing on the target shape combination by using the color set and the target assembly structure to obtain a color model file corresponding to the target part. By adopting the method, the visualization accuracy can be improved.

The embodiment of the invention discloses an image labeling method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a target image which comprises a to-be-labeled skin disease region; inputting the target image into the trained segmentation model for processing, and outputting an initial segmentation region; the segmentation model is obtained based on sample image training of a marked skin disease area, the segmentation model comprises an encoder, a decoder and a Mama bridging module connected between the encoder and the decoder, and the Mama bridging module is used for performing multi-scale fusion on high-level semantic features and low-level detail features which are extracted by the encoder and have different scales; and performing color feature screening on the initial segmentation region based on an HSV color model to obtain a target region image.

The invention discloses a cross color suppression method and system for multi-color 3D printing. The cross color suppression method comprises the following steps: establishing a steady-state operation reference before material switching, executing a pre-switching withdrawing action, guiding a new material to be stably injected and fused with a residual melt, reconstructing a cooperative relationship between extrusion flow and a nozzle moving path, and implementing dynamic pressure balance regulation and control; according to the method, a local path fine tuning instruction adaptive to material characteristics is generated, progressive flow climbing is executed, state verification after switching is completed, and other systematized steps are carried out, so that accurate control over the material switching process is achieved; the color crossing phenomenon in multi-color 3D printing is effectively inhibited, the color boundary is clear and sharp, the mechanical performance of a transition area is uniform and stable, meanwhile, material waste and time delay caused by excessive pre-extrusion in a traditional method are avoided, the appearance quality and structural reliability of a multi-color printed piece are remarkably improved, and the application prospect is wide. And the method is particularly suitable for manufacturing requirements of high-precision color models and functionally graded materials.

This application provides a method and system for processing surface defects on silicon wafers, an electronic device, a computer-readable storage medium, and a product. The processing method includes: acquiring image information, whereby the image information characterizes defect features on the silicon wafer surface, and the defect features characterize the peak and valley differences on the silicon wafer surface within a preset unit area; processing the image information to convert it into a surface feature model, where the surface feature model includes grayscale values ​​corresponding to the defect features, with different defect features corresponding to different grayscale values; coloring the surface feature model to generate a color model, where the color model includes a preset set of colors, with different colors corresponding to different grayscale values; and outputting defect identification results based on the color model. In this way, the nanometer-level peak and valley differences on the silicon wafer surface are transformed from discrete numerical values ​​into a visualized color model, thereby solving the problem of not being able to intuitively present defect features and improving the efficiency and accuracy of defect identification.

The application discloses an eye fundus image coloring method, a computer readable storage medium and an electronic device, relates to the technical field of medical image processing, and comprises the following steps: acquiring a gray eye fundus image of a patient with a diagnosis; inputting the gray eye fundus image as a lightness V channel into a pre-trained image coloring model to obtain a hue H channel and a saturation S channel corresponding to the lightness V channel output by the image coloring model; the image coloring model comprises an SPD downsampling module for performing a lossless downsampling operation; and performing color space conversion on the lightness V channel, the hue H channel and the saturation S channel after combination to obtain a color eye fundus image.

The application provides a color painting image reconstruction system and method based on brightness and saturation information matching, and relates to the field of color painting image reconstruction.The application comprises HSI color model conversion, Origin software fitting and reconstructed RGB image;the HSI color model conversion is to convert the measured different color RGB color space into HSI color space;the Origin software fitting is to import the HSI color space data into Origin software for data fitting;the reconstructed RGB image is to reconstruct the obtained HSI color space after fitting to obtain the RGB color image.Through the color painting image reconstruction system, the object color information is efficiently reconstructed, the spectral reflectance characteristics are accurately presented, and a scientific and reliable solution is provided for subsequent 3D object color reconstruction and artistic product digital copying.

Embodiments of the present application disclose a light balance method and device, equipment and a storage medium. The L channel picture in the LAB color model of the acquired to-be-processed image is divided into two regions, and a first target picture and a second target picture are generated. The brightness values of the first target picture and the second target picture are calculated respectively to obtain a first brightness value and a second brightness value. According to the sizes of the first brightness value and the second brightness value, the brightness difference of the first target picture and the second target picture is calculated to generate a brightness difference picture. The brightness difference picture is merged with the to-be-processed image to obtain a target image with balanced light. The L channel picture representing brightness is divided into two regions, the brightness values of the two regions are compared, the brightness difference on the same image is obtained, and the brightness difference picture is generated. The to-be-processed picture is adjusted based on the brightness difference picture to obtain an image with uniform brightness, so that the image meets the standard and the waste piece rate of the photographing device is reduced.

