443 results about "Chromaticity" patented technology

The invention relates to the technical field of machine vision and optical detection, in particular to a tea color automatic evaluation method and system based on machine vision. The method comprises the following steps: constructing a stable and repeatable optical measurement environment and calculating radiation baseline parameters; performing multi-band polarization sequence imaging on the tea sample to obtain original data with spectrum and structural characteristics; separating mirror surface and diffuse reflection components on the surfaces of the tea leaves, and performing spectrum consistency correction by combining a radiation baseline; performing multiband spectrum reconstruction and normalization processing on the basis of the corrected diffuse reflection image, mapping to a CIE Lab color space, extracting pixel-level brightness, chromaticity and color distribution statistical indexes, and generating a color feature vector in combination with spatial weighting matrix optimization; a final score is calculated by constructing a quantitative evaluation and grade judgment model and integrating perception color separation, drying calibration color separation and a penalty term, and a grade result is output. According to the method, objective, accurate and automatic evaluation of the color of the tea leaves is realized.

The invention discloses a vehicle-mounted HUD optical performance comprehensive detection method, and aims to solve the problems that a traditional detection method depends on manual operation and is low in detection efficiency and large in error. Project loading and test starting, picture height position calibration, multi-parameter test, brightness and chromaticity test, ghosting test, static distortion test and binocular parallax test items are automatically implemented through detection equipment, and accurate control (such as application of camera pixel-millimeter ratio) of advanced image recognition algorithm and equipment in the test process is carried out. The vehicle machine controls the step number of the curved mirror motor, and the industrial personal computer controls the motion module, so that high-precision measurement of optical parameters is realized, and the test efficiency and the test accuracy are improved.

The invention relates to the field of image processing, and discloses a figure image optimization method based on green screen image matting, which is used for carrying out optimization processing on a figure image. The figure image optimization method based on green screen image matting comprises the steps of performing fractional order differential transformation and other processing on an input green screen figure image, constructing a continuous chromaticity representation space, generating a chromaticity gradient field and multi-resolution chromaticity representation, establishing a geometric topology constraint model according to chromaticity information, generating an initial transparency field, and performing collaborative optimization. Optimizing a boundary curve, generating an accurate boundary contour, establishing a color mixing model, realizing natural transition between a foreground and a background, generating an optimized image, performing subsequent processing such as material optical attribute analysis and the like, and finally outputting an image completely fused with a background environment. According to the method, the foreground can be accurately separated, the edge can be optimized, natural transition and material simulation can be realized, and finally a high-quality figure image which is seamlessly fused with the background and has a physical reality sense is generated.

The invention discloses a brightness and chrominance consistency correction method and system for an LED spliced screen, and the method comprises the steps: firstly recognizing the batch features of a module, dividing the batch features into correction partitions, and respectively collecting the original brightness and chrominance data of a high-gray linear region and a low-gray nonlinear region; and then, calculating the geometric intersection of the original color gamuts of all the partitions to determine the maximum common target color gamut, and eliminating the metamerism phenomenon caused by multi-batch mixed use from the physical level. Then, constructing a non-linear dual-order correction model, and generating a correction coefficient set containing a main linear conversion matrix and a low-gray-scale non-linear offset item; when the control system runs, the linear matrix is dynamically called according to the input gray level, and bias compensation is superposed. According to the invention, the problems of cross-batch visual angle chromatic aberration and color temperature drift and pocking marks under low gray level of the spliced screen are effectively solved, and the high-fidelity display consistency of the full gray level is realized.

The invention discloses a multi-channel multi-color-temperature intelligent light source system for consistency calibration of a camera module and an optimization method, and the calibration system generates PWM signals through an STM32F103ZET6 main control unit of a light source controller to control the light output of each group of cold and warm LEDs so as to provide a stable and adjustable illumination environment; measuring reference brightness and chromaticity parameters of cold light and warm light of each group of LEDs, setting a target brightness value and a color temperature value, and converting the target brightness value and the color temperature value into target chromaticity coordinates; the initial PWM duty ratio of the cold and warm light LED is calculated, a piecewise polynomial nonlinear compensation algorithm is adopted to correct the duty ratio, and the LED is driven to output mixed light meeting the target brightness and color temperature; collecting a large amount of brightness and color temperature data by cyclically and gradually adjusting the duty ratio, and generating an error thermodynamic diagram; and the compensation model coefficient is optimized and updated according to error analysis, so that the light source output keeps high-precision consistency in a full range. Compared with the prior art, the method has the advantages that the brightness and color temperature consistency of the light source can be automatically calibrated and continuously optimized, the accuracy and stability of OTP burning calibration of the camera module are greatly improved, and the production test efficiency and the product imaging quality are improved.

