110 results about "Image scale" patented technology

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.

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 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 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 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.

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.

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 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.

The invention relates to the technical field of image processing, and discloses an image scaling method and device, equipment and a medium, and the method comprises the steps: respectively obtaining the height information and width information of an initial image and a target image, determining a longitudinal scaling based on the height information, and determining a transverse scaling based on the width information; mapping a target pixel position in the target image to a floating point coordinate position in the initial image based on the transverse scaling and the longitudinal scaling respectively to obtain a corresponding transverse floating point coordinate and a corresponding longitudinal floating point coordinate; determining a pixel mean value in a preset pixel position range in the initial image according to the transverse floating point coordinates and the longitudinal floating point coordinates, and obtaining a neighborhood pixel mean value; and limiting an interpolation weight range according to a preset sharpness adjustment parameter, and performing linear interpolation on the neighborhood pixel mean value to obtain a target pixel of the target image so as to zoom the initial image. The invention aims to improve the visual quality and detail retention effect of the zoomed image.

The invention relates to an image scaling method, system and device and a storage medium, and is applied to the field of image processing, and the method comprises the steps: receiving an original image comprising a plurality of original pixel points and a scaling coefficient; taking a direction parallel to one side of the original image as an x direction, taking a direction perpendicular to the x direction as a y direction, and recording coordinate information of a plurality of original pixel points; determining a plurality of scaled target pixel points according to the scaling coefficient and the plurality of original pixels, and generating a configuration table including a y-direction configuration table according to the coordinate information and the scaling coefficient; reading the DDR according to the y-direction configuration table to obtain image data of the original image and caching the image data; transmitting the image data to a plurality of parallel data channels, and intercepting the image data according to the configuration table to obtain a plurality of pixel point data; and calculating a plurality of target pixel values according to the data of the plurality of pixel points, and integrating the plurality of target pixel points to obtain a scaled image. The technical effect of the invention is that the picture zooming is more efficient and faster.

The application discloses an image recognition algorithm robustness evaluation method and a computer storage medium, relates to the safety technical field of deep learning, and particularly relates to a black box migration method of directional adversarial samples. The transformed sample is input into a pre-trained neural network to obtain network output; a loss value of the output and a target category is calculated through a pre-set loss function; the gradient of the loss value to the target sample is calculated, and updating is performed according to a learning rate. The application is characterized in that a large-scale image scaling operation and random transformation are used in each gradient updating iteration, the migration ability of the generated adversarial sample is enhanced, and the accuracy of the model robustness evaluation is improved.

When tracking skin regions or regions of interest frame by frame, the same coordinates may not be detected due to factors such as the image scale and the relative position to the light source. [Solution] The biological information acquisition device comprises an imaging unit that images a living body and generates a plurality of frame image data, a face recognition unit that identifies a face image included in the frame image data and identifies a plurality of feature points in the face image, and a detection unit that detects the pulse wave signal of the living body. The detection unit selects a tracking feature point from the plurality of feature points, tracks the tracking feature point included in each of the plurality of frame image data, and acquires the pulse wave signal based on the tracking feature point detection light amount of each of the plurality of frame image data.

The application designs an end-to-end image scaling network structure, which learns the loss of high-frequency information distribution to strengthen feature learning and solves the ill-posed problem, thereby improving the image reconstruction effect. First, considering the ill-posed problem existing in image super-resolution, the high and low frequency information of the input image is separated to learn the high frequency information distribution in a more flexible way, and the learned information is randomly sampled and applied to image reconstruction to improve the image reconstruction effect. Secondly, a long skip connection is added to the up-sampling network to relieve the network depth, obtain faster convergence speed and better performance. Finally, a multi-stage training strategy is proposed for the optimization of the down-sampling and up-sampling sub-networks. A new effective image scaling method is constructed to provide a more efficient framework for image scaling in practical applications. Combined with the joint loss, the model loss is iteratively optimized to minimize the final loss of the model, thereby improving the accuracy and robustness of image reconstruction.

The application relates to the field of image enhancement recognition, in particular to a construction method of a bilinear residual filter applied in coal powder particle measurement, which comprises the following steps: based on the basic principle of bilinear interpolation, inserting a scaling operator B to design a bilinear residual unit in the bilinear residual filter; from the mathematical point of view, constructing a filter through Taylor expansion and optimizing the filter by combining an optimization objective function; designing a sampling operator to make the scaling operator meet the requirement of low-frequency span in the sampling process; and considering the reciprocal relationship between scaling multiples to obtain the final optimized bilinear residual filter. The filter designed by the application can not only accurately restore edge details in the image scaling process, but also maintain stable segmentation performance in a complex environment, and provides a better solution for image processing, target detection and coal particle size measurement and the like.

The invention discloses an all-optical diffraction neural network gesture recognition method for a three-dimensional space, belongs to the technical field of crossing of optical calculation and artificial intelligence, and aims to solve the problems of insufficient generalization ability and recognition failure caused by the fact that an existing all-optical diffraction neural network cannot adapt to imaging scale zooming and defocusing effects caused by target movement in the three-dimensional space. The method comprises the following steps: step 1, constructing a physical model and setting parameters of the physical model; 2, establishing a three-dimensional space imaging geometric mapping relation; step 3, dynamic modeling of an input layer light field; 4, calculating light field propagation from the dynamic imaging surface to a first diffraction layer of the D2NN diffraction layer; 5, carrying out network structure constraint and forward propagation; step 6, detector layer signal extraction and category determination; and step seven, D2NN diffraction layer training and parameter optimization are carried out. According to the invention, the problem of identification failure is effectively solved; and the generalization ability and robustness of the all-optical diffraction neural network to a three-dimensional space dynamic target are significantly improved.

