135 results about "Color analysis" patented technology

The invention discloses a personalized customized vi design method and system, and relates to the technical field of visual identification design, the design method comprises the following specific steps: S100, data acquisition: docking with an enterprise internal management system to obtain core data, using a web crawler tool to collect Internet related information, and using the web crawler tool to store the core data; according to the method, data in multiple aspects of industry attributes, brand positioning and color preference of target audiences of an enterprise are deeply analyzed by utilizing an artificial intelligence technology, highly intelligent color analysis and recommendation are realized, and the color matching degree of the industry, the color preference of the target audiences and multiple dimensions of color coordination correction factors are comprehensively considered. According to the method, the color scheme meeting the enterprise image and the market demand can be accurately recommended to the user, the intelligent color recommendation not only improves the design efficiency, but also ensures that the color scheme is highly matched with the enterprise positioning and audience preference, and the visual impact and brand recognition degree of the design are enhanced.

The invention relates to the technical field of sorting control, in particular to a traditional Chinese medicinal material intelligent sorting control method and system based on image recognition, and the method comprises the following steps: collecting an original traditional Chinese medicinal material image based on image collection equipment, calculating the signal-to-noise ratio and the definition index in the original traditional Chinese medicinal material image, and obtaining a traditional Chinese medicinal material sorting result; and adjusting exposure and focusing setting of the camera according to a calculation result, and performing contrast and sharpening processing on the original image. According to the invention, by analyzing the signal-to-noise ratio and the definition of the original image and correspondingly adjusting the exposure and focusing setting of the camera, the details of the medicinal material image are remarkably improved, so that the edge detection and color analysis can be executed more accurately, the accurate capture of the characteristics of the traditional Chinese medicinal material is ensured, the misjudgment caused by poor image quality is reduced, and the accuracy of the traditional Chinese medicinal material image is improved. The sorting mechanical arm is adjusted in real time, classification errors are directly corrected, the flexibility and efficiency of the whole sorting system are improved, and therefore the operation efficiency is greatly improved while the sorting accuracy of the traditional Chinese medicinal materials is guaranteed.

Systems and methods for a sample database are provided, where the sample database is for matching a target coating. In one embodiment, the system comprises a sample database stored on a storage device. The sample database includes a sample coating formula and a sample image feature with at least one sample coating formula linked to at least one sample image feature. At least one sample image feature includes a spatial micro-color analysis that includes a value determined by a sample pixel feature difference between at least two sample pixels.

The invention provides a form and color analysis combined cell image analysis method and application and instrument thereof, and belongs to the technical field of artificial intelligence. The method comprises the following steps: S1, acquiring an image sample; s2, image data preprocessing: carrying out key area cutting and size standardization operation on all collected image samples; step S3, image data annotation: carrying out centroid point selection and classification annotation on the image samples of which the sizes are adjusted; s4, establishing a cellular morphology segmentation and classification model based on the U-Net network model, and performing network training; and S5, verifying the identification effect and precision of the network on the cell information by using the verification set and the test set. The technical scheme of the invention can realize multi-dimensional analysis, is convenient, efficient and non-toxic, has the characteristics of high precision, high automation degree, strong expansibility and the like, and has a very good practical application value.

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.

The invention relates to the technical field of computer vision, in particular to a fabric color fastness analysis method and system based on image processing. Comprising the following steps: acquiring a multispectral image of a to-be-detected fabric, and synchronously acquiring multi-factor data; processing the multispectral image to generate an enhanced image; performing image recognition on the gray sample card in the enhanced image to generate a calibration image; performing spectral data projection on the calibration image through a dimension reduction algorithm, and performing color analysis and quantification on color change to generate color difference features; extracting texture features by using a conditional generative adversarial network, carrying out image recognition and calculation on the calibration image through a decoupling algorithm, and generating a region credibility graph; and inputting the chromatic aberration features, the texture features, the multi-factor data and the regional credibility map into an image feature fusion model for mapping, performing analysis through gradient visualization, and outputting an abnormal feature saliency map. According to the method, a color fastness objective analysis closed loop is created, so that the accuracy and the universality of an analysis result are improved.

