1403 results about "Color space" patented technology

The invention discloses an image processing method applied to printed matter surface color difference detection, which relates to the technical field of image processing, and comprises the following steps: acquiring a digital image of a printed matter to be detected, performing illumination non-uniformity correction, and converting the corrected digital image into a CIELAB color space; performing region segmentation on the digital image based on double constraint conditions of color gradient and texture boundary to generate a detection region graph; feature parameters are extracted based on the detection area graph, and a feature data set is generated; establishing a dynamic reference model by utilizing process parameters and material characteristics of the printed matter; and calculating the distance between the feature data set and the dynamic reference model, identifying color difference regions and generating color difference scores, and screening and grading the color difference regions according to the color difference scores. According to the method, a multi-layer image pyramid and homomorphic filtering combined illumination correction technology is adopted, and an adaptive weight fusion mechanism based on image features is introduced, so that the illumination nonuniformity is effectively eliminated while the definition of printing details is kept.

The invention discloses an embedded power transmission line insulator chain defect identification method, system and device and a storage medium, and belongs to the technical field of defect identification, and the method comprises the steps: obtaining an insulator chain image, carrying out the insulator chain region positioning of the insulator chain image through color space change and image processing, and obtaining the coordinates of a target insulator chain; based on the target insulator chain coordinate, feature extraction is carried out on an insulator sheet, and an insulator sheet state vector is constructed; judging the state vector of the insulator sheet to obtain a suspected defective insulator list; calculating the insulator sheets in the suspected defect insulator list and detecting the surfaces of the insulator sheets to obtain the defect types and positions of the surfaces of the insulator sheets; and according to the defect type and position, the associated defect type code and description, generating structured power transmission line insulator defect report data, thereby improving the accuracy of defect identification, the fineness of defect classification and the practicability of a result report.

The invention discloses an artificial intelligence assisted makeup and makeup robot collaborative makeup method, and belongs to the technical field of artificial intelligence and robots. The method comprises the following steps: collecting user face data through a multi-modal sensor, extracting multi-scale features based on improved DenseNet and HRNet networks, and generating a three-dimensional face model; a high-fidelity virtual makeup effect is generated by using a conditional generative adversarial network in combination with user requirements, and real-time comparison and display are performed through an augmented reality interface; the makeup difference is quantified through multi-dimensional feature point mapping, multi-color space analysis and texture comparison, and correction suggestions are dynamically generated; operation is executed through the seven-degree-of-freedom redundant mechanical arm, a hybrid force control strategy and a visual monitoring system are integrated, and safe and accurate execution is ensured. The method solves the problems of uncontrollable traditional makeup effect, low execution precision and lack of dynamic feedback, has the characteristics of high adaptability, real-time response and zero-damage safety, and can be widely applied to the fields of family beauty, film and television makeup and special crowd nursing.

The invention discloses an image multi-modal feature extraction method, a ground feature classification and recognition method and a GIS image generation method. Comprising the following steps: firstly, extracting texture features from a target image by adopting a multi-directional statistical method fusing rotation invariant coding of a local binary pattern and a gray-level co-occurrence matrix, and extracting color features from the target image by adopting an LAB-HSV dual-color space collaborative analysis method to obtain features of different modes of the target image; and then according to the information values of the extracted color features and texture features, adjusting the weight ratio corresponding to the color features and the texture features so as to optimize the recognition precision of the classification model on complex ground features. And finally, according to a weight ratio corresponding to the color feature and the texture feature, performing weighted fusion on the color feature and the texture feature to obtain a corresponding multi-modal feature vector.

The invention provides a high-resolution machine vision detection method and system for precise chromatic aberration detection and a medium, and the method comprises the steps: carrying out the calibration of a camera based on a camera calibration tool, and synchronously calibrating a light source parameter and a color parameter; setting shooting parameters based on the calibrated camera, obtaining a workpiece image in real time, preprocessing the workpiece image, mapping the preprocessed image to a standard color space from an original RGB space, and extracting color features of the preprocessed image; comparing the color feature with the color feature of the standard sample, calculating a color difference index, and comparing with the color difference index based on a set color difference threshold to obtain a detection result; by calibrating various parameters of the camera, the visual detection precision is ensured, and by performing color space mapping on the workpiece image and analyzing the difference between the color difference index of the color feature and the color difference threshold value, the color abnormal area is accurately analyzed, and the detection precision of the abnormal color is improved.

