210 results about "Lab color space" patented technology

The invention discloses a product surface paint layer color difference detection method and system. The method comprises the following steps: converting an original image from an RGB color space to an LAB color space; then foreground segmentation is carried out to realize extraction of a to-be-detected object; lAB values of foreground pixels in the LAB image are extracted, a K-means algorithm is used for clustering the LAB values of the foreground pixels, and color grading of the foreground pixels is achieved; using a region merging denoising function to optimize the clustering labels, eliminating noise points with too small areas, and enabling the noise points to be automatically fused into regions with peripheral color levels; chromatic aberration between the clustering centers is calculated, and clustering center pairs with the distance larger than a threshold value serve as significant chromatic aberration pairs; and covering masks of different colors in each stage of color area, and generating a visual result of color grading and a visual result of an area with remarkable color difference. According to the invention, accurate and efficient image color difference detection is realized, and the method has a wide application prospect.

The invention provides a three-dimensional setting printing correction method and device, equipment and a storage medium, and the method comprises the steps: scanning a three-dimensional setting, and obtaining a three-dimensional point cloud model, color grid data and a multispectral texture mapping graph; based on the three-dimensional point cloud model and the color grid data, determining an offset defect labeling graph and a parallax offset matrix; performing two-dimensional mapping and global correlation calculation on the multispectral texture mapping graph to generate a texture anomaly thermodynamic graph and an LAB color space deviation vector; inputting the parallax offset matrix, the offset defect labeling diagram, the texture anomaly thermodynamic diagram and the LAB color space deviation vector into a preset decision tree model, and generating a root cause classification label and a printing quality score; and based on the root cause classification label and a preset historical printing parameter database, constructing a reinforcement learning model, taking the printing quality score as a reward function of the reinforcement learning model, and outputting a printing correction scheme.

The invention relates to the technical field of image processing, in particular to a printing image-text defect detection method based on machine vision, and the method comprises the steps: obtaining an RGB image of a to-be-detected printed matter, and carrying out the Lab space conversion, and obtaining a target image; performing normalization processing on the L channel value of each pixel point in the target image to obtain a normalized brightness value so as to divide the pixel points in the target image into a preset number of illumination level intervals; obtaining a color fluctuation statistical parameter of any one illumination level interval, and obtaining an optimal color difference threshold value corresponding to any one illumination level interval; according to the optimal color difference threshold value corresponding to the illumination level interval to which each pixel point in the target image belongs, performing Lab color space template comparison on the target image to obtain an image-text color difference defect result of the to-be-detected printed matter, thereby improving the accuracy of image-text printing defect detection based on the color difference threshold values.

The invention relates to the technical field of underwater image enhancement, and provides a deepwater image enhancement method based on multi-color space coupling, which comprises the following steps: determining an optimal channel, a moderate attenuation channel and a severe attenuation channel in an RGB color space based on a pixel average value, carrying out asymmetric histogram cutting pre-correction on the optimal channel, and carrying out deep-water image enhancement on the optimal channel; designing a loss function to enable an attenuation channel to approach an optimal channel through iterative compensation; in the Lab color space, clustering and segmenting the brightness component L into a plurality of pixel blocks by using an unsupervised pixel clustering model PCM, and performing local contrast enhancement and guided filtering-based noise reduction processing on each pixel block; and finally, carrying out nonlinear stretching on the chrominance components a and b by adopting a self-adaptive exponential function to improve the color naturalness. Through a multi-color space cooperative enhancement mechanism, the limitation of single color space processing in a traditional method is effectively solved, and the color correction precision, brightness enhancement and noise reduction balance and the naturalness optimization effect of the deep water image are improved.

