375 results about "Pixel intensity" patented technology

The invention provides a distortion correction method and system for image projection, and relates to the technical field of image processing, and the method comprises the steps: generating a geometric deformation parameter matrix and a light field attenuation vector of a projection surface through a constructed bidirectional distortion detection model, carrying out the regional analysis of the geometric deformation parameter matrix, and obtaining a curvature feature, dividing a priority correction level through curvature features, and determining a nonlinear compensation factor according to the priority correction level and a light field attenuation vector; constructing a self-adaptive projection network based on the distortion sensitive features of the projection image and the nonlinear compensation factor, and performing reverse coordinate mapping on the projection image through the self-adaptive projection network to obtain an initial correction image; determining an artifact suppression weight map through the high-frequency detail threshold value and the low-frequency smooth threshold value of the initial correction image; according to the invention, geometric deformation parameter optimization and light field attenuation compensation of image projection can be realized, so that the distortion correction precision and the anti-artifact capability are improved.

A method comprises receiving a user input to select a first point and a second point from a medical image, extracting a plurality of blood vessel regions from a plurality of frame images of the medical image, determining a plurality of first regions associated with the first point and a plurality of second regions associated with the second point, determining a first frame image and a second frame image with the contrast agent arriving at the first point and the second point, based on a change in pixel intensity for each of the plurality of first regions and the plurality of second regions, and computing the blood flow velocity from the first point to the second point based on a time interval between the first frame image and the second frame image and on a distance between the first point and the second point.

The invention relates to the technical field of network video monitoring, in particular to a terminal equipment collaborative video monitoring method and system, which comprises the following steps of: monitoring video data based on terminal equipment, extracting a motion vector, a pixel intensity value and a color histogram of each frame, carrying out space-time alignment, calculating an average value and a variance of each parameter, and calculating the average value and the variance of each parameter; time sequence characteristics are analyzed, parameter changes of key frames are extracted, and an initial behavior characteristic set is established; according to the method, the motion vector, the pixel intensity and the color histogram of each frame of video are analyzed, and the data are subjected to space-time alignment and statistical analysis, so that the understanding of a behavior mode is improved, and clustering analysis helps a system to efficiently identify abnormities, monitor in real time and calculate parameter deviation to ensure that abnormal behaviors are found in time; the adaptability and efficiency of the system to the monitoring environment are further improved through data exchange and collaborative analysis between the terminal devices, and the coverage range and the response speed of the monitoring system are remarkably enhanced through the highly collaborative processing mode.

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 relates to the technical field of safety monitoring, in particular to an intelligent video monitoring method and system, and the method comprises the following steps: extracting the pixel intensity change and color histogram of each frame from a real-time video stream, calculating the information entropy value of each frame, analyzing the pixel change and color distribution, and generating information entropy data. According to the method, the frame with high information entropy value is automatically screened out by analyzing the pixel intensity change of the video frame and the information entropy data generated by the color histogram in real time, so that the abnormal behavior or the unconventional activity can be positioned more quickly, the processing logic reduces the dependence on continuous monitoring, reduces the consumption of human resources, and improves the efficiency of monitoring. Compared with the prior art, the method provided by the invention has the advantages that the dynamic time-space diagram is further constructed by extracting the specific motion trail of the dynamic object and the mutual relation among multiple objects from the key frame set, so that the adaptive capacity of the monitoring system to a complex scene is improved, and data support is provided for early warning of an event.

A method comprises receiving a user input to select a first point and a second point from a medical image, extracting a plurality of blood vessel regions from a plurality of frame images of the medical image, determining a plurality of first regions associated with the first point and a plurality of second regions associated with the second point, determining a first frame image and a second frame image with the contrast agent arriving at the first point and the second point, based on a change in pixel intensity for each of the plurality of first regions and the plurality of second regions, and computing the blood flow velocity from the first point to the second point based on a time interval between the first frame image and the second frame image and on a distance between the first point and the second point.

The invention relates to the technical field of image analysis, in particular to a hydraulic engineering construction quality monitoring method and system based on image recognition, and the method comprises the following steps: analyzing the pixel intensity and gray gradient based on the image data of a construction region, building the adjacency characteristics of a local region, and generating an adjacency matrix through the spatial connection characteristics. According to the method, the pixel intensity and the gray gradient in the image data are analyzed, the spatial correlation characteristic weight distribution is established, the spatial characteristic analysis capability of the crack and the slope protection deformation is improved, the distribution characteristics of the crack and the deformation are optimized through path connection and edge weight extraction, the method is more accurate, the reliability of crack edge characteristic identification is enhanced, and the identification accuracy of the crack and the slope protection deformation is improved. The crack propagation direction and the slope protection deformation trend are comprehensively mastered by calculating node dynamic offset and connectivity characteristics, the prediction capacity of construction dynamic change is improved, local and global structure change characteristics are integrated, and quality monitoring from crack microscopic characteristics to the overall state of the slope protection is achieved.

