140 results about "Luminance distribution" patented technology

The invention discloses a machine vision-based intelligent detection method for steel galvanized surface defects, which comprises the following steps: S1, acquiring and preprocessing a steel galvanized surface image to obtain a standardized image; s2, constructing a specular reflection probability graph according to the brightness distribution and the gradient magnitude, and calculating a reflection intensity value; s3, calculating a structure tensor matrix, determining a main direction angle and an anisotropic consistency coefficient, and generating a direction feature matrix; s4, establishing a multi-scale direction adaptive phase kernel function, and performing phase modulation in a frequency domain by adopting an improved phase stretching transformation algorithm; s5, inverse Fourier transform is executed, and a phase response matrix is extracted; s6, performing weighted fusion to obtain a comprehensive phase response diagram; and S7, setting a threshold value according to the noise variance and the statistical characteristics, executing binarization and morphological processing, and outputting a defect region and boundary coordinates. According to the invention, high-precision identification and boundary positioning of steel galvanized surface defects are realized.

The invention discloses a sludge treatment automatic monitoring method based on computer vision, and relates to the technical field of sludge treatment automatic monitoring, and the method comprises the following steps: collecting sludge image information through computer vision, extracting an edge integrity index and a brightness distribution index, and constructing a fusion judgment function based on the time sequence change of the two indexes, determining whether a thin water film exists on the sludge surface or not according to an output result of the fusion judgment function; under the condition of determining that a thin water film exists on the sludge surface, acquiring a pixel highlight gradient polymerization rate, a texture missing fluctuation frequency and an image saturation nonlinear deviation value of a corresponding image region, and establishing a specular reflection image feature recognition matrix; by constructing a multi-dimensional image feature recognition and dynamic regulation and control mechanism, the problem of image misjudgment caused by thin water film mirror reflection in the sludge dewatering process is solved, and accurate recognition and automatic closed-loop control of the real water-containing state of the sludge are achieved.

The invention discloses a visual filter tip modeling recognition and control system and a data interaction method thereof, and relates to the technical field of image recognition and intelligent control, and the method comprises the following steps: carrying out the continuous image collection of a filter tip through an industrial camera, and obtaining an image sequence of the filter tip in a conveying process; for each frame of image in the image sequence, extracting a transverse brightness change curve in a gray domain, and constructing an image brightness distribution matrix based on the spatial distribution of the filter tip in the image recognition area; according to the invention, by introducing a frequency domain analysis and risk prediction control mechanism, intelligent perception, quantitative evaluation and dynamic suppression of periodic optical interference are realized. The system can extract interference frequency characteristics, calculate the stability and occupancy ratio, predict and identify the risk in combination with a historical misjudgment model, dynamically adjust the duty ratio of the LED light source according to the risk result, break the resonance relation with the conveying rhythm, and suppress false characteristic interference. The method improves the accuracy, stability and yield of an identification system, and has good engineering application value.

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

The invention provides a laser stripe center extraction method based on gray coefficient binarization, and aims to solve the problems of non-uniform brightness distribution of laser stripes, difficulty in extraction of weak stripes, breakage of center lines and the like. The method comprises the following steps: enhancing an image; realizing self-adaptive threshold segmentation through a local gray scale statistical model and a peak variable coefficient by using a gray scale coefficient binarization method; extracting a sub-pixel-level center point by combining a secondary positioning method of skeleton constraint; and carrying out fitting smoothing by adopting bicubic interpolation and a smooth spline method. According to the method, the problem of extraction of strong and weak stripes under complex working conditions is effectively solved, and the precision, connectivity and robustness of stripe center positioning are remarkably improved while background noise is suppressed.

