214 results about "Pixel intensity" patented technology

The invention discloses a full-automatic cable vibration high-precision measurement method based on a machine vision technology, and relates to the technical field of structural vibration measurement. The method comprises the following steps: installing and calibrating equipment, and ensuring that a cable plane coincides with an imaging plane; high-frame-rate video acquisition is carried out, and comparison verification is carried out in combination with an accelerometer; screening effective pixels based on the pixel intensity time sequence variance, and constructing a pixel intensity space-time matrix by using the time sequence of the pixels; sVD decomposition is adopted to extract an accumulated energy ratio gt; 90% of the main modes are reconstructed, and real signals are revealed; a plurality of inhaul cables are automatically positioned and coded through gradient features and Hough transform; sub-pixel-level vertical displacement is calculated by applying an optical flow method, and median filtering is combined to resist noise; fourier transform is carried out to obtain a spectrogram, and a fundamental frequency is identified; and calculating cable force based on a string pulling theorem. According to the method, remote, full-field and multi-target synchronous monitoring is realized, the displacement measurement precision reaches a sub-pixel level, and a high-precision and full-automatic solution is provided for health monitoring of structures such as bridges.

The invention belongs to the field of medical image processing and analysis, and provides a breast ultrasound image lesion segmentation method and system based on elastic semantic decoupling, and the method comprises the steps: obtaining a disclosed breast ultrasound image data set, carrying out the data enhancement of a breast ultrasound image, and randomly dividing a training set and a test set according to a corresponding proportion; an elastic semantic decoupling segmentation network is constructed, and the elastic semantic decoupling segmentation network is mainly composed of an encoder, a decoder and a jump connection; training the constructed elastic semantic decoupling segmentation network model by using the data of the training set to generate a model weight; testing the model by using the image data of the test set, and evaluating a segmentation result to obtain a trained elastic semantic decoupling segmentation network model; and performing focus segmentation prediction by using the trained elastic semantic decoupling segmentation network model. According to the method, challenges of pixel intensity similarity fluctuation and space detail loss can be coped with at the same time, so that the accuracy and robustness of breast ultrasound image lesion segmentation are improved.

The invention provides a dual-mode image fusion method and device based on tuple disturbance and a storage medium, and relates to the technical field of image fusion processing. The method comprises the following steps: constructing a model comprising an encoder, a feature fusion network and a decoder by collecting a cross-modal image group; an encoder is trained based on a tuple disturbance comparison learning framework, cross-modal sharing and complementary features can be mined, learnable positive and negative sample pairs are constructed through a tuple disturbance module, dependence on the same mode is avoided, and generalization is enhanced; the feature fusion network performs complementary feature fusion and multi-scale semantic aggregation training by using cross-modal deep features to improve the quality of a fused image; multi-scale semantic aggregation solves the problem of detail loss, and spatial feature calibration strengthens details, so that a fused image retains bimodal characteristics, and semantic consistency is enhanced; and a combined loss function is constructed, a pixel intensity and gradient structure difference optimization model is integrated, the performance and robustness are improved, key information of the fused image is ensured, and the visual effect and the accuracy are better.

The invention relates to the technical field of defect detection, in particular to a dynamic defect real-time detection system based on deep learning, and the system comprises an image sequence preprocessing module which is used for defining a three-dimensional space-time neighborhood for pixel data of each frame based on an input dynamic image frame and adjusting the neighborhood block form according to the local intensity change direction of the pixel data, and obtaining a space-time filtering frame sequence, analyzing the space-time filtering frame sequence, converting pixel data in a logarithm domain, and separating illumination components by iteratively updating a local pixel intensity estimation value. According to the method, the three-dimensional space-time neighborhood is defined for the input dynamic image frame, the neighborhood block form is adjusted according to the local intensity change direction, the weighted average of the neighborhood pixel data can retain the dynamic defect characteristics and suppress the background noise influence, and the local pixel intensity estimation value is updated through logarithm domain conversion and iteration. And illumination variation factors are reliably separated, so that the robustness and adaptability of the image are enhanced.