Disclosed is a surveillance camera capable of detecting a color of a vehicle using vehicle logo detection. This specification describes a method that, instead of processing an entire image of a vehicle, detects the vehicle's logo and expands a bounding box of the detected logo to analyze a dominant color within the expanded bounding box for higher accuracy. During a dominant color analysis process, color correction may be performed based on metadata, environmental factors, and a lighting condition. In addition, more accurate vehicle color detection may be achieved through color distance measurement. According to this specification, at least one of a surveillance camera, an autonomous vehicle, a user terminal, and a server may be associated with an Artificial Intelligence (AI) module, a robot, an Augmented Reality (AR) device, a Virtual Reality (VR) device, and a 5G service.

The invention discloses a system and a method for highly automatically detecting sludge sedimentation layering based on color difference, and particularly relates to the field of sewage treatment. Comprising an automatic test box body module, a visual acquisition module, a color analysis processing module, a data management transmission module and a field data analysis module, the automatic test box body module is used for completing filling, discharging and cleaning of a sludge sample, stabilizing a detection environment and supporting a standardized sedimentation test; the visual acquisition module is used for regularly shooting sludge sedimentation images, embedding time, places and biochemical pool serial number information, and retaining traceable visual detection data; and the color analysis processing module is used for identifying digital characteristics of color mutation characteristics in the image and accurately positioning the height of the sludge layering interface. The method has the advantages of being high in interface positioning precision, high in anti-interference performance, high in data visualization and high in expansibility.

The invention discloses an adhesive testing system, method and device, and relates to the technical field of adhesion testing, and the method comprises the following steps: obtaining a substrate surface image after an adhesive is stripped; a flexible isolation plate is arranged between the adhesive and the base material, and the flexible isolation plate is a hollowed-out isolation plate so that the adhesive and the base material can be bonded in a point-shaped mode. Performing color analysis on the surface image of the base material to determine a damage mode corresponding to each bonding point; the destruction mode comprises an adhesive body destruction mode, a base material body destruction mode and a bonding interface separation mode. And determining a test result of the adhesive based on the proportion of the bonding interface separation mode in the damage mode corresponding to all the bonding points. By arranging the hollowed-out isolation plate between the adhesive and the base material, even if the base material is an elastic base material with low modulus, stripping of the adhesive and the base material can be smoothly achieved. Based on the visual identification part, automatic evaluation of the adhesion effect of the adhesive can be realized, so that the adhesion effect of the adhesive can be evaluated more accurately.

The invention discloses an abnormal state identification method, system and device based on local color area mutation, and belongs to the technical field of safety monitoring. The method comprises the following steps: extracting a detection frame from a video stream according to a preset unequally spaced frame sequence; positioning local key areas (such as shoulders) of the body of the target constructor in the detection frame; in the region, extracting a maximum aggregation connected domain of target safe color pixels through color analysis, and calculating the area of the maximum aggregation connected domain; and comparing the ratio of the area to the total pixel area of the detection frame with a fixed preset threshold value, and if the ratio exceeds the threshold value, judging that an abnormal state exists and triggering an alarm. According to the method, the local color area mutation is taken as an agent index, a fixed parameter strategy is combined, the problems of large calculation amount, easy environmental interference and complex deployment in the prior art are solved, and the method has the advantages of low calculation cost, strong robustness, fast identification response and easy deployment.

