162 results about "Image scale" patented technology

Methods for multiple instance learning of tissue sample images are described. The methods may comprise, for example, receiving a whole slide image from a needle core biopsy sample from a subject; identifying a tissue region in the whole slide image; selecting a set of image patches from the identified tissue region; resampling the set of image patches at a plurality of image scales to generate a plurality of resampled image patches; generating image representations for the plurality of resampled image patches; extracting feature vectors based on the image representations; providing the feature vectors as input to a trained machine learning model configured to predict a gene alteration state; and outputting the predicted gene alteration state for the needle core biopsy sample for the subject.

Systems and methods include techniques associated with one or more machine learning systems analyze, compare, and process one or both of model outputs or model inputs. Content verification may include generating content using a first trained machine learning system and then verifying the initial generation using one or more second trained machine learning systems, such as by generating content associated with a prompt, comparing output labels, or comparing output responsive to changing parameters. Additionally, data preparation may include image scaling and batching methods to improve machine learning outputs.

The invention relates to the technical field of machine vision detection, in particular to an automobile part surface defect online detection method and system. The method comprises the following steps: acquiring a grayscale image, calculating the size of a structural element for each pixel based on a local Gaussian Laplacian operator response variance, filtering to obtain a substrate image according to the size of the structural element, and differentiating to obtain a texture image. Determining a Gabor scale and a gray-level co-occurrence matrix statistical direction by using the size, and extracting a cooperative direction gray-level co-occurrence matrix feature; and a weight is set based on the size and is subjected to weighted fusion with a multi-scale rotation invariant local binary pattern feature to generate a texture saliency map, and texture defects are judged. On the substrate image, taking the gray value as the height, and determining a neighborhood calculation curvature feature based on the size to detect the substrate defect. According to the scheme, the image scale can be adaptively analyzed, the background texture is effectively inhibited, and therefore different types of tiny defects such as scratches and pits can be reliably detected.

In a projection exposure method for exposing a substrate arranged in the region of an image plane of a projection lens with at least one image of a pattern arranged in the region of an object plane of the projection lens, using radiation from a wavelength range around a design wavelength < 260 nm, a substrate (SUB) is coated with a relatively thick radiation-sensitive photoresist layer (RS) and exposed using a projection lens (PO). This lens produces a focus at a design focus position (FOCO) at the design wavelength and, for other wavelengths outside the wavelength range, offset focus positions from an axially extended focus area (ΔFOC) around the design focus position (FOCO). The projection lens is designed as a single-waisted or double-waisted system.During an exposure time interval, different wavelengths from the wavelength range around the design wavelength are used such that the axial extent of the focus area is at least as large as the layer thickness. The projection lens is optically corrected for chromatic aberration (CHV) such that, in the region of the design wavelength, the image scale is essentially independent of the wavelength, so that a change in wavelength of 1 picometer results in a maximum variation of 2 nm between the position of an image point corresponding to an object point within an image field.

The invention discloses a displacement monitoring method and system based on a movable monocular camera, and particularly relates to the technical field of camera displacement monitoring. The method comprises the following steps: initially and synchronously acquiring images of a reference target and a to-be-measured target, and acquiring an initial pixel size and an initial center coordinate of the reference target and an initial center coordinate of the to-be-measured target; when the pose of the monocular camera changes, the image is collected again, and the pixel size and the center coordinate after the reference target changes are obtained; calculating an image scaling factor, determining an image coordinate transformation parameter, and constructing a dynamic coordinate transformation relation to obtain theoretical pixel coordinates of the target to be measured; and calculating the displacement according to the deviation between the actual pixel coordinate and the theoretical pixel coordinate of the target to be measured. According to the invention, stable calculation of the displacement of the to-be-measured target can be realized under the condition that the pose of the monocular camera changes, and the applicability and measurement consistency of the displacement monitoring process are improved.