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 relates to a micro-expression recognition method based on double-flow feature fusion, and belongs to the field of computer vision. The method comprises the steps of obtaining N frames of images of a micro-expression video from a start frame to a vertex frame, and calculating an optical flow field between adjacent frames by adopting an optical flow algorithm so as to effectively extract optical flow features of micro-expressions; the method comprises the following steps: acquiring N frames of images of a micro-expression video from a start frame to a vertex frame, converting the N frames of RGB face images into a CIE Lab color space, calculating a pixel difference between two frames of converted Lab images, and extracting pixel stream features of micro-expressions; and inputting the optical flow features and the pixel flow features into a constructed double-flow three-dimensional convolutional network for feature extraction and fusion, and classifying micro expressions. According to the method, the dynamic and subtle changes of the micro-expression are effectively captured by combining the optical flow and the pixel difference characteristics. By integrating spatial and temporal information, richer feature representations are provided. The improved attention mechanism further focuses on fine facial changes, and the accuracy of micro-expression classification is improved.

The invention provides a textile surface defect detection method and system based on image features, and relates to the technical field of textile surface defect detection.The method comprises the steps that an RGB image of a to-be-detected textile is obtained and zoomed to 512 * 512 pixels, and then at least five semantic tags are output through a semantic segmentation model; the method comprises the following steps: adopting hyperspectral imaging with the wavelength of 400-1700 nm and the resolution of 10 nm, calculating the pixel reflectivity through a reflectivity formula, comparing the reflectivity of 5-8 characteristic wavebands, marking as a pollutant if the average deviation exceeds a threshold value, matching the type and position of a stain with a spectral angular distance, and after a standard sample and a semantic region are converted into an LAB color space, extracting a minimum rotation enclosing rectangle and dividing a grid, invalid pixels are filled with 3 * 3 neighborhood effective pixel weighted mean values, and a grid LAB mean value is calculated. A grid pollutant pixel corresponding to an image to be detected is filled with a standard sample in a mean value mode, then channels A and B are clustered through a sliding window, a pilling area is preliminarily judged according to the standard deviation of a channel L, and the pilling area is confirmed by combining the structured light three-dimensional point cloud and comparing with a threshold value.

Provided in the embodiments of the present description are an image processing method and apparatus, and a device and a storage medium. The method comprises: acquiring an image to be matted which has a pure-color background; determining a ternary graph corresponding to the image to be matted; selecting a target image from an A-channel image and a B-channel image, in a LAB color space, of the image to be matted, wherein the color of the target image is closer to the background color of the image to be matted; by taking the target image as a guide image, performing guided filtering processing on the ternary graph to obtain an Alpha image; and on the basis of the Alpha image, performing matting to obtain a foreground area from the image to be matted. By means of the solution, details of an edge part of a foreground area can be retained to the greatest extent; in addition, the processing speed of a matting process can be increased.

The invention provides a facial skin flaw enhancement method based on a Lab color space. The method comprises the following steps: firstly, acquiring an RGB face image and converting the RGB face image into a CIE Lab color space with uniform perception; then, performing differentiation treatment according to the manually selected skin flaw type: for the vascular flaw, extracting statistical characteristics of a component and driving adaptive nonlinear transformation, and generating a grey-scale map which highlights the red flaw; for pigment flaws, nonlinear transformation is carried out on the component L, and then collaborative linear weighting and feature amplification are carried out on the component L, the component a and the component b, so that a grey-scale map with highlighted pigment spots is generated. And finally, coloring the grey-scale map in the Lab color space through adjustable parameters to generate a high-contrast color enhanced image. The method overcomes the dependence on hardware and training data in the prior art, can clearly and adaptively enhance various flaws such as acnes, couperose streaks and color spots, shows robustness under different illumination, and can be widely applied to clinical beauty, later photography and real-time video processing.