The invention provides a method for enhancing the contrast of an interference fringe image in a coherent dispersion spectrometer, and the method comprises the steps: carrying out the normalization processing of the interference fringe image collected by the coherent dispersion spectrometer, so as to obtain a normalized interference fringe image; performing image scale decomposition based on a local Laplacian filter, and decomposing the normalized interference fringe image into a base layer image and a detail layer image; performing base layer contrast expansion based on an HVS perceptual model and operations of wavelet transformation and unsharp mask processing on the base layer image and the detail layer image to obtain an enhanced base layer image and an enhanced detail layer image; and re-fusing and post-processing the processed base layer image and the processed detail layer image by adopting addition operation to generate a final interference fringe image. An interference fringe image is processed based on local Laplacian decomposition and an HVS perception model, so that the problem of low contrast of the existing interference fringe image is solved.

The present application relates to the technical field of computers. Disclosed are an image scaling method and apparatus. The method comprises: receiving an instruction for instructing the scaling-down and scaling-up of an image frame, and calculating the ratio of the height of the adjusted image frame indicated in the instruction to the height of a display screen of a target vehicle; and on the basis of the ratio, scaling down or scaling up an original image frame, which comprises information required to be displayed in a dialog service flow interface, so as to obtain a target image frame, which comprises the information required to be displayed in the dialog service flow interface, and displaying the target image frame in the dialog service flow interface.

The present application relates to a kind of transmission electron microscope image sample geometry characteristic parameter measurement method and device, wherein method includes: obtaining transmission electron microscope image, and the transmission electron microscope image is preprocessed;The transmission electron microscope image after pre-processing is input to semantic segmentation model and is carried out pixel-level segmentation generation segmentation mask to sample area and background area;Geometric characteristic parameter of sample in the transmission electron microscope image is automatically calculated based on segmentation mask;The geometric characteristic parameter is realized pixel value to physical unit conversion by image scale information.This application can realize the automatic, high-precision measurement of sample geometric characteristic parameter.

Vision system (300) for amplifying and segmenting an image (100) of an environmental scene, comprising the following: an image amplifier (305) which amplifies a segmented image of the surrounding scene incident on an input of the image amplifier and provides an amplified and segmented image of the surrounding scene to an observer at an output of the image amplifier; and an input-side optical module (301) comprising at least two optical imaging means (302, 303, 304) that generate the segmented image of the surrounding scene and map it to the input of the image intensifier, wherein a first optical imaging means of the at least two optical imaging means generates a first image segment (102) of the surrounding scene with a first image scale, and, wherein a second optical imaging means of the at least two optical imaging means generates a second image segment (101) of the surrounding scene with a second image scale.

An image processing device includes a first memory, the first memory being used to store to-be-processed data of a plurality of first images; a scaling control component, the scaling control component being configured to read the to-be-processed data of the plurality of first images from the first memory, determine an image scaling factor corresponding to each of the first images, and send the to-be-processed data of each of the first images and its corresponding image scaling factor to a scaling component; and the scaling component, the scaling component being configured to perform scaling processing on the to-be-processed data of the corresponding first image based on the image scaling factor to obtain a target image, the target image including each target object in the first image, each target object being displayed in the target image based on a target ratio.

An imaging device for the scalable visual depiction of a region to be observed includes an optical image capture apparatus, optical and / or electronic scaling means for scaling the captured image, and an adjustable focusing apparatus. A robotic holding arm moves the image capture apparatus relative to the region to be observed and provides a mechanical scaling function through adaptation of an axial distance (d) between the image capture apparatus and the region to be observed. An input unit captures a user-side input command for scaling the captured image on a display unit. A control apparatus sets the image scaling, the mechanical scaling function and sets the focusing apparatus. The control apparatus executes the setting of the mechanical scaling function and the focusing apparatus by actuating the holding arm and the focusing apparatus, based only on the input command detected by the input unit for scaling the captured image.

The application relates to the field of measurement, in particular to a handheld electric drill drilling depth measurement display method and device, a handheld electric drill and a medium. The method comprises the following steps: acquiring a drill bit projection image before the start of the work of the handheld electric drill; judging whether the drill bit is perpendicular to the object to be punched according to the drill bit projection image before the start of the work of the handheld electric drill; if not, obtaining an initial drill bit projection image length according to the drill bit projection image before the start of the work of the handheld electric drill; when the work start information of the handheld electric drill is monitored, acquiring a working drill bit projection image in real time, and obtaining a working drill bit projection image length according to the working drill bit projection image; obtaining a working drill hole real depth and a working drill hole real effective depth according to a preset image scale, a real drill bit length, the initial drill bit projection image length and the working drill bit projection image length; and displaying the working drill hole real depth and the working drill hole real effective depth. The application has the effect of improving the drilling operation success rate.