The invention provides a low-illumination image enhancement method, and belongs to the technical field of image processing. The method comprises the following steps: converting an original RGB image into an HSV color space to obtain a chroma channel component, a saturation channel component and a brightness channel component; enhancing the saturation channel component and the brightness channel component to obtain a saturation channel enhanced component and a brightness channel enhanced component; fusing the saturation channel enhancement component, the brightness channel enhancement component and the chroma channel component to obtain a synthesized RGB image; and performing noise reduction processing on the synthesized RGB image to obtain an enhanced RGB image. Through the method provided by the invention, the brightness balance, the detail retention degree and the color naturalness of the output image are remarkably improved; therefore, the problems of overexposure, noise amplification, detail loss and the like which are easily caused by unbalanced brightness distribution under low illumination are solved.

The invention relates to the technical field of paper marking image acquisition optimization, in particular to an intelligent paper marking image acquisition system and method based on AI and terminal equipment. An original image is acquired through an image acquisition unit, and a color temperature distribution thermodynamic diagram is generated by a light source analysis unit through a convolutional neural network; the dynamic compensation unit divides compensation grids and executes exposure compensation, the self-adaptive correction unit outputs a target image through bilinear interpolation and color space conversion, and the quality evaluation unit compares a positioning mark color gamut value to trigger a grading re-shooting mechanism. The problems of insufficient complex illumination adaptability and poor image quality stability in the prior art are solved, highlight-shadow intelligent compensation, color space adaptive conversion and quality closed-loop control of the answer sheet image are realized through AI-driven multi-unit cooperation, the full-filling point recognition accuracy and the image qualification rate are improved, and the image quality is improved. And the high-efficiency examination paper marking requirement of large-scale examination is met.

The invention discloses an image digitization method, system and device and a storage medium, and relates to the field of data processing. The method comprises the following steps: acquiring spectral data, three-dimensional shape data and material reflectivity parameters of a target image; based on the three-dimensional shape data, analyzing surface deformation characteristics of the target image, and generating a geometric distortion distribution diagram; generating geometric correction parameters according to the surface deformation features, and reconstructing pixel positions of the geometric distortion distribution diagram to obtain reconstructed image data; and mapping the spectral data to a preset color space, matching a pre-established color database, and adjusting the color component of the reconstructed image data to obtain corrected image data. By implementing the technical scheme provided by the invention, a set of traditional Chinese painting digitization technical system considering high-precision acquisition, intelligent correction and efficient management can be developed.

The invention relates to the technical field of medical treatment, and particularly discloses an intelligent wound measuring and recording system and method.The method comprises the steps that multi-dimensional image data and three-dimensional depth information of a wound area are synchronously collected, hierarchical space division and nonlinear feature coding are adopted for preprocessing the multi-dimensional image data, and wound basic information is generated; constructing a wound measurement model based on the basic information of the wound, extracting wound features with space-time relevance, and extracting a wound contour of multi-dimensional image data by using an improved U-Net architecture in combination with an HSV color space and morphological gradient method; according to the method, the multi-dimensional image data and the three-dimensional depth information are synchronously acquired, and the advanced image processing technology is combined, so that the accurate extraction of the wound contour is realized, and the problem that wound details cannot be accurately identified and measured in a traditional method is solved; based on an ICP registration algorithm and a three-dimensional point cloud reconstruction technology, submillimeter-level wound volume measurement is realized, and the wound volume evaluation precision is improved.

The invention relates to a light-weight low-light image enhancement method based on illumination iterative adjustment, and the method comprises the steps: carrying out the HVI color space transformation of a to-be-processed image, obtaining an HVI image, separating an illumination intensity component, obtaining an illumination intensity feature map, and carrying out the convolution processing of the HVI image, and obtaining a shallow feature map; feature fusion and brightness adjustment are carried out on the illumination intensity feature map and the shallow layer feature map to obtain a depth feature map, up-sampling is carried out step by step through two layers of decoders to recover the size, channel adjustment is carried out on each layer of decoder by adopting convolution of a specified size, and up-sampling is realized through bilinear interpolation; a Mama-based structure refining module is introduced into the tail end of each layer of decoder to improve the structure restoration capability, the number of channels of HVI features output by the last layer of decoder is adjusted to a specified value through a convolution of a specified size, residual connection is carried out on the HVI features and an initial HVI image, an enhanced HVI image is obtained, and a restored image is obtained through inverse HVI transformation. Adaptive enhancement is carried out under various low light conditions, multi-directional spatial context aggregation is realized, and texture representation is improved.