The invention discloses a steel plate surface defect detection method based on a Swindow-Transform network structure and electronic equipment. The method comprises the following steps of: extracting scale features of an image by using each layer of Stage and outputting a feature map; convolution is carried out on the feature map by using different convolution kernels to obtain a scale feature map; carrying out global pooling on the scale feature map to extract a pooling vector, generating a fusion scale weight according to the pooling vector, and carrying out weighted fusion to obtain a fusion feature map; operating the fusion feature map, introducing a fusion scale weight to obtain classification branch features, performing weighted fusion, and inputting the classification branch features into a classification full-connection layer to obtain a classification prediction result; performing global average pooling on the fusion feature map to obtain global feature representation, processing pooling vectors, fusing fusion scale weights to form regression feature representation, and inputting the regression feature representation into a regression full connection layer to obtain a regression prediction result; and performing feature splicing on the classification feature representation and the global feature representation to generate an interaction weight, and weighting a classification prediction result and a regression prediction result to obtain final classification output and final regression output. The problem of surface defect detection is solved.

In various embodiments, a system can: access a failure image on which a first model has inaccurately performed an inferencing task; train, on a set of dummy images, a second model to learn a visual variety of the failure image, based on a loss function having a first term and a second term, the first term quantifying visual content dissimilarities between the set of dummy images and outputs predicted during training by the second model, and the second term quantifying, at a plurality of different image scales, visual variety dissimilarities between the failure image and the outputs predicted during training by the second model; and execute the second model on each of a set of training images on which the first model was trained, thereby yielding a set of first converted training images that exhibit the visual variety of the failure image.

The invention discloses a material granularity identification method and system based on appearance analysis, and the system generates a standardized image matrix through image collection and preprocessing, employs a two-channel neural network structure to extract spatial positioning features and morphological structure features, carries out the fusion of the features to form a guide mask pattern, and carries out the recognition of the granularity of a material based on an improved active contour evolution model. Position constraint and form constraint are introduced into an energy function at the same time, dynamic evolution of the contour is achieved, the system monitors boundary consistency in the evolution process, and local topology reinitialization is triggered when the boundary consistency is lower than a threshold value so as to guarantee segmentation stability. After evolution is completed, a final contour area is extracted, particle size distribution data are calculated in combination with image scale parameters, and a particle size recognition result is output, automatic recognition and statistical analysis of the material particle size are achieved, and the method is suitable for particle material detection and distribution evaluation scenes.

The application relates to the technical field of federated learning, and discloses an attack method, device, equipment and medium for a decentralized federated learning system. The method comprises the following steps: connecting the network identifier of a malicious client and the network identifier of each benign client to obtain a communication graph; restoring the image proportion of each benign client according to the gradient information of each benign client, determining state data in a simulation environment based on the image proportion of each benign client and the communication graph; determining a current attack model in the simulation environment based on a reinforcement learning mode and the state data; obtaining the loss value of each benign client in the tth round of training and the loss value of each benign client in the next round of training based on the current attack model; and determining an attack report of the decentralized federated learning system based on attack benefits. Through the attack report, the attack mode can be identified, and the anti-attack capability of the decentralized federated learning system can be improved.

The application provides a kind of unsupervised hyperspectral image classification method based on mixed space spectrum information, comprising the following steps: S1, obtains binary segmentation graph by entropy rate superpixel segmentation algorithm, applies binary segmentation graph on original hyperspectral image to obtain segmented superpixel block, converts input hyperspectral image into multiple homogeneous regions based on superpixel segmentation, removes redundant information and guides data purification;S2, optimize the redundant information in principal component domain by two-dimensional singular spectrum analysis method, enhance spatial spectral feature;S3, realize the unsupervised classification of large-scale hyperspectral image by anchor point graph clustering unsupervised classification method.The application is closer to actual engineering application compared with existing supervised classification method, can process larger image scale compared with existing unsupervised classification method, has the advantages of not needing prior information reference, high classification precision, fast classification speed and the like.

