31 results about "Pixel intensity" patented technology

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.

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

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.

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.

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).

One embodiment of the present invention provides a marker pose uncertainty estimation method. The method comprises the steps of: receiving a single image that includes a reference marker, and deriving a corner position for the reference marker on the basis of a pixel intensity variation of the reference marker; deriving corner position uncertainty for the corner position on the basis of the pixel intensity variation, and deriving the position and the pose of the reference marker on the basis of the corner position; and deriving the position and the pose uncertainty of the reference marker on the basis of the corner position and the corner position uncertainty.

This invention discloses a method, system, and apparatus for improving the security of media streams. The system receives a decoded media stream from a media decoding pipeline that receives and decodes an encoded media stream. The system identifies a set of features to be generated based on the decoded media stream and generates the set of features using the decoded media stream. Such features may include luminance histograms, pixel intensity data, motion vectors, edge detection, audio gain, pitch information, and other types of features. The system provides the set of features to a processor executing a machine learning model, wherein the apparatus prevents the processor executing the machine learning model from accessing the decoded media stream.

The present application belongs to the technical field of situation judgment, and particularly relates to a kind of electrode foil corrosion situation judgment method and system, comprising the following steps: S1, the gray scale image of electrode foil to be analyzed is obtained;Based on the composite gradient reflecting the local brightness and texture information of pixel point, the structure tensor is constructed, and the anisotropic diffusion coefficient is determined according to the structure tensor, the gray scale image is processed by iterative anisotropic diffusion filtering using the anisotropic diffusion coefficient, and the filtered image is obtained;S2, the anisotropic diffusion coefficient determined for each pixel point is fused with the pixel intensity of the filtered image to obtain a high-contrast corrosion significance map.The present application can smooth the complex texture and noise in the background area of electrode foil image while maintaining the outline edge information of the corrosion area, solve the contradiction between denoising and edge preservation in traditional filtering method, make the corrosion situation result more reliable, and improve the automation level of electrode foil product quality detection.

As deep neural networks are increasingly used for generating dense depth maps, depth perception has become an increasingly popular topic in the image community. However, the application of depth perception estimation can still be limited due to the need for large amounts of dense ground truth depth for training. It is contemplated that a self-supervised control strategy can be developed for estimating depth maps using color images and data provided by a sensor system (e.g., sparse lidar data). Such a self-supervised control strategy can utilize superpixels (i.e., groups of pixels that share common characteristics (e.g., pixel intensity)) as locally planar regions to regularize surface normal derivatives according to estimated depth and photometric loss. The control strategy can be operable to produce dense depth maps that do not require dense ground truth supervision.

An apparatus and a method performed by one or more devices (205; 231; 331; 1000) for providing information indicating the width (371; 471; 571) of the intensity peak (363; 463; 563) of an intensity peak (366; 466; 566) along a pixel line (352-m) including an intensity peak (363; 463; 563). The pixels correspond to pixel elements of an image sensing region (351) of an image sensor (231; 331). The intensity peak (363; 463; 563) is formed by reflected light sensed by the image sensor (231; 331). Calculate (601) a first value, which includes the magnitude of a low-order Discrete Fourier Transform (DFT) coefficient (473; 573) relating to the pixel intensity values ​​of pixels (366; 466; 566) along a pixel line (352-m) including intensity peaks (363; 463; 563). Calculate (602) a second value, which includes the magnitude of another high-order DFT coefficient (473; 573) relating to the pixel intensity values ​​of pixels (366; 466; 566) along a pixel line (352-m) including intensity peaks (363; 463; 563). Based on the first and second values, provide (603) the information indicating the width of the intensity peaks (371; 471; 571).

The application provides an infrared and visible light image fusion method based on second-order attention mixed features, first-order and second-order statistics of an infrared image and a visible light image are input, global features and local features of the infrared image and the visible light image are adaptively integrated, so that the fusion image obtains stronger feature expression, meanwhile, pixel intensity and texture details are retained, comparable contrast is maintained while more realistic edges are realized, the output result retains rich edge and texture information transmitted by the input image, has fewer artifacts, and better meets human visual perception; compared with other infrared and visible light image fusion algorithms based on deep learning, the application has better fusion effect.