The invention discloses a wafer probe mark accurate detection method based on deep learning, and belongs to the field of wafer probe mark detection, and the method comprises the steps: constructing a pin mark image denoising preprocessing network, employing a small target feature protection and enhancement strategy based on HSV color space and local contrast joint adjustment, and carrying out the recognition of a small target feature; denoising and contrast optimization are carried out on the needle mark image; a multi-scene training sample is generated through mosaic splicing and mix fusion; a dense small target enhancement module is introduced into the backbone network to enhance needle mark feature expression, and a multi-scale feature fusion module is arranged in the neck network to extract full-scale features; and establishing an anchor frame optimization system adaptive to the minimum needle mark target, and adopting an optimizer and learning rate collaborative optimization training strategy to realize model adaptive convergence. According to the method, high-precision detection and robust identification of the wafer probe mark can be realized under a complex background, and the detection accuracy and stability are remarkably improved.

A method for analyzing a sample includes introducing the sample into a vessel including a cation-exchange resin, where the cation-exchange resin has a high affinity to one or more interferent species in the sample, directing the sample through the cation-exchange resin to thereby capture one or more interference species in the cation-exchange resin to purify the sample, and releasing the purified sample from the vessel. Additionally, a method for analyzing a sample includes receiving an image depicting a diagnostic test region and a color reference chart, converting a test region image portion of the image to an analytical color space, determining first and second coordinate values characterizing the test region image portion in the analytical color space, and providing a quantitative measurement of an analyte of interest in the sample based on the first and / or second coordinate values and a predictive formula based on the color reference chart.

The invention discloses a tea leaf fermentation degree determination method with image recognition, which comprises the following steps: shooting tea leaves through a high-definition camera and a camera in different tea leaf processing stages according to different tea leaves to obtain high-definition images of the tea leaves, preprocessing the collected tea leaf images, and determining the fermentation degree of the tea leaves according to the preprocessed tea leaf images. Through denoising, contrast enhancement and color correction technologies, an acquired image is preprocessed, the image is converted into a proper color space for extracting color features in the tea image, and dominant hue and color distribution on the surface of tea are analyzed. The invention relates to the technical field of tea leaf detection, and the method can realize non-destructive, real-time and accurate tea leaf fermentation degree monitoring, and automatically judges the fermentation state of tea leaves by shooting tea leaf images and analyzing visual features of colors, forms and textures of the tea leaves in combination with a machine learning algorithm. Therefore, the automation level and the production efficiency of the tea processing process are improved.

The invention relates to the technical field of computer vision and image processing, and discloses a low-light image enhancement method based on a conditional diffusion model and an attention mechanism. The method comprises the following steps: converting an input low-light image and a normal-light image from an RGB color space into an HVI color space, and decomposing the HVI color space; obtaining a brightness component and a horizontal and vertical component; performing iterative brightness recovery on the obtained brightness component through a conditional diffusion model to obtain an enhanced brightness component; performing color preservation and local and global detail enhancement on the obtained horizontal and vertical components through a residual attention module to obtain enhanced horizontal and vertical components; and synthesizing the obtained enhanced components into an HVI image, converting the HVI image back to an RGB image, and finally obtaining an enhanced image. According to the method, the problem that an enhancement algorithm in the prior art is not easy to obtain a local detail which is fully reserved, the color is kept undistorted, and the generalization ability is improved to adapt to images under different conditions is solved.

The invention provides an underwater multi-color space image enhancement method based on depth information guidance, which is characterized in that a model of the method is based on a training model framework capable of utilizing physical priori knowledge and multi-color information, and comprises a depth information generation module, a multi-color space feature fusion module and a depth enhancement module, the relation between a depth image and an underwater scene is enhanced through attention estimation, scene reconstruction training and fusion of multi-color information, so that insensitivity of a single RGB color space to brightness and saturation image attributes is made up; the feature representation capability is improved, and the scene adaptability of the model is enhanced.

