199 results about "Hue" patented technology

The invention discloses an SAR image orientation sensitive target detection method based on sub-aperture image saturation characteristics, and belongs to the technical field of synthetic aperture radar image target detection. The azimuth sensitive target detection is realized through the following steps: 1) sub-aperture image generation: dividing an azimuth frequency spectrum into a plurality of sub-bands, and generating a plurality of sub-aperture images through inverse Fourier transform; 2) color synthesis and feature extraction: allocating different hues to each sub-aperture image by using an HSV color space, synthesizing an RGB color image, converting the RGB color image to the HSV color space, and extracting a saturation channel in the RGB color space as an azimuth sensitivity feature map; and 3) target detection: carrying out threshold segmentation on the saturation feature map, and identifying a pixel region with a high saturation value, namely, an orientation sensitive target. According to the method, the color saturation change caused by the scattering difference of the target in different sub-apertures is utilized, effective detection of azimuth sensitive targets such as artificial buildings and vehicles is achieved, and the method has the advantages of being simple in calculation and high in robustness.

The invention relates to a balanced color perception enhancement method for rail transit target detection, and belongs to the technical field of rail transit and computer vision. According to the method, brightness channel adaptive histogram equalization enhancement CLAHE-LC, a multi-segment tone channel mask mechanism MSHCM-M and a three-stage hybrid mechanism non-maximum suppression TSLSH-NMS technology are combined, so that the problems of complex illumination, multi-target shielding, color interference and the like in a rail transit scene are solved, and the accuracy and recall rate of target detection are improved. The method can be seamlessly integrated into an existing deep learning target detection framework, is compatible with multi-label, multi-category and multi-scale features, and remarkably improves the precision and recall rate of target detection in a rail transit scene. Experimental verification shows that the real-time performance is guaranteed, meanwhile, the detection performance is obviously improved compared with a traditional scheme, and the method is suitable for being applied to an actual rail transit safety monitoring and intelligent maintenance system.

The invention provides a semantic segmentation and image enhancement method and system for a complex background of a transformer substation, and relates to the technical field of computer vision, and the method comprises the following steps: collecting original image data of the transformer substation, and combining heat map saliency analysis and a spectrum residual error method based on a reciprocal function to obtain a semantic segmentation and image enhancement result; preprocessing the collected original image data to obtain a hue saturation brightness image; performing brightness component extraction on the hue saturation brightness image, and establishing an end-to-end image enhancement model based on an IAT model; performing brightness component enhancement by using the image enhancement model to obtain an enhanced image; and performing diffusion processing on the enhanced image based on a diffusion coefficient function, and performing semantic segmentation processing on the image after diffusion processing by using a semantic segmentation model in combination with a Laplacian operator to obtain a semantic segmentation result. According to the method, image distortion and excessive problems caused by image enhancement are avoided, and the readability of the image is ensured, so that a reliable data basis is provided for subsequent analysis.

An image capturing apparatus for capturing an object and generate an image, displaying the image, performing signal processing for the image, and accepting an input from a user, wherein the image is displayed with a vectorscope superimposed thereon, the vectorscope indicating a distribution of color components included in the image based on a hue and a color density and an index for specifying a range of a skin color region in the image, a display position of the index on the vectorscope is specified based on the hue and the color density values, and a size of the index is specified by the range of the skin color region value, and processing is performed for decreasing a detail level of an image signal of the image having a color component included in the index.

The invention discloses a night enhanced imaging and dynamic denoising method for an automobile data recorder, relates to the technical field of vehicle-mounted image processing and computational photography, and is used for solving the problems of insufficient brightness and too strong noise of night videos of the automobile data recorder under a low-illumination condition. Estimating the zero-mean Gaussian noise intensity of a read link and the Poisson noise intensity increasing along with the brightness in the same frame, converting the zero-mean Gaussian noise intensity and the Poisson noise intensity into brightness equivalent parameters, and introducing a unified control function G as the scale reference of all enhancement and noise reduction links; defining a sharing priori H and carrying out unified constraint in dense optical flow, depth time sequence denoising and local tone mapping; and in combination with the motion mask and the background model, performing differential space-time denoising and proportional write-back, and in cooperation with motion blur detection, spatial variation motion kernel and constrained deconvolution and multi-frame detail recharge, obtaining target brightness and outputting an RGB frame after proportional write-back.