The invention discloses an intelligent monitoring system for camellia oleifera processing based on artificial intelligence, and relates to the technical field of camellia oleifera fruit processing, and the system comprises a temperature monitoring module which comprises a multi-point temperature sensing array which is arranged along the axial direction of a presser and is installed at the front end, the middle part and an oil outlet of a barrel, the temperature sensor is used for collecting temperature and forming temperature distribution data in the barrel; the color detection module is arranged at an outlet of the oil outlet pipeline, obtains oil chromaticity parameters and a yellow-red ratio through spectral imaging, and extracts color feature data; and the volatile matter detection module is arranged above the discharged oil collection cavity and is used for detecting the concentration of volatile organic compounds through a gas sensing array and generating flavor characteristic data. By arranging the temperature monitoring module, the color detection module and the volatile matter detection module, real-time collection of multi-dimensional information in the squeezing process is achieved, and based on an asynchronous triggering and weighted fusion mechanism, space-time alignment and conjoint analysis are conducted, a joint feature vector is constructed, and a real-time prediction result is output.

The invention discloses a full-color LED screen optical adjustment and intelligent energy-saving detection method, and relates to the field of LED screen adjustment and energy saving. Ambient light data is collected through a bionic distributed sensor network, and features such as illumination intensity and color temperature are extracted through PCA dimension reduction; constructing a human eye visual perception model based on the CIE chromaticity diagram, and introducing a visual adaptation factor to dynamically adjust color gamut mapping; dividing a screen dimming area, and calculating an optimal brightness value by adopting a local contrast enhancement algorithm; the RGB ratio is optimized through a genetic algorithm, and the color difference between the mixed spectrum and the target color gamut is minimized; power consumption is monitored in real time, an energy consumption prediction model is established, and an optimization strategy is triggered when a predicted value exceeds a threshold value. The color gamut coverage rate is high, the local contrast ratio is excellent, and the comprehensive energy-saving rate is greatly improved; blue light harm is reduced, screen flicker is eliminated, the visual comfort is improved, the functions of peep prevention and block chain energy consumption evidence storage are achieved, and the display quality, the energy efficiency performance and the use safety of the LED screen are comprehensively improved.

The invention relates to the technical field of constructional engineering quality detection, and discloses a method for testing the apparent quality of mirror-surface bare concrete. The method comprises the following steps: acquiring a multiband multispectral image of the surface of a component; through image fusion and feature decoupling, an independent specular glossiness distribution diagram, a microscopic fluctuation topological diagram and a chromaticity coordinate mapping diagram are obtained; registering and associating the three images to construct a three-dimensional total element apparent characteristic field; performing gridding scanning on the characteristic field to extract glossiness uniformity, texture roughness and chromaticity stability indexes of each grid; inputting a pre-trained apparent quality analysis network to output a local quality evaluation value and a defect type label of each grid; and integrating and generating an overall quality index and generating a repair guidance map according to the defects. According to the method, multi-dimensional, full-field and intelligent fine detection of the apparent quality is realized, the gloss, the texture and the color can be independently and quantitatively analyzed, and the defect can be accurately positioned.

The noise performance of the image sensor is improved by using hundreds of pixel-to-pixel comparisons that all contribute to information about a given pixel. This allows accurate determination of image chromaticity even at extremely low signal-to-noise ratios. In other embodiments, filters of a non-conventional spectral transmittance function are employed to reduce metamerism, enabling resolution of scene information that is not visible to the human eye. Yet other embodiments relate to manufacturing a sparse matrix of transparent pedestals on an array of image photosensors. When a color resist is applied thereafter, these pedestals produce a change in thickness of the resulting resist layer, resulting in the pixels having a different spectral response despite the use of the same color resist. Yet other embodiments relate to image sensor arrangements that enable generation of red, green, blue, and NIR output data using only four conventional (red, green, blue, cyan, magenta, yellow) color resists. Numerous other novel features and arrangements are also detailed.