Methods and systems are provided for correcting pixel intensity variation in an image. In one example, a method comprises: acquiring reference images at different sensor temperatures and / or exposure times; generating a plurality of parameters via identifying a cut-off pixel number using a break-up point between a linear region and a non-linear region of an intensity curve for each reference image; sending coordinates of identified pixels to be corrected to an image sensor; capturing a pixel-corrected image via the image sensor by replacing a pixel intensity at coordinates of the pixels to be corrected with a local statistic intensity; and outputting the pixel-corrected image for display and / or storage.

This invention proposes a video power grid frequency estimation method and system based on illumination region detection and fusion. The invention constructs a power grid frequency estimation region dataset and uses this dataset to train an illumination region detection model, resulting in an optimized illumination region detection network. Feature frames to be estimated from the video are extracted. These feature frames are then input into the optimized illumination region detection network for detection, yielding multiple estimation regions. The average pixel intensity within each estimation region is calculated, resulting in multiple illumination sequences. Each illumination sequence is preprocessed to obtain a preprocessed illumination sequence. A short-time Fourier transform is performed on the preprocessed illumination sequences, and quadratic interpolation is used to estimate the power grid frequency signal for each region. Finally, the multiple power grid frequency signals from each region are fused to obtain the final power grid frequency estimation result. This invention improves the efficiency of illumination region detection, the accuracy of video power grid frequency estimation, removes noisy regions from the image, and expands the applicability of video power grid frequency estimation.

An inspection system includes a scanner and a computing device. The scanner is configured to scan an inner surface of a hole in an object. The scanner is configured to generate a plurality of images. Each one of the images represents one of a plurality of sections of the inner surface of the hole. The computing device is coupled to the scanner to receive the plurality of images. The computing device is programmed to combine the plurality of images to form an image. The image represents the inner surface of the hole. The computing device is programmed to evaluate a surface characteristic of the inner surface based on pixel-intensity values of the image.

Arrangements and method, performed by one or more devices (205; 231; 331; 1000), for providing information indicative of an intensity peak width (371; 471; 571) of an intensity peak (363; 463; 563) along a pixel line (352-m) of pixels (366; 466; 566) comprising the intensity peak (363; 463; 563). Said pixels corresponding to pixel elements of an image sensing area (351) of an image sensor (231; 331). Said intensity peak (363; 463; 563) being formed of such reflected light sensed by the image sensor (231; 331). A first value is computed (601) comprising a magnitude of a lower order Discrete Fourier Transform, DFT, coefficient (473; 573) regarding pixel intensity values for pixels (366; 466; 566) along the pixel line (352-m) that are comprising the intensity peak (363; 463; 563). A second value is computed (602) comprising a magnitude of a another, higher order DFT coefficient (473; 573) regarding pixel intensity values for pixels (366; 466; 566) along the pixel line (352-m) that are comprising the intensity peak (363; 463; 563). Said information indicative of the intensity peak width (371; 471; 571) is provided (603) based on said first and second values.

Embodiments of the present application provide a kind of laser speckle contrast image correction method, device and system, it is related to image processing technical field.The method includes: obtaining multiple speckle images;According to each speckle image, determine light intensity distribution diagram;Determine the original speckle contrast image of target speckle image;According to light intensity distribution diagram, determine local light intensity variation coefficient diagram, wherein each pixel point in local light intensity variation coefficient diagram is used to indicate the variation coefficient of pixel intensity in the neighborhood of pixel point in light intensity distribution diagram;According to the value of each pixel point in local light intensity variation coefficient diagram, the value of pixel point in corresponding position in original speckle contrast image is corrected, to obtain corrected speckle contrast image and output.Selecting the present application, can reduce the influence of uneven illumination on laser speckle image, so that blood flow image luminance is uniform and contrast is accurate, to improve the accuracy of blood flow velocity estimation.

A method and architecture for encoding image data solely based on pixel intensities, eliminating the need for encoding pixel positions. This is achieved by utilizing low-discrepancy (LD) sequences, such as Sobol or Van Der Corput (VDC) sequences, for deterministic encoding of the image intensity values. The position hypervectors, which were previously required for representing pixel positions, are no longer needed. The corresponding index of the LD sequence is used to encode the intensity values.

