156 results about "Pixel intensity" patented technology

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 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.

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.

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 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 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.

This invention discloses a method and system for visual enhancement of underwater robots in turbid waters, relating to the fields of machine vision and image restoration technology. The method includes: acquiring a sequence of original color images collected in real-time by the underwater robot; mapping the pixel intensity of the original color image sequence to a complex space to construct a complex domain analytic signal; extracting the multi-directional spatial gradient tensor of the original color image, and calculating pseudo-polarization information and environmental characterization parameters accordingly; constructing a virtual phase compensation operator using the pseudo-polarization information and the environmental characterization parameters; wherein the pseudo-polarization information drives the compensation intensity of the virtual phase compensation operator, and the environmental characterization parameters are used to locally weight the compensation intensity. Its beneficial effects include: correcting edge blurring and texture obfuscation caused by wavefront phase disturbances, and significantly improving the edge steepness of structural contours in the image.

The invention relates to the technical field of computer vision and intelligent medical examination, in particular to an animal pathogenic microorganism target image detection model construction system which comprises a sample collection module used for controlling digital microscopic imaging equipment to collect an original microscopic image matrix containing target microorganisms; the ideal reference construction module is used for constructing an ideal reference light field image matrix; the knowledge parameterization injection module is used for generating a theoretical simulation state image matrix, a biological factor tensor for controlling geometric deformation and a flaking factor tensor for controlling pixel intensity attenuation; the double-track difference module is used for generating a real light field residual matrix and a theoretical simulation residual matrix; the coupling judgment and model construction module is used for calculating the Euclidean distance of the feature vectors of the two residual matrixes; according to the method, rapid and accurate inversion from real observation to theoretical parameters is realized, and the efficiency of model construction and iteration is greatly improved.

The invention discloses a sample crystal quality detection method, electronic equipment and a storage medium. The sample crystal quality detection method comprises the following steps: acquiring a plurality of morphology graphs of a to-be-detected sample; wherein each morphology graph corresponds to an incident angle for performing X-ray irradiation on the to-be-detected sample, and the sizes of all the morphology graphs are the same; determining a rocking curve of the pixel according to the pixel intensity of the pixels at the same position in all the morphology graphs, and obtaining a rocking curve of the pixel at each position; wherein the rocking curve is a corresponding relation curve of the incident angle and the pixel intensity; and determining the crystal quality of the sample to be detected according to the rocking curve of each pixel and the position of the pixel. According to the invention, micron-sized high-spatial-resolution crystal quality analysis can be realized, and the crystal quality of the sample can be quantitatively evaluated.

The invention relates to the field of respiratory dynamics image evaluation, and provides a dynamic DR-based respiratory dynamics analysis method, which comprises the following steps of: acquiring a dynamic X-ray video sequence of a chest through dynamic DR equipment; a pre-trained deep learning model is adopted to segment the lung field area and eliminate rib interference, and a rib-free image is generated; generating initial grid points covering the lung field based on a first frame image of the rib-free image; intercepting a template area by taking a previous frame of grid point as a center, intercepting a search area at a corresponding position of the target image, and calculating grid point displacement through pixel intensity statistical correlation matching to obtain grid point displacement data; performing polynomial fitting correction on the grid point displacement data according to grid coordinates to obtain single-frame displacement; according to the frame rate of the dynamic X-ray video sequence, single-frame displacement is converted into a real-time speed field, and the displacement is accumulated to generate a displacement field relative to the initial frame; and superposing velocity field or displacement field visualization results in the dynamic X-ray video sequence.

The application discloses a kind of based on foreground perception's no ROI marking medical image classification method, comprising: the pre-processing of three-dimensional medical image data, obtain standardized three-dimensional image data;In the condition of no ROI marking, based on pixel intensity distribution and spatial connection relationship, self-adapting foreground mask is constructed and three-dimensional minimum bounding box is determined;Accordingly, the foreground three-dimensional image data is obtained by cutting;It is input three-dimensional convolutional neural network to extract three-dimensional convolutional feature, after channel attention weighting and multi-layer semantic feature fusion, input full connection layer and output the class probability of corresponding preset classification category by Softmax function.The application can automatically obtain foreground area without artificial ROI marking, reduce redundant background interference, and improve image classification accuracy and stability through three-dimensional convolution, channel attention and multi-layer semantic fusion.

