3022 results about "Magnification" patented technology

The present disclosure generally relates to user interfaces. In some examples, the electronic device transitions between user interfaces for capturing photos based on data received from a first camera and a second camera. In some examples, the electronic device provides enhanced zooming capabilities that result in visual pleasing results for a displayed digital viewfinder and for captured videos. In some examples, the electronic device provides user interfaces for transitioning a digital viewfinder between a first camera with an applied digital zoom to a second camera with no digital zoom. In some examples, the electronic device prepares to capture media at various magnification levels. In some examples, the electronic device enhanced capabilities for navigating through a plurality of values.

The invention discloses a method, system and equipment for monitoring the running state of a solid state disk and a storage medium, and belongs to the technical field of monitoring the running state of the solid state disk, and the method comprises the following steps: S1, monitoring core indexes during running of different solid state disks, including a health degree index, a performance index and an environment and hardware index; field mapping tables are established for different models of solid state disks, and SMART attributes of different manufacturers are uniformly converted into self-defined indexes; the main control temperature, the writing speed, the reading error rate and the environment temperature are combined, and the temperature values of different areas of the NAND are predicted; and the temperature, the voltage and the write-in magnification are combined to correct the residual life of the solid state disk. According to the method, the SMART attribute indexes are unified, the NAND temperature is accurately predicted, and the service life is predicted in combination with multiple factors, so that the operation and maintenance efficiency of the solid state disk can be effectively improved, and a safety guarantee is provided for stable operation of the solid state disk.

A data-centric system for analyzing agricultural crops, consisting of: a data acquisition module configured to capture images of agricultural fields using cameras, unmanned aerial vehicles (UAVs) or sensors, with the sensors collecting data on soil moisture, temperature, light, humidity and pH; a data preprocessing module configured to: resize the acquired images to a standardized dimension suitable for input to a deep learning model; apply noise reduction using a Gaussian filter; improve image contrast through histogram equalization; and perform image magnification through rotation, reflection, and scaling transformations; a feature engineering module configured to extract the following features: color features, which include color histograms, mean, and standard deviation of color channels; texture features using Gray-Level Co-occurrence Matrix (GLCM) properties, which include contrast, dissimilarity, homogeneity, energy, angular moment (ASM), and correlation; shape features, which include contour area, perimeter, aspect ratio, and roundness; and other features, which include the green pixel ratio and edge density; a classification module configured to: implement deep learning-based classification models selected from the group consisting of Support Vector Machine (SVM), Artificial Neural Network (ANN), Convolutional Neural Network (CNN), ResNet18, Random Forest (RF), SegNet, VGGNet, Naive Bayes (NBG), Decision Tree (DT), K-Nearest Neighbors (KNN), and DeepLab; detecting and classifying weed species in the images of agricultural fields; and diagnosing plant diseases based on the features extracted from the images of agricultural fields; an output module comprises a user interface configured to display the classification and recognition results; and a recommendation module configured to suggest treatment solutions for diagnosed plant diseases through the output module's user interface.

The current disclosure provides methods and systems to provide a magnification capability to a software application for reviewing medical images, where a selected portion of a medical image displayed by the software application can be viewed within a magnification window at a higher resolution than other portions of the medical image. A higher-resolution medical image of the selected portion can be generated from the medical image using a trained convolutional neural network (CNN). In a first embodiment, the selected portion of the medical image is cropped and inputted into the trained CNN to generate a higher-resolution medical image corresponding to the selected portion. In a second embodiment, the medical image is inputted into the trained CNN to generate a higher-resolution medical image when the medical image is displayed, and the selected portion of the higher-resolution medical image is cropped and displayed at a time of selection.

