236 results about "Image scale" patented technology

Techniques are described for using computing devices to perform automated operations for analyzing visual data of images acquired at a building to determine building information that includes building dimensions. The automated determination of building dimensions and other building information may include determining estimated camera height for one or more camera devices while acquiring the images based on identified visible structural building objects of defined types, using the determined image scale information to further determine resulting building dimensions, and associating the building dimension data with a floor plan generated from analysis of the images. Information about such determined buildings may be used in various automated manners, including for controlling device navigation (e.g., autonomous vehicles), for display on client devices in corresponding graphical user interfaces, for further analysis to identify shared and / or aggregate characteristics, etc.

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.

Processing image data using deep neural networks is critical to many systems that desire to understand objects and their environment using camera sensors. Image scaling is a fundamental processing task required when managing image data. Although it is possible to scale image data using standard computer or graphics processors it would be highly advantageous in terms of throughput, latency and power consumption to manage image scaling using dedicated neural network hardware. The inventions contained herein provides methods to use existing neural network hardware to preform image scaling functions. Further, the inventions contained herein describe additional circuitry that can be added to neural network hardware to further enhance image scaling capabilities and efficiencies.

The invention relates to the field of computer image processing, and provides a remote sensing multispectral image fusion method and system based on multi-stage feature correction, and the method comprises the steps: obtaining a spectrum mask through a low-resolution multispectral image and a panchromatic image, and covering the panchromatic image through the spectrum mask to obtain a corrected panchromatic image; performing scale feature enhancement on the low-resolution multispectral image, and performing down-sampling and convolution on the corrected panchromatic image to obtain image scale features; obtaining attention block input, and mapping the attention block input to obtain a mapping vector group; performing image correction by using the mapping vector group to obtain a feature correction vector, performing iteration on the feature correction vector to obtain a target feature correction vector, and obtaining a fusion feature matrix; and iterating the fusion feature matrix to obtain target fusion features, and carrying out feature refinement and convolution on the target fusion features layer by layer to obtain a high-resolution multispectral image. According to the invention, a high-fidelity and high-resolution multispectral image can be obtained.

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 discloses a method for three-dimensional reconstruction and volume quantification of a middle pulp edema area, particularly relates to the technical field of medical image processing, and aims to preprocess an original image and lay a foundation for smooth three-dimensional reconstruction and volume quantification of the middle pulp edema area. According to the method, points in a section contour are extracted from an OCT B-scan image and mapped to correct positions in a three-dimensional space, and then the upper surface and the lower surface of the edema area are presented by using a visualization means, so that three-dimensional reconstruction of the edema area is realized, a parameterized space of the mesoplasm edema area can be obtained, and a foundation is laid for subsequent volume quantification; the method comprises the following steps of: constructing an inter-frame adjacency relation, reasonably filling a blank area by using a symmetrical frame unit, further dividing the symmetrical frame unit into infinitesimal elements in a parameterized space of a medium pulp edema area, calculating the volumes of all the infinitesimal elements, and realizing volume quantification of the edema area by combining an image scale.

The curvilinear polygon image scaling method includes: placing partial derivatives at points on edges of a curvilinear polygon image to obtain a vector gradient field; integrating the vector gradient field to obtain an auxiliary grayscale image; segmenting the edges; modulating, on the auxiliary grayscale image, to grayscale values of edgelets based on the image scaling parameter and vector gradient field and modulating to nearby grayscale values using a smoothing function to obtain an updated auxiliary grayscale image; intercepting the updated image using a threshold and extracting a polygon contour. If differences between edge placement errors of edgelets, that are between the currently extracted and the original curvilinear polygon image, and the image scaling parameter are less than the preset small value, then the currently extracted contour is used as a scaled image; otherwise, modulating iteratively until the difference is less than the preset small value.

The invention provides an omni-directional universal AEB method and system based on pure vision, and the method comprises the steps: obtaining the scale change information of a pixel point corresponding to a panoramic image based on the scene flow information of laser point cloud manufacturing, and generating a panoramic image scale data set; constructing a deep learning network model, training the deep learning network model through the panoramic image scale data set, and generating a panoramic image scale prediction model; and inputting the to-be-measured panoramic image data into the panoramic image scale prediction model to obtain predicted scale change information of the panoramic image scale data, and according to the predicted scale change information, based on the perspective view angle, calculating the collision time between the obstacle and the vehicle, and predicting the collision risk. According to the invention, through panoramic perception of the environment, the traveling track of the vehicle can be analyzed in real time, the potential collision risk can be predicted, the method does not depend on any specific obstacle detection algorithm and is not limited to a white list of any obstacle category, and the adaptability and robustness of the system are significantly improved.

