699 results about "Static image" patented technology

The invention relates to the technical field of remote sensing monitoring, in particular to an intelligent forest disease and pest monitoring method and system based on unmanned aerial vehicle remote sensing, and the method comprises the following steps: collecting multispectral data by an unmanned aerial vehicle, extracting reflectivity and smoothing the reflectivity, carrying out differential recognition on abnormal pixels, extracting curves and screening significant changes, segmenting scab boundaries, and classifying health states. And generating a pest and disease map layer prediction trend. According to the method, the reflectivity time sequence is constructed, differential processing is carried out, the vegetation change trend is dynamically captured, abnormal areas are identified by combining slope offset and persistence analysis, significant pixels are screened according to main peak wavelength offset, boundary information is extracted, the scab positioning precision is improved, and the recognition resolution of the lesion state is enhanced through reflectivity combined analysis; accurate description of disease spot dynamic changes is realized, static image dependence limitation is broken through, monitoring time continuity and space response capability are enhanced, and disease and insect pest change capture efficiency and state classification accuracy are effectively improved.

An apparatus and method for static image of time series measured data to time series translation is disclosed. The apparatus comprises at least a processor configured to receive a static image of time series measured data, convert that static image from its initial domain to a usable time series within another user-selected domain, then to validate the conversion against a confidence threshold.

The invention belongs to the technical field of ship collision avoidance, discloses a ship collision avoidance early warning method and system based on vision and AIS data collaborative perception, and firstly provides an enhanced YOLO (E-YOLO) target detector which can detect a ship in a complex environment in a high-precision and real-time manner and perform ship tracking and visual trajectory extraction in combination with a DeepSORT algorithm. Secondly, a homography coordinate conversion method based on multiple frames of static images is provided, conversion between a pixel coordinate system and a world coordinate system can be realized without accurate camera calibration, and the fusion precision of visual data and AIS data is improved. AIS and visual information are matched through a K-D tree algorithm, AIS data are projected to visual coordinates, and accurate matching of heterogeneous data is achieved. The system is provided with ship illegal behavior judgment and collision early warning rules, behaviors such as overspeed and abnormal AIS closing can be recognized, and early warning is triggered. Experiments prove that the method provided by the invention is strong in adaptability and high in robustness, and can effectively improve the ship monitoring and traffic management level of the bridge water area.

The invention relates to the technical field of path planning, in particular to an emergency evacuation path planning method and system and a storage medium. The method comprises the following steps: obtaining building layout data; performing building structure topology analysis on the building layout data to generate building structure layout data; arranging monitoring equipment according to the building structure layout data to obtain monitoring equipment deployment data; performing environment perception on the monitoring equipment based on the monitoring equipment deployment data so as to generate environment perception data; performing static image acquisition on the environment sensing data to obtain a static environment image; performing channel connection on the static environment image to generate building channel distribution data; and performing potential dangerous channel area identification on the building channel distribution data through the static environment image to generate potential dangerous channel area data. According to the invention, by integrating environment perception, dynamic video stream analysis and intelligent path scheduling, the flexibility and accuracy of emergency evacuation path planning are improved.

The invention provides a relatively controllable video generation system based on an AIGC large model, and relates to the technical field of video generation, and the system comprises a user input module which is used for receiving a demand of a needed video input by a user; the language construction module is used for generating a video character script based on a large language model according to a demand input by a user; the graph generation module is used for generating a static image of a video scene according to the video character script; the image-to-video module is used for converting the static image into a dynamic video shot; and the video generation module is used for combining the plurality of dynamic video shots into a complete video. According to the relatively controllable video generation system based on the AIGC large model provided by the invention, the video is quickly generated based on the AIGC, the video production efficiency is improved, and a plurality of software platforms do not need to be switched for use.

The invention relates to the technical field of medical image processing, and discloses a craniofacial dynamic reconstruction method and system based on multi-modal data fusion, and the method comprises the steps: arranging a multi-modal data collection device in a target craniofacial region, and obtaining a static CT image, a static MRI image, a dynamic expression video sequence and a surface electromyogram signal; preprocessing the static CT image, the static MRI image, the dynamic expression video sequence and the surface electromyogram signal; inputting the preprocessed data into a multi-scale finite element model, and simulating a coupling relationship between muscle contraction force and skin deformation by adopting a biomechanical driving strategy to generate a dynamic craniofacial model; and fusing the geometric error and the motion consistency score of the real data by adopting a linear regression method, and outputting a comprehensive reconstruction quality index. According to the method, the problems of low craniofacial dynamic modeling accuracy and poor robustness in the prior art can be solved.

The invention discloses a pulse neural network training method based on adaptive threshold integer activation neurons, and the method specifically comprises the following steps: inputting a neuromorphic data stream or a static image, generating a pulse sequence through time coding, and taking the pulse sequence as the input of a model; in the backbone network, performing down-sampling and extracting multi-scale features by using an SNN module based on LIF neurons activated by an adaptive threshold integer; the visual tasks are divided into image recognition, image detection and image segmentation, and are respectively accessed to different processing modules for processing and outputting; according to the invention, on the basis of a Meta-SpikeFormer element module, the spiking neural network based on LIF neurons activated by an adaptive threshold integer value is used, so that the memory overhead during training is reduced, the network performance of direct training is improved, the application range of the spiking neural network is expanded, and the network is better applied to image classification, recognition and segmentation tasks.