The invention discloses an image scaling method and system, an intelligent terminal and a storage medium, and the method can calculate a scaling ratio, a partition parameter and a jitter address value according to a first size of an input image and a second size of an output image, and updates a head address of the current image scaling, so as to read partition image data in the input image, the method comprises the following steps of: performing gray conversion on read image data to obtain initial gray data, acquiring corresponding first gray energy and a maximum gradient value, performing scaling interpolation calculation to obtain first scaled data, acquiring second gray energy of the first scaled data, performing recovery processing on the first scaled data to obtain recovered data, and performing data processing on the recovered data to obtain the image data. And time-space domain filtering is carried out on the recovered data, so that scaling processing on the whole input image is realized, the high-frequency signal loss and the mosaic effect can be obviously improved in the scaling processing process, the calculation overhead is greatly optimized through channel combination and parameterization design, and the method has universal adaptability to various images and has good image visual effect and algorithm robustness.

The application provides a kind of deep learning optical imaging system design method based on complex function neural network, comprising the following steps: step one, object distance, system focal length, F number as input, according to the ideal optical system imaging relationship, determine image distance, imaging scale ratio;Step two, according to the imaging scale ratio and the object image of ideal optical system, the image of ideal optical system is calculated;Step three, construct complex function generator network model, step four, construct discriminator network model, step five, after the establishment, training of the above-mentioned complex function neural network deep learning model, step six, use phase.The deep learning optical imaging system design method based on complex function neural network provided by the application solves the correlation between the advantages and disadvantages of the design result and the experience value of the designer, without the designer providing initial structure parameters;Greatly improve the computing ability and optimization speed.

The invention belongs to the field of wheel-rail tribology, and particularly discloses a wheel-rail fatigue crack intelligent quantitative characterization method based on microscopic analysis images, which comprises the following steps: carrying out image enhancement and noise reduction preprocessing on scanning electron microscope and optical microscope images of wheel-rail rolling contact fatigue cracks; inputting the image into a YOLOv8-CSSTAM model for target detection, and outputting an image crack prediction frame and a proportional scale prediction frame; on the basis of an automatic calibration algorithm of an image scale, calculating the widest connected region of the image in the scale prediction frame, and performing OCR character recognition to obtain the image scale; performing median filtering, threshold segmentation, edge smoothing and small area filling processing on the crack image in the crack prediction frame; carrying out Harris corner detection on the processed image to obtain candidate corners; and screening the angular points to obtain crack feature angular points, and then calculating crack size features. The method can quickly and accurately obtain crack information in the wheeltrack rolling contact fatigue crack image, wherein the crack information comprises the crack depth, the crack width and the crack propagation angle.

The invention relates to the technical field of image recognition processing, and discloses an image segmentation method based on scale space prediction, which comprises the following steps of: inputting an original image into a scale space prediction network A, performing feature extraction and segmentation prediction on the image on a plurality of different scales, and obtaining segmentation prediction maps corresponding to the scales, forming a segmented image scale space of the image; and inputting the scale space of the segmented image into a segmented image reconstruction network B, carrying out step-by-step up-sampling fusion reconstruction on the segmented prediction image of each scale, and outputting a final segmented image with the same resolution as the original image. According to the method, the scale space of the segmented image is predicted through the scale space prediction network A, the problem that details are lost due to information mixing in a traditional single feature fusion method is avoided, different scale targets from subtle to macroscopic are accurately captured and expressed, the segmented image reconstruction network B adopts a step-by-step up-sampling fusion mechanism, and the fusion efficiency is improved. The boundary of the final segmentation image is clearer and more continuous, and the geometric accuracy of segmentation is improved.

The invention discloses an image scale reconstruction method and a related device, and the method comprises the steps: carrying out the timestamp matching of a camera and a millimeter-wave radar, obtaining a radar-image matching frame pair, carrying out the monocular depth estimation of an image frame, obtaining a depth map, and carrying out the calculation of the depth map according to the external parameters from the millimeter-wave radar to the camera; transforming radar point cloud in the radar-image matching frame into a camera coordinate system to obtain target radar point cloud, performing perspective projection according to internal reference of a camera, mapping to an image plane, performing ratio operation on a physical depth value detected by the millimeter wave radar and a relative depth value in a depth map to obtain a scale factor, and performing calculation on the scale factor; and determining a scale factor median from all scale factors corresponding to the current image frame as a scale reference value of the current image frame. The millimeter-wave radar and the camera are introduced to work cooperatively, physical distance information measured by the radar is mapped to an image plane through timestamp synchronization, and the actual distance between an object in an image and the camera is determined by combining the advantages of the millimeter-wave radar.