A method for detecting flashing lights, implemented by a device comprising at least one image acquisition device and a computer, characterized in that it comprises: an acquisition (100) of an image stream; for at least one image of said image stream, a segmentation (120) of the image comprising: a calculation (121) of a differential image resulting from the pixel-by-pixel difference in intensity between the image under consideration and a preceding image in the stream, as well as an extraction (122) of the segments of the differential image exhibiting absolute values ​​of intensity difference greater than a predefined threshold; and for each of the segments of said segmentation, an association (130) of the segment with a flashing light tracking marker in the image stream. Abstract figure: Figure 1

Provided herein is a method of imaging and analyzing plant microspores. The method comprises obtaining one or more two-dimensional micrographs of a microspore population, wherein the microspore population has not been labeled; sending the one or more two-dimensional micrographs to an image processing system; segmenting, via the image processing system, the one or more two-dimensional micrographs to distinguish individual microspores, thereby creating a segmented population of microspores; obtaining one or more z-stacks of the population of segmented microspores or the population of microspores to be segmented; and using one or more of pixel intensity information and intensity signal processing within the one or more z-stacks of segmented cells to find nuclear edges and nuclear geometry and determining whether cells are uninucleate or binucleate based on the nuclear edges and nuclear geometry. Also provided herein is a method of imaging and analyzing plant microspores comprising determining microspore viability.

An LED dome display interpolation method based on edge detection. It belongs to the technical field of LED dome display. The technical problem of image information loss which is not solved by the existing uniform point extraction technology is solved. The method comprises the following steps: S1, extracting the pixel intensity variation of the input image in four directions, i.e. 0°, 45°, 90° and 135°, and generating a pixel intensity variation matrix; S2, performing binaryzation processing on the pixel intensity variation matrix to generate a binary contrast guided decision map, and marking the edge region which needs to be directionally filtered; S3, performing edge diffusion processing on the edge region which needs to be directionally filtered based on contrast to obtain a contrast guided edge decision map; S4, performing interpolation processing on the pixel points according to the marking information of the contrast guided edge decision map to obtain the final display image of the input image.

A method for depth estimation of a video includes capturing a current video frame and one or more previous video frames of a scene using a camera of an electronic device; estimating a depth map for each of the current video frame and the one or more previous video frames; capturing one or more event frames over a time period between the current video frame and each of the one or more previous video frames using an event-sensor of the electronic device, wherein each of the one or more event frames includes pixels which have changed in video frames; aligning the depth map of the one or more previous video frames with the depth map of the current video frame using a change in pixel intensities indicated by the one or more event frames; and fusing the aligned depth map of the one or more previous video frames with the depth map of the current video frame to generate an updated depth map of the video.

The invention provides an infrared and visible light image fusion method based on a second-order attention mixed feature, and the method comprises the steps: inputting the first-order and second-order statistics of an infrared image and a visible light image, and adaptively integrating the global features and local features of the infrared and visible light images, thereby enabling the fused image to obtain a stronger feature expression, and improving the image fusion precision. Pixel intensity and texture details are reserved at the same time, comparable contrast is kept while a more vivid edge is achieved, an output result keeps rich edge and texture information transmitted by an input image, artifacts are few, and visual perception of human beings is better met. Compared with other infrared and visible light image fusion algorithms based on deep learning, the method has a better fusion effect.

In a 3D imaging system based on optical triangulation, this provides indicators such as measured and / or estimated values ​​of intensity peak width in image data from an image sensor. [Solution] An arrangement and method performed by one or more devices for providing information indicating the intensity peak width of an intensity peak along a pixel line of a pixel containing an intensity peak. The intensity peak is formed from such reflected light sensed by an image sensor. A first value is calculated, including the magnitude of a lower-order Discrete Fourier Transform (DFT) coefficient for the pixel intensity value of the pixel along the pixel line containing the intensity peak (601). A second value is calculated, including the magnitude of another higher-order DFT coefficient for the pixel intensity value of the pixel along the pixel line containing the intensity peak (602). Information indicating the intensity peak width is provided based on the first and second values ​​(603).