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.

Provided in the embodiments of the present disclosure are a point cloud coarse registration method and apparatus, and a device. The method comprises: using a plurality of acquisition devices to simultaneously perform, on the same horizontal plane, point cloud acquisition on a target object, so as to obtain a plurality of pieces of point cloud data of the target object, the point cloud data obtained by adjacent acquisition devices overlapping partially; respectively performing discretization projection on the plurality of pieces of point cloud data, so as to obtain a bird's eye view corresponding to each piece of point cloud data (S102); respectively performing two-dimensional image scale-invariant local feature detection on the plurality of bird's eye views obtained, so as to obtain feature data corresponding to each bird's eye view (S103); performing feature matching on the plurality of pieces of feature data obtained, so as to obtain a point cloud transformation matrix (S104); and, on the basis of the point cloud transformation matrix, performing coarse registration on the plurality of pieces of point cloud data of the target object (S105).

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.

Techniques are described for performing automatic operations using a computing device to analyze visual data of images acquired at a building to determine building information including building dimensions. Automatic determination of building size and other building information may include determining an estimated camera height for one or more camera devices based on an identified visible structure building object of a defined type while acquiring an image; further determining the resulting building size using the determined image scale information; and associating the building size data with a plan view generated from the image analysis. Information about such determined buildings may be used in a variety of automated ways, including for controlling device navigation (e.g., autonomous vehicles), for display on client devices in corresponding graphical user interfaces, for further analysis to identify shared and / or aggregated features, and the like.

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.

Techniques for image scaling based on emotion detection are described. In some embodiments, the techniques include acquiring one or more images of a user, processing the one more images to generate emotion-specific three-dimensional (3D) positions of ears of the user based on a 3D head geometry and an emotion of the user, where the emotion is identified based on the one or more images of the user, and processing one or more audio signals to generate one or more processed audio signals based on the three-dimensional positions of the ears. Further embodiments include systems and non-transitory computer-readable media that perform the steps of the method.

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 an image segmentation method and device, equipment and a storage medium, and relates to the technical field of medical image processing, and the method comprises the steps: obtaining an initial medical image, and carrying out the enhancement of the initial bottom features of the initial medical image through a target neural network, so as to obtain a target medical image; performing down-sampling on the target medical image in the diagonal direction on different image scales so as to obtain target semantic information and target index values corresponding to the target medical image on different hierarchies; and performing weighted fusion on each piece of target semantic information to obtain fused semantic information, performing up-sampling on the fused semantic information based on the target index value to obtain a mask corresponding to the to-be-segmented target, and performing image segmentation based on the mask. According to the image segmentation method, up-down sampling is carried out based on diagonals, and semantic information of different levels is fused for image segmentation, so that loss of fine-grained information is reduced, and the image segmentation precision is improved.

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.

Techniques for image scaling based on emotion detection are described. In some embodiments, the techniques include acquiring one or more images of a user, processing the one more images to generate emotion-specific three-dimensional (3D) positions of ears of the user based on a 3D head geometry and an emotion of the user, where the emotion is identified based on the one or more images of the user, and processing one or more audio signals to generate one or more processed audio signals based on the three-dimensional positions of the ears. Further embodiments include systems and non-transitory computer-readable media that perform the steps of the method.

An electronic device including a display, a first camera, a second camera, at least one processor and memory storing computer-executable instructions, when being executed by the at least one processor individually or collectively, causes the electronic device to identify an object region in a first image captured by the second camera; determine a first zoom magnification based on an area of the object region and at least one pre-set reference ratio; display, through the display, a preview screen comprising a second image scaled from at least part of the first image based on the first zoom magnification; receive a user input for the second image; and drive a zoom operation of the first camera such that a zoom magnification of the first camera is adjusted based on a second zoom magnification determined based on the user input.

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.