168 results about "Color analysis" patented technology

The invention relates to the technical field of power line deicing, in particular to an intelligent power line deicing system installed by using an unmanned aerial vehicle. The system comprises an image acquisition module, a feature extraction module, a data analysis module, a deicing control module and a data storage module. According to the invention, image sampling is carried out on an ice layer attached to the surface of a power line through the image acquisition module, and corresponding feature extraction is carried out on image information by using an edge detection unit, a texture detection unit and a color analysis unit in the feature extraction module; and through information fusion of the data analysis module and construction of a corresponding ice layer thickness prediction model, a corresponding ultrasonic deicing frequency range is selected according to the ice layer thickness, and efficient removal of the ice layer on the surface of the power line is realized.

The invention provides a typical culture gene identification method for historical blocks in southern Fujian, which comprises the following steps of: dividing culture gene identification elements into a macroscopic class, a mesoscopic class and a microscopic class according to an identification principle; carrying out data collection, data arrangement, picture labeling and data division; the construction model comprises four sub-models, namely a building feature target detection model, a street aspect ratio judgment model, a color analysis model and a plane layout judgment model, and the four sub-models act together to form a fusion model to identify microscopic features, mesoscopic features and macroscopic features in the picture; and carrying out model training, model evaluation and model optimization on the fusion model by utilizing the training set and the test set obtained by constructing the database, exporting the trained model into a deployable format, and deploying the trained model on computer equipment to carry out typical culture gene identification on the southern Fujian historical block. According to the method, the accuracy and efficiency of typical culture gene identification of the southern Fujian historical block are improved, and culture genes contained in the picture can be identified and classified by inputting the picture.

The invention relates to the technical field of vehicles, and discloses a method and system for determining and controlling the color of an atmosphere lamp based on a picture color extraction algorithm, electronic equipment, a storage medium and a vehicle, and the method comprises the steps: responding to a user instruction, and based on a current scene, selecting a picture conforming to the current scene atmosphere from a preset picture source; performing color analysis on the picture based on a picture color extraction algorithm, and extracting at least one group of colors capable of representing the main style of the picture according to preset preference setting; in response to the extracted color, converting the color into an atmosphere lamp control signal; and in response to the analyzed color information of the atmosphere lamp control signal, dynamically adjusting the light emitting color of the atmosphere lamp so as to enable the current scene to be coordinated and consistent with the current visual element. According to the method, the color of the atmosphere lamp can be dynamically calculated and adjusted, main visual elements including a screen and a lamp effect in the cabin are unified, immersive cabin experience is provided for a user, and the individual requirements of the user are met.

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 discloses a face makeup real-time generation method based on a two-stage diffusion model, and the method comprises the steps: carrying out the preprocessing of a face-free face image of a user, analyzing the color and contour features of the face of the user, obtaining the personal color information of different users, and classifying the personal color information into a predefined color type, namely, the personal color analysis results of different users; on the basis of a two-stage diffusion model, generating a face makeup image according to the face-free face image uploaded by the user and the personal color analysis result; and carrying out refinement post-processing and optimization post-processing on the generated face makeup image. According to the method, the makeup effect can be finer, accurate adjustment can be carried out according to the individual requirements of the user, it is ensured that the finally generated makeup image is more attractive visually, and meanwhile the stability of the generation model is improved.

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.

The invention discloses a detection method for identifying bad appearance of a product by using an AI image, which belongs to the field of appearance detection and comprises the following steps of: acquiring a product surface image by using a high-resolution industrial camera under a control illumination condition; preprocessing the collected image to improve the image quality; carrying out AI image recognition on the preprocessed image, and analyzing whether flaws exist on the surface of the product or not; evaluating the bad appearance of the product according to the analysis result, and outputting an evaluation result; the specific process of analyzing whether flaws exist on the surface of the product comprises the following steps: constructing a texture analysis model, importing an image into the texture analysis model for AI analysis, and outputting a first analysis result; constructing a shape analysis model, importing the image into the shape analysis model for AI analysis, and outputting a second analysis result; and constructing a color analysis model, importing the image into the color analysis model for AI analysis, and outputting a third analysis result. According to the invention, the appearance of the product can be comprehensively identified, and the identification accuracy is improved.