The invention discloses an atmosphere lamp control method and device based on visual perception and an automobile, and the method comprises the steps: partitioning a display screen, obtaining a display partition matrix, and enabling partition elements or partition element combinations in the display partition matrix to correspond to corresponding lamp strips; performing multi-dimensional saliency scoring on the candidate color of each partition by using a K-means clustering module, a vision priority detection module or a spatio-temporal context weighting module according to the preprocessed display image, and determining a typical color according to the saliency comprehensive score of each candidate color; and respectively mapping the typical color of each display subarea to the corresponding lamp strip. Visual intelligent perception is achieved, the intelligent degree of atmosphere lamp control in the vehicle is improved, and the problems that in an existing automobile atmosphere lamp control technology, screen partition is simple, color picking is not intelligent, simple screen average color picking or partition color picking is mostly adopted, dominant hues and key details perceived by human eyes cannot be accurately reflected, and the color picking efficiency is poor are solved. And the light color and the picture content are separated.

Provided are a color correction matrix optimization method, an electronic device, and a medium. The method includes performing correction processing on a color source matrix of an acquired color chart image based on a color correction matrix to obtain an output color matrix; determining a global hue error and a saturation constraint regularization term based on Lab coordinates of the output color matrix and Lab coordinates of a target color matrix of a target color chart image and constructing a loss function; and updating the color correction matrix based on a value of the loss function until the value of the loss function converges to obtain an optimal color correction matrix to perform color correction on a to-be-processed image based on the optimal color correction matrix.

The invention discloses a mode switching control method and related equipment, and the method comprises the steps: obtaining an image of a target region through employing image collection equipment when the image collection equipment is in a night mode that a light supplement lamp is turned on, and taking the image as a detection image; performing block processing on the detection image to obtain a plurality of first image blocks; performing hue detection processing on the plurality of first image blocks in the detection image to obtain hue detection information of the detection image; obtaining the difference between the hue detection information of the detection image and hue reference information, wherein the hue reference information is used for representing the hue characteristics of the image of the target area in the night period; and if the difference reaches a hue evaluation threshold value, switching the image acquisition equipment to a daytime mode in which the light supplement lamp is turned off. According to the technical scheme provided by the embodiment of the invention, the accuracy, reliability and robustness of mode switching of the image acquisition equipment can be improved.

Systems and methods for visualizing time-varying two-dimensional (2D) color maps are disclosed. The 2D color map may include luminance values ​​corresponding to hues of varying intensities that change over time. In some 2D color maps, the hue used for the intensity scale may change over time, while the luminance of the pixels may remain constant. In some 2D color maps, both hue and luminance may change over time relative to intensity.

The application provides an image hue adjusting method and device, a computing device and a storage medium, and the method comprises the following steps: acquiring color information corresponding to an image to be adjusted, wherein the color information comprises hue values of various colors in the image to be adjusted; determining an adjusting angle and an adjusting ratio corresponding to the color to be adjusted; determining a hue adjusting mode according to the hue value of the color to be adjusted and the adjusting ratio; and calculating an adjusted hue value based on the hue adjusting mode, the hue value of the color to be adjusted, the adjusting angle and the adjusting ratio. The image hue adjusting method provided by the scheme makes the transition of the adjusted color natural, and improves the user experience.

This application provides an image data processing method, an electronic device, and a computer storage medium. The image data processing method includes: obtaining an image to be processed in a preset color space, wherein the preset color space is a color space that reflects the hue of the image to be processed; determining multiple color components corresponding to the background reference color system of the image to be processed, and obtaining at least a portion of individual color components among the multiple color components; obtaining a color range corresponding to each individual color component based on the pixel number distribution of the individual color components; and obtaining a background color range of the image to be processed based on the color range corresponding to each individual color component. This application embodiment makes the determined background color range more accurate.

The invention belongs to the technical field of point cloud precise registration, and particularly relates to a point cloud precise registration method for a large-scale building scene. The method comprises the following steps: S1, acquiring a point cloud, and performing hue correction on the point cloud to obtain hue information after hue correction; s2, constructing a hybrid adaptive residual optimization framework according to the point cloud and the hue information, and obtaining an optimal transformation parameter through optimization iteration; and S3, performing point cloud fine registration according to the optimal transformation parameter to obtain a registered point cloud. According to the point cloud precise registration method for the large-scale building scene, high-precision, high-robustness and high-efficiency point cloud precise registration under the conditions of low overlapping rate, strong symmetric structure, illumination variation and geometric noise interference is realized through a hybrid adaptive residual optimization framework fusing geometry and robust hue.

