21 results about "Pixel correlation" patented technology

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.

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 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 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 scalar of a display device of an information handling system includes a scalar processor and zero-gray luminance enhancer. The scalar processor generates image data by processing raw image data, including grayscale levels, received from the information handling system. The image data is used by the display device to control pixels of a display screen of the display device in rendering images on the display screen. The zero-gray luminance enhancer generates zero-gray enhancement values, each zero-gray enhancement value uniquely corresponding to one of the grayscale levels. The zero-gray enhancement values control luminance associated with the pixels.

This invention discloses a method for secure encryption of medical data, relating to the field of data encryption technology. The method includes implementing access control based on security levels, acquiring original medical images after authorization verification, stacking the images into a three-dimensional cube by channels, dynamically dividing them into blocks according to security levels, generating a chaotic encryption key based on the block information, and performing encryption operations on each three-dimensional block to obtain encrypted ciphertext. By destroying pixel correlation through channel stacking and segmentation, combined with differentiated keys generated by chaotic mapping, the key space and encryption efficiency are significantly improved, effectively resisting brute-force and statistical attacks. Permutation and substitution operations enhance the randomness of the ciphertext, ensuring encryption adapts to security levels and operates in a closed loop, guaranteeing controllable access permissions, improving the targeting and security of medical image encryption, and enabling flexible access for authorized users while ensuring efficient data encryption.

An apparatus comprising a set of pixels configured to shape a beamlet approaching the set of pixels and a set of pixel control members respectively associated with each of the set of pixels, each pixel control member being arranged and configured to apply a signal to the associated pixel for shaping the beamlet.

The application discloses a kind of information security encryption methods based on cloud platform, belong to information processing technical field, including the following steps: S1, the channel value of the image to be encrypted is extracted, and the final key of each channel is generated for pixel point;S2, substitution and confusion are carried out, and the value level encryption channel value of pixel point is obtained;S3, iteration operation is carried out, and the joint key of each pixel point is determined;S4, cross-channel association is carried out, and the secondary encryption channel value of pixel point is determined;S5, according to the three secondary encryption channel values of pixel point, generate RGB matrix, determine the encrypted image stored by cloud platform.The cross-channel secondary encryption of the application realizes three-channel cyclic association, destroys channel independence, avoids single-channel cracking risk;Three-layer operation forms complete confusion and diffusion, and the slight change of single pixel can be diffused to the whole image, effectively resists statistical attack, differential attack and pixel correlation analysis and other common attack means.

This document describes various techniques for monitoring noise to assess sensor aging in systems and applications. The systems and methods described herein can monitor sensors—for example, at given time intervals and / or continuously—to generate information representing sensor noise. For instance, data (e.g., sensor data) received from a sensor are used to determine noise (e.g., temporal noise) associated with points (e.g., pixels) corresponding to the sensor. This noise is then used to generate representations—such as histograms—of noise distributions associated with the sensor. The systems and methods described herein can also monitor this noise information generated for the sensors to detect degradation in performance as the sensors age, such as specific components of the noise distributions.

The invention relates to a method for recognizing an object (2) in a search image (1), comprising the following steps: a) providing a pattern vector (5) describing the object (2) by means of coordinates of characteristic pixels (7); b) moving the pattern vector (5) over different positions of the search image (1); c) determining a respective success value (15) at the different positions; and d) recognizing the object (2) at the positions depending on the success values (15); wherein a first direction (8) and a second direction (9) different from the first direction (8) are associated with each characteristic pixel (7), wherein a first total intensity value (10) of a first number (11) of one-dimensionally arranged pixels in the first direction (8) and a second total intensity value (12) of a second number (13) of one-dimensionally arranged pixels in the second direction (9) are determined, respectively, wherein a difference value (14) between the first total intensity value (10) and the second total intensity value (12) is determined, respectively, wherein the success value (15) is determined depending on the respective difference value (14).

This invention discloses a method and system for detecting tomato diseases based on a hybrid self-attention mechanism. The method includes inputting an image of a tomato leaf disease to be identified into a PACA-ConvNeXt model. The PACA-ConvNeXt model includes a ConvNeXt convolutional neural network. PACA attention mechanism modules are inserted into each level of the ConNeXt Block module of the ConvNeXt convolutional neural network. The PACA attention mechanism module includes pixel attention blocks and coordinate attention blocks. For the input feature map, each level of the ConNeXt Block module calculates the pixel correlation and coordinate information correlation of the feature map based on the PACA attention mechanism module to obtain a hybrid attention feature map containing pixel correlation and coordinate information correlation. Based on the hybrid attention feature map output by the last level of the ConNeXt Block module, the type of tomato disease is predicted.

Processing circuitry (e.g., a GPU) obtains a first MSAA intermediate buffer from rendering of a first image, the first MSAA intermediate buffer comprising a plurality of sub-samples of each pixel of the first image. The processing circuitry determines, for a first pixel of a first image, a first surrounding subsample pattern based on a plurality of first subsamples in a surrounding region of the first pixel. Further, the processing circuitry obtains, based on the first surrounding subsample pattern, a first hybrid weight vector for a first position, the first position being one of a plurality of positions defining an output pixel position within the first pixel, the output pixel locations are used to up-sample the first pixel as a plurality of output pixels in an up-sampled image that is higher than the first image resolution. The first mixed weight vector comprises a plurality of first mixed weights, and the plurality of first mixed weights respectively correspond to a plurality of first candidate sub-samples associated with the first pixel. The processing circuitry generates an up-sampled image accordingly.

