152 results about "Pixel correlation" patented technology

The invention provides a super-resolution binocular image generation method and system based on geometric structure consistency, and the method comprises the steps: extracting the deep features of a low-resolution binocular image through employing a convolutional neural network or a Transform model, achieving the information interaction of a left image and a right image in combination with a cross attention module, and constructing a pixel incidence matrix of the left image and the right image; acquiring a pixel corresponding relation of the left image and the right image by using the pixel incidence matrix, and constructing a continuous parallax field; performing spatial warping on the deep features based on a continuous parallax field to obtain warping features, and aligning the deep features of the left and right images; and merging the deep features and the warping features after spatial alignment, inputting the merged features into a feature up-sampling module based on implicit two-dimensional expression, and outputting a high-resolution binocular image of the same scene. The invention provides a binocular image super-resolution technology comprising binocular image feature extraction, continuous parallax field construction based on implicit two-dimensional expression, left and right image feature space alignment and feature upsampling based on implicit two-dimensional expression.

An event filter for an event based imaging sensor is provided by the present disclosure, the event filter comprising: input circuitry configured to receive an indication of a pixel position associated with a current event detected by the event based imaging sensor; event potential determining circuitry configured to determine an event potential associated with the current event; and filtering circuitry configured to determine whether to pass the current event based on the event potential; wherein the event potential determining circuitry is configured to determine the event potential for the current event based on combining contributions associated with pixels in a local region of pixels around the pixel position of the current event, where a given contribution associated with a given pixel in the local region is dependent on a time decay factor which causes the given contribution to decrease with increasing time difference between a time of detection of a previous event for the given pixel and a time of detection of the current event. A method of filtering and a computer program are also provided.

The present application relates to image processors and methods for processing images. A method of computing a warp result is disclosed that can include performing, for each target pixel in a set of target pixels, a warp computation process that includes receiving, by a first set of processing units in an array of processing units, a first weight and a second weight associated with the target pixel; receiving, by a second set of processing units in the array, values of neighboring source pixels associated with the target pixel; computing, by the second set, a warp result responsive to the values of the neighboring source pixels and the pair of weights; and providing the warp result to a memory module.

The invention relates to a method (50) comprising the steps of: - using a sensor to acquire (110) an image of the sample composed of grains; - applying (120), to the image (1), a convolutional neural network in order to obtain a contour map (C) associating, with each pixel of the image, a value equal to one unit when that pixel belongs to a contour of a grain and otherwise equal to zero; - applying (130), to the contour map (C), a segmentation algorithm in order to obtain a segmented image (S) comprising a plurality of regions, one region being separated from a neighbouring region by a contour having a thickness of a single pixel; and - applying (140), to the segmented image (S), one or more geometric processing operations in order to calculate physical quantities for each grain so as to characterise the lithology of the sample (3).

A computer-implemented method includes encoding a radiance field of an object onto a machine learning model; conducting, based on a set of training images of the object, a training process on the machine learning model to obtain a trained machine learning model, wherein the training process includes a first training process using a plurality of first test sample points followed by a second training process using a plurality of second test sample points located within a threshold distance from a surface region of the object; obtaining target view parameters indicating a view direction of the object; obtaining a plurality of rays associated with a target image of the object; obtaining render sample points on the plurality of rays associated with the target image; and rendering, by inputting the render sample points to the trained machine learning model, colors associated with the pixels of the target image.

Techniques are provided for facilitating detection threshold determination for infrared imaging systems and methods. In one example, a method includes capturing, by an imaging device, a thermal image of a scene. The method further includes determining temperature difference data indicative of a difference between temperature data of the thermal image associated with a background of the scene and temperature data of the thermal image associated with gas detection. The method further includes determining detection threshold data based on sensitivity characteristics associated with the imaging device and the temperature difference data. The method further includes generating a detection threshold image based on the detection threshold data. Each pixel of the detection threshold image corresponds to a respective pixel of the thermal image and has a value indicative of a detection threshold associated with the respective pixel of the thermal image. Related devices and systems are also provided.