The invention belongs to the technical field of multi-source remote sensing image fusion, and relates to a structure-texture collaborative interference radar altimeter image and optical image fusion method and system. The method comprises the following steps: reading an optical image and a wide-swath interference radar altimeter image, and carrying out preprocessing and space-time registration; respectively decomposing the registered optical image and radar altimeter image into a structural layer and a texture layer by adopting a multi-scale local total variation decomposition method; fusing the structure layer by adopting a fusion rule based on weighted local energy and local gradient consistency to generate a fused structure layer; fusing the texture layer by adopting a fusion rule of combining partition filtering and maximum absolute value to generate a fused texture layer; adding the fusion structure layer and the fusion texture layer, and reconstructing to obtain a gray fusion image; and performing intensity-hue-saturation inverse transformation with the chromaticity and the saturation of the original optical image by taking the gray level fusion result as intensity to generate a final natural color fusion image.

The application discloses a boride identification method, system and related device, and relates to the field of alloys.The method comprises the following steps: obtaining a metallographic image of a titanium-aluminum alloy; converting the metallographic image into a first image represented by color in terms of hue, saturation and brightness; determining a saturation threshold and a brightness threshold; dividing each pixel point in the first image into a boride category and a background category according to the saturation threshold and the brightness threshold; obtaining a binary image of the first image according to the classification results of the pixel points; identifying at least one target region composed of multiple target pixel points in the binary image by using a continuous region identification algorithm; and determining the target region as a boride region.The application screens the pixel points by using saturation and brightness, so that the possibility of selecting a shallow corrosion structure is reduced; after a suspected boride region is selected, the continuous region identification algorithm is used to identify and screen according to the shape characteristics of the boride. Therefore, the application can effectively improve the identification accuracy of the boride.

This invention belongs to the field of analytical chemistry technology, specifically relating to a composite indicator and testing method for determining cadmium content in crude cadmium. The composite indicator comprises xylenol orange, thymol blue, and sodium indigo disulfonate, with a mass ratio of xylenol orange, thymol blue, and sodium indigo disulfonate of 1.0–1.5:0.3–0.6:0.5–1.0. This invention utilizes the ternary compound of xylenol orange, thymol blue, and sodium indigo disulfonate, employing the blue background of sodium indigo disulfonate and the reddish-purple and yellow hues of xylenol orange for optical color adjustment. This transforms the color change at the titration endpoint from the traditional reddish-purple to yellow to a bluedish-purple to yellowish-green, significantly increasing the color difference and creating a clear and sharp color change interface, greatly improving the accuracy and reliability of visual endpoint determination.

The present application relates to a kind of color titration identification method, system and equipment based on combination prediction model, belong to solution titration identification technical field, its method includes: using hue measured value trains ARIMA model;Judge the difference between the hue measured value after each titration and the hue predicted value of ARIMA model whether greater than first threshold value;If yes, then determine the current titration point as color mutation inflection point, and at least three consecutive hue measured values after color mutation inflection point are used to train linear regression model;Based on the linear regression model after training, iteratively execute three times hue value prediction and titration operation, until the average difference between the predicted value and the measured value for three times is less than second threshold value, determine the first drop point in the first three drop points as color smooth inflection point.The present application uses different models to identify color in different reaction stages, which can adapt to the demand of multi-color titration formula, and improve the accuracy and anti-interference ability of color titration.

This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for histogram-based frame dropping for smooth motion. A display processor may be configured to generate a data structure indicative of a spread of color tones between a set of image frames associated with a first frame rate of a source application. The display processor may also be configured to output a rate-matched set of image frames at a second frame rate associated with a display panel by dropping a first subset of image frames associated with the set of image frames based on the spread of color tones. The display processor may be configured to output the rate-matched set of image frames at the second frame rate by duplicating a second subset of image frames of the set of image frames based on the spread of color tones.

This product management system uses indicators to detect when a product is not present on the product display surface, while minimizing its impact on store design. [Solution] The product management system comprises a camera 2 that photographs the product placement surface 10a, an indicator M placed on the product placement surface 10a and hidden by the product G, and a product detection unit that detects, based on the detection of the indicator M by the camera 2, that the product G corresponding to the detected indicator M is not present on the product placement surface 10a. The indicator M is made of a paint that includes invisible light reflective paint that reflects invisible light, and is transparent or has the same hue as the product placement surface 10a.