This invention provides a method, apparatus, and system for correcting laser speckle contrast images, relating to the field of image processing technology. The method includes: acquiring multiple speckle images; determining a light intensity distribution map based on each speckle image; determining an original speckle contrast image of the target speckle image; determining a local light intensity variation coefficient map based on the light intensity distribution map, wherein each pixel in the local light intensity variation coefficient map represents the variation coefficient of pixel intensity in the neighborhood of the pixel in the light intensity distribution map; correcting the values ​​of corresponding pixels in the original speckle contrast image based on the values ​​of each pixel in the local light intensity variation coefficient map, obtaining and outputting a corrected speckle contrast image. Using this application can reduce the influence of uneven illumination on laser speckle images, resulting in more uniform brightness and accurate contrast in blood flow images, thereby improving the accuracy of blood flow velocity estimation.

The application provides a cable lead seal ultrasonic fan scanning image denoising method and system, and relates to the technical field of power cable detection. The method comprises the following steps: S1, obtaining an original ultrasonic fan scanning image of a cable lead seal defect, and converting the original ultrasonic fan scanning image into a two-dimensional rectangular coordinate pixel matrix; S2, performing edge pixel detection: estimating a local intensity gradient through least square plane fitting, and extracting an edge pixel set of the lead seal defect; S3, calculating the distance from a pixel point to be processed to the nearest edge point in the edge pixel set; S4, if the distance is greater than a threshold parameter, constructing an adaptive elliptical neighborhood that does not cross the edge, and calculating the pixel intensity after denoising; and S5, if the distance is less than or equal to the threshold parameter, based on the dynamic clustering of the pixel intensity, dividing the neighborhood into different connected regions, and performing weighted average denoising. The method reduces the global noise, improves the accuracy of extracting boundary features, and improves the accuracy and efficiency of cable lead seal defect identification.

A system and method for coding video in which deblocking artifacts are reduced by using modified filtering that is based, at least in part on pixel intensity associated with a coding unit, such that filtering levels are increased as pixel intensity increases. In some embodiments, an offset value or indicator of an offset value for parameters associated with deblocking filter parameters can be associated with a filtering level that is based at least in part on an intensity value.

This invention discloses an image correction and precise ray tracing method for vignetting regions in a focused light field camera, belonging to the field of light field imaging technology. The invention includes: acquiring an original light field image and a white image; performing pixel intensity correction on the original image based on the grayscale values ​​of the white image to compensate for pixel intensity attenuation in sub-image edge regions caused by the vignetting effect; performing inverse ray tracing on the pixels in the vignetting region of the corrected image, sequentially determining the first intersection point of the light rays on the microlens array and the second intersection point on the main lens from the pixel center, and tracing the rays to the object space to obtain the object point coordinates. This invention can effectively improve the image feature recognition capability of vignetting regions, turning previously unusable vignetting regions into effective imaging areas, and significantly improving the data utilization efficiency of the light field camera.

A method for analyzing the debris structure of a baked goods article includes the following steps: receiving visual data of the baked goods article at a mobile device, the visual data including real-time distance measurements from the mobile device to the baked goods article, wherein the visual data is collected in a light-controlled environment; segmenting the received visual data to identify the outline of the baked goods article; segmenting regions within the outline of the baked goods article; and using the results of the previous steps, estimating multiple debris structure features of the baked goods article by separating regions within the baked goods article based on pixel intensity, wherein regions with lower intensity correspond to open spaces or pores (i.e., holes, cavities), while regions with higher intensity correspond to debris.

This invention discloses a CT image enhancement method and apparatus based on global consistency optimization, comprising: performing Gaussian fitting on the pixel intensity distribution of a low-dose CT image to obtain a fitting result containing multiple categories of Gaussian distribution components; calculating the probability of each pixel intensity belonging to each category of the Gaussian distribution component based on the fitting result, and assigning a pixel label to each pixel accordingly; constructing a global optimization objective function containing consistency constraints of adjacent pixel labels, and adjusting the pixel labels through iterative optimization to maximize the objective function, obtaining optimized pixel labels; and performing adaptive deviation reduction processing on the intensity value of each pixel by shrinking it towards the mean of the Gaussian distribution component corresponding to each optimized pixel label, thereby generating an enhanced CT image. This invention can simultaneously achieve noise suppression and detail preservation under low radiation dose conditions, and is applicable to scenarios such as industrial non-destructive testing, medical diagnosis, and high-resolution microscopic imaging.