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.

Systems and methods for varying an intensity of pixels of displays are provided. A display subsystem may include a display driver for receiving image frame data and commands from an image or data source, including pixel intensity values for bit planes of an image frame; a parser for receiving the image frame data and the commands, and configured to determine a drive waveform having an pixel drive value and a pixel drive time interval for each bit plane of the image frame data; a display backplane for receiving the drive waveform, the display backplane including a pixel array comprising an array of pixels, each pixel driven by a pixel circuit; and display driver circuitry for driving the pixels in accordance with the drive waveform; and wherein an intensity of the pixel varies for each bit plane according to the pixel drive value and the pixel drive time interval.

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 an unsupervised image registration method for a photoacoustic fast scanning system. The unsupervised image registration method comprises the following steps: acquiring data by using a fast scanning photoacoustic microscope; carrying out preprocessing steps of hooping, normalization, filtering, affine transformation and the like on the data to form a photoacoustic image data set; constructing a registration model based on a convolutional neural network and a space conversion module, fusing an attention mechanism to distribute weights for different pixels, adopting a two-stage training strategy, and introducing mutual information and a smooth item as a loss function; and predicting the registered photoacoustic image based on the training weight. According to the method, the registered image can be predicted in an extremely short time under the condition that a truth value is not input, an attention mechanism is introduced into the convolutional network, key factors are endowed with higher weights during feature extraction, the registration efficiency is effectively improved, mutual information is introduced as a loss function, the probability relation between pixel intensities is effectively modeled, and the registration accuracy is improved. And the similarity of the photoacoustic images acquired at different time points can be evaluated more accurately.

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.

A method for processing an image comprising image data. The image data comprises pixel intensity values, said pixel intensity values being associated with respective pixel locations. For a plurality of zones of the image, based on a plurality of pixel intensity values in the respective zone of the image, a value of a characteristic pertaining to the plurality of pixel intensity values, is determined. At least a spatial filtering process is performed on data representative of the values of the characteristic for the plurality of zones, to obtain filtered values of an image characteristic at respective locations. The filtered values are interpolated from to determine a local value of the image characteristic at a said pixel location. The determining and / or the interpolating is performed using fixed function circuitry. The spatial filtering process is performed using a programmable processor.

The invention discloses an unmanned equipment target autonomous identification and tracking system based on a visual processing technology, and relates to the technical field of YY. The system comprises an image acquisition module which acquires image data according to an acquisition device, performs frame synchronization acquisition based on a clock signal, and preprocesses the image data; and the grid division module is used for determining seed number points through the number of pixel points based on a Voronoi diagram division method, and carrying out preliminary edge detection on the image to determine the side length of a grid. According to the method, a grid region needing to be additionally subdivided is further screened out by using an edge detection method of a Sobel operator, a system can adaptively adjust the size and the shape of a local window of an image in cooperation with a division method of a Voronoi image, better adaptability to image features is achieved, and the accuracy of image segmentation is improved. The pixel intensity difference and the SAD value are calculated based on the Voronoi region, so that the target with both subtle change and overall displacement in the image can be effectively identified.

A depth sensor device for measuring a depth map of an object comprises a projector unit configured to illuminate different locations of the object during different time periods with an illumination pattern, a receiver unit comprising a plurality of pixels, the receiver unit being configured to detect on each pixel intensities of light reflected from the object while it is illuminated with the illumination pattern, and to generate an event at one of the pixels if the intensity detected at the pixel changes by more than a predetermined threshold, and a control unit configured to generate for each of the different time periods a total number of detected events and pixel information indicating for each event the pixel that detected the event, and to calculate from the pixel information and the total number a position of the image of the illumination pattern on the pixels with sub-pixel accuracy.

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.

The invention discloses a non-welding bottle type container surface defect intelligent discrimination system based on magnetic powder detection, relates to the technical field of intelligent discrimination, and aims to improve the problem of low discrimination accuracy caused by non-surface physical defects in magnetic powder detection. Screening a plurality of defect positions according to a gray value in the image, obtaining a defect area of each defect position, constructing a support vector machine model according to the defect areas and the gray value, calculating a category index and a misjudgment rate of each defect position, setting a classification threshold value, and performing classified display on each defect position. After obtaining the pixel intensity value of each pixel point in the image, calculating a complex frequency domain signal value, performing sampling adjustment on a camera in combination with a misjudgment rate, setting a similar range, screening out classification fuzzy positions from all defect positions, and marking the classification fuzzy positions; and obtaining the probability that each mark position has defects, calculating the average gray value of each mark position, and carrying out reclassification on the display categories of the mark positions by utilizing a logistic regression algorithm.