The invention discloses an electrostatic induction and optical imaging principle-fused online monitoring device and method for wear particles of a water-lubricated bearing, and belongs to the field of state monitoring of mechanical equipment. The device comprises a square insulation detection cavity, a detection electrode array, an electromagnetic shielding shell, a high-speed optical imaging module and a signal processing unit, the square insulation detection cavity is installed in a bearing lubrication water loop. The detection electrode array is fixed to the outer wall of the cavity and used for capturing electrostatic signals generated when the abrasion particles pass through. The electromagnetic shielding shell wraps the detection cavity and is grounded through a wire to eliminate environmental electromagnetic interference. The high-speed optical imaging module integrates a high-magnification microscope lens and a CMOS sensor, is coaxially mounted with the detection cavity, and obtains the morphology characteristics of the wear particles in real time; and the signal processing unit performs multi-modal data fusion analysis on the electrostatic signal and the optical image. Through cooperative detection of electrostatic induction and optical imaging, the monitoring precision and reliability of the wear particles of the water-lubricated bearing are remarkably improved.

The application discloses a method, electronic device, and storage medium for image processing, which includes: in response to determining that the trigger of an effect display function is detected, adding a virtual model to a collected image to be processed to obtain a presentation video frame; magnifying-displaying an image area of the virtual model in the presentation video frame, and processing the virtual model into a target three-dimensional effect model in response to determining that a magnification stop condition is reached; fusing the target three-dimensional effect model and a target object in the image to be processed to present a target video frame.

The invention relates to an image processing method and device, electronic equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: in response to a zooming operation, obtaining a current zooming magnification of a current image frame and a target zooming magnification currently issued by a camera application; determining a zoom scene type according to the current zoom ratio and the target zoom ratio; obtaining a corresponding target zooming curve according to the zooming scene type, and obtaining a transition zooming magnification between a current zooming magnification and a target zooming magnification by using the target zooming curve; and processing the acquired image data based on the transition zoom magnification to obtain the image data under the transition zoom magnification. The gradual change of the field angle of the image data in the focus-varying scene is realized, the sudden change of the field angle is avoided, and the picture change is smoother.

The present application provides an array-type image detection device, including two columns of imaging arrays arranged at intervals along a first direction, each column of the imaging array includes a plurality of imaging modules arranged at intervals along a second direction, each imaging module includes a magnifying lens and an imaging chip arranged in sequence along its optical axis; each imaging module magnifies the image to be detected located in its detection area and images it in its imaging area and is acquired by the imaging chip located in its imaging area; and except for the detection areas located at the head and tail sides of the second direction, each detection area has overlapping parts with other detection areas at both ends along the second direction. The array-type image detection device provided by the present application can effectively solve the problems of low resolution of existing contact image sensors, difficulty in detecting high-resolution images, and detection blind spots when performing magnified detection, and can obtain a clear magnified image of the image to be detected within the full-frame range.

The invention discloses a metallographic detection method and system based on image recognition, and belongs to the technical field of metallographic detection. The detection method comprises the following steps: image acquisition and preprocessing: acquiring a metal metallographic image by an optical microscope, performing median filtering and denoising, and extracting a grain boundary by adaptive binarization; and field-of-view and scale calibration: selecting a corresponding scale according to the shooting magnification. The multi-parameter automatic measurement comprises grain size analysis, grain size rating, second phase analysis and intelligent report generation, a report is automatically generated based on a preset template and comprises an original image, a binary image, measurement data and a histogram, and report fonts and layout can be adjusted in real time through a visual control. According to the detection method disclosed by the invention, the efficiency, the precision and the reliability of metallographic structure analysis of the metal material are remarkably improved through the full-process design of image acquisition standardization, preprocessing intellectualization, data analysis automation and report generation templating.

The invention discloses a method for measuring the particle size of metal powder by using a scanning electron microscope, which comprises the following steps: step 1, sample treatment: adhering a small conductive adhesive tape on a sample table, sprinkling metal powder on the adhered conductive adhesive tape, and purging to obtain the conductive adhesive tape adhered with the metal powder; step 2, parameter setting: setting acceleration voltage, working distance, diaphragm, image contrast, brightness, magnification times and scanning speed of a scanning electron microscope to observe the conductive adhesive tape adhered with the metal powder obtained in the step 1 to obtain an electronic image of metal powder particles; and processing and calculating the electronic image of the metal powder particles by using software to obtain the average particle size of the metal powder particles. According to the method disclosed by the invention, a solvent is not needed to disperse the sample, pollution of the solvent to the sample is effectively avoided, and the result is more accurate than that of directly measuring the particle size by using length software.