A pixel includes a semiconductor substrate, a low-κ dielectric, and a photodiode region in the semiconductor substrate. The semiconductor substrate has a substrate top surface forming a trench. The trench extends into the semiconductor substrate and has a trench depth relative to a planar region surrounding the substrate top surface. The low-κ dielectric is located in the trench between the trench depth and a low-κ depth relative to the planar region. The low-κ depth is less than the trench depth. The photodiode region is in the semiconductor substrate and includes (i) a bottom photodiode portion below the trench and (ii) a top photodiode portion adjacent to the trench. The top photodiode portion begins at a photodiode depth relative to the planar region that is less than the low-κ depth and extends toward and adjacent to the bottom photodiode portion.

A method or device implementing a method for determining the depth of a pixel by a depth prediction model learned in a learning phase comprising receiving (31) first and second images acquired by two cameras of a vision system, cropping (32) these images to generate a first and second cropped image comprising pixels corresponding to objects in the scene seen by the two cameras, predicting (34) first depths associated with the pixels of the first cropped image by another depth prediction model, and learning the depth prediction model by minimizing a difference between the first and second depths associated with the pixels of the first cropped image predicted by the depth prediction model. Figure for the abstract: Figure 3

Method for processing an initial image (I) of an object under investigation, wherein the initial image (I) was reconstructed from measurement data (pin) acquired during a relative rotational movement between a radiation source (C2, C4) of a computed tomography system (C1) and the object under investigation, wherein a frame rate splitting (Fk) of the initial image (I) into at least a first and a second image (Ik) is performed, the first image is modified by means of a first function (G1), wherein the first function (G1) causes a contrast increase within the first image, and the modified first image and the second image are combined (+), for the first image, before the frame rate splitting (Fk) is performed, wherein the non-linear function (Hk) causes a limitation of image values ​​with large absolute values, wherein the second image is modified by means of a second function (Gk).which effects noise reduction limited to an image value range of the second image, wherein the extent of the image value range is determined depending on the magnitude of the noise in the second image, and which includes pixel-dependent parameters so that adaptation to locally varying noise is achieved.

An apparatus for generating X-ray images is provided. The apparatus obtains a first pixel frame dataset and a second pixel frame dataset (350-(t-1), 350-t) from readouts of sensor pixels (SP1-SPN) at a first time and a second time (t-1, t). These sensor pixels are spatially offset from each other by a first distance (dy) in the sensor scanning direction (y). Between the first and second times, the object point projections (122a, 122b) on the sensor have a time shift greater than the first distance by a distance (h). A first data element (Li(t-1)) from the first dataset is combined (236) with a second data element (Li(t)) from the second dataset to generate a portion of an image 400, wherein the first and second data elements are associated with different sensor pixels. A corresponding X-ray detector, imaging system, and method are also provided.

A computer-implemented method for encrypting an image, wherein image data is generated by collecting a signal having bandwidth, and the image data includes data corresponding to a plurality of pixels of the image, wherein each pixel has an associated phase value; and the method includes the step of modifying each phase value of the phase values ​​associated with each of the plurality of pixels based on a first encryption key, wherein after the step of modifying each phase value of the phase values, the rate of phase change between adjacent pixels does not exceed the bandwidth.

An optical detector unit (10) includes an optical sensor unit (14) disposed on a substrate (12) to detect received photons, which should allow for partitioned observation applications with reduced manufacturing costs under a specific streamlined and simple setup. According to the invention, this is achieved by the optical sensor unit (14) comprising a plurality of detector arrays (24), each array forming a corresponding detector spectral channel (26), wherein each detector array (24) includes a plurality of sensor pixels (16), each pixel generating a multispectral sensor signal as part of a corresponding detector channel (26), and an aperture element (30) is disposed above the optical sensor unit (14) relative to the substrate (12), the aperture element (30) comprising a flat, laterally extending refractive element (32), the refractive element... The element defines a top surface (34) and a bottom surface (36), wherein the top surface and the bottom surface (34, 36) are respectively provided with a top aperture layer (38) and a bottom aperture layer (40), the top aperture layer (38) and the bottom aperture layer (40) are respectively provided with a plurality of top apertures (42) and a plurality of bottom apertures (44), the top apertures (42) and the bottom apertures (44) are arranged in pairs to form a plurality of dual apertures (46), and for each sensor pixel in the sensor pixel (16), a single dual aperture in the dual apertures (46) associated with each sensor pixel is provided.

The application discloses a horizontal visibility estimation method and device, equipment and storage medium, and relates to the technical field of meteorological monitoring. The method comprises the following steps: acquiring pixel correlation data corresponding to an effective pixel image shot by a target stationary satellite, and constructing a first-level pixel feature corresponding to the effective pixel image. According to the first-level pixel feature and a target deviation revision model obtained by pre-training, aerosol optical depth deviation revision processing is performed to obtain a target aerosol optical depth after deviation revision. According to the target aerosol optical depth and the pixel correlation data, a second-level pixel feature corresponding to the effective pixel image is constructed. According to the second-level pixel feature and a target visibility estimation model obtained by pre-training, horizontal visibility estimation processing is performed to obtain a target estimated horizontal visibility corresponding to the effective pixel image in a shooting period, which can greatly improve the accuracy of the horizontal visibility estimation result and provide high-quality data support for atmospheric pollution assessment.