The invention relates to the technical field of medical image processing, in particular to a medical image segmentation method based on a frame prompt enhanced SK-VM + + network, and the method comprises the steps: collecting a to-be-segmented image in a data set, and carrying out the three-channel copying of the to-be-segmented image; extracting focus bounding box information in the text annotation file to generate a binary mask; generating a box prompt visualization graph by using the binary box prompt mask; fusing the binary box prompt mask and the copied three-channel image to obtain a four-channel tensor; and taking the four-channel tensor as input, constructing an SK-VM + + model, and performing segmentation processing by using the SK-VM + + model. The medical image segmentation method solves the problem that the existing medical image segmentation method is difficult to accurately deduce the spatial context only by means of the pixel intensity in a low-contrast and high-noise environment, and is easy to cause false detection and missing detection.

The invention discloses a binocular camera global ultra-sensitive depth direction vibration measurement method, and relates to the technical field of structural vibration measurement. The method comprises the following steps: completing internal reference and external reference calibration of a binocular camera based on a Zhang's calibration algorithm, and ensuring the three-dimensional reconstruction precision; in image processing, effective pixels with high dynamic response are screened by calculating pixel intensity time-history variance, a pixel intensity space-time matrix is constructed, and singular value decomposition is adopted to extract front k-order modals to reconstruct the pixel intensity matrix so as to reveal real signals originally submerged in imaging noise and rounding errors. And multi-direction cost aggregation is carried out by using a semi-global matching algorithm, the parallax calculation robustness in a low-contrast scene is improved, and a high-precision depth map is generated. And depth information is converted into three-dimensional space information in combination with camera parameters, so that non-contact measurement of the vibration time travel curve in the full-field depth direction is realized.

A system includes a tracker, an autostereoscopic display, and at least one processor. The at least one processor is configured to track an eye position of a given eye of a given user relative to an image plane of the autostereoscopic display, using the tracker. The at least one processor is further configured to detect when the eye position of given eye is no longer trackable by the tracker. When the eye position is no longer trackable, the at least one processor is configured to generate or retrieve an image for display to the given eye, increment a tracking counter, map a value of the tracking counter to a transition level, and perform at least one of modifying pixel intensity values of the image based on the transition level to fade out the image, or interpolating a virtual depth in the image based on transition level, and display the image.

The invention relates to a camera function test method, in particular to an intelligent automobile camera function simulation test method for a strong light environment, which comprises the following steps of: firstly, determining a corresponding relation between a position of a strong light source in a world coordinate system and a pixel position in an image coordinate system according to camera parameters; secondly, determining the influence radius of a light source according to the geometrical characteristics of strong light, finally outputting a pixel position influenced by the strong light as a strong light influence area, establishing a camera imaging physical model according to an optical-electrical-digital signal propagation process, and determining the specific numerical value of the influence of the strong light environment on the pixel intensity according to the physical characteristics of the strong light source; on the basis of the geometric model, a more real strong light influence area is simulated in combination with a strong light pixel threshold value; based on the strong light influence area and the corresponding pixel value, superposing the strong light influence area and the corresponding pixel value to a strong light-free image acquired by a real vehicle to synthesize a strong light simulation image, and testing the camera function; the method has the advantages of low test cost, high test efficiency and accurate and controllable analogue simulation parameters.

The invention relates to an artificial intelligence image segmentation processing method and device, equipment and a medium. The method comprises the following steps: acquiring pixel intensity distribution of an input image and extracting local features by using a convolutional neural network to generate a pixel intensity change feature map; on the basis of the feature map, boundary area probability distribution is calculated by using a conditional random field model, and a zigzag edge is smoothed by using a graph cut algorithm to generate a continuous segmentation boundary; splicing the continuous segmentation boundary and an input image, inputting the spliced continuous segmentation boundary and the input image into a Transform enhanced U-Net network, balancing edge alignment loss and region overlapping precision loss through a dynamic weighted loss function, and generating an optimized segmentation mask; and performing threshold binarization processing on the optimized segmentation mask, removing isolated noisy points in combination with morphological closed operation, and generating a segmentation image matched with the physical defect form. By adopting the method, the continuity of segmentation boundaries and the matching precision of defect forms can be improved, and the problems of edge sawteeth and noise interference in traditional segmentation are solved.

A method for detecting defect in image is provided. The method obtains a number of original images, determines a first reference image from the original images, and performs a histogram matching on the original images excluding the first reference image according to the first reference image, to obtain a plurality of matched images. The method further generates a synthesized image according to pixel intensities of the matched images and pixel intensities of the first reference image; and uses the synthesized image as a second reference image to perform an image comparison with a test image, to generate a result of defect detection. A related device and a related non-transitory storage medium are also provided.