The invention relates to the technical field of biomedical detection, and discloses a portable urine component detection system and method based on a fluorescence sensor array. The system comprises a fluorescence sensor array, different fluorescence probes are used for detecting different trace components in urine, and fluorescence color gradient changes are generated through a probe-target object reaction, so that bilirubin, glucose and albumin in the urine are detected. The detection system comprises a micro-fluidic chip which integrates a capillary driving channel and a colorimetric reaction area and is used for sample transmission and reaction area positioning; an ultraviolet excitation light source and a multi-angle optical sensor are arranged in the camera obscura type color acquisition module, and the camera obscura type color acquisition module eliminates ambient light interference and captures fluorescence chromaticity signals; and the intelligent data processing system is integrated with an intelligent analysis end of the smart phone, establishes a quantitative relationship between chromaticity parameters (RGB and hue saturation) and the concentration of a target object through a machine learning model, and outputs a detection result in combination with an ambient light correction algorithm.

The invention relates to a light color control system, a video player, a smart home system and a computer equipment storage medium. The control system comprises an embedded graphic display system which is used for collecting video signals from a video source and obtaining image pictures; the light color processing unit is used for performing color analysis on the image picture to obtain color data; the light address coding unit is used for carrying out address coding on light equipment according to the color data to generate light control parameters, and outputting the light control parameters to a light driver to drive the light equipment to emit light, so that the light color of the light equipment changes along with the image picture output to the display screen by the video player; according to the technical scheme, the equipment cost in light color control is reduced, and the light color control effect is improved.

The invention relates to an intelligent color box printing color analysis control method, and provides a reinforcement learning decision closed-loop control method integrating distributed color detection, space-time multi-modal data processing, feature extraction and self-supervised clustering and multi-scale reward modeling. Multi-variable normalization and synchronization are completed through multi-point real-time collection of color, process and environment data, color change meta-state features are extracted based on a self-encoding neural network, and a meta-state distribution model related to space and working conditions is established through self-supervised clustering. And the color risk is evaluated in combination with a multi-scale reward function, so that color parameter optimal adaptive adjustment and cooperative control driven by reinforcement learning are realized. In an actual control link, the system can dynamically optimize a meta-state aggregation and strategy network structure based on a feedback signal, a stable and self-adaptive color consistency closed-loop control system is constructed, and the color consistency and process intelligence level of a printed product under a complex working condition are effectively improved.

The invention discloses a tobacco curing barn temperature and humidity regulation and control system and method based on deep learning optimization, and the system comprises a data collection module which is used for collecting environment data and tobacco leaf image data in a tobacco curing barn; the data processing module is used for carrying out data preprocessing on the tobacco leaf image data and the environment data and carrying out color analysis on the tobacco leaf image data so as to obtain color feature data of the tobacco leaf image; the data prediction module is used for predicting the color change trend of the tobacco leaves and the temperature change trend in the tobacco curing barn according to the preprocessed environment data and color feature data; the equipment control module is used for dynamically regulating and controlling heating equipment in the tobacco curing barn according to the temperature change trend and the color change trend; according to the invention, PID parameters are dynamically adjusted through the output of the data prediction module, so that the heating equipment of the flue-curing barn is accurately controlled, the temperature and humidity in the flue-curing barn are accurately controlled, the temperature fluctuation is reduced, and the quality consistency and color uniformity in the flue-curing process of tobacco leaves are ensured.

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.

This invention provides a portable colorimetric detection system for astaxanthin based on a smartphone, belonging to the field of colorimetric detection technology. Its features include: an optical detection platform for accommodating the Yeast lipolyticis fermentation broth sample and providing a standardized optical detection environment; a smartphone with its camera pointed at the sample area of ​​the optical detection platform for acquiring images of the fermentation broth sample; a color analysis module embedded in or connected to the smartphone for extracting color feature parameters from the acquired images; and a quantitative model module pre-stored with a linear regression model based on the color parameters of the fermentation broth and the astaxanthin concentration, the model being constructed based on the (R+G) / 2 parameter, used to calculate the astaxanthin concentration in the sample based on the color feature parameters. The advantages of this invention are: simplified detection steps, convenient and rapid sample detection, and real-time sample monitoring.