A method of forming an imaging calibration device for a biological material imaging system is provided. A first set of one or more discrete regions is provided upon or within a retaining member. Each said region of the first set comprises a selected tissue stain material, wherein each of the selected tissue stain materials exhibits a predetermined optical response inside a colour-stable region of a chromaticity diagram defined by the ellipse x2 / a2+y2 / b2=1, where a=0.21, b=0.135, the rotation is −0.07 rad and the translation is (0.38,0.333). A second set of one or more discrete regions is also provided upon or within the retaining member. The second set is formed of one or more coloured filters having a predetermined optical response outside the colour-stable region.

An image highlight removal method and system, a storage medium and a program product relate to the field of general image data processing or production, the method comprising: receiving an input image, converting the input image to a brightness and chrominance separated color space, identifying a highlight pixel region, and generating a highlight mask; expanding outwards by taking the boundary of the highlight mask as a reference, and constructing an annular analysis area; calculating a pixel gradient amplitude and a pixel gradient direction, and generating a linear structure feature data set; calculating a pixel color mean value and a pixel color standard deviation, and generating an abnormal region feature data set; performing defect distribution reasoning based on direction information in the linear structure feature data set and the abnormal region feature data set to generate a structure prior guidance graph; and carrying out source patch constraint according to the structure prior guidance graph, and processing the highlight pixel region to obtain an output image. According to the invention, the highlight removal process can be optimized, and the integrity of equipment defect features in the image is ensured.

The invention provides a display optimization method based on an HK effect, a control unit and head-mounted display equipment, and is applied to the technical field of near-eye optical imaging. The display optimization method comprises the following steps: acquiring real-time eye movement data of a user, wherein the real-time eye movement data comprises a pupil size and a fixation area; determining color features of the virtual image to be optimized in the fixation area, wherein the color features comprise image chromaticity, image hue angle and image brightness; on the basis of an HK effect model, according to the pupil size and the color features, the perception brightness of the user in the watching area is determined; if HK effect brightness compensation is determined to be performed according to the perception brightness, determining an HK effect brightness compensation amount for the fixation area based on the perception brightness and the pupil size; and controlling an optical display unit to generate and display an optimized virtual image according to the HK effect brightness compensation amount. According to the method, the fusion effect of the virtual content and the real environment is improved, and the high-standard visual experience requirement of a user in diversified and dynamic environments is met.

A method provides distance calibration data to a colorimetric range sensor system having a colorimetric range sensor optical pen configured to focus different wavelengths at different distances along a distance measurement axis. The colorimetric range sensor optical pen is arranged in a certain relationship relative to a spherical calibration object having a nominal spherical calibration surface. The relative movement of the colorimetric range sensor optical pen with respect to the nominal spherical calibration surface is controlled to perform a helical scan of a portion of the nominal spherical calibration surface. Distance indication data is determined to correspond to the distance between the colorimetric range sensor optical pen and a surface point on the nominal spherical calibration surface when the helical scan is performed. Distance calibration data for the colorimetric range sensor system is determined based on the distance indication data.

The present application relates to the field of multi-modal chiral structural color anti-counterfeiting, and provides a multi-modal micro-scale dome chiral structural color anti-counterfeiting label, a preparation method and application thereof.The multi-modal micro-scale dome chiral structural color anti-counterfeiting label is prepared from a transparent material and has a micro-scale dome structure on the surface, the label pattern can be customized, and the chroma, brightness, chirality and polarization of the pattern color can be programmed and regulated according to requirements.The preparation method of the multi-modal micro-scale dome chiral structural color anti-counterfeiting label is simple, universal, low in cost and suitable for large-scale production.