The embodiment of the invention provides a video generation method and device, electronic equipment and a computer readable storage medium, and relates to the technical field of artificial intelligence. According to the method, a reference image and a text cue word are obtained, and a complete background image, an external expansion background image and a target object image are generated according to the reference image, so that the generation of a video clip depends on a unified and expanded background reference, and the structure and color continuity of a background region is kept in a segmentation generation process; on the basis, multi-stage background modeling of a complete background image and an external expansion background image is combined, so that background consistency of the generated video clip is kept, and dynamic change of the target object under rich semantic guidance is realized. The continuous and complete video content is obtained after splicing, the static and steady state characteristics of the background can be maintained, the cross-segment background texture and hue drift can be significantly reduced, and the stable presentation of the background area under the main body action change can be realized.

The invention provides a microscopic image vignetting correction method, which comprises the following steps of: firstly, converting an image to be processed into an HSV color mode, and decomposing the image into a hue channel image H, a saturation channel image S and a brightness channel image V; then, an optimization equation is constructed and solved, and a brightness imbalance factor matrix L and a dark field layer D are obtained; and finally, calculating a corrected image of the V-channel image, re-synthesizing the corrected image, the original H-channel image and the S-channel image into an HSV image, and converting the obtained HSV image into an RGB image to complete correction. According to the method, the optimization equation is constructed based on the imaging physical principle, the sparsity characteristic of the brightness imbalance factor matrix is utilized, and the brightness imbalance factor matrix is subjected to regularization constraint through the L1 norm, so that the brightness imbalance factor matrix of the vignetting image is solved and obtained, and then efficient vignetting correction is achieved.

The application discloses a light source color resolution evaluation method, device and equipment and a storage medium, and relates to the technical field of light source quality testing. The spectral power distribution of a test light source is acquired under multiple exit angles, and the bidirectional reflectance factor of a test color sample is combined to calculate three stimulus values and white point three stimulus values. The hue angle, brightness value and saturation value of each test color sample under the exit angle are calculated according to the three stimulus values and the white point three stimulus values. The comprehensive color error value of the test light source relative to a reference light source under the exit angle is obtained according to the aforementioned three parameters. The color resolution index of the test light source is obtained according to the neutral density index of the test light source and the comprehensive color error value corresponding to each exit angle. The spectral power distribution is collected under multiple exit angles, the color rendering performance of the light source in the front is considered, and the spectral output difference under different angles is also considered, so that the color rendering consistency and the directional characteristics of the light source in the spatial distribution can be comprehensively reflected.

U I T T R E K S E L Disclosed is a remote sensing image enhancement method and system based on homomorphic filtering topographic correction. The method includes: after acquiring a remote sensing image to be enhanced, converting the image from a red—green—blue (RGB) color space to a hue—saturation—value (HSV) color space, and extracting a brightness component; performing homomorphic filtering processing on the brightness component; combining’ the processed. brightness component with saturation and. hue, and. inverting the component back to the RGB color space to complete image enhancement; acquiring grayscale distribution of the enhanced image, and calculating image information entropy; further calculating contrast of the image through grayscale values of pixels; calculating an enhancement score of the image based on the information entropy' and. contrast, and. comparing' the enhancement score with a preset score to determine whether the image enhancement is completed. The present invention can effectively improve the image quality of remote sensing images. (+ Fig. 1)

The application discloses a kind of artificial rainbow formation method and system based on visual feedback, its method includes: the last fully connected layer of convolutional neural network AlexNet is modified, it outputs four values, respectively corresponding hue, saturation, color brightness and arc length;AlexNet is initialized using pre-training weight and is trained, and the obtained AlexNet model is used as identification model;According to AlexNet model, sampling image is identified to obtain identification result;Using random forest regression model to predict control parameter, according to the predicted control parameter adjustment spraying system.The artificial rainbow formation method of the application considers incident angle, spatial position, time parameter, water pressure and air pressure size and the density and size of water droplet of water mist, improves the existing artificial rainbow calculation method;By real-time feedback and output decision to the rainbow image sprayed by spraying device, the incident angle of sunlight and spray head can be automatically controlled by spraying device, so that better rainbow effect is obtained.

A system maintains stable image colors of a user, such as face tones, when the user is at a computer display device and the user's image is transmitted to other persons, such as in an online meeting. The system adjusts the user's image based on factors such as the light, color and brightness of the computer display device, foreground light and color, background light and color, and light incident on the user and background.