The invention relates to the related technical field of computer vision, in particular to a multi-exposure image enhancement method for a dynamic scene, which comprises the following steps of: 1, inputting a multi-exposure image into a global brightness correction network (GBCNet), and carrying out iterative correction on the multi-exposure image by learning parameters of a nonlinear mapping curve LE by the global brightness correction network (GBCNet), therefore, global brightness correction is realized. According to the multi-exposure image enhancement method oriented to the dynamic scene, a global brightness correction network (GBCNet) is provided, the global brightness of an image is adjusted by using a learnable image brightness mapping curve, the discreteness of global brightness distribution is reduced, a local content enhancement network (LCENet) is provided, a multi-scale convolution block (Rep-MSB) capable of being reparameterized is designed, and the multi-exposure image enhancement method oriented to the dynamic scene is realized. By combining semi-instance normalization and multi-scale feature extraction, local contrast and detail representation are enhanced, and the complexity of the model is reduced by using a structure re-parameterization technology in an inference stage.

The invention discloses a web end picture compression processing and uploading method based on multi-dimensional image features, and the method comprises the steps: recognizing a real format through file header verification, and carrying out the precise preprocessing; extracting multi-dimensional image features including size information, content complexity, brightness distribution and metadata; a lightweight CNN model is integrated for semantic recognition, and images are divided into portraits, documents, sceneries and the like; a regional compression mechanism is activated based on semantic types, and higher weights are distributed to key regions such as human faces and characters; the multi-dimensional features and a user set target are integrated through a weighted regression model, an optimal compression parameter is calculated, and the precision is ensured by adopting two-stage iterative optimization; selecting optimal output formats such as WebP, AVIF and the like according to the equipment capability, the network state and the content characteristics; hash verification is implemented to ensure transmission integrity; and finally, continuously optimizing the compression strategy through front-end incremental learning.

The invention relates to the technical field of automobile testing, and discloses a method and system for evaluating and testing visual field glare of a driver in a cabin, and the method comprises the following steps: starting from the position of the eyes of the driver, capturing a brightness distribution image of the whole visual field, and dividing the image into a plurality of sub light source regions around a glare source; dividing the visual field into a main sight area and a residual light sight area according to the attention distribution of the driver, and endowing sub light source areas falling into different areas with different weight values; calculating a unified glare index according to the weighted sum of the glare intensity of each sub light source area and the background brightness in the cabin; and finally, comparing the index with a preset threshold value, and outputting an evaluation result. According to the method, the large-area light source is divided into the sub-light sources, weighted calculation is carried out in combination with the attention of the driver, the problem that a traditional glare evaluation model is not suitable for a close-range non-point light source scene of the automobile cabin is solved, a scientific evaluation system matched with the automobile cabin is established, and the glare evaluation result is more accurate.

The invention relates to a pavement crack intelligent detection method, system and device based on image recognition and a medium. The method comprises the steps of obtaining an original pavement image, and performing preprocessing according to global brightness distribution of the original pavement image to obtain an image after illumination equalization; inputting the illumination equalization image into a pre-trained convolutional neural network to extract multi-scale texture features, and generating a multi-scale texture feature map; mapping the feature map into a crack confidence map through a segmentation network; performing threshold segmentation based on the confidence map to obtain a crack binary image; performing sub-pixel-level edge refinement on the binary image to obtain a sub-pixel-level crack edge set; and based on the edge set, crack width and length quantization data are calculated through geometric parameters, and a detection result is output. By adopting the method, local shielding interference can be effectively resisted, a scene with dramatic illumination change is adapted, sub-pixel-level precise positioning and quantification of the crack edge are realized, the recognition capability and detection continuity of the fine crack are improved, and the reliability and generalization capability in a complex road environment are enhanced.

An apparatus comprising an interface and a processor. The interface may be configured to receive pixel data of an environment. The processor may be configured to process the pixel data arranged as video frames, generate a luminance distribution map of the video frames in response to a low-pass filter operation, determine a plurality of defogging intensity weights for the luminance distribution map, perform adaptive smoothing to each of the plurality of defogging intensity weights, and generate defogged video frames in response to the video frames and the plurality of defogging intensity weights with the adaptive smoothing. The plurality of defogging intensity weights may each correspond to one of a plurality of luminance intervals of the luminance distribution map. The adaptive smoothing may be configured to prevent brightness differences in the defogged video frames.