A far-infrared camera mounted in a vehicle generates an image frame. When an image of a large animal is identified in the image frame, a pixel intensity of the large animal image is determined. An estimated distance to the large animal from the far-infrared camera based on the pixel intensity is determined. When the animal is classified as a tracked animal, and future trajectories of the tracked animal and the vehicle intersect, a component in the vehicle is actuated.

The invention discloses an explosion-proof intelligent video monitoring control system, and relates to the technical field of intelligent security and protection, and the system comprises a collection module which is used for collecting multispectral video data, and generating a partition time sequence original data set; the feature extraction module is used for extracting pixel intensity change and constructing a native fluctuation spectrum; the linkage factor construction module is used for constructing a multi-channel disturbance parameter matrix based on the multi-channel native fluctuation spectrum in the same area and extracting a multi-dimensional eddy current spectrum linkage factor; the anomaly judgment module is used for carrying out high-dimensional density peak clustering, identifying an abnormal high condensation region and calculating a spectrum gathering factor; the path modeling module is used for performing time sequence tracking on the abnormally high condensation area and establishing a multi-dimensional diffusion path model; and the risk early warning module is used for setting a multi-level response threshold value and realizing graded risk early warning of the abnormal region. Through multi-channel disturbance modeling, abnormal accumulation area discrimination and diffusion tracking, the ability of sensitive perception and dynamic evolution monitoring of early risks in a complex scene is improved.

There is provided a system and method for determining a three-dimensional model of an object using a projector and a light sensor arranged in a stereo configuration. The method including: receiving pixel intensity values captured by the light sensor for a plurality of images of a scene containing the object, wherein the images of the scene each capture different projection patterns emitted by the projector onto the scene; determining, using the pixel intensity values from a set of the plurality of images, correspondence between a projector plane and a camera plane to form a three-dimensional model of the object, the correspondence determined using a trained artificial neural network model that uses a combination of volume rendering and geometric representation; and outputting the three-dimensional model of the object.

The invention discloses an underwater image distortion correction method based on a multi-medium refraction model, and relates to the technical field of image processing, and the method comprises the steps: S1, obtaining a preliminary distortion parameter set through collecting original underwater image data and extracting optical interference characteristics in the data, S2, analyzing the influence of medium change according to the preliminary distortion parameter set, and carrying out the correction of the distortion of an underwater image. The method comprises the following steps: S1, processing image pixel distribution by adopting a convolutional neural network model, integrating pixel intensity adjustment and local contrast enhancement, and determining a refraction distortion degree, S3, if the refraction distortion degree exceeds a preset threshold value, applying an adaptive correction algorithm for the image pixel distribution in combination with refraction deviation compensation and dynamic threshold value setting, and obtaining a corrected image version; according to the underwater image distortion correction method based on the multi-medium refraction model, the underwater image definition and the object recognition precision are remarkably improved, and the security check efficiency is optimized.

Image data analysis, and particularly identifying cluster or polony locations for performing base-calling in a digital image of a flow cell during DNA sequencing is described. A method may include generating a first plurality of flow cell images of a cellular sample immobilized on a support by conducting one or more cycles of sequencing reactions. The cellular sample may include a plurality of concatemer molecules therewithin. For the first plurality of flow cell image, pixel intensities, and a respective color purity of each of the pixel intensities may be determined. A base calling template may include base calling locations based on the pixel intensities and the respective color purity of the pixel intensities. The base calling template may be for registering a second plurality of flow cell images of the support in one or more subsequent cycles of the one or more cycles.

The invention provides a radial artery compressor control method and related equipment, and can be applied to the technical field of image processing. The method comprises the following steps: acquiring a first laser blood image acquired by an image acquisition component for a hand region of a target object; determining a target speckle contrast according to the pixel intensity of the pixel points in the first laser blood image, and determining the current blood flow rate of the hand region according to the target speckle contrast; under the condition that the current blood flow rate indicates that the radial artery has the occlusion risk, the ulnar artery air bag is controlled to deflate, and target pressure is determined according to the maximum pressure pulse wave amplitude of the target object in the ulnar artery air bag deflation process; and the ulnar artery air bag is controlled to be inflated until the pressure applied to the ulnar artery by the ulnar artery air bag reaches the blocking pressure, and the radial artery air bag is controlled to be inflated or deflated until the pressure applied to the radial artery by the radial artery air bag reaches the target pressure. In the application, the blood flow of the radial artery is ensured to be smooth so as to avoid radial artery occlusion.