The application relates to a multi-scale feature extraction system which comprises a multi-layer feature extraction module, a multi-layer upper feature fusion module and a multi-layer lower feature fusion module; the multi-layer feature extraction module comprises N first convolutional layers which are connected in sequence; the multi-layer upper feature fusion module comprises N-2 upper sampling layers which are connected in sequence; the multi-layer lower feature fusion module comprises N-2 down sampling layers which are connected in sequence; the Nth first convolutional layer is connected with the 1st upper sampling layer through a second convolutional layer, and the N-2th upper sampling layer is connected with the 1st down sampling layer; a residual module is arranged at the output end of each upper sampling layer and the output end of each down sampling layer. The application realizes the extraction and fusion of multi-scale features, overcomes the technical defects that the existing method based on the image scale of a fan blade cannot realize accurate detection and positioning for small-scale defects, and thus cannot comprehensively recognize the defects of the fan blade.

The invention discloses a vegetable oil nutritional ingredient detection method based on AI vision, particularly relates to the field of image processing and detection, and aims at solving the problems that existing vegetable oil nutritional ingredient detection depends on chemical reagents, operation is complex, and real-time evaluation cannot be achieved. According to the method, sample microscopic images of vegetable oil are collected and binarized, a box counting dimension method is adopted to establish a double logarithmic relation between image scales and the number of covered boxes, global fractal dimensions are calculated, and local fractal dimension spatial distribution statistics of channels with different colors are extracted at the same time; further extracting a texture feature set of contrast, correlation, energy and homogeneity by constructing a gray-level co-occurrence matrix of the image, finally forming a multi-dimensional feature vector for describing the structural form of the oil sample, inputting the multi-dimensional feature vector into a regression prediction model, and outputting the nutrient content of the vegetable oil. Identification of a mapping relation between complex structure characteristics and component contents of the vegetable oil and nondestructive detection of nutritional components of the vegetable oil are realized.

The invention discloses a face image super-resolution processing method and system based on an open source gap, a terminal and a storage medium, and the method comprises the steps: obtaining a plurality of low-resolution face image flows of gap equipment, and carrying out the preprocessing, and obtaining a low-resolution face image training set; performing model training according to the low-resolution face image training set to obtain a face image super-resolution processing model; and obtaining an original face image flow of a target user, performing preprocessing to obtain a target face image flow, inputting the target face image flow into the face image super-resolution processing model, and outputting a target high-resolution face image. According to the method, the adaptive super-resolution of any image scale is realized through proportion vector input of the implicit representation network, the processing delay is reduced by accelerating frame operator fusion, and the processing efficiency is improved through global coordinate modulation, feature fusion and pixel synthesis, so that the super-resolution image effect is better.

To provide an image forming apparatus that can prevent disturbance of the ends of bristles of a discharge device and maintain a stable discharge effect over a long period.SOLUTION: An image forming apparatus according to the present invention has an intermediate transfer body 6, primary transfer means 5, secondary transfer means 9, intermediate transfer body cleaning means 12, and a discharge device 27. In a high humidity environment, the image forming apparatus changes current or voltage to be supplied to the cleaning means according to an image ratio, and in a low humidity environment, it maintains the current or voltage to be supplied to the cleaning means constant irrespective of the image ratio.SELECTED DRAWING: Figure 1

The application provides a data processing method and device, the method comprising: obtaining an original image, performing padding processing on the original image according to a preconfigured image scale space to obtain a padding processing image, so as to normalize the scale of the padding processing image, performing interpolation down-sampling processing on the padding processing image to obtain an interpolation down-sampling processing image with details information gain, extracting features of the interpolation down-sampling processing image according to a fine-grained global feature extraction network model, performing encoding processing on the features by using an encoder, and performing decoding processing on the encoded features by using a decoder based on an attention mechanism to obtain a formula recognition result in the original image. Therefore, the application can more accurately recognize fine-grained information in a formula.