The invention provides a panel lamp light spot uniformity detection method and system and electronic equipment, and the method comprises the steps: analyzing the non-uniform characteristics of brightness gradient distribution according to the intensity and range information of a light spot region, carrying out the image processing of a projection image region, carrying out the quantitative evaluation of the brightness uniformity of a screen, and determining the specific region and degree data of the non-uniform brightness; if the dynamic influence range of the light interference exceeds a preset threshold value, meeting room layout adjustment and equipment position change are determined according to a light interference distribution diagram and equipment configuration parameters, the adjusted light distribution mode change is identified, and the detection priority is determined; according to the detection priority and the brightness deviation index, a brightness compensation method is adopted to carry out local correction on the specific area with the uneven brightness, and corrected brightness distribution data are obtained; and identifying the image definition index and the color rendition degree index after the brightness distribution is corrected, obtaining an area coverage proportion for clearly presenting the information, and judging whether the projection display effect reaches a preset definition standard value or not.

The application belongs to the technical field of artificial intelligence, and particularly relates to a digital cultural creative content generation method based on artificial intelligence. The method comprises the following steps: step 1, calculating global text embedding; step 2, obtaining latent variables after manifold transformation; step 3, taking the transformed latent variables as initial states, constructing a potential energy function in a latent space, which comprises text semantic constraints and a global regularization term, and realizing diffusion update of the latent variables through a discrete random differential equation; adopting an inverse Fourier transform method to generate a local texture map; and step 4, taking the generated local texture map as a local driving factor, constructing a partial differential equation based on diffusion and reaction mechanism, and realizing spatiotemporal evolution of image brightness distribution under a preset initial condition, so as to realize adaptive synthesis of image structure layout and generate image content. The application realizes intelligent generation from text description to high-quality image content.

The invention relates to an SMT patch defect identification method and system. The method comprises the following steps: acquiring a first image of a welding spot in a first illumination mode, and judging whether the first image has abnormal optical characteristics or not according to the brightness distribution characteristics of the first image; if the optical characteristics of the first image are abnormal, adaptively switching to a second illumination mode, and acquiring a second image of the welding spot in the second illumination mode; acquiring rough position information of a welding spot area in the first image; acquiring fine edge information of a welding spot area in the second image; screening and fusing the fine edge information according to the rough position information to reconstruct a welding spot contour; and on the basis of the reconstructed welding spot contour, welding spot geometric information is calculated, and defect judgment is carried out, so that the misjudgment probability under the condition that the optical characteristics of the welding spots are abnormal is effectively reduced, and the SMT patch defect identification accuracy is remarkably improved.

The invention relates to the technical field of visual information detection, and discloses an identification information content intelligent detection method based on visual ergonomics. The method comprises the following steps: acquiring identification visual data, and obtaining a high-resolution image of a target identification and related environmental parameters; based on the collected data, extracting visual ergonomics characteristic functions including brightness distribution, color contrast and text definition indexes; constructing a self-adaptive detection model framework by utilizing the characteristic functions; and performing iterative optimization on the model framework according to a performance objective, operation limitation and environmental constraint of the detection task: inputting feature representation and outputting a detection result evaluation value, performing content compliance analysis on the evaluation value to generate a deviation metric value, judging whether the deviation metric value reaches a preset stable condition or not, and if so, outputting a final detection conclusion, and if not, adjusting the internal parameters of the model and repeating the process until a preset stable condition is met. The method can realize intelligent detection of the identification information.

The invention discloses an office lighting optical system with double light-emitting paths and a light quality control method, and relates to the technical field of lighting, the system comprises a double-channel module which provides central direct light and peripheral reflected light, and parameters of the double-channel module are independently adjustable; the in-situ acquisition module acquires brightness distribution information of a typical observation area; the dynamic execution mechanism module adjusts the light shielding angle of the lamp; the core processing and control unit executes a rhythm-comfort level fusion optimization algorithm, takes a target illumination parameter generated by a human body physiological rhythm as a guide, takes a constraint that a glare value is controlled to be lower than a preset threshold value, and collaboratively optimizes light source brightness, color temperature and shading angle parameters according to priorities; the control method comprises the steps of target parameter generation, glare value in-situ calculation, multivariable optimization decision and instruction execution and feedback learning, visual comfort is guaranteed, meanwhile, the system fits the physiological rhythm of the human body, the system has the self-optimization capacity, the light quality is stable and reliable, and the system is suitable for office lighting scenes.