The present disclosure relates to a computer-implemented method of classifying an object from a real-world environment in which an entity 32 operates, comprising: receiving a hyperspectral image 31 of the real-world scene from a hyperspectral camera mounted to the entity; obtaining a hyperspectral signature for each pixel of the hyperspectral image; identifying a material associated with each pixel based on the hyperspectral signature; clustering the pixels of the hyperspectral image into sub-objects based on the identified materials; comparing the sub-objects to sub-objects of known objects; and identifying the object (60, 62, 64, 66, 68 , Fig. 4) based on the comparison. Sub-objects may be classified into primary and secondary ones. The primary may have the largest size or highest prediction probability. The primary may be the sub-object whose material has the lowest occurrence frequency. Feasible search zones may be identified. Sub-objects may be weighted according to their prediction confidence. The entity may be a tank

A method for estimating distance to an object in a scene using an imaging system (400) comprising a camera array (410) with multiple imagers (540), an image processing pipeline module (420) and a controller (440). The image processing pipeline module (420) comprises an upstream pipeline processing module (510), an image pixel correlation module (514), and a parallax confirmation and measurement module (518). The method comprises: providing images (412), including a current image, captured by the multiple imagers (540) to the upstream pipeline processing module (510) for processing; aligning, by the image pixel correlation module (514), portions of images, including the current image, captured by different imagers of the multiple imagers to compensate for parallax; processing, by the parallax confirmation and measurement module (518), the current image to detect and measure the parallax by keeping track of various pair-wise measurements and calculating a parallax difference that is a best fit to a sample data; and determining distance to an object in the scene using the measured parallax.

A driving device includes a pulse intensity modulation module, a pulse width modulation module, a pulse density modulation module, and a driving module. The pulse intensity modulation module receives an image processing signal related to a pixel and generates a pulse intensity according to the image processing signal. The pulse width modulation module receives the image processing signal and generates a pulse width according to the image processing signal. The pulse density modulation module receives the image processing signal and generates a pulse density according to the image processing signal. The driving module receives the pulse intensity, the pulse width, and the pulse density and generates a driving signal to the pixel according to the pulse intensity, the pulse width, and the pulse density.

This information processing device can improve resolution of a taste that can be reproduced in the oral cavity of a person. The information processing device: causes a display unit to display an image which is indicated by image data indicating the image and in which taste data indicating one or more tastes is associated with each of at least some pixels; and outputs, on the basis of an operation received via the image displayed on the display unit, the taste data associated with the pixel designated by the operation.

The present application relates to a kind of based on dynamic spectrum diagram topology infringement counterfeit commodity intelligent identification system, belong to image visual identification technical field.System includes data acquisition, pre-processing, time sequence feature extraction, pixel correlation diagram construction, graph topology feature calculation, infringement counterfeit identification etc.Module.Data acquisition module generates dynamic spectrum sequence containing coordinate pixel etc.Information, time sequence feature extraction module extracts attenuation half-life etc.Feature, pixel correlation diagram construction module takes pixel as node, time sequence feature as node feature and based on DTW distance calculation edge weight, graph topology feature calculation module obtains node connection close and information propagation feature.This system discards traditional commodity appearance comparison mode, utilizes commodity physical characteristic time sequence law identification, avoids high imitation product visual deception, significantly improves identification accuracy.

The present disclosure provides concepts for processing pixels of interest of a flat or curved 2D image plane in a 3D volume of an anatomical structure to generate a 2D image of the anatomical structure. The method includes, for each of the pixels of interest: determining a compounding direction associated with the respective pixel, the compounding direction being based, at least in part, on a shape of one or more features of the anatomical structure included in the 3D volume; identifying one or more compound pixels in the vicinity of the respective pixel in the 3D volume based on the compounding direction; and determining an updated value of the respective pixel based on values of the compound pixels. The method further includes generating the 2D image based on the updated values of the pixels of interest. For each of the pixels of interest, a gradient of a feature of the anatomical structure included in the 3D volume in the vicinity of the respective pixel is identified, and the compounding direction is determined further based on the identified gradient. The method allows for adaptive compounding based on the shape of the anatomical structure included in the 3D volume, such that blurring and ghosting artifacts are reduced, while maintaining the benefits of compounding techniques (e.g., noise reduction).