The invention belongs to the technical field of condition judgment, and particularly relates to an electrode foil corrosion condition judgment method and system, and the method comprises the following steps: S1, obtaining a grayscale image of a to-be-analyzed electrode foil; constructing a structure tensor based on the composite gradient reflecting the local brightness and texture information of the pixel points, determining an anisotropic diffusion coefficient according to the structure tensor, and performing iterative anisotropic diffusion filtering processing on the grayscale image by using the anisotropic diffusion coefficient to obtain a filtered image; and S2, fusing the anisotropic diffusion coefficient determined for each pixel point with the pixel intensity of the filtered image to obtain a high-contrast corrosion significance map. According to the method, the contour edge information of the corrosion area can be kept while the complex texture and noise of the background area of the electrode foil image are smoothed, the contradiction between denoising and edge protection of a traditional filtering method is solved, the result of the corrosion condition is more reliable, and the automation level of electrode foil product quality detection is improved.

The invention relates to the technical field of defect detection, in particular to an electronic component welding spot detection method and system, and the method comprises the following steps: based on a circuit board imaging image, extracting a welding spot gray scale gradient direction and a pixel change trend, marking a gray scale path, recognizing a pixel space communication relation, and analyzing the welding spot gray scale consistency and edge stability. According to the method, the gray gradient direction and neighborhood pixel intensity change of the welding spot region are extracted, so that the local structural features of the welding spot are effectively recognized, the gray consistency and edge stability of the welding spot are optimized, and the welding spot defect classification and partition recognition label set is obtained. The morphological operation processes isolated pixels and cavity pixels, background noise and fragments are effectively removed, accurate closing of welding spot contours is ensured, the defect classification precision is improved, the defect type recognition capability is enhanced, false detection is reduced, and the precision and efficiency of defect classification and region recognition are optimized.

The invention discloses an image-text character recognition method and system based on AI intelligent examination, and relates to the field of character recognition. The method comprises the steps of calculating image features through image pixel intensity and local noise and generating a self-adaptive enhanced image, recognizing an effective region in combination with region weight, a local expansion index and geometric distortion, performing time sequence feature analysis on a candidate text region to generate a character sequence, correcting a character confidence value and extracting a key field, and obtaining a character sequence; and calculating non-linear verification to form an overall check value, generating a self-adaptive optimization signal according to the check value and a feedback error, and dynamically updating the recognition model and the field extraction model. Through adaptive image enhancement, candidate region recognition, character confidence correction, key field extraction and logic verification, high-precision image-text character recognition is realized, and model dynamic optimization and adaptive updating are supported.

The invention relates to the technical field of image processing, in particular to a circuit board defect detection system and method. According to the method, a plurality of detection sub-regions are obtained through division, and spectral image parameters, circuit board design information and process execution historical data are obtained based on circuit board feature information of each region; further obtaining pixel intensity ratio anomaly, function influence degree and historical defect frequency factors, and performing coupling analysis to obtain circuit board region evaluation information to realize region importance grading; the resource waste caused by full-region non-difference detection is avoided, the calculation load is greatly reduced, the detection delay is shortened, and the real-time requirement of high-speed production is met; according to the method, lightweight feature extraction is carried out on multispectral imaging information to obtain a fused multispectral image, the defect detection rate of a core area is guaranteed, the process of a non-core area is simplified through lightweight processing and low-standard detection, the dynamic balance of detection precision and efficiency is improved, and the overall detection quality is improved.

The invention relates to the technical field of bridge construction monitoring, and discloses a bridge construction crack dynamic detection method and system based on AI visual identification. According to the method, pixel-level time domain and space domain feature separation and fusion are carried out by analyzing an obtained multi-frame continuous image sequence, a target structure feature map fused with spatio-temporal context information is generated, and a potential crack region is positioned according to the target structure feature map. And constructing a region evolution model by analyzing a pixel intensity multi-direction discrete change rule of the candidate region, and outputting quantitative description of a crack growth trend by using a crack growth prediction model. And a structural integrity dynamic score is generated through comprehensive growth trend and multi-scale texture analysis, and real-time detection and evaluation of the crack are achieved. According to the method, the accuracy and robustness of crack detection in a complex construction environment are improved, and the dynamic development trend of the crack can be effectively predicted.