The application relates to the technical field of image processing, and particularly discloses a power transmission line image enhancement method and system based on super-resolution reconstruction, which comprises the following steps: firstly, carrying out gray scale statistics on a power transmission line original inspection image to obtain global brightness distribution characteristics; secondly, carrying out contrast layering on the original image according to the characteristics to obtain a brightness level image set; thirdly, constructing a scale space and a multi-scale gradient field to generate a significant geometric structure feature map; fourthly, splitting a low-frequency basic layer and a high-frequency detail layer through guided filtering, combining the feature map to complete structure perception reconstruction, and obtaining a hierarchical super-resolution image; fifthly, obtaining an original brightness level mapping relationship through brightness field backtracking, obtaining a composite super-resolution image through Bayesian inference; and finally, obtaining a final enhanced image through visual optimization; and the application can improve the quality and precision of the power transmission line image.

The embodiment of the invention provides an image optimization method and related equipment. The method comprises the following steps: determining a first brightness threshold for indicating a dark region based on an image, image parameters and a preset first parameter; through the first brightness range corresponding to the first brightness threshold, the first distribution data corresponding to the first brightness range of the image is constructed, and brightness optimization of the dark region of the image can be realized based on the first distribution data; according to a preset second parameter, adjusting data of a brightness range beyond the first brightness range in the original distribution data of the brightness of the image, and updating the original distribution data in combination with first distribution data corresponding to the first brightness range to obtain target distribution data; the brightness distribution of the image can be optimized through the target distribution data, and the target image is obtained. By utilizing the method, the brightness distribution of the image can be balanced, so that the global brightness distribution of the image is optimized, and the display effect of the image is improved.

To provide an X-ray inspection device, an X-ray inspection system, and an X-ray inspection method capable of preventing deterioration of inspection accuracy of an article even if a conveyance speed of the article is increased.SOLUTION: The X-ray inspection apparatus includes a conveyor that conveys an article, an electromagnetic wave irradiator that irradiates the article with a first electromagnetic wave in a first energy band and a second electromagnetic wave in a second energy band, an electromagnetic wave sensor that detects the first electromagnetic wave and the second electromagnetic wave with which the article is irradiated, and a controller to which a detection result of the electromagnetic wave sensor is input. The control unit generates a first transmission image based on a detection result of the first electromagnetic wave and a second transmission image based on a detection result of the second electromagnetic wave, performs image processing including subtraction processing on the first transmission image and the second transmission image by using a luminance distribution related to a background other than the article shown in the first transmission image and the second transmission image, and determines presence or absence of a foreign substance contained in the article based on a difference image obtained by the subtraction processing.SELECTED DRAWING: Figure 2

The invention discloses a non-uniform illumination underwater image reply method based on discrete entropy gain, which is applied to the technical field of computer vision and image processing, and aims to solve the problem that an existing method based on image enhancement or a physical model is difficult to give consideration to global brightness balance and local detail reservation. Particularly, the problems of light spot overexposure and gradual change shadow caused by an artificial light source cannot be effectively inhibited are solved; according to the method, an underwater image under non-uniform illumination is modeled by using a vignetting component, a central overexposure region in the modeled vignetting component is constructed by using an exponential term, and an edge shadow region in the modeled vignetting component is constructed by using a polynomial term. Dynamic modeling of exponential term construction is realized through brightness distribution characteristics in the centroid region; optimal modeling of polynomial construction is achieved through discrete entropy minimization, and image attenuation caused by the influence of an extra light source of an underwater medium is effectively recovered through a gain function.