The invention discloses a multi-mode nuclear magnetic image brain tumor segmentation system based on optical flow method pixel correlation, relates to the field of image processing, and solves the problems that complex physiological correlation among multiple modes cannot be captured and specific characteristics of a tumor area are difficult to reproduce in an existing missing mode completion method. The segmentation system comprises a data preprocessing module, a cross-modal optical flow estimation module, a pixel association weight calculation module, a missing modal complementation module and a fusion segmentation module. The system is simple in structure and reasonable in design, breaks through the limitation that a traditional optical flow model depends on a gray level consistency hypothesis, and adapts to the characteristic that the gray level difference of the multi-mode MRI is remarkable. By capturing the consistency of the gray gradient direction of the same anatomical structure in different modals, cross-modal pixel correlation mapping is accurately established, optical flow estimation deviation caused by gray mismatching is avoided, a reliable correlation basis is provided for subsequent deletion completion, and the accuracy of cross-modal information transmission is guaranteed.

The present document relates to an electronic device, a method, and a non-transitory storage medium for changing a screen displayed on a display. According to one embodiment, the electronic device may comprise: a display; at least one processor including a processing circuit; and a memory for storing instructions. The instructions, when executed individually or collectively by the at least one processor, may instruct the electronic device to: display, on the display, a screen including a first image indicating first information; on the basis of performing a security function for the screen, identify information related to at least one pixel of an inner part or an outer part with respect to a boundary between an object indicating the first information included in the first image and a background excluding the object; on the basis of the information related to the pixel, obtain a second image in which at least one of the inner part or the outer part is adjusted; obtain a third image in which the background included in the first image is blurred; obtain a fourth image in which the first information is processed so as not to be visually identified at a specified distance or more, by using the second image and the third image; and replace the first image displayed on the screen with the fourth image. Various other embodiments are also possible.

This invention relates to the field of image processing technology, specifically disclosing an AI-based 3D reconstruction method for human organ tissue slices. The method includes: acquiring a sequence of consecutive slice images and preprocessing them to generate a tissue foreground mask; using previously registered slices as references, estimating the homography matrix through a feature matching neural network and transforming it to obtain candidate registration results; calculating the pixel correlation between the candidate and the reference foreground mask, retaining those not less than a threshold and taking the largest to form a registration slice sequence; semantically segmenting the registration sequence to obtain a target structure mask sequence, and stacking it in voxels according to the interlayer spacing to output the 3D reconstruction result. By using multi-reference registration based on foreground correlation for optimal selection, the accumulation of misregistration is suppressed, and the alignment stability is improved; post-registration segmentation and voxel stacking realize multi-structure 3D reconstruction and sub-structure display. This method enhances registration reliability and supports multi-label 3D display.

A display driver includes an image processing circuit and a driver circuit. The image processing circuit is configured to receive spatial distribution information of a physical quantity related to a display panel. The image processing circuit is further configured to generate output voltage data by processing input pixel data associated with sub-pixels of a pixel. The driver circuit is configured to drive the display panel based on the output voltage data.

The application discloses a multi-modal image data automatic recognition system and method based on machine learning, relates to the technical field of image data processing, and comprises the following steps: image data is called, pixel point feature information is extracted, and a pixel point feature sequence is constructed; feature matching degree analysis is performed on the pixel points, and hierarchical division is performed; spatial feature joint index analysis is performed on the pixel points in the same layer, and hierarchical pixel correlation index is obtained by integrating the analysis data; interlayer pixel classification analysis is performed on the pixel points between adjacent layers, pixel classification correlation analysis is performed on the adjacent layers, and pixel classification correlation index of the adjacent layers is determined; correlation evaluation of pixel layer correlation logic is performed on non-repeated image data, and the best image matching combination is output; the application realizes accurate recognition of multi-modal image data, reduces the misrecognition rate and interference rate caused by coarse recognition and coarse matching, and improves the accuracy of image recognition.

