582 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 provides a machine learning-based OLED display screen image defect detection method, which comprises the following steps of: acquiring brightness distribution data and chromaticity distribution data of an OLED display screen through an image acquisition device, calculating a spatial difference between the brightness data and a standard template to obtain an abnormal degree index, determining a geometric center coordinate of a defect area, and calculating the abnormal degree index according to the abnormal degree index. Judging whether the neighborhood contains normal pixels or not; if not, a defect feature texture map in the brightness data is extracted through the convolutional neural network, and after the defect feature texture map is matched with a preset defect type, defect positioning information and a quality early warning instruction are generated and sent to the detection terminal. According to the invention, high-precision automatic detection and classification of image defects of the OLED display screen can be realized, and the accuracy and detection efficiency of defect identification are effectively improved.

An end-to-end color imaging system chromaticity characterization method based on hyperspectral calibration belongs to the field of color imaging, color display and imaging chromaticity measurement, and comprises the following steps: firstly, acquiring or calling hyperspectral cube data, and measuring red, green and blue three-channel spectral sensitivity curves and calibration coefficients of imaging equipment; fixed weight linear mapping is adopted, a pixel-level RGB image and a corresponding XYZ tristimulus value image are generated, and paired calibration samples are formed. And an end-to-end deep network is constructed to map any RGB image into an XYZ image. The method has good prediction precision and robustness, and can be widely applied to scene chromaticity measurement, color imaging color management, display calibration, camera color recovery and other chromaticity consistency demand scenes.

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 provides a low-gray-scale image imaging defect visual detection method and system for an OLED display screen. The method comprises the following steps: aiming at original image data of a display screen, extracting a gray scale picture with a pixel brightness mean value lower than a preset brightness threshold value as a first image, carrying out color characteristic analysis on the first image, and generating a second image marked with a chromaticity feature vector; identifying the chromaticity feature vector according to the second image, generating a feature vector matrix for representing chromaticity distribution of each pixel in the second image, and performing color jump analysis through the feature vector matrix to obtain a third image; extracting a jump region in the third image, and setting marks for distinguishing different dispersion distribution for chromaticity distribution dispersion distribution according to the jump region to obtain a fourth image; and identifying dispersion distribution according to the fourth image, combining with a chromaticity abnormal pixel region in the hopping region, performing pixel classification in the hopping region, and setting defect marks for pixels with imaging defects represented by a classification result to obtain a fifth image.

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 a partial discharge detection method and device based on discharge spectrum chromaticity, and belongs to the technical field of power systems, and the method comprises the steps: collecting a plurality of discharge optical signals of switch cabinet equipment; for each discharge optical signal, carrying out chromaticity analysis on the RGB monochromatic light signal, and respectively calculating color chaos degree, spectrum color gamut deviation degree, spectrum chromaticity dynamic coupling parameters and hue purity ratio; calculating a chromaticity difference weight based on the full-band optical signal and the RGB monochromatic light signal; performing weighted integration on the color chaos degree, the spectrum color gamut deviation degree, the spectrum chromaticity dynamic coupling parameter and the hue purity ratio based on the chromaticity difference weight to obtain a chromaticity comprehensive magnitude; calculating the mean value of the comprehensive chromaticity values of the plurality of discharge optical signals; and comparing the chromaticity comprehensive magnitude mean value with the plurality of judgment chromaticity ranges, and determining a discharge type detection result of the switch cabinet equipment. According to the invention, the problem of low accuracy of partial discharge detection in the prior art can be solved.

The invention provides a collectable walnut pairing method and device. The method comprises the following steps: classifying a plurality of collection walnuts based on a classification algorithm to obtain a first set; marking the amusement walnuts in the first set as first amusement walnuts; acquiring a first image of the target collection walnut, acquiring a second image of the first collection walnut, and acquiring texture complexity, contour variation and chromaticity dispersion of the second image; calculating texture similarity, color similarity and boundary similarity based on the first image and the second image; determining a texture similarity weight, a boundary similarity weight and a color similarity weight based on the texture complexity, the contour variability and the chromaticity dispersion; and based on the texture similarity, the texture similarity weight, the boundary similarity, the boundary similarity weight, the color similarity and the color similarity weight, determining a first collection walnut most similar to the target collection walnut. According to the invention, high matching between walnuts can be realized more accurately in a complex environment.