The application discloses a vehicle PVC decal defect detection method and system based on AI vision, which comprises the following steps: acquiring test image data of a vehicle decal; adjusting camera parameters to obtain a first image sequence with uniform brightness distribution in the case that the test image data has an overexposed area; the overexposed area is an area with a brightness value greater than a brightness threshold; performing image optimization processing on the first image sequence to obtain a second image sequence; the image optimization processing comprises at least one of the following: illumination compensation processing, denoising processing and contrast optimization processing; performing feature capture on potential low-complexity areas in the second image sequence through convolution operation to generate a feature vector set containing decal edges and surface textures; and inputting the feature vector set into a trained defect classification model to obtain a defect detection result.

A bending angle measuring device capable of accurately measuring the bending angle of a workpiece. [Solution] A laser light irradiation unit irradiates a linear laser beam onto a first flange on the front side of the workpiece and a second flange on the rear side. A camera 35 captures the reflected light of the laser beam irradiated onto the first and second flanges. An exposure time adjustment unit 321 adjusts the exposure time of the camera 35 so that the luminance distribution of the reflected light in each line falls within a reference range. An image angle calculation unit 33 extracts pixels within the line image with the exposure time adjusted so that the luminance distribution of the reflected light falls within the reference range. The image angle calculation unit 33 calculates the image angle of a straight line obtained by linearly approximating the pixels of the extracted multiple lines. A bending angle conversion unit converts the image angle into a bending angle of the workpiece.

The application discloses a FAST core array broadband beam effect elimination method based on deep learning, relates to the technical field of radio astronomy image processing, and constructs an end-to-end image restoration system, which comprises the following five steps: acquiring an observation image containing a broadband beam effect; extracting multi-scale features and introducing a spatial perception mechanism; performing feature frequency domain enhancement processing and multi-scale context fusion; and outputting a multi-scale prediction image and a restored image from which the broadband beam effect is removed. The application realizes the function of improving imaging quality by constructing a deep learning model, avoids cumulative errors caused by step-by-step processing, can effectively remove broadband beam distortion, and makes the reconstructed image more close to the real sky brightness distribution in overall fidelity and structural details, and has higher physical consistency.

According to the face image processing method and device, the storage medium and the computer equipment provided by the invention, after the to-be-processed face image is acquired, the face region in the face image is firstly determined, and the key points of the five sense organs are marked, so that a space coordinate basis of the key region is provided for subsequent processing; and then, a brightness channel image is separated from the face region, and the scene category of the face image is judged by quantifying the illumination distribution characteristics of the face region, so that a decision basis is provided for subsequent processing. For example, in a dark scene, five-sense-organ brightness hierarchical correction is performed to maintain skin pore-level textures while brightening; performing dynamic range compression in a strong backlight scene to balance light and shade transitions; limited histogram equalization is performed in a normal illumination scene to prevent over-sharpening or noisy explicit of the image. In addition, noise reduction processing, detail restoration and feature enhancement are sequentially carried out on the image after enhancement processing, and the finally generated image can greatly improve the recognition accuracy and the living body detection passing rate.

The invention provides a brightness modulation adaptive watermark embedding method for AI sample protection and a terminal, and the method comprises the steps: generating a watermark identification field for each sample in a training data set needing protection, encrypting the content in the watermark identification field, and coding the encryption result into a watermark bit stream; constructing an image brightness distribution modulation model based on the atmospheric scattering model; processing the input image I (x) through an image brightness distribution modulation model to obtain a brightness modulation image; in a brightness modulation process, embedding the generated watermark bit stream into a brightness modulation image in a self-adaptive steganography mode according to content characteristics of an input image I (x) to obtain a watermark protection image; according to the method, image content characteristics are combined, watermark embedding of minimum distortion embedding is supported, authentication and tampering prevention of image data are achieved, and the method can be used for protection of AI image samples in a high-reliability scene.

Embodiments of the present application provide an image display method and an electronic device. In the method, the spatial geometric characteristics and the luminance distribution characteristics of a to-be-rendered image are determined, the lighting characteristic parameters of a preset virtual light source are determined, and the image display parameters of the to-be-rendered image under virtual light source relighting are determined according to the spatial geometric characteristics and the luminance distribution characteristics of the to-be-rendered image. The method is beneficial to enhancing the authenticity of the lighting effect and optimizing the visual effect of image display.