A method includes receiving data indicating at least two different base colors to be respectively emitted from base lights on opposite sides of at least one transition light of a light array. Each light of the light array has adjustable colors and is covered by one or more light guides, and receiving data of a target color between the two base colors in at least one coordinate dimension on a chromaticity color space chart. The method also includes determining at least one adjustment color on an opposite side of the target color from a mixed color on the color space chart, and providing the at least one adjustment color to be emitted from the at least one transition light when the base colors are being respectively and simultaneously emitted from the base lights on opposite sides of the at least one transition light.

The invention relates to a color gamut mapping method and device, computer equipment, a chip and a chip module. The method comprises the steps of querying a pre-constructed color gamut boundary corresponding relation based on a target direction axis coordinate value in converted color coordinate values of a sampling point of a to-be-processed image and a chromaticity coordinate value of the sampling point, and obtaining an input color gamut boundary and an output color gamut boundary corresponding to the sampling point; according to the input color gamut boundary and the output color gamut boundary, mapping the color coordinate value to obtain a target color coordinate value corresponding to the sampling point; smoothing the target color coordinate value to obtain a processed target color coordinate value corresponding to the sampling point; and obtaining a target image corresponding to the to-be-processed image based on the processed target color coordinate value. By adopting the method, the color gamut mapping accuracy can be improved.

The present invention provides a color and luminance compensation method and system, and an MR head-mounted display device. The color and luminance compensation method comprises the following steps: acquiring gaze point coordinates of a user to determine a corresponding color and luminance compensation matrix, wherein the color and luminance compensation matrix comprises a chromaticity compensation element and a luminance compensation element for a preset first color in an image to be compensated, and a chromaticity compensation element for at least one other second color, and a value of each compensation element is determined on the basis of a luminance value of the first color at a target position corresponding to the gaze point coordinates; and inputting the color and luminance compensation matrix into a display module, so as to perform, by means of the display module, synchronous color and luminance pre-compensation on a plurality of pixel points in said image. In the present invention, synchronous pre-compensation of color and luminance non-uniformity can be performed in real time on the basis of the gaze point position of human eyes, thereby presenting an optimal effect to the human eyes.

This invention relates to the field of industrial circuit board anomaly monitoring technology and discloses a vision-based industrial circuit board anomaly monitoring method, comprising the following steps: Step 1: Acquire image data and performance test data of all circuit boards, and classify them into image datasets and performance datasets; Step 2: Based on the image datasets, evaluate the appearance defects of each circuit board, generate corresponding bridging area ratios, color deviations, and pin deviations, and construct a three-dimensional defect scoring matrix by comprehensively utilizing image processing, color analysis, and geometric measurement technologies; Step 3: Based on the performance datasets, evaluate the functional defects of each circuit board, generate corresponding functional data sets, achieving high accuracy in multi-dimensional monitoring; Step 4: Based on the image datasets, bridging area ratios, color deviations, pin deviations, and functional data sets, classify the defect types of each circuit board into connection defects, scratch defects, and electrical defects, and output corresponding control commands, achieving high efficiency in intelligent repair.

The invention discloses an RGB-LED spectrum closed-loop system for colorimetry teaching and a color matching method. The system comprises a multi-source input module, a spectrum database module, a dynamic modeling module, an optimization algorithm engine module, a duty ratio calculation module, a PWM driving module and a closed loop verification module. The system is provided with a multi-source input interface, target color coordinates can be obtained, and chromaticity conversion of RGB input is supported; on the basis of the constructed high-precision linear model, solving a normalized strength ratio under constraints by combining an optimization algorithm engine and taking minimization of chromatic aberration as a target; calculating an actual PWM duty ratio according to the actual PWM duty ratio; meanwhile, a spectral irradiation sensor is integrated to collect a mixed color spectrum in real time, actual color coordinates are calculated, and an offset connecting line with an arrow between a target point and an actual point is dynamically drawn in a chromaticity diagram, so that a set of complete visual teaching tool is formed. Compared with a manual color matching scheme, the color mixing precision and the color matching efficiency are greatly improved, and the teaching requirements of colorimetry can be better met.