The invention relates to the technical field of crop disease and insect pest image recognition, and particularly discloses a crop disease and insect pest image recognition method based on a large model, and the method comprises the steps: obtaining a multi-angle leaf image through high-resolution imaging equipment under a controllable illumination condition, and obtaining a target image in a unified format; extracting scab texture complexity features in combination with a local binary pattern and a gray-level co-occurrence matrix algorithm, and performing multi-channel statistical analysis on RGB and HSV color spaces to generate color heterogeneity feature vectors; further fusing the two types of features into a composite disease feature vector, inputting the composite disease feature vector into a probability model constructed based on a support vector machine and a Monte Carlo Dropout mechanism, and outputting probability distribution and confidence score of disease and pest categories; and dynamically adjusting a model training strategy according to a confidence level, triggering a feedback mechanism for a low-confidence sample, generating a synthetic image by using a conditional generative adversarial network, and optimizing model parameters in combination with incremental learning to realize stable identification modeling of rare or complex disease types.

The invention belongs to the field of burn grading, and particularly relates to a burn grading detection method based on three-dimensional reconstruction, and the method comprises the steps of depth inspection, human body three-dimensional modeling, model compression, curved surface labeling, burn surface area calculation and burn indexing. According to the scheme, the semantic segmentation network used for dividing the burn area is constructed, the semantic segmentation network is accelerated by utilizing FPGA hardware, a network model is compressed by adopting a pruning method, and the FPGA hardware storage and calculation pressure is reduced; according to the method, two thresholds of HSV and CIELAB color spaces are combined, erythema recognition specificity is improved, blister morphological characteristics are quantified through edge detection, convex hull defect analysis and brightness variance, voice content keywords and acoustic emotion characteristics are fused, patient pain is quantified, and an automatic complete solution is provided for burn indexing through three-dimensional reconstruction and space mapping. And the defects of traditional subjective diagnosis are overcome.

The invention relates to the technical field of computer vision, and discloses a vehicle paint make-up color matching method based on computer vision, and the method comprises the steps: collecting a vehicle paint image in real time through an industrial camera, constructing a multi-dimensional color feature extraction system after preprocessing, and building a color feature database through a color space conversion technology; and positioning a target color card by combining a feature matching algorithm. Meanwhile, a historical paint make-up case information base is constructed, a multi-factor weight distribution model is established, an optimal matching scheme is solved by adopting an improved particle swarm optimization algorithm, and finally a result is output through an interactive verification platform and tracked and fed back. According to the method, multiple factors are comprehensively considered, the precision and efficiency of color matching are improved, and the method is suitable for paint make-up color matching of vehicles of different vehicle types, years and paint surface types and has high practicability and popularization value.

The invention discloses an image denoising device based on adaptive local enhancement and dynamic multi-scale dependent fusion, and the device comprises a data preprocessing module which is used for carrying out the pixel normalization processing, color space conversion and size and resolution adjustment of a to-be-denoised image, and obtaining a standard image; the adaptive local convolution enhancement module dynamically adjusts a receptive field of a convolution kernel according to noise characteristics on the standard image, and extracts an original feature map on the standard image; the long and short term dependency modeling module based on beam scanning captures long and short term dependency on the original feature map by using a beam scanning technology, and fuses the long and short term dependency with a standard image to obtain a preliminary de-noised feature map; the multi-level representation information extraction module is used for extracting deep-level de-noising feature maps of different scales in the preliminary de-noising feature maps; and the refined reconstruction module carries out fusion and refined reconstruction on the original feature map and the deep denoising feature map, and outputs a noiseless image. According to the invention, efficient and accurate image denoising processing can be realized.

The invention provides an offshore wind power GIS equipment partial discharge mode identification method based on an improved SAE network, and belongs to the technical field of electrical digital data processing.The method includes the steps that a partial discharge experiment platform meeting the IEC60270 standard is constructed, four typical defect models are designed, and PRPD spectrograms are collected; hSV color space threshold segmentation is utilized to remove interference elements, and image features are enhanced in combination with a partial discharge physical mechanism equation. The improved SAE network integrates a three-layer convolution encoder and an up-sampling decoder, introduces an ECA attention mechanism to optimize a feature channel weight, and combines a pre-trained # imgabs0 # PDCN model to enhance the feature extraction capability. Physical parameter constraints are introduced in the training process, and after global pooling dimensionality reduction and full connection layer processing, high-precision identification of four defect types of point discharge, creeping discharge, air gap discharge and suspended metal discharge is realized, and the technical problems of low accuracy and poor generalization ability of offshore wind power GIS equipment partial discharge mode identification are solved.