Embodiments of the present invention provide systems, methods, and computer storage media for cutting down a user's larger input video into an edited video comprising the most important video segments and applying corresponding video effects. Some embodiments of the present invention are directed to adding face-aware scale magnification to the trimmed video (e.g., applying scale magnification to simulate a camera zoom effect that hides shot cuts with respect to the subject's face). For example, as the trimmed video transitions from one video segment to the next video segment, a scale magnification may be applied that zooms in on a detected face at a boundary between the video segments to smooth the transition between video segments.

The invention discloses a device and a method for detecting organic matter components in space dust. The device comprises a pyrolysis cabin and an electronics box which are communicated end to end, a transparent window is arranged between the pyrolysis cabin and the electronics box, a front cover plate capable of being opened and closed is arranged at a head port of the transparent window, and a silicon dioxide aerogel plate is embedded in the front cover plate; the electronics box is internally provided with a macro magnification camera, an infrared laser unit, an infrared temperature measurement camera, a double-optical-comb spectrum unit, a main control unit and a power supply unit. According to the invention, dust or fragments floating in the space can be effectively captured and collected under the approaching environment of the space orbit of the earth or asteroid and other stars, and the captured sample is heated by using high-power infrared laser, so that organic matters and water in the sample are heated, decomposed and volatilized; and then carrying out qualitative and quantitative analysis work on volatile matters by using a double-optical comb spectrometer, so as to realize high-precision, high-sensitivity and high-resolution in-situ detection analysis on space dust.

The invention discloses a DBT reconstruction method based on three-dimensional coordinate displacement, a medium and electronic equipment, and the method comprises the steps: firstly obtaining multi-angle X-ray projection data, and carrying out the preprocessing of the X-ray projection data; then, the preprocessed projection data is corrected in a three-dimensional coordinate system; after interpolation processing is conducted on the projection data, an autoregressive source moving average separation method is adopted for processing, and a cross-sectional image of any depth is obtained; and finally, carrying out image enhancement on the cross-sectional image. According to the method, DBT reconstruction is carried out through three-dimensional displacement by utilizing multi-angle X-ray projection, and the structure in the reconstruction layer is effectively focused through multi-angle projection displacement correction and magnification standardization, so that the structure in the reconstruction layer is clearer, artifacts in the reconstruction layer are reduced, the contrast of the image of the reconstruction layer is improved, and observation of breast lesions is more facilitated.

An audio processing method implemented by an electronic device includes entering a multi-channel video recording mode, detecting a shooting operation of a user, simultaneously recording, after detecting the shooting operation, a first video image and a second video image using a first camera and a second camera, and recording audio of a plurality of sound channels, where the audio includes panoramic audio, first audio corresponding to the first video image, and second audio corresponding to the second video image. The electronic device further records the first audio based on a feature value such as a zoom magnification corresponding to the first display area.

The invention relates to the technical field of pathological image analysis and mining, and particularly discloses an ovarian cancer subtype classification method based on prototype learning and multi-view deep embedding clustering, and the method comprises the following steps: S1, collecting a tissue pathological image of an ovarian cancer patient and a corresponding full-view digital pathological image; and S2, generating a multi-view data set. According to the ovarian cancer subtype classification method based on prototype learning and multi-view deep embedding clustering, the problem that in the prior art, patch-level labels are generally lacked in the field of multi-instance pathological images, so that many natural image processing methods cannot be applied to the field of pathological images is solved. A ResNet backbone network is used for extracting features of pathological images under the maximum magnification, a small number of pathology prototypes are introduced to guide deep embedded clustering through pathology expert priori knowledge, a pathology image spectrogram is introduced to serve as a reference view, and the accuracy and stability of clustering are enhanced.