The invention belongs to the field of computer vision, and particularly relates to a cloud cover density calculation method combining image features and superpixel partition merging and a corresponding device thereof. The method comprises the following steps: 1, preprocessing an original image P0 containing the sky and the cloud layer to obtain a gray level image H1, an HSV image P1, a Lab image P2, a color enhancement image E1 and a gray level enhancement image E2 which correspond to different color spaces; 2, P0 is divided into a plurality of superpixel areas, and background elimination is carried out by combining characteristic information of H1, P1, P2, E1 and E2; gradually combining the superpixel regions belonging to the cloud layer or the sky; and 3, extracting values of an S channel and a Lab color space three channel of the superpixel region, generating a four-dimensional feature vector, and inputting the four-dimensional feature vector into a K-means algorithm for clustering. And 4, calculating the cloud cover density according to the clustering result of the K-means algorithm. The problem that the sky and cloud layer image segmentation task is difficult to process by the existing traditional image segmentation technology is solved.

The invention discloses a method for dynamically monitoring the drying quality of fruits and vegetables by fusing chromatic aberration-shrinkage rate analysis. The method comprises the following steps: placing a fruit and vegetable sample on a rotatable objective table, drying according to a preset program, and carrying out multi-angle shooting by using a digital camera according to a timestamp to obtain an RGB color image; the method comprises the following steps: preprocessing by an image analysis system, converting an image into a Lab chromaticity space, calculating a chromatic aberration value delta E and a comprehensive shrinkage rate S, integrating data to generate a change curve, and constructing an appearance form model. In actual drying, real-time data and the model are compared, and early warning is carried out when the color difference value exceeds 3.5 or the shrinkage rate deviation exceeds 15% and image acquisition is abnormal. The drying equipment is provided with a high-precision electric rotary objective table, a specific annular light source array, a high-resolution camera and a standard calibration color card, and illumination and color measurement accuracy is ensured through calibration. Through multi-step operation, accurate and dynamic monitoring of fruit and vegetable drying quality is achieved, and drying process optimization is assisted.

The invention relates to the technical field of LED control, and discloses a multi-mode LED swimming pool lamp control method, system and device. The method comprises the following steps: calculating switching path pre-calculation data based on a remote controller mode switching instruction and color characteristic parameters of E light modes; according to the switching path pre-calculation data, carrying out progressive modulation on the RGBW four-channel PWM duty ratio to obtain a linear transition modulation sequence; on the basis of the linear transition modulation sequence, trajectory calculation is carried out on dynamic change mode color change in a CIE Lab color space, and color transition trajectory parameters are obtained; and closed-loop control is carried out on RGBW four-channel LED currents according to the color transition track parameters, PWM control signals are obtained, progressive adjustment of the PWM duty ratio is achieved, and the visual abrupt sense and the flicker phenomenon caused by traditional abrupt change type switching are eliminated.

The invention discloses a non-contact heart rate estimation method and system based on multi-modal signal fusion, and relates to the technical field of image processing, and the method comprises the steps: positioning a region of interest in a face video, extracting HSV and Lab color space signals, and fusing the HSV and Lab color space signals; suppressing ambient light interference through background brightness compensation to obtain a color conversion signal and normalizing the color conversion signal; adjusting the bandwidth filtering signal of a band-pass filter by using the historical heart rate to generate an rPPG signal; performing time domain and frequency domain analysis on the rPPG signal, and performing weighted averaging by combining two results to obtain a heart rate estimation value; a historical data smoothing strategy is adopted to correct a heart rate estimation result to ensure accuracy. According to the method, high-precision non-contact heart rate estimation is realized through extraction and fusion of HSV and Lab color space signals, background brightness compensation, signal normalization and processing of human face video signals based on a historical heart rate adjustment band-pass filter.

The invention discloses a nozzle voltage adjustment method and device based on camera automatic identification printing color blocks and a storage medium, and relates to the technical field of printing device calibration, and the method comprises the steps: controlling a printing device to output a pattern containing each nozzle independent test area and color blocks; acquiring chromaticity distribution data through an image acquisition device, and extracting characteristic parameters such as brightness and saturation; determining a reference characteristic parameter based on a reference selection rule, and calculating a deviation value between each test area and the reference; an adjustment instruction is generated according to the mapping relation between the deviation value and the voltage adjustment amount, verification printing and iterative optimization are carried out after implementation until the precision is met, and through dynamic selection of a reference color block, LAB color space chromatic aberration calculation, voltage sensitivity coefficient matrix mapping and aging compensation, the adjustment precision and efficiency are improved, the dependence on artificial experience is reduced, and the adjustment precision and efficiency are improved. The method is suitable for nozzle calibration of high-precision printing equipment.