The application provides a cloud image efficient synthesis method, belonging to the field of image processing, comprising the following steps: calculating the edge distance transform of the mask of the foreground image to generate a multi-level boundary information graph; converting the background image and the foreground image to the Lab color space, and inputting the Lab channel, the mask and the multi-level boundary information graph into a generator network to output a preliminary fusion image in the Lab color space; using a brightness discriminator and a chroma discriminator to respectively judge the local texture consistency of the preliminary fusion image and a real fusion image in the L channel, and the global color harmony of the preliminary fusion image and the real fusion image in the a and b channels; performing the adversarial training of the generator network and the discriminator based on a total loss function until the network model converges, and using the converged generator network to generate a final fusion image. The application can guarantee the high realism and consistency of the fusion image in the details and the overall color tone.

The present disclosure provides an image processing method and device, electronic equipment and storage medium, relates to the technical field of image processing, and particularly relates to the field of computer vision. The specific implementation scheme is: for a pixel in a first image, determining a saturation adjustment weight according to at least one of a hue value, a saturation value and a lightness value of the pixel; determining a target lightness value according to a numerical range in which the lightness value of the pixel is located; adjusting the saturation value of the pixel according to the saturation adjustment weight, and adjusting the lightness value of the pixel according to the target lightness value, to obtain a color-enhanced image.

Provided is a method with which an evaluation index of an engraved mark formed on an object by laser marking can be quantified. According to one embodiment of the present invention, provided is a method for quantifying an evaluation index of an engraved mark formed on an object by laser marking, the evaluation index including at least contrast, color unevenness, hue, and engraved mark density. The method comprises: a step of calculating, as the contrast, the absolute value of the difference between the most frequent value of a laser irradiation region and the most frequent value of a laser non-irradiation region; a step of calculating, as the color unevenness, a standard deviation of the laser irradiation region; a step of calculating, as the hue, a ratio of the most frequent value of the laser irradiation region for each the colors of red, green and blue to the sum of the most frequent values of the laser irradiation region for the respective colors; and a step of calculating, as the engraved mark density, the ratio of the area of a laser marking good region to the total area of the laser marking good region and a laser marking defective region in the laser irradiation region.

The application provides an adaptive axial resolution 3D velocity field measurement method and system, and relates to the technical field of optical measurement. The method uses excitation sources with different wavelengths to synthesize laser spectra with color changing along the depth direction through a spatial light modulator; then a light beam is separated from the laser spectrum, a corresponding spectral image is extracted, depth information and RGB color information of the spectral image are obtained, the RGB color information is converted into HSV color information, and a corresponding relationship between the hue H and the depth is established; then tracer particles are uniformly scattered in the flow field to be measured, the flow field to be measured is excited by using the laser spectrum, and time sequence images of the flow field to be measured are collected by using a color camera; tracer particle coordinates and HSV color information of the tracer particles are extracted from the time sequence images; based on the corresponding relationship between the hue H and the depth, depth information of the tracer particles is obtained, a three-dimensional particle field is reconstructed by combining the tracer particle coordinates, and a three-dimensional velocity field of the flow field to be measured is obtained by using a variational optical flow model.

In a method to improve backwards compatibility when decoding high-dynamic range images coded in a wide color gamut (WCG) space which may not be compatible with legacy color spaces, hue and / or saturation values of images in an image database are computed for both a legacy color space (say, YCbCr-gamma) and a preferred WCG color space (say, IPT-PQ). Based on a cost function, a reshaped color space is computed so that the distance between the hue values in the legacy color space and rotated hue values in the preferred color space is minimized. HDR images are coded in the reshaped color space. Legacy devices can still decode standard dynamic range images assuming they are coded in the legacy color space, while updated devices can use color reshaping information to decode HDR images in the preferred color space at full dynamic range.

[Problem] To provide an endoscope diagnosis program, an endoscope diagnosis device, a control method for an endoscope diagnosis device, and a program for generating an endoscope diagnosis trained model which each make it possible to use endoscope images that have been accumulated to date to diagnose the presence or absence of a lesion, without limitations on the type of endoscope device. [Solution] A computer is made to function as: a diagnostic-use image acquisition unit 66 that acquires a diagnostic-use image of a site that is subject to diagnosis which has been captured with an endoscope device 20; a color tone correction unit 67 that performs color tone correction on the diagnostic-use image in accordance with a reference color tone, which is a color tone of an image of a site subject to diagnosis having been captured in advance with the endoscope device; and a lesion presence / absence diagnosis unit 68 that inputs the corrected diagnostic-use image into a trained model and that performs a process for diagnosing the presence or absence of a lesion from a result output by the trained model, said trained model having being generated by machine learning of a plurality of training images which have been subjected to color tone correction with the reference color tone.