The invention discloses a fine tracking detection light path structure for a free space laser communication terminal, and belongs to the technical field of satellite laser communication. The system comprises a receiving light path convergence lens, an inclined end face optical fiber, a relay amplification lens and a fine tracking detector. A fiber core of the inclined end face optical fiber is located at the focus of the receiving light path convergence lens, and the fiber core of the inclined end face optical fiber and the center point of the fine tracking detector meet the object image conjugate relation with the relay amplification lens. The optical axis of the receiving light path convergence lens, the optical axis of the relay amplification lens, the optical axis of the inclined end face optical fiber and the normal of the inclined end face optical fiber are in the same plane; reflected light beams of the optical fiber with the inclined end face are collected through the relay amplification lens and imaged to the high-resolution area array detector, high-precision detection on the angle of the received laser beams is achieved under the condition that extra laser energy loss is not introduced, and the result is not affected by angle drifting of all optical elements of a receiving branch.

The invention discloses a wafer defect detection method and system for an IGP processing unit, and a storage medium. The detection method comprises the following steps: acquiring a wafer image; the method comprises the following steps: constructing a multi-dimensional and multi-level low-magnification imaging feature recognition network, extracting different-dimensional features of an image by using a deep convolutional neural network formed by convolution kernels, introducing a feature pyramid structure, and realizing fusion and extraction of multi-level features through a top-down path and transverse connection in combination with features of different levels. Further processing the high-dimensional features extracted by the convolutional neural network model by adopting a multi-granularity scanning structure through a deep forest model to complete multi-dimensional and multi-level extraction of wafer defect features; and training the constructed multi-dimensional and multi-level low-magnification imaging feature recognition network, and carrying out wafer defect detection. By introducing structures such as a feature pyramid and a depth forest, a multi-dimensional and multi-level low-magnification imaging feature recognition network is constructed, and the wafer defect recognition capability is improved.

A variable magnification optical system comprising a first lens group, a second lens group, a third lens group, and a rear group in order from object side is configured so that at varying magnification the distances between adjacent lens groups are varied; at a predetermined object distance the variable magnification optical system has focused states with different amounts of aberration; the rear group includes a first focusing lens group and a second focusing lens group disposed closer to an image side than the first focusing lens group, the first and second focusing lens groups moving along different trajectories at focusing; at the object distance the first and second focusing lens groups move at transition from a first focused state to a second focused state; and the following conditional expression is satisfied:−6.80<f1 / f2<−0.05where f1 and f2 are the focal lengths of the first and second lens groups, respectively.

The invention relates to a projection zoom lens, which comprises a first fixed group, a zoom group, a second fixed group and a focusing group which are sequentially arranged along an optical axis direction, the first fixed group comprises a first lens, a second lens and a third lens, the zoom group comprises a fourth lens, a fifth lens and a sixth lens, and the focusing group comprises a fourth lens, a fifth lens and a sixth lens. The second fixed group comprises a seventh lens, an eighth lens, a ninth lens, a tenth lens and an eleventh lens, the focusing group comprises a twelfth lens, a thirteenth lens and a fourteenth lens, the first lens to the fourteenth lens are sequentially arranged in the optical axis direction, and the zooming group and the focusing group can move in the optical axis direction for focusing; the wide-angle end and the long-focal-length end of the wide-angle long-focal-length lens are combined, the focal length ranges of the wide-angle end and the long-focal-length end are reasonably set, pictures are kept clear in large-range zooming and focusing, and the wide-angle long-focal-length lens has the advantages of being small in number of moving groups, invariable in total length, simple in zooming, small in size, low in cost, free of defocusing under the environment condition of large temperature difference, stable in working performance, high in projection quality and wide in application range.