The invention provides a tomato leaf health status assessment system and method, and relates to the technical field of tomato leaf health assessment, and the method specifically comprises the steps: firstly, carrying out the preprocessing of a tomato leaf image, converting the image into a Lab color space, and carrying out the segmentation and extraction of a leaf region based on a chromaticity threshold value; and then leaf area chromaticity channel statistical magnitude is counted, pixel-level anomaly detection is carried out through an adaptive threshold value, and an abnormal area mask and a normal area mask are generated. Performing connected domain analysis on the abnormal region, extracting local color and texture features, and extracting corresponding features of the normal region as a reference; the health deviation degree is obtained by calculating the feature distance between each abnormal region and the normal region, and the comprehensive health assessment index is generated by combining the area proportion of the abnormal regions after normalization. And finally, judging a health state according to a preset threshold rule and outputting an evaluation result. According to the invention, a comprehensive and accurate decision basis is provided for agricultural producers, and the automation level and evaluation accuracy of tomato disease monitoring are improved.

The invention discloses an eye disease analysis method based on an eye image, and the method comprises the steps: carrying out the image segmentation of a sclera region through employing a convolutional neural network architecture u-net network model of image segmentation, carrying out the recognition of a hyperemia part of the sclera region, and calculating the proportion of the hyperemia part through employing FrangiFilter blood vessel recognition in combination with the color recognition of an LAB color space; by combining the image algorithm with a traditional image processing and deep learning model, the segmentation precision of the sclera region, the identification precision of sclera congestion, the robustness, the high accuracy and the rating accuracy are improved under the condition that the operation speed is not reduced.

The invention discloses an ultrasonic image enhancement method based on an ultrasonic Gaussian kernel and non-sharpening mask sharpening. The method comprises the following steps: acquiring an original image; removing the noise of the original image by using an ultrasonic Gaussian kernel to obtain a denoised image; performing Gaussian blur processing on the denoised image to obtain a Gaussian blur image; extracting high-frequency information in the original image; sharpening the extracted high-frequency information part to obtain a sharpened image; performing weighted fusion on the sharpened image and the original image to obtain a weighted image; the weighted image is converted to a Lab color space, and the brightness channel contrast is enhanced; and restoring the contrast-enhanced image to the RGB color space to generate a final enhanced image. According to the method, the Gaussian kernel denoising and the non-sharpening mask sharpening technology are combined, noise in the ultrasonic image is effectively removed, image details are enhanced, and the identifiability of the image is further improved through self-adaptive contrast enhancement.

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.

The invention discloses a sandstone slice image component identification method based on a GL-SLIC algorithm and semi-supervised learning. The method comprises the following steps: firstly, acquiring a sandstone slice image amplified by 200 times under single polarization, converting an RGB image into a CIE-Lab color space, and extracting a GLBP texture feature vector; then a GL-SLIC superpixel segmentation algorithm is constructed, and adaptive segmentation based on texture and color features is realized in combination with a Gabor filter and a local binary pattern; then implementing a region merging algorithm to generate complete mineral particles and pore fragments; finally, a classifier based on VGG16 and a discriminator based on ResNet18 are constructed, a semi-supervised self-training framework is adopted, a model is initialized by using about 6% of manual labeling samples, a high-confidence-coefficient pseudo-label extension training data set is generated through iteration, and automatic recognition of sandstone components such as quartz, pores, kaolinite, rock debris and a matrix is achieved. According to the method, the recognition accuracy of 96.3% on a test set is achieved, compared with a traditional method, the segmentation precision and the recognition accuracy are remarkably improved, the data labeling cost is greatly reduced, and an efficient technical scheme is provided for automatic analysis of geological images.