The invention relates to the technical field of water quality detection, in particular to a wastewater chroma removal rate measuring method, device and equipment and a storage medium. The fitting curve is generated by obtaining the first absorbance and the first concentration data set, basic data is provided for subsequent fitting curve generation, a scientific basis is provided for colorimetric analysis, and the method can also be used for calculating the chromaticity removal effect. In combination with a fitting curve, a second absorbance data set and a removal rate calculation formula, the efficient adsorption time with the highest chromaticity removal rate can be determined, and the problem that the adsorption time is difficult to accurately measure is solved; in a water tank system of grease sample constant-temperature Raman detection equipment, a periodic concentration calculation formula is constructed according to efficient adsorption time and the number of parallel filter elements, the current concentration is calculated in combination with a real-time filtration cycle period, and the chromaticity removal rate is obtained, so that the chromaticity removal effect of a sequencing batch water inlet treatment system is accurately evaluated, and a reliable basis is provided for system optimization.

The present application belongs to the technical field of intelligent agriculture, and discloses a monitoring method for a fruit bottom colorizing and light reflecting film, comprising the following steps: S1, forming a grid map for an orchard; S2, collecting fruit bottom images to obtain array type multi-angle image data; S3, performing edge processing on the image data to form fruit images; S4, performing fruit surface color analysis on the fruit images to form color feature data; S5, performing edge and dispersion analysis on dark color patches in the color analysis process to form lesion feature data; S6, inputting the color feature data and the lesion feature data of S4 and S5 into a preset fruit maturity and lesion grade library to obtain preliminary maturity and sunscald grades; S7, performing feature weighting judgment to output maturity grades and / or sunscald lesion probabilities; S8, if the lesion probability is greater than a preset threshold, automatically recording a collection time stamp, a lesion probability value, an image slice and a map location, and triggering an early warning of adjusting the light reflecting film to weaken the radiant light; if the lesion probability is less than or equal to the preset threshold, normally recording data.

The invention discloses a safety shoe rapid detection method based on deep learning and color analysis, and the method comprises the steps: obtaining a human body posture data set, and randomly dividing the data set into a training set, a verification set and a test set according to a proportion of 8: 1: 1; training a target detection and attitude estimation model by using the training set and the verification set to obtain personnel positioning information and key point information; acquiring color data of the safety shoes, and determining a color range of the safety shoes in the HSV color space; obtaining a to-be-detected video stream and performing preprocessing; reasoning the video stream by using the trained target detection and attitude estimation model, and positioning ankle part coordinates and confidence information; and by taking the coordinates of the ankle part as the circle center, whether the safety shoe is worn or not is judged by checking whether an element with a pixel value greater than zero exists in the mask or not.

The invention discloses a computer vision environment perception method and system, and relates to the technical field of visual perception, and the method comprises the steps: analyzing an environment image and composition elements in personalized composition suggestions, calculating the proportion of different element regions to the environment image, obtaining a weight sudoku, and obtaining a weight sudoku; the method has the advantages that the limitation that only existing composition can be analyzed by a traditional method is solved, the balance of rule constraint and data driving is realized through condition triggering, and the method is high in practicability, high in efficiency and high in reliability. According to the method, the satisfaction degree and the participation degree of a user on an environment image are increased, the problem of local contrast imbalance in traditional color analysis is effectively solved by calculating the total color value and the difference value of adjacent areas, the performance of the method in the aspects of environment perception and image processing is further improved, and a breakthrough of intelligent creation assistance is achieved.

Systems, devices, and methods operate on a fluorescence image to ensure a white balance correctness of the fluorescence image; perform a color analysis of the fluorescence image, including: converting the fluorescence image to an HSV color space, comparing a saturation parameter of the first converted image to a first saturation threshold condition and comparing a value parameter of the first converted image to a first value threshold condition, wherein the first value and saturation threshold conditions are based on a correlation between a first color and the first bacterial fluorescence signature, and identifying areas of the fluorescence image where the saturation parameter satisfies the first saturation threshold condition and the first value parameter satisfies the value threshold condition, discard the identified areas which are smaller than a size threshold, and outline the remaining identified areas on the fluorescence image, thereby to generate an overlay image.