The invention discloses a monocular vision bridge structure three-dimensional reconstruction method, which comprises the following steps: camera calibration: adopting a Zhang camera calibration method to shoot a plane calibration plate image, and resolving an internal parameter matrix K and an external parameter matrix M of a monocular camera; monocular depth and pose estimation: obtaining a bridge structure video shot by a monocular camera, processing video frames through a MonoViT model, and outputting a depth map and a camera pose of each frame; constructing a neural radiation field (NeRF) model fused with depth information: taking a 5D vector as an input, the 5D vector comprising three-dimensional space coordinates (x, y, z) and a visual angle direction (theta, phi), and outputting a density sigma and a color RGB through an MLP network; model training and three-dimensional reconstruction: taking the depth map, the camera pose and the corresponding video frame as training data, and optimizing NeRF model parameters through a total loss function; and after training is completed, inputting any visual angle parameter, synthesizing a new visual angle image through volume rendering, and realizing three-dimensional reconstruction of the bridge structure.

This invention provides an image processing method that enables highly accurate acquisition of information about a subject from an image. [Solution] The image processing method includes an acquisition step of acquiring first information relating to the color of a subject, a first database of color models, and second information relating to ambient light; a determination step of determining the state of ambient light using the second information; and a modification step of changing at least one of the processing relating to the first information or the processing relating to the first database based on the determination result of the state of ambient light.

The invention belongs to the technical field of image recognition, and particularly relates to a garbage classification method and system based on image enhancement, and the method comprises the steps: carrying out the preprocessing of an obtained garbage image, and sampling a reference region block according to a local color variance, so as to extract a reference color model and a reference texture model; calculating a color consistency index, a texture homology index and a structure regularity index of each pixel point; determining a self-adaptive color weight and a self-adaptive texture weight according to a color statistical characteristic and a texture distribution characteristic in the reference region block; and based on the self-adaptive color weight, the self-adaptive texture weight and a preset structure weight, carrying out weighted fusion on the three indexes to generate a material authenticity map. According to the method, the enhancement effect on the complex stained junk image can be remarkably improved by fusing the multi-dimensional features and adopting the self-adaptive weight.

Food contact materials with a color difference ΔE <20 from the L*a*b* coordinates to a color number starting with "5" of the RAL color chart according to the color model according to EN ISO 11664-4, to be determined according to DIN 5033 (1979), based on compositions containing at least one polyester or polystyrene and a colorant consisting of at least one blue anthraquinone dye and at least one ethylene oxide-propylene oxide block copolymer with a central propylene oxide unit between two ethylene oxide units, a molar mass determined from the OH number to be determined according to DIN / ISO 4629, in the range of 5500 to 7500 g / mol, a mass fraction of ethylene oxide units in the range of 45 to 55 wt.% and a flocculation point to be determined according to EN 1890 method B in the range of 70°C to 85°C.

Food contact materials with a color difference ΔE <20 from the L*a*b* coordinates to a color number starting with "3" of the RAL color chart according to the color model according to EN ISO 11664-4, to be determined according to DIN 5033 (1979), based on compositions containing at least one polyester or polystyrene and a colorant consisting of at least one red azo dye and at least one ethylene oxide-propylene oxide block copolymer with a central propylene oxide unit between two ethylene oxide units, a molar mass determined from the OH number to be determined according to DIN / ISO 4629, in the range of 5500 to 7500 g / mol, a mass fraction of ethylene oxide units in the range of 45 to 55 wt.% and a flocculation point to be determined according to EN 1890 method B in the range of 70°C to 85°C.

The application discloses a stereoscopic forming method, a stereoscopic forming device and a storage medium, which mainly forms a current printing layer through solidification, then forms a pigment layer, solidifies the pigment layer to adhere to the current printing layer, forms a color model through superposition, realizes one-time printing of the color model, does not need to be colored separately, and is flexible in color. The main technical scheme of the application is as follows: a stereoscopic forming method, comprising the following steps: adding printing material to a construction window, the printing material being in a covering position of the construction window and corresponding to a current printing layer of a to-be-printed model, controlling a printing platform to be close to the construction window, so that the printing material is solidified on the printing platform to form the current printing layer of the to-be-printed model, controlling the printing platform to be away from the construction window, removing the printing material on the construction window, spraying a layer of pigment layer on the construction window, the pigment layer corresponding to the current printing layer, and controlling the printing platform to be close to the construction window, so that the pigment layer is solidified on the current printing layer. The application is mainly used for printing a model.