The invention discloses a method for optimizing micro-crack segmentation based on deep learning and super-resolution reconstruction, and the method comprises the steps: cutting an image in real time, obtaining an image block which takes a component as a target main body, and synchronously recording a homography matrix for geometric mapping; selecting an amplification strategy to improve the resolution, and recording a scale mapping relation; inputting the enhanced image block into a double-flow network; adaptive fusion and reconstruction are carried out on the two branch features, and a high-resolution texture image is output; generating a geometrically corrected ortho-image, fusing the geometrically corrected ortho-image with original illumination information, and outputting a corrected image with a known pixel size; identifying cracks, spalling and honeycomb diseases in parallel; generating a unified defect confidence map; calculating real geometric parameters of the BIM in a BIM global coordinate system through coordinate back projection; and generating quantitative defect reports and maintenance suggestions. The method has the advantage that seamless connection between the detection result and the BIM global coordinates is realized.

Methods, systems, and devices for wireless communications are described. The described techniques may enable transmitting and receiving devices to increase an effective aperture size of transmitting and receiving antenna arrays, which may increase an achievable rank of spatially multiplexed communications. For example, one or both of the transmitting device and the receiving device may use a reflective component to reflect signals transmitted between the devices, which may allow for larger effective aperture sizes without increasing a physical distance between antenna elements. In some examples, the reflective component may be a static concave mirror associated with a predetermined weight vector and focal point. In some examples, the concave mirror may be a reflective intelligent surface (RIS) of reflective elements with reflective properties which may be dynamically adjusted to various weight vectors. An orientation of the RIS may be adjusted to steer a beam in various directions in space.

The invention discloses a visual tracking image acquisition method and device, electronic equipment and a readable storage medium, relates to the technical field of robot inspection, and accurately establishes a mapping relation between an actual image coordinate and a camera coordinate through a camera checkerboard calibration algorithm to obtain an internal reference matrix and a distortion coefficient. A shooting holder is controlled to collect a large-range image containing an inspection target, and a visual tracking algorithm is used to identify the contour of the target. And calculating a target magnification factor and a holder fine tuning parameter based on the contour coordinate data, the internal reference matrix and the distortion coefficient. And rotating the holder according to the fine tuning parameters, accurately positioning the inspection target to the center of the global image, and executing image magnification operation according to the target magnification times to complete target image acquisition. Through cooperation of calibration data support, algorithm contour recognition and parameter driving adjustment, the collection effect of a target image is not affected by repeated positioning precision of the robot, it is ensured that the collected target image is centered and clearly amplified, and a high-quality image basis is provided for subsequent detection.

An apparatus and control method for controlling an online meeting is provided for receiving, from a camera, a captured video of the meeting room, transmitting, via a first server, the captured video of the meeting room to an online meeting client, specifying an ROI (Region Of Interest) in the meeting room, controlling an optical zoom magnification of the camera for capturing a still image of the ROI in the meeting room, and transmitting, via a second server that is different from the first server processing the captured video of the meeting room, the still image that the camera captures after the control for the optical zoom magnification to the online meeting client.

The invention relates to a cloth flaw detection method, device and equipment and a storage medium, and the method comprises the following steps: carrying out the multi-angle light source irradiation of the surface of cloth, obtaining a texture image, and carrying out the grid segmentation of the texture image into detection units; and extracting a gray value sequence of each unit, calculating an edge difference by using a gradient operator, generating a texture edge change curve to identify an abnormal wave crest, and determining a defect candidate region. And carrying out local amplification imaging based on the candidate region coordinates, and extracting defect shape feature parameters. The defect types are determined according to the parameters, quality grade division is performed on the cloth according to the parameters, and a quality grade report is finally generated, so that the technical problems of low defect identification precision and easy missing detection of fine defects caused by uneven illumination and texture interference in the existing cloth defect detection method are solved.

The invention discloses an unmanned aerial vehicle autonomous inspection system and method based on AI identification, the system comprises an unmanned aerial vehicle body carrying a high-power optical zoom lens, an edge computing device and a function module, and the autonomous inspection of a power distribution tower is realized by fusing front-end AI identification and edge computing technologies. A Yolo structure is adopted to construct a lightweight tower recognition model, a dynamic route planning module is combined to realize single-point reference route generation and three obstacle crossing modes, and real-time coordinate correction in an RTK-free environment is supported. The edge computing device integrates a semi-supervised learning engine and a multi-sensor data fusion module, meets miniaturization design, and supports breakpoint continuous flight control and precise landing. The method covers automatic route generation, visual tracking, zoom cooperative control and self-adaptive task scheduling, solves the problems that a traditional unmanned aerial vehicle depends on manual operation, the data quality is poor and the efficiency is low, realizes whole-course automation of tower inspection in a complex environment, and is high in inspection efficiency, and the picture definition reaches the pin level.