The invention relates to the technical field of industrial circuit board abnormity monitoring, and discloses an industrial circuit board abnormity monitoring method based on visual inspection, which comprises the following steps of: 1, acquiring image data and performance test data of all circuit boards, and classifying the image data and the performance test data to form an image data set and a performance data set; 2, evaluating the appearance defect of each circuit board according to the image data set, generating a corresponding bridging area proportion, chromaticity deviation and pin deviation, and constructing a three-dimensional defect scoring matrix by comprehensively applying technologies such as image processing, color analysis and geometric measurement; 3, according to the performance data set, evaluating the functional defect of each circuit board, generating a corresponding functional data set, and realizing high multi-dimensional monitoring precision; and 4, according to the image data set, the bridging area proportion, the chromaticity deviation, the pin deviation and the function data set, the defect type of each circuit board is divided into a connection defect, a scratch defect and an electrical defect, corresponding control instructions are output, and the intelligent repairing efficiency is high.

An image enhancement method, a vehicle, a computer device, a program product and a storage medium are characterized in that the method comprises the steps that an image of a YUV domain is obtained according to an original image of an RGB domain, and the image of the YUV domain comprises an original brightness diagram and an original chromaticity diagram; obtaining illumination information and relative brightness information of the original brightness map according to the original brightness map; obtaining an adjusted brightness map according to the illumination information, the relative brightness information and an original brightness map; obtaining an adjusted chromaticity diagram according to the illumination information, the relative brightness information and the original chromaticity diagram; and obtaining an adjusted RGB domain image according to the adjusted brightness diagram and chromaticity diagram. Through the method, the image quality can be improved to a great extent, and a better image enhancement effect is achieved.

The invention provides a laser interference processing optimization method and system for improving the performance of a micro-nano structure, and relates to the technical field of laser micro-nano machining.The method comprises the steps that firstly, micro-nano structure sample processing parameters are extracted, a sensitive tracking model is constructed to analyze parameter sensitivity, and a lifting-retaining parameter set is obtained; according to the simulation modeling, processing and lifting a dynamic model diagram, analyzing the period consistency of the dynamic model diagram and a reference design diagram through foreground and background maximum flow cutting, and determining a structure period; if the local photoetching model diagram is of a periodic structure, projecting the actual local photoetching model diagram to a local photoetching model diagram of one type in a sine manner, analyzing the curve slope, and optimizing a periodic interference angle and a light beam wavelength; and if the structure is a non-periodic structure, interpolating a gradient processing chromaticity layout for the second-class local photoetching model diagram according to a chromaticity tradeoff criterion, and analyzing and optimizing a dynamic phase in real time. According to the method, high-precision optimization of the processing parameters of the micro-nano structure with consistency in different periods is realized, and the overall performance and stability of the micro-nano structure are effectively 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 belongs to the technical field of LED driving, and particularly relates to a software compensation color correction method for LED lamp beads. By means of a light splitting test system in a lamp bead packaging link, key parameter values such as the position and the wavelength of a light-emitting wafer of each lamp bead and a lamp bead collection temperature value are comprehensively recorded, optical correction values are measured and calculated, and the optical correction values are stored in registers in the lamp beads; the application device is powered on to output the target brightness value, and the lamp bead calls the optical correction value in the register to achieve accurate control over the color of the lamp bead. The method provided by the invention thoroughly breaks through the limitation of a traditional Bin scheme, 96% of brightness / chromaticity deviation can be eliminated, lamp beads of different batches under different working conditions have completely consistent optical characteristics, and the method is particularly suitable for scenes such as Micro-LED display, medical endoscopes and the like which have extremely strict requirements on color precision.

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 discloses a multi-frame fusion fixed image noise dynamic compensation system and method, particularly relates to the technical field of image processing, and is used for solving the problem that fusion boundary noise has artifacts due to existing independent noise compensation. The method comprises the following steps of: establishing a mapping relation between pixel-level imaging parameter difference degree and fusion weight by acquiring a multi-frame image sequence of different imaging parameters and identifying a transition region of brightness gradient dramatic change; extracting a frequency domain energy skewness feature and a chroma phase change boundary feature of the transition region, and generating a noise visibility correction factor through nonlinear fusion; dynamically constraining the compensation intensity range to realize multi-frame noise compensation collaboration; and finally, performing cooperative compensation and fusion output within the constraint range. Fusion boundary noise discontinuity is effectively eliminated, and the visual quality of high-dynamic-range imaging is remarkably improved.