The invention discloses an SAR image orientation sensitive target detection method based on sub-aperture image saturation characteristics, and belongs to the technical field of synthetic aperture radar image target detection. The azimuth sensitive target detection is realized through the following steps: 1) sub-aperture image generation: dividing an azimuth frequency spectrum into a plurality of sub-bands, and generating a plurality of sub-aperture images through inverse Fourier transform; 2) color synthesis and feature extraction: allocating different hues to each sub-aperture image by using an HSV color space, synthesizing an RGB color image, converting the RGB color image to the HSV color space, and extracting a saturation channel in the RGB color space as an azimuth sensitivity feature map; and 3) target detection: carrying out threshold segmentation on the saturation feature map, and identifying a pixel region with a high saturation value, namely, an orientation sensitive target. According to the method, the color saturation change caused by the scattering difference of the target in different sub-apertures is utilized, effective detection of azimuth sensitive targets such as artificial buildings and vehicles is achieved, and the method has the advantages of being simple in calculation and high in robustness.

A slide fastener slider, includes: a slider body; a puller, and a lock pin. The lock pin is made of a stainless steel material. A black oxide film is formed on a surface of the lock pin. The surface of the lock pin has brightness L* satisfying 31.70≤L*≤35.90, and a* of a value satisfying −0.708≤a*≤1.929, the brightness L′ and the a* of the value according to definitions in a CIELAB color space defined in JIS Z8781-4 (2013).

The invention belongs to the technical field of image processing, and particularly discloses a low-light environment image adaptive enhancement method based on unmanned aerial vehicle inspection, which comprises the following steps: preprocessing a low-light RGB image shot by an unmanned aerial vehicle; the preprocessed low-light RGB image is converted to an HSV color space, and brightness and color components are obtained through separation; performing dual-channel attention mechanism processing: respectively optimizing brightness and color components by adopting a brightness branch and a color branch; wherein the brightness branch enhances the dark part contrast through a convolutional network, and the color branch suppresses a color channel with significant noise based on a channel attention mechanism; and reconstructing the optimized color and brightness components into an HSV color space, and converting the HSV color space into an RGB image format to realize brightness enhancement and color denoising. The problems of overexposure, color distortion, excessive noisy points and the like existing in the image shot by the unmanned aerial vehicle camera in the low-light environment in the prior art can be effectively solved, and the image quality under the night or weak light condition is improved.

The invention discloses a method and device for detecting color reducibility of a mobile phone camera and a medium, and relates to the technical field of image quality detection.The method comprises the steps that after geometric alignment is conducted on a color card image set, color block areas are divided, and a cross-light-source color block observation matrix is obtained through color block feature extraction; converting the cross-light-source color block observation matrix into a target color space coordinate, and performing illumination correction to obtain a cross-light-source color coordinate matrix; inputting the cross-light-source color coordinate matrix into a deep learning model, outputting a color block level high-dimensional color embedding matrix, and obtaining a high-dimensional color embedding vector set corresponding to the tested mobile phone camera in combination with an attention weight; calculating a color reducibility error index based on the high-dimensional color embedding vector set in combination with the standard reference library; and performing qualification judgment according to the color reducibility error index to generate a qualification detection conclusion. According to the invention, a stable and reliable technical basis is provided for camera quality detection under a multi-light-source imaging condition.

The invention provides a video content enhancement method for a low-illumination environment, and the method comprises the steps: achieving the data preprocessing based on an original low-illumination video frame sequence through frame synchronization, color space conversion and local brightness analysis, generating a noise sensitivity thermodynamic diagram through multi-feature unsupervised learning, and constructing a noise perception gating mechanism through the combination of affine transformation. Dynamic modulation of the characteristic channel is realized; in the multi-scale network structure, a channel attention module is used for carrying out layer-by-layer self-adaptive adjustment on a noise sensitive area; a basic illumination image and an edge enhancement image are generated through double-branch decoding, and then weighted fusion is carried out in combination with a noise thermodynamic diagram, so that brightness balance and detail enhancement are realized; a noise smoothing regular term is introduced during end-to-end training, so that the network achieves dynamic balance between an enhancement effect and noise control.