A steel wire rope remaining life prediction method of a four-rope grab crane comprises the steps that 1, an electromagnetic device is used for magnetization in the axial direction of a steel wire rope; 2, a Hall sensor is installed at the fixed end of the steel wire rope; 3, a Hall sensor monitors changes of a magnetic field around the steel wire rope in real time; 4, whether missing values and abnormal values exist in the collected digital signal sequence or not is checked; if missing values exist, a linear interpolation method is adopted for filling; 5, extracting characteristics related to the damage and residual life of the steel wire rope from the cleaned digital signal data; 6, establishing a magnetization prediction model; 7, magnetic field change characteristic data of the steel wire rope in different use stages are collected, a least square method is adopted for the magnetization prediction model to estimate model parameters, and a loss function is minimized; and 8, predicting the real-time residual life of the steel wire rope. The method can predict the service life of the steel wire rope at high frequency and accurately predict the residual service life of the steel wire rope.

The invention belongs to the technical field of information science, and particularly relates to a disease survival prediction method based on a multi-scale pathological image and multi-modal data, which comprises the following specific steps: digital pathological image data processing, gene representation data processing, multi-modal data interaction and survival analysis. According to the method, image blocks corresponding to two magnification factors of x40 and x20 are used as input of a model, and integration of multi-scale information is realized by adopting an early fusion strategy; compared with a method of repeatedly calculating the same position by adopting a multi-view WSI, the method provided by the invention adopts a non-overlapping image segmentation and blocking mode, so that the over-fitting of the model can be prevented to a certain extent, and the efficiency of the model is improved.

This imaging device includes a storage unit configured to store zoom setting information that is set by an operation and includes at least one of a zoom time, a zoom speed, or a target zoom magnification, and a control unit configured to perform, on the basis of a first operation acting as a trigger for starting zoom variation that causes a zoom magnification to change to a wide-end side or a tele-end side, the zoom variation reflecting the zoom setting information.

The invention belongs to the technical field of projection light field regulation and control, and particularly relates to a method and a system for enhancing manufacturability of a projection type structure photomask pattern. Determining an initial mask pattern according to the target structured light field and the magnification of the projection system; calculating an initially generated structured light field by adopting a vector diffraction imaging model, calculating a numerical value of a target function, and judging whether the numerical value meets a preset threshold value or not; if the target function does not meet the preset threshold value, carrying out iterative optimization on the mask pattern by adopting an optimal gradient method until the preset threshold value of the target function is met, and outputting a continuous numerical value mask; pixel splitting or fusion is carried out on the continuous numerical mask according to the required mask manufacturing precision; local integral approximation operation is adopted, binarization processing is carried out on the mask, and the manufacturability of the mask is enhanced. According to the method, the projection type structure light field which is high in degree of freedom, high in efficiency, low in calculation power requirement and robust can be optimized, the mask pattern which can be manufactured can be obtained, and the manufacturing precision of the mask can be adjusted at will so as to adapt to different mask pattern preparation processes.

Various embodiments provide a Raman spectroscopy probe. The probe comprises an elongate body having a proximal end and a distal end. The probe comprises an optical transmission line within the elongate body for guiding light for inducing Raman scattering in a tissue between the proximal end and the distal end in a distal direction and for guiding Raman scattered light between the distal end and the proximal end in a proximal direction. The probe comprises a magnification layer arranged at or on the distal end. The magnification layer has an exposed surface for contacting the tissue, wherein the magnification layer is positioned such that the light for inducing Raman scattering impinges on the magnification layer. The magnification layer is for inducing surface-enhanced Raman scattering (SERS) at the exposed surface.