This invention discloses an AI-based automatic analysis color diagnosis system for fundus lesions, relating to the field of ophthalmic disease diagnosis technology. It includes a fundus image acquisition module and an AI color analysis module. This AI-based automatic analysis color diagnosis system for fundus lesions, through its fundus image acquisition module, AI color analysis module, and analysis model construction module, inputs acquired fundus color images into a fundus color image processing and analysis model to obtain fundus color feature data. It then identifies the patient's lesions, diagnoses the patient's ophthalmic disease based on the identified lesions, and determines the location of the lesions in the fundus images using the fundus color feature data. This constructs a fundus image color evaluation system, improving the accuracy and reliability of ophthalmic diagnosis and assisting in the diagnosis and treatment of fundus diseases.

The invention discloses a digital ink-jet printing color analysis method and system based on image processing, and relates to the technical field of printing color analysis. According to the method, a multi-angle fabric image is obtained, the brightness gradient is calculated, the reflectivity is measured, and surface scattering distribution is deconstructed by combining an initial absorption baseline; then, according to the law of conservation of energy, utilizing reflectivity and scattering distribution to iteratively update an absorption baseline, and dynamically stripping the absorption and scattering coefficients of the fabric; and finally, inputting the dynamic coefficient as a bottom layer parameter into a Coubella-Miscang model to reversely solve the target spectral reflectivity, and matching an optimal ratio in an ink ratio database, so that high-precision color prediction which does not need manual intervention and adapts to various complex fabrics is achieved, the matching degree of printing colors and design intentions is improved, and the printing quality is improved. The problems that in the prior art, correction coefficients and simple linear hypotheses are set depending on artificial experience, color prediction adaptability is poor in the face of complex fabric, and automatic high-precision real color matching cannot be achieved are solved.

The application discloses a kind of machine vision-based fiberboard production quality on-line detection method and system, belong to production detection technical field, the present application is detected by the bark residual rate of bark wood image analysis Skin quality, and then combined according to the wood characteristics of wood raw material and average bark thickness Adjusting peeling machine parameters, the color difference value is analyzed by the color of fiber bundle after drying within the color of preset moisture content range To detect drying quality and adjust drying machine parameters by it, and by pixel gray value Judge glue spraying quality, Realize real-time detection in multiple production links, improve quality problem positioning accuracy, effectively improve material utilization and the qualified rate of fiberboard finished product.

The invention discloses a fiberboard production quality on-line detection method and system based on machine vision, and belongs to the technical field of production detection.The peeling quality is detected according to the skin residual rate of peeled wood image analysis, and then the parameters of a peeling machine are adjusted according to the wood characteristics of wood raw materials and the average skin thickness; the color difference value is analyzed according to the color of the dried fiber bundle and the color within the preset water content range so as to detect the drying quality and adjust the parameters of the drying machine, and the glue spraying quality is judged according to the pixel gray value, so that real-time detection in multiple production links is realized, the quality problem positioning precision is improved, and the production efficiency is improved. And the material utilization rate and the qualified rate of fiberboard finished products are effectively improved.

The invention relates to the technical field of leather manufacturing, and discloses a leather selecting and color matching method and device.The leather selecting and color matching device comprises a second conveying belt, a portal frame is installed outside the second conveying belt, a working groove is formed in the top of the portal frame, a protective shell is arranged at the top of the portal frame, and a color analysis module is arranged in the protective shell; a mounting block is arranged in the protective shell, a camera is arranged on the outer wall of the mounting block, a driving structure is fixedly connected to the middle of the mounting block, a fiber brush is arranged at the bottom of the protective shell, an extending opening is formed in the bottom of the protective shell, and the position and the size of the extending opening correspond to those of the working groove. And a fiber brush arranged at the bottom of the protective shell is fixedly connected to the inner wall of the extending opening, so that the problems that much dust possibly exists in a traditional color screening workshop after leather dyeing, the dust can be adsorbed to the surface of an image acquisition camera, the camera is shielded, and the acquisition precision is reduced are solved.