A method for generating images from a semantic map, which assigns to each pixel of the images a semantic meaning of an object to which this pixel belongs. The semantic map is provided as a map tensor comprising channels which each indicates all the pixels of the images to be generated, to which the semantic map assigns a specific semantic meaning; a set of variable pixels of the images to be generated is provided, which are to vary from one image to the next; using values taken from a random distribution, a noise tensor with channels is generated, those values of the noise tensor which relate to the set of variable pixels being reused for each image to be generated; the channels of the map tensor are merged with the channels of the noise tensor to yield an input tensor, which is mapped by a trained generator onto an image.

This application relates to a method for detecting a picked object performed by a computer device. The method includes: detecting trigger location information of a trigger operation; in a process of rendering a second image frame by using a graphics processing unit, obtaining location information of a pixel associated with each three-dimensional model in the second image frame, and matching the location information of the pixel associated with each three-dimensional model with the trigger location information; storing, by using the graphics processing unit, a model identifier of a three-dimensional model associated with a pixel with location information successfully matched, to a storage location in a color buffer that corresponds to the successfully matched pixel; and obtaining the model identifier in the color buffer by using a central processing unit, and determining, based on the obtained model identifier, the picked object specified by the trigger operation.

The invention relates, in one aspect, to a method for processing a three-dimensional image associated with a plurality of cross-sectional images and containing a component to be detected, said method comprising, for each associated cross-sectional image, performing the following steps: binarizing (E31) the associated cross-sectional image in order to obtain a mask consisting of a plurality of pixels, the pixels are associated with section image pixels corresponding to the section of the component; determining (E3121, E3122) a surface characterizing the cross section from said mask; determining (E3123) a parametric function describing the surface; defining a constant displacement field on each pixel column based on the column coordinates of the pixels so as to apply a flattening transformation to the surface represented by the parametric function; and correcting (E314) the cross-section of the component in the relevant cross-sectional image by applying the determined displacement field to the image pixels.

Dental treatment monitoring systems and methods may include accessing an input image of teeth taken at a particular time during dental treatment, and determining virtual-camera parameters that represent an estimated position and orientation of a virtual camera for producing a generated image from a time-projected 3D model of the teeth. The virtual-camera parameters may be iteratively adjusted by: generating a first generated image by modifying the virtual-camera parameters based on a first jaw in the generated image; determining a pixel-associated cost based on a comparison of the first generated image to the input image; generating a second generated image by modifying the first virtual-camera parameters based a second jaw in the first generated image; and determining a pixel-associated cost based on a comparison of the second generated image and the input image. The generated image may be generated from the time-projected 3D model using the adjusted virtual-camera parameters.

The embodiment of the invention provides an image processing method and device and a readable storage medium. The method comprises the following steps: acquiring a first image and a second image, wherein the second image is an image obtained by performing style editing processing on the first image; performing Lab color space conversion on the first image and the second image to obtain a first brightness channel and a first color channel of the first image and a second brightness channel and a second color channel of the second image; fusing and filtering the first brightness channel and the second brightness channel to obtain a third brightness channel; based on the pixel correlation of the first brightness channel and the second brightness channel, reconstructing the second color channel to obtain a third color channel; and outputting a fused image based on the third brightness channel and the third color channel. The fused image keeps the style of the second image and keeps the texture features of the first image, and detail distortion is avoided.

A method for detecting zones corresponding to a surface of liquid cargo the method including obtaining inspection data associated with pixels of zones of interest in the inspection image of the cargo which is tilted, the pixels having signal levels, determining a vertical derivative field from the obtained inspection data, determining a horizontal derivative field from the obtained inspection data, and determining, based on the determined vertical derivative field and on the determined horizontal derivative field, zones where a first order vertical derivative is positive, and an absolute value of the first order horizontal derivative is smaller than a predetermined threshold.

A method is provided for generating a medical image indicating the presence of uric acid. A base image indicates the presence of uric acid on a pixel-by-pixel basis. Pixel values of the base image are modified using the effective atomic number and the electron density values associated for the tissue associated with those pixels.