Embodiments of the present specification disclose a skeleton screen detection method and device, electronic equipment, medium and program product. The method comprises: obtaining a screenshot of a page to be detected; based on the screenshot of the page to be detected, extracting a target color model corresponding to the page to be detected; and based on the target color model, determining a target skeleton screen detection result corresponding to the page to be detected.

Embodiments of the present disclosure disclose a three-dimensional reconstruction method, a color information acquisition method, an apparatus and a device, wherein the reconstruction method comprises: processing a pixel three-dimensional coordinate set of a pixel point set of a target object surface by using an SDF network to obtain an SDF value set of the pixel point set; processing the pixel three-dimensional coordinate set by using a neural feature network to obtain a neural feature map of the pixel point set; processing, by using a decoding network, based on the SDF value set, the neural feature map and a camera line-of-sight set, to obtain a color information set of the pixel point set; and determining a three-dimensional structure color model of the target object based on a three-dimensional structure model of the target object and the color information set. The embodiments of the present disclosure can realize real-time rendering of the three-dimensional structure model while ensuring a high-quality mesh, and obtain a three-dimensional structure color model of the target object with color information.

One variation of a method for modeling a human body includes: driving a sensor block along a path above a platform occupied by a user and recording a sequence of depth images and color images, via the sensor block, at each capture position in a sequence of capture positions along the path; compiling the set of depth images into a low-density 3D model of the user and a high-density 3D model of the user; extracting a set of color patches, from the sequence of color images, corresponding to discrete regions on a surface of the low-density 3D model of the user; projecting the set of color patches onto the surface of the low-density 3D model to generate a 3D color model of the user; and extracting a value of a dimension, projected from the low-density 3D color model onto the high-density 3D model, from the high-density 3D model.

The application discloses a kind of based on plane Gauss's color model construction method, system, device and medium, method includes the position parameter of initialization Gauss ball, depth order, shape attribute, color attribute and opacity;According to shape attribute, the shortest axis of Gauss ball is determined, and then flat loss is calculated;According to the shortest axis and observation direction, the local normal of Gauss ball is obtained and then normal alignment loss is calculated;According to opacity, sparsity loss is calculated;Gauss ball is colored and color loss is calculated, according to flat loss, normal alignment loss, sparsity loss and color loss, construct final loss function, and shape attribute, local normal, opacity and color attribute are updated by back propagation algorithm.The application improves normal estimation accuracy by flattening Gauss ball, and then improves the color quality, by combining color model, high-quality rendering of a variety of materials is realized, and can be widely applied in computer vision technical field.

The application is suitable for the field of image technology, and provides a chart image processing method and device, electronic equipment and readable storage medium. The method comprises the following steps: acquiring a to-be-processed chart image; converting the color model of the to-be-processed chart image into an HSV color model to obtain a converted chart image; generating a chart mask of each water depth area according to the HSV color of each pixel point in the converted chart image; extracting the corresponding water area contour in the converted chart image according to the chart mask of each water depth area; determining the longitude and latitude information of the water area contour of each water depth area based on a coordinate registration model; and constructing the corresponding chart vector contour according to the longitude and latitude information of each water area contour and the attribute information of the to-be-processed chart image. The application can automatically process the chart image in the whole link and ensure the image processing quality.

The invention discloses an image coloring model construction method, an image coloring method, equipment and a medium. The method comprises the following steps: generating corresponding category descriptions based on a reference grayscale image, a target color image and a text description; constructing a basic image generation model, and constructing a grid latent feature and a binary mask by coding the reference grayscale image data and the target color image data; extracting a color prior representation, performing mask forward noise addition and prediction on a target coloring region in combination with a reference region of a binary mask injection grid latent feature, and calculating a basic denoising loss function; extracting a cross attention graph of text description color vocabularies and object vocabularies in a denoising process, and constraining weight distribution through a color-object alignment loss function; finally, the model parameters are jointly optimized, and an image coloring model is obtained through combination. According to the method, accurate pixel-level region positioning can be realized, semantic color coverage of the whole image is automatically enriched and complemented, and color object dislocation interference is explicitly eliminated in a training reasoning process.