The invention discloses a fabric texture fidelity rendering method and system based on a scanned image and a target color number, and solves the technical problems of poor color perception consistency and texture detail loss. The method comprises the steps of preprocessing a standardized input image through illumination compensation, performing double-space color mapping in an LAB color space, partitioning the image by adopting K-means clustering according to texture features of a gray-level co-occurrence matrix, establishing a color mapping function containing global mapping transformation and local adaptive adjustment, and adaptively determining a fusion coefficient according to regional variance; decomposing the image into a base layer and a detail layer by adopting guide filtering, and performing spectrum constraint on the detail layer to ensure that the spectrum amplitude is consistent with the original; extracting material parameters such as a normal map, a roughness map and a metallity map; according to the method, fabric texture details are completely reserved while color perception consistency is guaranteed, rendering reality is improved, batch processing of multiple groups of target color numbers is supported, and the method is suitable for application scenes such as costume design, e-commerce display and virtual fitting.

The invention relates to the field of computer vision, and discloses an image classification and defect detection method based on self-supervised contrast learning, which comprises the following steps: converting an image to be detected into an LAB color space, and generating feature representation information of principal component analysis based on separated information of a channel A, a channel B and a channel L; extracting a target potential feature vector from the feature representation information through a first encoder; extracting high-level semantic information from the target potential feature vector through a first semantic feature encoder; performing classification detection on the advanced semantic information through a classifier network; wherein the construction of the first encoder and the first semantic feature encoder comprises the following steps: respectively inputting a training sample set into the first encoder and the second encoder; and performing parameter optimization on the first semantic feature encoder and the second semantic feature encoder by adopting a comparative learning mode.

The invention discloses a line sequence automatic detection algorithm based on machine vision and an implementation method thereof, and belongs to the technical field of machine vision and image processing. Aiming at the problems of low efficiency and poor accuracy of traditional manual detection of a multi-core cable, the invention provides the following technical scheme: preprocessing a collected cable image, and enhancing the image by adopting adaptive histogram equalization of an LAB color space; determining a scanning band at the center of the image and converting the scanning band to an HSV color space; the comprehensive value of the HSV color gradient is calculated, the peak value is detected by adopting an adaptive threshold value, and the boundary of each wire core is accurately positioned; establishing an HSV (hue, saturation and value) feature library containing 10 standard colors, and realizing color matching through weighted distance measurement; and an automatic detection mode and a manual assistance mode are provided. According to the method, the detection accuracy reaches 95% or above, the processing time of a single image is shorter than 1 second, the technical problems of automatic color recognition and sorting detection of the multi-core cable are effectively solved, and the method can be widely applied to cable production quality control.

The invention discloses a color image enhancement method and device based on an LAB color space and a storage medium, and belongs to the technical field of computer vision. The method comprises the following steps: extracting a region of interest from a color image of a product; converting the color image from an RGB color space to an LAB color space to obtain a brightness channel image and a color channel image; respectively performing image enhancement on the brightness channel image and the color channel image based on the gray average of the pixels in the region of interest to obtain a brightness channel enhanced image and a color channel enhanced image; and converting the brightness channel enhanced image and the color channel enhanced image from the LAB color space to the RGB color space to obtain an enhanced color image. According to the embodiment of the invention, the color image is converted from the RGB color space to the LAB color space, the brightness channel image is utilized to enhance the defect with the brightness difference, and the color channel image is utilized to enhance the defect with the color difference, so that the enhancement effect of the defect in the color image is improved.