The invention belongs to the technical field of image processing, and particularly relates to a textile color difference analysis method and system based on image processing, and the method comprises the steps: automatically collecting textile image data of each batch, and carrying out the automatic analysis of the preprocessed textile image data, and obtaining a textile image collection quality index; after the textile image acquisition quality index of the to-be-detected textile is analyzed to be qualified, performing automatic color analysis and comparison on the preprocessed textile image data, and automatically obtaining color features in the textile image; through a color difference analysis method based on color feature matching, a first color difference value between a to-be-detected textile image and a reference sample image is automatically analyzed, a second color difference value between a to-be-detected textile and a reference sample is analyzed through a color difference meter, and according to the first color difference value and the second color difference value of the to-be-detected textile, the first color difference value and the second color difference value of the to-be-detected textile are calculated. And the textile color difference value of the to-be-detected textile is analyzed. The automation degree of textile color difference analysis is improved.

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 color correction, in particular to a baked porcelain false tooth color adjusting method and system based on image analysis, and the method comprises the following steps: identifying teeth, gingiva and surrounding tissues in an image, determining a region boundary by using an edge detection technology, and calibrating a region according to gray difference. According to the method, teeth and surrounding tissues are accurately segmented by using an edge detection technology, accurate identification of a tooth area in an image is realized, a clear starting point is provided for color adjustment, a wave band with high reflectivity is selected, the quality of image data is optimized, the precision of subsequent color analysis is ensured, and hue, brightness and saturation are adjusted in a CIELAB color space; the matching degree of denture colors and natural teeth is improved, the visual coherence is enhanced, neural network analysis and time sequence data are integrated to predict long-term color changes, a real-time feedback mechanism automatically adjusts color parameters according to illumination conditions, the visual effect is continuously optimized, and the naturalness and consistency of colors in different environments are guaranteed.

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 a printer control system which comprises an image processing module used for detecting the quality of printing output in real time and monitoring abnormity in the printing process; the behavior analysis module is used for predicting a printing demand of a user by utilizing a machine learning model based on printing historical data of the user, and pre-loading a file or setting related printing parameters in advance; the error correction and feedback module is used for immediately stopping the printing task when the image processing module detects abnormity; the user interaction module allows a user to check the printing progress in real time through an interface and prompt errors; a printing control method of a printer comprises the following steps that S1, a system reads an image to be printed, color analysis is conducted on each pixel of the image, the RGB color value Ii, c (t) of each pixel is obtained, and the ideal color value # imgabs0 # is obtained through a reference image to calculate the color complexity C (t); according to the method, the printing accuracy and reliability are improved, unnecessary rework and consumable waste are reduced, the printing speed is increased, and the efficiency is improved.

The invention relates to a video clipping method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: determining an editing template, and obtaining a to-be-clipped video clip; the video clip to be clipped is a video clip of which the video duration is greater than a duration threshold; the duration threshold value is determined according to an editing template; performing color analysis and shot language analysis on the to-be-clipped video clip, and determining a first video style of the to-be-clipped video clip according to analysis results of the color analysis and the shot language analysis; and determining a second video style matched with the editing template, and determining a target clip in the to-be-clipped video clip according to the duration threshold and the relationship between the first video style and the second video style. By adopting the method, the wonderful moments in the video can be automatically identified so as to adapt to the editing template.

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 provides a flotation froth color analysis method based on multispectral dual-branch depth fusion, and relates to the technical field of flotation froth color analysis, comprising: collecting and preprocessing a multispectral image of froth; the method comprises the following steps: inputting a preprocessed multi-spectral image # imgabs0 # into a multi-spectral double-branch deep fusion network, firstly performing multi-spectral shallow feature extraction on # imgabs1 #, and obtaining a multi-spectral shallow feature matrix F1 reflecting foam space information and spectral information; the F1 is input in parallel into two branches aiming at foam spectral features and foam spatial features, and a spectral feature matrix FM and a foam spatial feature matrix Fs are obtained; fM and Fs are input into a multispectral feature fusion module for intelligent weighted fusion, and a fusion feature matrix F of comprehensive foam spectral features and spatial features is obtained; and inputting the F into a foam color characterization module, and outputting to obtain foam color state prediction. The method can be used for predicting and analyzing the foam color.

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.