The invention discloses a dust concentration detection method for a mining site, and relates to the technical field of dust concentration detection. The method comprises the following steps: building a dust quality prediction device, and capturing dust images with different qualities; segmenting the color dust image into a set number of channel images in a set color model; screening out candidate channels of which the similarity is higher than a set threshold value; calculating the characteristic parameters of the screened candidate channels, fitting the characteristic parameters with the concentration, and screening out the optimal characteristic parameters; converting the information of the dust image, and enriching a dust image data set; calculating a transmission graph of the dust image; gray channels of all the dust images in the data set are calculated, and correlation and a mean value are calculated in the gray channels and the transmission images; and establishing a coal dust quality detection model, and detecting the dust picture by using the coal dust quality detection model to obtain the dust concentration.

The invention relates to the technical field of image recognition, in particular to an intelligent garbage classification system based on X-ray transmission and deep learning. A scanning module of the system adopts multi-angle dual-energy X-rays to obtain high-energy and low-energy projection data, and reconstructs a cross section attenuation image through a filtering back projection algorithm; the image processing module calculates an effective atomic number of a pixel point by using a dual-energy imaging algorithm, and performs mapping based on a color model so as to realize material distinguishing of the organic matter, the mixture and the inorganic matter; the garbage identification module inputs the mapping transmission image into a deep learning model, completes identification of specific categories of metal, plastic and the like, and outputs a classification instruction in combination with a garbage classification library; and the garbage sorting unit moves the garbage carrier to a collection area according to the instruction. The data management unit stores the transmission image and the classification result, and continuously optimizes the recognition model through incremental learning. According to the invention, transmission type identification and intelligent classification of garbage can be realized, and the classification precision and the system stability are improved.

The purpose of the present application is to provide a method for rendering a three-dimensional digital factory display picture based on an HSV model, which aims to solve the technical problem that the visual experience of operators viewing the three-dimensional digital factory display picture is poor, and it is difficult to adjust the text and background of the picture according to the color value requirements of different users. The method for rendering the three-dimensional digital factory display picture of the present application adopts an HSV color model. The HSV model focuses on the measurement of H and S components, making it very easy for the HSV color system to understand the three-dimensional digital factory display picture, and the color classification is simple and natural. The effect obtained by changing the HSV value is very clear and predictable for the text part and the background part, improving the visual experience of operators viewing the three-dimensional digital factory display picture, and being more consistent with the human visual system. According to the color value requirements of different users, the text and background of the picture are adjusted accordingly, better meeting the visual needs of users and achieving better visual effects.

The invention discloses a video coloring model construction method, a video coloring method, equipment and a medium. The method comprises the following steps: sampling a key frame from a grayscale video sequence; extracting a scene description text and generating a color reference image; constructing a basic video generation model, and introducing a parameter-frozen main branch and a parameter-trainable control branch; constructing a composite visual sequence and setting a time sequence mask sequence; extracting spatio-temporal control features from the control branches, and injecting the spatio-temporal control features into the main branches in a residual superposition manner; an alignment loss function training control branch is introduced, deep target semantic features of the color reference image are extracted, and middle features of main branch denoising are mapped to a semantic space; and setting a loss function to jointly construct a total training target and minimize the total training target, carrying out iterative optimization on specified parameters to obtain a video coloring model, and executing a video coloring operation. According to the method, effective decoupling of single-frame content color editing and long-sequence time sequence color propagation can be realized, and the color fidelity and dynamic time sequence consistency of the generated video are improved.

The present disclosure relates to a document image processing method, device, medium, equipment and model training method. The processing method comprises: performing image segmentation on the document image through a hue saturation value color model to generate a three-channel space image, filtering a background area of the document image to generate a binary edge image, splicing and fusing the three-channel space image and the binary edge image to generate target input data, performing flip recognition on the target input data according to an image flip recognition model, determining a target flip category of the document image, and performing reverse flip on the document image according to the target flip category to obtain a target image. Thus, the text features in the document image are recognized through the image flip recognition model, and the document image is flipped according to the flip angle, so that the uploaded document image is not disturbed by the shooting angle, and the user can intuitively view the information in the document image.