The invention relates to a wallpaper surface defect detection method, which comprises the following steps of: acquiring a wallpaper surface image by using a line-scan digital camera, correcting the image, establishing an average background model, and performing reflection compensation based on the average background model; s200, the image after reflection compensation is separated into R, G and B color channels, a gray level co-occurrence matrix (GLCM) of each channel is calculated to extract contrast features, the dynamic weight of each sub-block is calculated, a weighted RGB image is obtained, then the RGB image is converted into an LAB color space, an L channel is extracted, and # imgabs0 # is obtained; fusing the weighted RGB image and the L channel; carrying out three-layer decomposition by using a Daubechies8 wavelet basis, and carrying out reconstruction to obtain a background image; and carrying out dual-channel Ostu segmentation on the defect candidate image, carrying out decision fusion after segmenting the two channels, and carrying out noise removal and non-defect region filtering to obtain a defect region.

The invention relates to the technical field of image processing, in particular to a forest pest intelligent identification method based on unmanned aerial vehicle remote sensing, which comprises the following steps: analyzing the position distribution condition of pixel points in each suspected pest connected domain in an LAB color space coordinate system, and obtaining the green sensitivity of each suspected pest connected domain; dividing all suspected pest and disease connected domains into high green sensitivity connected domains and low green sensitivity connected domains based on green sensitivity; acquiring an enhancement coefficient of a remote sensing grayscale image of the to-be-detected man-made forest according to a grayscale difference condition of pixel points between the high green sensitivity connected domain and the low green sensitivity connected domain and the edge feature parameters; enhancing the remote sensing grayscale image of the to-be-detected man-made forest according to the enhancement coefficient to obtain an enhanced remote sensing image of the to-be-detected man-made forest; and performing intelligent forest disease and insect pest identification based on the enhanced remote sensing image of the to-be-detected man-made forest. According to the invention, the recognition degree of the forest regions with diseases and pests in the man-made forest is improved.

The invention relates to a color adjustment method and device, equipment and a medium. The method comprises the following steps: acquiring first lattice point data of each lattice point in an LAB color space; calculating a first color attribute of each grid point in the LAB color space based on the first grid point data of each grid point; adjusting the original saturation of each grid point based on a preset hue and saturation adjustment lookup table to obtain a first saturation; correcting the original hue of each lattice point to obtain a first hue of each lattice point; adjusting the first hue of each lattice point based on a preset hue and saturation adjustment lookup table to obtain a second hue; calculating second lattice point data of each lattice point in the LAB color space based on the first saturation and the second hue of each lattice point; and updating the initial three-dimensional lookup table based on the second lattice point data of each lattice point in the LAB color space. By adopting the method provided by the embodiment of the invention, the adjustment quality and the imaging effect can be improved.

The invention relates to the technical field of image data processing, in particular to a production speed regulation and control method and system for packaging bag printing. The method comprises the following steps: acquiring and segmenting a printed packaging bag image, and correcting rotation offset by using a histogram of oriented gradients to ensure image alignment. Then, the corrected image is converted into an LAB color space, the LAB color space is compared with a preset standard template, the ink color unevenness is calculated, and the printing quality is quantified. And finally, a relation model of the printing quality and the production speed is constructed based on historical data, the target production speed is determined, regulated and controlled, and balance of the printing efficiency and the quality is achieved. According to the invention, the influence of artificial experience dependence and image dislocation on quality evaluation is overcome, and the accuracy and stability of printing speed regulation and control are improved.

The invention relates to the technical field of batteries, in particular to a battery collector plate and a battery. The battery collector plate comprises copper and an electric connection part, and the electric connection part is used for realizing electric connection between a battery cell tab and an electrode terminal; the color parameters of the surface, used for being connected with the battery cell tab and / or the electrode terminal, of the electric connection part comprise L1 and a1, L1 is the numerical value of an L channel in the Lab color space, a1 is the numerical value of an a channel in the Lab color space, S = L1a1, and S is larger than or equal to 900 and smaller than or equal to 2500. By controlling the product S of L1 and a1 on the surface of the electric connection part to be within a proper range, light reflection can be reduced, the welding quality and stability are improved, and the structural strength of the battery collector plate is ensured.