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 method of image processing. The method comprises obtaining a set of image data, the set being associated with one or more parameters representative of one or more image capture characteristics for the set and comprising pixel intensity values representing image pixels having respective pixel locations in an image. The method comprises, for a given pixel intensity value in the set: determining an estimated noise value based on at least: the one or more parameters associated with the set, and a representative intensity value derived from one or more pixel intensity values in the set. The method comprises associating the estimated noise value with the given pixel intensity value.

This disclosure provides systems, methods, and devices for image signal processing that support image signal processing of exposure bracketed image frames. In a first aspect, a method of image processing includes receiving, by at least one processor, image data comprising first image data of a first exposure duration, second image data of a second exposure duration, and third image data of a third exposure duration, wherein the first exposure duration is equal to the third exposure duration; determining, by the at least one processor, fourth image data by subtracting corresponding pixel intensity values of the third image data from the second image data; and determining, by the at least one processor, a first output image frame by combining the fourth image data with the first image data. Other aspects and features are also claimed and described.

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.

An image projector includes a spatial light modulator (SLM) with a two dimensional array of pixel elements controllable to modulate a property of light transmitted or reflected by the pixel elements. An illumination arrangement delivers illumination to the SLM. A collimating arrangement collimates illumination from the SLM to generate a collimated image directed to an exit stop. The illumination arrangement is configured to sequentially illuminate regions of the SLM, each corresponding to a multiple pixel elements. A controller synchronously controls the pixel elements and the illumination arrangement so as to project a collimated image with pixel intensities corresponding to a digital image.

A face identification system, and a non-transitory computer readable medium with instructions, that when executed, perform a method for identifying a face image are described. The face identification system comprises a camera for facial image capture, encryption circuitry for image encryption, feature extraction circuitry for processing encrypted images, a face recognition system for identification, and an identification notifying system for result transmission. The encryption circuitry includes a pixel-bit converter, transforming pixel intensities into binary digits, and a 2D cellular automata function, working with a 3×3 matrix of binary-state cells transitioning by update rules. A gray code generator maps binary numbers to unique gray-scale values. The system integrates advanced encryption techniques with efficient feature extraction and recognition algorithms, addressing challenges in biometric authentication. This approach enhances security, accuracy, and computational efficiency compared to conventional systems, making it suitable for various applications requiring secure and efficient facial recognition.

The invention discloses an on-line defect detection system for float glass production, relates to the technical field of glass defect detection, and solves the technical problem that the quality of a finished product does not reach the standard due to the fact that tiny defects or inclusions in a glass plate are not considered in the prior art. The method comprises the following steps: irradiating a glass plate by laser and collecting to obtain a light propagation image, preprocessing the light propagation image to obtain a gray-scale light image, taking a mode of the light intensity of pixel points in the gray-scale light image as standard light intensity, dividing abnormal pixel points based on the standard light intensity, and obtaining an abnormal feature map in the gray-scale light image based on the abnormal pixel points. Extracting light reaction features in the abnormal feature map; and matching the light reaction characteristics with standard geometric characteristics to obtain a defect shape, obtaining the pixel type and the pixel defect intensity of the defect shape based on the number of abnormal pixel points in the defect shape and the light intensity, and matching the defect shape, the pixel type and the defect pixel intensity with standard defect data to obtain corresponding defect data.

The invention provides a subway lightweight video positioning method based on a sparse optical flow algorithm, and the method comprises the steps: collecting a front image in a subway driving process, and carrying out the ROI region segmentation of the image; carrying out gray processing on the ROI image and carrying out angular point identification; performing optical flow tracking on the identified angular point by using an L-K optical flow identification algorithm based on an image pyramid, and estimating a new position of the angular point in the current frame according to the pixel intensity change in the local window; the target points are screened, noise is filtered, effective displacement tracks of the target points are screened, and confidence coefficient assignment is carried out on the effective target points; converting the pixel coordinate system into a three-dimensional world coordinate system to obtain a three-dimensional coordinate of the target point; solving the real displacement and instantaneous speed of each target point between adjacent frames; weighting and solving the actual displacement and the real-time speed of the whole vehicle; the actual distance between the stations of the subway line is obtained, and whether the subway arrives at the station is judged according to the running distance and the real-time speed between the subway stations. According to the method, metro positioning is achieved only through the vehicle head camera and the vehicle-mounted computer, a large amount of trackside equipment is reduced, light weight of metro positioning is achieved, and the equipment maintenance cost is reduced.