The invention relates to the technical field of collapsibility evaluation, in particular to a loess collapsibility evaluation calculation method based on image semantic segmentation, which comprises the following steps of: analyzing spatial heterogeneous characteristics of a monitoring area, adjusting sampling step length and resolution, and calculating temperature, humidity, stress and fracture form change based on parameters such as reflectivity, chromaticity and texture entropy of a remote sensing image; and judging the relationship between fracture communication and a pore structure, and completing collapsibility grading. According to the method, multi-dimensional fusion is realized through reflectivity, chromaticity and texture feature information of remote sensing images, a spatial heterostructure is automatically analyzed, a high-change area and a stable area are automatically identified in different monitoring areas, a sampling strategy is adaptively adjusted according to information distribution, and the resolution and the sampling density are linked to respond to regional dynamics. Real-time tracking and grading judgment of structural changes such as microscopic fracture extension and pore aggregation are achieved, automatic marking of spatial partition attributes and risk categories is achieved in combination with multi-source data, and spatial information content and grading types are greatly enriched.

The invention discloses a motor bearing fault diagnosis method and system based on a multi-dimensional chromaticity symmetric dot matrix, and belongs to the technical field of bearing fault diagnosis, and the method comprises the steps: determining a fault mode needing to be diagnosed, and collecting a current signal and a multi-dimensional vibration signal when an electric agricultural machinery equipment drives a motor bearing to operate in each mode; performing time-frequency spectrum analysis on the collected current signal to obtain a rotating speed signal; decoupling the rotating speed signal from the multi-dimensional vibration signal to obtain a multi-dimensional reconstructed vibration signal; performing feature extraction on the obtained multi-dimensional reconstructed vibration signal, and taking a standard deviation as a color feature to obtain a multi-dimensional chromaticity symmetric dot matrix picture; and outputting a prediction result of fault diagnosis by taking the result as an input signal of the SE-CNN neural network. The method does not depend on a rotating speed sensor, and fault features can be accurately extracted in a working environment with strong bottom noise. The robustness and reliability of fault diagnosis are improved, and the application range of fault diagnosis is expanded.

The invention discloses an art design intelligent color matching auxiliary system and method based on artificial intelligence, and relates to the field of art design. Comprising an image analysis module, and the image analysis module comprises a noise reduction preprocessing unit for carrying out noise suppression on an input image by adopting a bilateral filter and a semantic segmentation unit for analyzing an image semantic region and outputting a segmentation mask by using a DeepLabv3 + model; a color space conversion unit that inputs and converts the RGB image into a CIELAB space; a style feature extraction unit; a palette optimization module; and a rendering output module. According to the method, the four-dimensional statistical vector of the LAB color space is extracted, high-level features such as the quantized lightness centrality, the chroma discreteness and the distribution skewness are extracted, the problem of feature expression distortion of a traditional method is solved, the style loss function based on the Gram matrix is constructed, gradient descent optimization is combined, and it is ensured that the palette meets the aesthetic rule and style consistency at the same time.

A light-emitting device includes: a first light-emitting unit, a second light-emitting unit and a third light-emitting unit. A chromaticity coordinate x of the first light-emitting unit is 0.65>x>0.49, and a chromaticity point of the first light-emitting unit is below a blackbody radiation curve. A chromaticity coordinate x of the second light-emitting unit is 0.48>x>0.4, and a chromaticity point of the second light-emitting unit is above the blackbody radiation curve. A third light-emitting unit, wherein a chromaticity coordinate x of the third light-emitting unit is 0.31>x>0.22, and a chromaticity point of the third light-emitting unit is above the blackbody radiation curve. The CIExy of the light-emitting device conforms to the blackbody radiation curve during a tuning process.

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 invention relates to the field of LED display screen correction, and in particular to a method and device for binarizing a light point image, and an LED display screen correction method, comprising obtaining a light point image, determining a target area from the light point image according to a preset target area determination strategy; obtaining probability density distribution statistics of the target area according to a preset probability density function model and the grayscale values ​​of each pixel in the target area; determining a light point binarization threshold according to a preset threshold determination strategy and probability density distribution statistics; and performing image binarization processing on the light point image according to the light point binarization threshold. The method and device provided by the present invention can accurately distinguish light point pixels from pixels between light points, improve the light point image binarization processing effect, and thereby improve the correction effect and correction efficiency of LED display screen correction in brightness and chromaticity correction.

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.