The present application relates to a method and device for determining the optimal shooting distance of a fixed depth-of-field camera. The method includes: determining an initial region of interest focused on by a fixed depth-of-field camera, and determining a first region parameter of the initial region of interest; determining the image scaling ratios before and after the camera is advanced based on the initial shooting distance between the camera and the subject before advancement and the current shooting distance between the camera and the subject after advancement; adjusting the first region parameter according to the image scaling ratio to obtain a second region parameter after the camera is advanced, and determining the current region of interest corresponding to the second region parameter; obtaining image clarity by fusing multi-scale gradients of the image captured in the current region of interest, and recording the correspondence between the clarity and the current shooting distance; after the camera is advanced, selecting the shooting distance with the highest clarity as the optimal shooting distance based on the correspondence. The present application can accurately determine the optimal shooting distance of a camera.

This application discloses a method and apparatus for scientific chart data reconstruction based on semantic understanding and feature adaptation, relating to the field of scientific chart data reconstruction technology. The method includes: acquiring an image of the scientific chart to be reconstructed and identifying the data representation area and coordinate frame area; performing semantic parsing on the scale numbers within the coordinate frame area and constructing a mapping relationship between image pixel coordinates and image scale coordinates; identifying data markers within the data representation area and obtaining data sequences with different marker styles through multimodal feature vector clustering; applying the mapping relationship based on the image pixel coordinates of the data markers in each data sequence to obtain the scale coordinates corresponding to each data marker, thereby performing scientific chart reconstruction to obtain a reconstructed scientific chart image. This application solves the fundamental problem that existing tools are limited in scope due to their reliance on predefined templates, enabling a single technique to cover the vast majority of chart types in scientific publications.

The present invention provides a method for expanding and constructing a noise-modified multi-scale machine vision task dataset, comprising: randomly acquiring a plurality of images, performing image preprocessing and feature extraction to obtain feature images; constructing corresponding image expansion models based on the image scales and image types of the plurality of images, combining adaptive noise addition and a sliding window algorithm; performing multiple image construction processes on the feature images of the macro image using the image expansion model based on the image type to obtain an expanded macro image; and / or, according to the image expansion model, performing data enhancement on the feature images of the micro image and adding mechanical noise, Gaussian noise, and background noise in preset proportions to generate an expanded micro image; and constructing an expanded macro dataset and / or micro dataset based on the expanded macro image and / or the expanded micro image. The present invention can achieve the expansion and construction of a multi-scale image dataset, thereby improving the authenticity of multi-scale simulated images.

The invention discloses an image scale obtaining method and device based on face key points. The method comprises the following steps: constructing a face image information set; standard face key point coordinates are extracted from each panoramic face image; calculating the Euclidean distance between every two standard face key point coordinates; according to the original scale parameter information matched with each panoramic face image, calculating the pixel distance of the standard face key point pair to obtain a distance matrix of the face image information set; performing matrix element statistics and screening processing to obtain a target key point pair, and determining an actual physical distance value corresponding to the target key point pair as a target reference value; and constructing a recognition model, determining a pixel distance value of a target key point pair in the to-be-recognized face image, and performing conversion on the target reference value and the pixel distance value to obtain target scale parameter information. According to the invention, the technical problems of poor generality and high cost of actual scale calculation of the face image in the prior art are solved.

The invention provides a three-dimensional registration method for neutron / X-ray heterogeneous images of a cylindrical multilayer structure. The method comprises the following steps of: adjusting the perpendicularity of an image pair by an edge extraction operator and PCA (Principal Component Analysis); the edge contour extraction module determines the height mapping of the heterogenous cross-sectional image; calculating a circle extraction algorithm and a circle center positioning translation amount; an image super-resolution reconstruction algorithm is used for realizing scale change of the image; and realizing image rotation matching based on a characteristic fault and phase cross-correlation method. The method can effectively correct geometric and modal differences between different-source images, realizes accurate alignment of metal and nonmetal regions, has significant advantages in the aspects of registration precision, edge preservation and modal feature alignment, has high adaptability and stability for complex structures and multi-modal feature characterization, and can be widely applied to the field of image registration. The method is suitable for three-dimensional registration of neutron / X-ray heterogenous images of cylindrical multi-layer structures such as cylindrical lithium batteries and the like.