The application discloses an inverse ISP method based on a spiral diffusion model and camera perception adaptation, and relates to the technical field of computer photography and image signal processing. The method comprises the following steps: S1, constructing an inverse ISP training sample, each training sample comprising an RGB image, a target RAW image corresponding to the RGB image and a camera label; S2, constructing an inverse ISP network based on a spiral diffusion model, introducing a time-varying weight map related to pixel intensity at different time steps in the diffusion process; S3, setting a camera perception low-rank adaptation module in the inverse ISP network, comprising a plurality of low-rank adaptation branches corresponding to different camera labels, and selecting a corresponding low-rank adaptation branch to participate in network calculation according to the input camera label; S4, in the training stage, training the inverse ISP network based on the forward probability distribution of the spiral diffusion model; in the sampling stage, inputting the RGB image and the camera label into the trained inverse ISP network, and obtaining the target RAW image based on the reverse iterative sampling process of the spiral diffusion model.

The present disclosure relates to normalizing pixel intensity values of images. Qualitative images that must be compared need to have actual pixel intensity values that are within a suitable range. Typically, the qualitative images to be compared are rendered to appear visually similar by adjusting display settings. A first image and a second image are received that have different actual pixel intensity values but appear visually identical. One or more normalization parameters are determined and used with one or more contrast and illumination parameters associated with the first and second images. Thereafter, normalized pixel intensity values are determined and a normalized image is generated using the one or more normalization parameters. The pixel intensity values of the normalized image can be compared to the actual pixel intensity values of the first image or the second image.

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.

A method of reducing a digital storage size of an image of a sample for use in digital pathology operations is disclosed herein that can include receiving the image of the sample in a digital format; automatically dividing, by a computer processor, the image into multiple tiles that collectively form the image; and determining, by the computer processor and for each tile of the multiple tiles, a tile figure of merit (FOM) value depending on pixel intensities of pixels forming each tile. The method can further include comparing the tile FOM value for each tile to a first FOM threshold value and a second FOM value and digitally compressing the corresponding tile using one of a first compression quality, a second compression quality, and one a plurality of intermediate compression qualities depending on the tile FOM value.

The present application relates to the field of character recognition, and discloses a kind of character recognition methods of drug and reagent expiration date verification, comprising: obtaining the image of the character region of the curved object to be identified, collecting local curvature parameters and real-time temperature data, resampling based on local polar coordinate system, eliminating the geometric distortion of character region, determining anisotropic constraint operator according to temperature data, implementing stroke topology compensation by adjusting pixel intensity gradient distribution, diffusing against environmental color development, extracting two-dimensional skeleton features and implementing graph isomorphism matching with standard character dynamic grid, determining the recognition result, the present application uses physical prior constraint to solve the recognition problem caused by narrow curved distortion and temperature fluctuation, on the basis of ensuring the stability of recognition scheme, realize the essential improvement of expiration date verification accuracy in high-load supervision scene.

This application relates to the field of aquaculture information technology, and discloses a method, device, computer equipment, and storage medium for monitoring fish in net cages. The method involves transmitting acoustic signals into the water within the net cage via a transducer array and receiving the echoes to generate an acoustic image. Clutter suppression and connected component analysis are performed on the image to extract candidate target regions. Pixel intensity statistical features, geometric shape features, and swimming motion features of each region are extracted to form a feature vector. The feature vector is input into a pre-trained deep learning classification model to obtain a confidence score. Specific fish targets are selected based on the confidence score. Finally, their swimming motion features are calculated and visualized. This application effectively overcomes the technical challenges of distinguishing different fish species and the significant interference from the net cage structure in mixed-culture models, achieving non-invasive and accurate identification and behavioral monitoring of fish.