The invention relates to a sunspot counting method and system based on deep learning, and the method comprises the steps: obtaining the image data of a solar photosphere layer and the data of a magnetic field activity region in a corresponding time period, carrying out the preprocessing, carrying out the positioning processing through the data of the magnetic field activity region, determining a region of interest, obtaining a binary mask image through segmentation, and carrying out the error correction and reconstruction. A pseudo label corresponding to historical data is obtained, the pseudo label and the preprocessed image data serve as training data of a segmentation model, and a reconstructed mask image is obtained; further filtering out penumbra-free black pieces in the image through a pixel intensity histogram, carrying out dichotomy clustering on a part in which penumbra black pieces are reserved, and counting the number of clustered umbra as the number of single black pieces; the reconstructed mask image is subjected to image recognition based on historical data, and the number of sunspot groups is obtained; and utilizing a Wolf number function model to obtain a final sunspot counting result according to the obtained sunspot group number and the single sunspot number.

The present application belongs to the technical field of engineering structure health monitoring, and discloses a photovoltaic flexible support cable force non-contact monitoring method and system based on video vibration. First, a cable video sequence is acquired, and a dynamic sensitive area is adaptively divided along the cable image. The time sequence average value of the pixel intensity in each area is calculated, the two-dimensional image information is converted into a one-dimensional vibration time history signal, and the light drift and image noise are suppressed through difference and wavelet denoising. After filtering the signal of each dynamic sensitive area, the initial frequency set is formed by preliminarily extracting the multi-order dominant frequency estimate value. The frequency observations from all areas are fused and analyzed, and the real frequency cluster is identified. The weighted centroid frequency of the cluster is used as the multi-order vibration frequency. Finally, the multi-order frequency is substituted into the model, and the real-time cable force value is output. The whole process of the method does not need to contact the measured cable, and provides an innovative and reliable solution for long-term online safety monitoring of the flexible support cable force in complex environments.

To provide a contour detection method and a contour detection system capable of detecting particles with less error.SOLUTION: The contour detection method is a contour detection method executed by a contour detection system that detects contours of particles that are stacked circular-shaped objects, and includes a measurement step (S1) of performing two dimensional measurement or three dimensional measurement of the particles, an obtaining step (S2) of obtaining a measurement result of shapes of the particles, and a determination step (S3 to S10) of identifying the particles based on shape feature values that are indices of the circular-shaped objects and pixel intensities that are indices of region identification of the particles in the measurement result, and detecting the contours of the particles.SELECTED DRAWING: Figure 3

A display driver drives a color sequential liquid crystal display. The display driver obtains pixel intensity data for a pixel of the display, including a first color sub frame (CSF) pixel intensity value and a second CSF pixel intensity value for a second CSF following the first CSF. Selection logic is applied to the pixel intensity data to select a start time for a modulation pulse: for a given second CSF pixel intensity value, in response to a first first CSF pixel intensity value, a first start time is selected from a plurality of predefined potential start times; and in response to a second first CSF pixel intensity value greater than the first first CSF pixel intensity value, a second start time later than the first start time is selected. The liquid crystal material of the pixel is driven with a modulation pulse starting at the selected start time.

An image data set is obtained (110), the set being associated with parameter(s) representative of image capture characteristic(s) (e.g. exposure) for the set and comprising pixel intensity values repr

A method, performed by an electronic device, of controlling a brightness of infrared lighting in a vehicle is provided. The method includes obtaining a proximity frame according to a preset frame rate between a plurality of image frames obtained according to frames per second through a camera, determining whether infrared lighting of the camera is close to an occupant in the vehicle based on a pixel intensity value of the obtained proximity frame, and adjusting the brightness of the infrared lighting based on a result of determining whether the infrared lighting is close to the occupant.

The embodiment of the invention provides a blood vessel segmentation method, computing equipment, a computer storage medium and a computer program product. The blood vessel segmentation method comprises the following steps: acquiring original medical image data containing blood vessels; performing feature extraction on the original medical image data to generate image feature data; based on the image feature data, modifying a pixel intensity value of a pixel in the original medical image data to obtain first medical image data; performing edge detection on the first medical image data to obtain edge information of the blood vessel, and generating second medical image data containing a blood vessel boundary mask; and based on the blood vessel boundary mask, performing blood vessel segmentation on the second medical image data to obtain a target blood vessel image. The technical scheme provided by the embodiment of the invention can improve the accuracy of blood vessel segmentation.