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 provides a compressor aerodynamic stability prediction method and device, the method comprising color space conversion on an initial flow field cloud image of the compressor to obtain a target flow field cloud image, wherein the target flow field cloud image comprises at least two color channels; for any pixel point in the target flow field cloud image, the gradient amplitude of the pixel point on different color channels is calculated, and a color gradient map is generated according to the gradient amplitudes of multiple pixel points; based on a preset threshold and the gradient amplitudes, the color attribute of each pixel point in the color gradient map is adjusted to obtain a color mask image; the connected region of a target color in the color mask image is determined as a target region of a shock wave, a main flow and a leakage flow interface; based on the distance between the target interface region and the blade leading edge of the compressor, a stability prediction result is generated, wherein the stability prediction result represents that the compressor is in an unstable or stable state.

The computer implemented tool, intended to be controlled by a user, for assistance in the development of colors of makeup products, comprises: a mapping mode adapted for the user to select a group of colors of makeup products from a makeup product color bank, configured to classify each color of the selected group of colors with the method for classifying a color of a makeup product according to a labels designed according to a subjective perception of makeup colors. The mapping mode being configured to display a map of the selected colors arranged in a table according to the respectively assigned labels, the table being organized by a major line for each color family labels, and a major column for each hue tone subfamily labels, each major line including a subline for each lightness subfamily labels, each major column including a subcolumn for each chroma subfamily labels.

The invention discloses an intelligent detection method and system for the flatness of a special-shaped steel plate, and the method comprises the steps: placing a to-be-detected steel plate obtained through sampling on a horizontal detection platform, and stamping a colored mark on the surface of the to-be-detected steel plate according to a preset height and / or pressure parameter through an ink printing plane parallel to the horizontal detection platform; acquiring a surface image of the embossed steel plate to be tested to obtain a plane test chart; performing chromaticity identification on the plane test chart to obtain a first identification area coated with a colored identifier and a second identification area not coated with the colored identifier; respectively calculating the area ratios of the first identification area and the second identification area in the plane test chart; and comparing the area ratio as a flatness identification parameter with a preset area ratio threshold. The detection process is simpler and more convenient, the flatness test requirements of steel plates in any shape can be met, the flatness and the position with flatness defects can be accurately detected, the application range is wider, and the detection effect and the detection precision are better.

The invention provides a coffee particle color value detection method and a colorimeter. The method comprises the steps that n color cards are irradiated through a first spectrum light source; the light detector receives the reflected light of the n color cards on the first spectrum light source to form first-class images of the n color cards; respectively acquiring image brightness values of the n color cards according to the first type of images of the n color cards; obtaining color card values of n color cards and a preset function model; according to the image brightness values of the n color cards, the color card values and a preset function model, performing fitting to obtain parameter values of the preset function model; irradiating the coffee particles through a first spectrum light source; light reflected by the coffee particles to the first spectrum light source is received through a light detector to form first-class images of the coffee particles; acquiring an image brightness value of the coffee particles according to the first-class image of the coffee particles; and calculating the chromatic value of the coffee particles according to the image brightness value of the coffee particles, a preset function model and the parameter value. According to the invention, the measurement accuracy and consistency of the chromatic value can be improved.

The application provides an image fusion method and device, electronic equipment and storage medium, which are applied to the field of computer images. The method comprises the following steps: performing pixel sampling on an initial insertion image to obtain original color channel values of each pixel point in the initial insertion image; determining color parameters of each pixel point; determining spatial dispersion degrees of each pixel point; determining a reference pixel point among each pixel point based on the color parameters and the spatial dispersion degrees; determining a background pixel point and a foreground pixel point among each pixel point based on the chroma difference values between each pixel point and the reference pixel point; correcting the transparency of the background pixel point to a target transparency, and generating a target insertion image based on the background pixel point and the foreground pixel point of the target transparency; and inserting the target insertion image into a preset insertion position of a target image, thereby improving the adaptability and instantaneity of image fusion.