The invention discloses an intelligent light color control system and method based on image feedback. The system comprises an image acquisition module, a color analysis module and an intelligent control module. The image acquisition module acquires environment illumination information in real time through a camera; the color analysis module converts the RGB image into multi-color space data, and calculates an error value between each channel and a target color; the intelligent control module adopts a PID algorithm to dynamically adjust the color temperature, color and brightness of lamplight, a proportional term corrects a current error, an integral term eliminates accumulated deviation, and a differential term predicts a change trend. The method comprises the following steps: acquiring ambient light data in real time, performing multi-color space error analysis, realizing accurate light matching through closed-loop PID control, and feeding back continuous optimization by using a light intensity sensor. The method solves the problems of slow response and low precision of traditional light color control, can adapt to a dynamic illumination environment, and is suitable for stage illumination, smart home and other scenes.

According to the automatic food screening equipment, when semi-finished products fried in the previous process are conveyed to a vibrating screen, the semi-finished products are evenly scattered to a conveying belt under the vibration effect of the vibrating screen, the conveying belt drives the semi-finished products to move forwards in the direction of a qualified product collecting line at a constant speed, and when the semi-finished products move to the position below an image sensor, the semi-finished products are conveyed to the image sensor. The image sensor collects image information of a semi-finished product and then transmits the image information to the image processor for geometric dimension and color analysis processing, when the image information does not accord with a set value domain, the image processor transmits the information to the central processor, the central processor sends out an instruction to open the high-pressure gas nozzle, and under the action of gas sprayed by the high-pressure gas nozzle, the semi-finished product is processed. The original running track of the semi-finished products which do not meet the requirements is changed, the semi-finished products fall into the rejected product collecting line in advance, and qualified products run to the tail end of the conveying belt, fall into the qualified product collecting line along a parabola track and are transferred to the next procedure. Therefore, the sorting work of the semi-finished products is completed by the food sorting equipment.

The application relates to a plasma waste liquid color analysis method, a system, a readable storage medium and equipment, the method comprising the following steps: collecting an image of a transparent container containing plasma waste liquid to obtain an initial image; wherein the plasma waste liquid is obtained after plasma is filtered by adopting a double filtration plasma replacement method; the initial image is preprocessed and color space conversion is performed to obtain an image to be analyzed; a color feature vector is extracted from the image to be analyzed; and the color feature vector is input into a pre-trained classification model to obtain a classification result; through computer vision technology, non-invasive and rapid identification of the nature of DFPP waste liquid is realized, objective and convenient reference indexes are provided for clinical doctors to adjust treatment parameters, evaluate the metabolic state of patients and the risk of cardiovascular diseases, detection cost is effectively reduced, and diagnosis and treatment efficiency is improved.

The invention discloses a tobacco leaf color correction method based on a convolutional neural network, and the method comprises the steps: 1, collecting a warehouse tobacco leaf image, carrying out the normalization and denoising preprocessing, and synchronously obtaining a color reference value under standard illumination as a supervision label; 2, constructing a feature extraction model fusing a SqueezeNet lightweight network and an SE channel attention mechanism, and enhancing the color feature adaptability; 3, the preprocessed image is used as input, a supervised learning strategy is adopted, an iterative training model is optimized through a loss function, and color mapping precision is improved; and 4, performing illumination correction on the tobacco leaf image by using the training model, and outputting a restored image under standard illumination. According to the method, the calculation cost is reduced through a lightweight network, key feature learning is enhanced by utilizing an attention mechanism, and the color correction accuracy can be remarkably improved by combining dual optimization of actual scene data and standard illumination calibration, so that the method can be effectively applied to automatic color analysis in tobacco leaf quality detection and warehouse management.