The invention relates to a displayer with a mouse keyboard replacing an OSD key and a method. The displayer comprises a USB Type-C interface, two USB Type-A interfaces, a USB concentrator, a power transmission controller, an image scaling integrated circuit and a display screen. After a shortcut key specified by the mouse or the keyboard is pressed for a specified duration, the USB concentrator generates a decoded OSD operation instruction, and the image scaling integrated circuit controls the operation of the display screen OSD according to the OSD operation instruction. By the adoption of the technical scheme, a user can directly control the OSD menu through the specific shortcut key on the mouse or the keyboard, operation is visual and smooth, and convenience and use experience of the user are greatly improved.

This invention discloses a method and apparatus for matching corresponding points based on Gaofen-2 satellite remote sensing imagery. The method includes: performing grayscale processing on the Gaofen-2 satellite remote sensing imagery and its matching reference base map to obtain a processed image; performing convolution operations on the processed imagery using a Gaussian function to obtain the image scale space corresponding to the processed imagery, and performing extremum detection on the image scale space to obtain a feature point set; processing the feature point set according to the distribution characteristics of the gradient directions of the neighboring pixels of the feature points to obtain SIFT feature vectors; obtaining matching feature points based on the calculated ratio of the nearest neighbor distance to the second nearest neighbor distance between the SIFT feature vectors and a set threshold; and determining the corresponding points of the Gaofen-2 satellite remote sensing imagery based on the matching feature points. This invention can improve the reliability and accuracy of image matching and effectively reduce the workload of measuring corresponding points.

The invention relates to a wafer image scale identification method and system and a readable storage medium, and the method comprises the steps: obtaining a defect image, extracting a color region in the defect image through a preset mask template, and judging whether there is a scale region in accordance with a preset length range from the color region; if the scale region conforming to the preset length range exists, obtaining scale position information, and obtaining at least one candidate region and a character string of scale scales and / or scale units through a character detection model; based on the at least one candidate area and the position information of the character string, performing splicing judgment processing on the character string to obtain combined scale information; determining the color and the position of the scale by counting the positions and communication blocks of various colors in the image; and an optical character positioning method is applied to obtain a possible area of the scale and the unit, character recognition is carried out based on the area determined in the previous stage, and finally post-processing such as correction is carried out on the recognized characters to obtain the scale and the unit of the scale.

The application provides a kind of image scale calculation method based on wire type image quality gauge wire diameter measurement, and belongs to the field of image processing technology.The method comprises the following steps: step 1, pre-processing the ray image containing the wire type image quality gauge;step 2, selecting the local area of the image quality gauge as ROI by rectangular frame;step 3, carrying out binaryzation processing on the ROI obtained in step 2;step 4, detecting straight line and endpoint coordinates by using straight line detection method on the result obtained in step 3;step 5, calculating the slope of the obtained straight line, and obtaining the wire diameter of the wire type image quality gauge by using trigonometric function;step 6, comparing the image quality gauge model and the nominal wire diameter, and calculating the scale.The application can calculate the wire diameter of different wire numbers of the wire type image quality gauge, and can obtain the scale of different ray images by comparing the actual nominal wire diameter of the wire type image quality gauge.

This application relates to an image scaling method, system, display device, and storage medium. The method includes: acquiring an RGBG-arranged image; extracting first data corresponding to the R channel and second data corresponding to the B channel from the RGBG-arranged image, rearranging the first and second data respectively, and merging the data; denoting the merged data matrix as matrix M; calculating the position of a target point in matrix M based on a scaling factor; selecting reference points based on the position of the target point in matrix M; calculating the weight values ​​of the reference points based on the number and position of the reference points; and calculating the sum of the products of the reference points and their corresponding weight values ​​to obtain the target value. This method directly interpolates and scales the RGBG data, simplifying the processing flow, improving processing efficiency, and fully considering the unique arrangement and color characteristics of RGBG-arranged image data. It can accurately estimate and retain color information in the original image, reducing color distortion and confusion.