The invention relates to a method and system for uniformizing pixel values in a pixel array. A method for homogenizing values of an array of pixels includes identifying pixels for which a target has been detected and correctable hole pixels for which no target has been detected, the correctable hole pixels being surrounded by at least one adjacent pixel for which the target has been detected; calculating an equivalent target detection confidence indicator for each correctable void pixel according to a target feature value calculated for adjacent pixels of the correctable void pixel, the equivalent target detection confidence indicator represents that a target detected in a neighborhood according to a parameter including the sensitivity of a hole pixel also exists at a position of a correctable hole pixel, and when the calculated equivalent target detection confidence indicator is below a detection threshold value, calculating a corrected target feature value associated with the hole pixel, wherein a target detected in the neighborhood will have a target detection confidence indicator at the correctable hole pixel.

A method for processing an image acquired by a fingerprint sensor, comprising: determining (200) a peak value (R) associated with a pixel in the image and a valley value (V) associated with the pixel; calculating (202) a normalized dynamic range (Dn) associated with the pixel as a ratio of the difference between the peak value (R) associated with the pixel and the valley value (V) associated with the pixel, and a reference value being a linear combination of the peak value (R) associated with the pixel and the valley value (V) associated with the pixel; comparing (204) the normalized dynamic range associated with the pixel and a dynamic range threshold (T1); and generating a result associated with the pixel, the result associated with the pixel indicating that the pixel is showing a finger to the fingerprint sensor only if the normalized dynamic range (Dn) is greater than the dynamic range threshold (T1). Figure for the abstract: Fig. 3

The present disclosure relates generally to medical imaging, and more specifically to machine-learning techniques to analyze and generate medical images to characterize tissue of a subject (e.g., to aid diagnosis and / or treatment of diseases). An exemplary method of displaying risk assessment of a tissue of a subject comprises: receiving a fluorescence image of the tissue of the subject; providing the fluorescence image to a trained generative adversarial (“GAN”) model, wherein the GAN model is trained using a plurality of unlabeled training images associated with a normal future outcome; obtaining, based on the GAN model, one or more pixel-wise anomaly scores associated with one or more pixels of the fluorescence image, wherein the one or more pixel-wise anomaly scores are indicative of abnormality of the tissue of the subject; and displaying, based on the one or more pixel-wise anomaly scores, the risk assessment of the tissue in the fluorescence image.

Methods and apparatuses for performing cross-component intra prediction, including: receiving a coded bitstream; obtaining, from the coded bitstream, a syntax element indicating a downsampling filter used for a cross-component intra prediction mode; obtaining a plurality of reconstructed sample values of a first component which are associated with a pixel of a second component based on the downsampling filter; determining a pixel value of a downsampled pixel of the first component, based on the plurality of reconstructed sample values; determining a pixel value of the pixel of the second component based on the pixel value of the downsampled pixel of the first component; and reconstructing a picture based on the pixel value of the pixel of the second component.

A display can include an array of pixels, such as light emitting diode pixels. A circular polarizer can be omitted from the display to improve efficiency. A polarizer-free display can use other non-polarizer techniques to mitigate reflection of ambient light and mitigate associated diffraction artifacts. A polarizer-free display can include a black matrix, a color filter element, or a pixel-defining layer with a tapered surface to make variations in reflectivity associated with a sub-pixel more gradual. Alternatively or additionally, a sub-pixel can have a footprint with one or more protrusions. Alternatively or additionally, different pixels can have sub-pixels with different elliptical footprints and / or different footprints with protrusions. Alternatively or additionally, a polarizer-free display can include a color filter element with a concave upper surface. Alternatively or additionally, a polarizer-free display can include sub-pixels with different anode heights.

In some embodiments, a spectrometer is proposed. According to some embodiments, the spectrometer includes: an optical sensor array including a substrate and a pixel array formed on the substrate; a spectral filter array formed above the pixels of the optical sensor array, the spectral filter array filtering incident light such that each pixel receives light with a spectral transmission profile associated with that pixel; a transparent spacer formed above the spectral filter array; and an opaque mask having an input aperture that allows light to pass through the transparent spacer and reach a portion of the spectral filter array. The spectrometer can be formed from the optical sensor array using photolithography techniques and a combination of bonding of certain layers.