The invention discloses a tunnel freezing degree prediction method based on lining surface image recognition, and relates to the technical field of image recognition, and the method comprises the following steps: collecting brightness distribution images and reflection direction change images of the same lining region in continuous time, arranging the brightness distribution images and the reflection direction change images according to a unified time coordinate, constructing a reflection change sequence, and carrying out the recognition of the brightness distribution images and the reflection direction change images; the method is used for the brightness sudden rising process in the thin water film engraving stage. And extracting a brightness increase rate and a reflection direction fluctuation amplitude based on the reflection change sequence, and constructing a reflection fluctuation trajectory. According to the method, the time separation of the water film stage and the real freezing stage is realized by constructing the reflection change sequence and combining the brightness increase rate, the reflection direction fluctuation and the texture attenuation characteristics, the freezing recognition accuracy is improved, and the mirror reflection misjudgment risk is reduced; and meanwhile, a freezing grade progressive curve is generated, and continuous growth reaching a set time length is taken as a control basis, so that the consistency of control response and freezing evolution is ensured, and invalid energy consumption is reduced.

An anti-glare film is attached on a surface of a display, and includes an anti-glare layer. The anti-glare layer is set to have a sparkle value falling within a range from 6 to 10, which is defined based on a value of a standard deviation of luminance distribution of the display under a state in which the anti-glare film is attached on the surface of the display, a value of specular gloss of 30% or less, which is measured with 60-degree specular gloss, a value of transmission image clarity of 60% or less, which has an optical comb of 0.5 mm, and a haze value of 50% or less. Consequently, satisfactory anti-glare property can be provided while appropriately suppressing sparkle on the display.

The invention discloses an OLED equipment fault diagnosis system based on artificial intelligence, and relates to the technical field of fault diagnosis, and the system comprises the following steps: capturing OLED pixel-level brightness distribution in real time through a microscopic camera, synchronously collecting driving current, voltage and spectral emission data, and aligning all spatio-temporal data by taking a driving signal rising edge as a reference; a defect area is segmented and positioned by adopting a self-adaptive threshold value, topological features are extracted, and a defect distribution tensor containing spatial attributes is constructed; fusing space-time and electro-optical parameters to construct a fourth-order degradation tensor, and coupling a space attenuation weight and a time sensitive function through a space-time convolution operator; microdefect evolution is analyzed by using a 3D-CNN branch, a macroscopic degradation track is learned by using an LSTM branch, and dual-scale features are interactively fused through a mask matrix; and calculating fault probability distribution, generating a fault positioning thermodynamic diagram and residual life prediction, and outputting a diagnosis report. According to the method, the tensor degradation model based on spatial-temporal feature fusion is constructed, so that the sensitivity of the system to hidden faults is enhanced.

Tone mapping methods and apparatus provide for receipt by a backend device may of metadata and an HDR image from a frontend device and the generation of a mapping curve based on feature parameters of the mapping curve included in the metadata. Tone mapping on the HDR image can be performed based on the mapping curve. The feature parameters of the mapping curve are determined by the frontend device based on a luminance distribution status of the HDR image. In this way, the mapping curve parameters are determined based on the luminance distribution status of the HDR image so that design of the mapping curve is more flexible, and display effect of the HDR image can be effectively improved.

The invention discloses a line light source detection method and system and a medium, and the method comprises the steps: adjusting the distance from an objective lens to a calibration object; processing the line scanning brightness image to obtain a brightness distribution diagram, and identifying a peak value position in the width direction of the brightness distribution diagram; calculating the offset of the peak position on each pixel column under the current line light source and the standard line light source; and extracting a relative gray value of the line scanning brightness image in the width direction, and analyzing whether the line light source in the current optical system meets the detection requirement by combining the offset of the current line light source and the standard line light source in the peak position in each pixel column direction in the length direction of the line light source. Whether the linear light source deviates and rotates relative to the plane where the camera is located or not and whether the linear light source deflects relative to the optical axis where the objective lens is located or not are judged, and then whether the linear light source of the current optical system meets the detection requirement or not is accurately analyzed.