The present application provides a multi-color light mixing method and apparatus, a system, and a computer device. The method comprises: acquiring target chromaticity coordinates and target luminance of target colored light, and chromaticity coordinates and luminance of four mixing colors; obtaining three determination regions on the basis of the chromaticity coordinates of the four mixing colors; determining a target determination region into which the target chromaticity coordinates of the target colored light fall, the target determination region being one of the three determination regions; and on the basis of chromaticity coordinates and luminance of three vertices of the target determination region and the target chromaticity coordinates of the target colored light, determining mixing proportions of the various colors required for forming the target colored light. The present application provides a novel light mixing algorithm. The three determination regions are set according to a preset directional projection manner on the basis of a color gamut range formed by four colors, so that the mixing proportions of the four colors are calculated by determining the determination region into which the target colored light falls, thereby completing light mixing on the basis of the mixing proportions, and controlling output of the target colored light.

A chromaticity attribute evaluation method and apparatus (200), a computer device, a storage medium, and a product. The chromaticity attribute evaluation method comprises: calculating a first chromatic aberration ΔE1 of a chromatic aberration-free group under an illumination light source, the chromatic aberration-free group being a combination of a standard sample and a chromatic aberration-free product (S100); calculating a second chromatic aberration ΔE2 of a group being evaluated under an illumination light source, the group being evaluated being a combination of a standard sample and a product being evaluated (S200); acquiring a false positive probability of determining a chromatic aberration-free group as "having a chromatic aberration" and a true positive probability of determining a group being evaluated as "having a chromatic aberration" (S300); inputting the false positive probability and the true positive probability into a quantization model of the chromatic aberration resolution threshold to obtain a chromatic aberration resolution threshold of the product being evaluated (S400); inputting the false positive probability and the true positive probability into a quantization model of the chromatic aberration determination bias to obtain an estimated determination bias value of the product being evaluated (S500); and on the basis of the chromatic aberration resolution threshold and the estimated determination bias value, acquiring the chromaticity attribute evaluation result of the product being evaluated (S600).

The invention discloses a method, a system and equipment for detecting the iron doping concentration in an iron-doped gallium nitride crystal. The method comprises the following steps: acquiring an image of the iron-doped gallium nitride crystal, and carrying out correction preprocessing on the image; calculating a chromaticity-brightness coordinate of the image after correction preprocessing in a CIExyY color space; wherein the chromaticity-brightness coordinate represents chromaticity information and brightness information of the iron-doped gallium nitride crystal; calculating an iron doping concentration corresponding to the chromaticity-brightness coordinate according to a preset correlation model; wherein the correlation model is a mapping model which comprises a mapping relation between the characterization chromaticity information and the iron doping concentration and a mapping relation between the characterization brightness information and the iron doping concentration. According to the method, rapid nondestructive detection of the iron doping concentration in the iron-doped gallium nitride crystal can be realized, the operation is simple and convenient, and the cost is low.

Embodiments of the present application provide a photovoltaic module shadow defect identification method and system based on multispectral imaging. In the method, through the multispectral image data of the surface of the photovoltaic module, the intrinsic features directly related to the material characteristics are extracted by using feature decomposition technology, and a multi-dimensional intrinsic color space capable of accurately reflecting the material characteristics of the module is constructed. By analyzing the relative relationship between the reflectivities of different wave bands, the adaptive clustering algorithm is used to divide the normal area, the shadow area and the defect area in the multi-dimensional intrinsic color space, and based on the material optical characteristics, the two-level identification strategy is used to identify the real defects in the shadow area, so that the accurate identification of the real defects of the photovoltaic module in the complex shadow environment is realized.

The invention discloses an image enhancement method, computer equipment and a readable storage medium, and belongs to the technical field of image signal processing. The method comprises the following steps: in response to the input of a YUV image, performing brightness enhancement on a first brightness image corresponding to a Y channel of the YUV image to obtain a second brightness image; performing global average pooling operation on the first brightness image and a first chromaticity image corresponding to a UV channel of the YUV image to obtain statistical characteristics; performing color cast correction on the first chromaticity diagram based on the statistical characteristics to obtain a second chromaticity diagram; and based on an inverse transformation formula from YUV to RGB, carrying out linear transformation and recombination calculation on the second brightness diagram and the second chromaticity diagram to obtain a nonlinear enhanced RGB image corresponding to the YUV image. According to the invention, color cast during image enhancement can be avoided, so that the classification precision of a downstream target detection network is improved.