The invention relates to the technical field of computer vision and remote sensing image processing, in particular to a road extraction method for a high-resolution remote sensing image, which comprises the steps of (1) preprocessing, (2) feature extraction and fusion, (3) attention optimization and (4) segmentation and post-processing. According to the road extraction method for the high-resolution remote sensing image, road features are enhanced through HSV color space conversion and edge detection, a DeepLabv3 + encoder is combined with a multi-scale feature interaction module to capture multi-scale contexts, feature representation is optimized through a coordinate-channel double-attention mechanism, and the road extraction efficiency is improved. The road breakpoints are repaired based on a multi-criterion voting mechanism, the problems of complex background interference, feature loss and connectivity deficiency are effectively solved, the precision and integrity of remote sensing image road extraction are remarkably improved, and the method is suitable for automatic road network analysis in the fields of smart cities, disaster emergency and the like.

The invention discloses an OK lens offset quantitative detection method and system based on a corneal topography map, and the method specifically comprises the following steps: carrying out the preprocessing of the corneal topography map, intercepting a lower half region, converting the lower half region into an HSV color space, and separating the brightness and chrominance information; extracting cornea sclera boundaries and OK lens red and green mark areas through dynamic threshold segmentation, and determining boundary key points and lens mutation point coordinates in combination with a sub-pixel level positioning technology; a standardization coefficient is dynamically calculated through the cornea diameter, and the pixel distance is converted into the actual physical offset. Through multi-modal feature fusion and dynamic parameter adjustment, the positioning error is reduced to + / -0.02 mm, the single time consumption is less than 5 seconds, and the method has the robustness of illumination interference resistance, motion blur resistance and individual cornea size difference, and provides an efficient and objective quantitative basis for evaluation of the wearing effect of the OK lens.

HDR color patches are sampled throughout an HDR color space parameterized by a parameter. Reference SDR color patches, input HDR color patches and reference HDR color patches are generated from the sampled HDR color patches. An optimization algorithm is executed to generate an optimized forward reshaping mapping and an optimized backward reshaping mapping. The optimized forward reshaping mapping is used to forward reshape input HDR images into forward reshaped SDR images, whereas the optimized backward reshaping mapping is used to backward reshape the forward reshaped SDR images into backward reshaped HDR images.

The invention relates to a photovoltaic sand and dust identification method based on color space fusion and lightweight learning, and the method comprises the following steps: S1, obtaining image data of a photovoltaic module, detecting a module region in an image, and obtaining a mask image of the module region; s2, extracting an ROI image of the photovoltaic module, and performing geometric correction processing; s3, converting the ROI image from an RGB color space to an HSV color space; s4, respectively carrying out threshold setting on the H, S and V three-channel images based on the HSV color space; s5, fusing threshold setting results of the H, S and V channels, generating a final sand and dust pollution mask pattern, and completing sand and dust region identification; and S6, calculating the sand-dust covering proportion of the polluted area on the surface of the component, and dividing pollution grades according to the sand-dust covering proportion. According to the invention, the dust area on the surface of the photovoltaic module can be accurately extracted under different illumination conditions, standardized quantitative evaluation of the pollution degree is realized, and the method is suitable for intelligent cleaning management and remote operation and maintenance scheduling scenes of a photovoltaic system.

The invention provides a weak light environment image contrast improvement method for a robot welding path, and relates to the field of image processing, and the method comprises the steps: firstly improving an original compression mode based on a brightness single factor through a contrast perception compression factor; secondly, a low-light image enhancement color space based on a polar coordinate structure is introduced, and a contrast perception compression factor is combined to construct a low-light image enhancement color space with a space decoupling capability; a welding path structure response diagram is constructed by fusing the direction consistency entropy and the direction responsivity, the sensitivity to the structural characteristics with directivity and coherence such as the welding path is enhanced, and the accuracy and robustness of structure extraction are improved; and finally, through combination of the robot welding path image after color enhancement and the welding path structure response diagram, background noise interference is effectively inhibited while welding edge details are highlighted, accurate enhancement of the welding path image is realized, the welding edge details are enhanced, and the definition and the structure continuity of the image are improved.