The invention relates to a knowledge-intensive visual question and answer automatic data generation method and device, and the method comprises the steps: constructing an original visual data set containing the professional knowledge of a target domain according to a static image, a video stream and multimedia content; extracting a representative frame sequence, converting the audio information into text information, and extracting character information in the static image to construct a structured visual instance database; according to the prompt text meeting the preset professional depth condition, establishing a three-level prompt system containing domain knowledge, an evaluation standard and a generation specification; generating a corresponding visual question and answer pair data set according to the dynamic cooperation of the main agent and the domain expert agent; generating a multi-agent quality evaluation system according to the quality evaluation result; and designing a difficulty grading mechanism according to the negative example sample. According to the method, the professionality, the accuracy and the diversity of the visual question and answer data are remarkably improved, and reliable data support is provided for training and evaluation of a multi-modal large model.

The invention discloses a laryngoscope imaging analysis method and system based on deep learning. The method comprises the steps of data acquisition, data preprocessing, lesion static detection, vocal cord dynamic analysis and laryngoscope imaging analysis. The invention relates to the technical field of laryngoscope analysis, in particular to a laryngoscope imaging analysis method and system based on deep learning, according to the scheme, two mutually supporting functional designs of lesion static detection and vocal cord dynamic analysis are adopted, detection of an intuitive lesion area is carried out through a static image, and assistance and support are carried out through vocal cord dynamic analysis; detecting by adopting an improved convolutional neural network method combined with feature optimization; the vocal cord dynamic analysis is carried out by adopting a residual error improved bidirectional long-short-term memory neural network method combined with bidirectional improved time sequence convolution, a powerful auxiliary reference is provided for static image lesion detection, meanwhile, the method can also be used as an independent detection function, and the overall connectivity and reusability of the method are improved.

The invention relates to the technical field of civil engineering structure safety monitoring and aircraft image detection, and discloses a safety quantification nondestructive monitoring method and system based on an aircraft concrete structure, and the method comprises the steps: constructing a multi-target path optimization model, and generating a track point sequence; the aircraft collects an image and calculates an image reliability score; identifying crack and spalling defects by using a deep learning model; calculating a structure risk index SFI based on a partial least squares regression model; a monitoring report is generated and uploaded to the cloud. The technical problem that in the prior art, the safety quantitative evaluation based on the in-flight image structure cannot be realized by depending on a manual inspection or static image recognition mode, especially under the conditions of a special-shaped curved surface structure, shielding of a complex area or violent illumination change is solved. Due to the fact that three-dimensional path optimization, an image reliability scoring mechanism and AI defect recognition are fused, intelligent recognition of structure surface covering and crack peeling is achieved, and the automation degree of concrete structure monitoring is improved.

Standard event-based cameras capture changes in luminance with high temporal resolution, but they do not measure static images. An event-based imaging system and methods that can capture static intensity is provided that eliminates the need for a dual-sensing system and significantly reduces the cost, footprint, and data bandwidth of the imaging system. The system is suited for applications where high temporal resolution and dynamic range are required, but where static scene information is also important. The system has a Dynamic Vision Sensor for capturing data and a processing unit for data aggregation, a fast approximate extraction of static information from noise, and scene reconstruction. The methods may use a model for reconstruction or may calibrate during system deployment by making measurements of known static luminance and recording average noise levels over a predetermined period to map between luminance and noise for reconstruction.

The invention discloses a flow field measurement system and method fusing event data and a blurred image, and the method comprises the steps: turning on a sheet-shaped light source, and enabling the sheet-shaped light source to illuminate tracer particles in a gas flow field; starting a gas flow field to enable the gas flow field to carry the tracer particles to flow through the transparent box body; capturing the motion change of the tracer particles in real time through an event camera, and collecting event data; blurred images of the tracer particles are synchronously collected through an industrial camera; based on the event data and the blurred image, reconstructing a clear moving image at any moment by using an image deblurring technology EDI; and carrying out time synchronization and space alignment on the clear moving image and the high-resolution static image, and extracting flow field information in the gas flow field in combination with the movement track and brightness change of the particles. According to the method, the high-speed dynamic capture capability of the event camera and the high spatial resolution of the industrial camera are combined, the bottleneck of the spatial-temporal resolution in the traditional flow field measurement technology can be effectively overcome, and meanwhile, the method has a relatively good effect in a complex flow field and a high-speed dynamic scene.

The invention relates to the technical field of tumor interventional operations, and discloses a visual navigation method based on a tumor interventional operation robot. The method comprises the following steps: acquiring real-time medical image data of a tumor area containing multi-modal imaging information so as to comprehensively present anatomical details; and performing three-dimensional reconstruction on the image data to generate a tumor area three-dimensional anatomical structure model capable of visually displaying a space structure. Key anatomical feature points are extracted based on the model, space coordinates are calculated, a surgical robot intervention path is planned according to the coordinates, and an initial navigation track is generated; and continuously collecting real-time pose data of the robot in an operation, dynamically matching the real-time pose data with the initial navigation trajectory, adjusting motion parameters according to a matching result, and generating a corrected navigation instruction. The method can reflect the intraoperative anatomy condition in real time, dynamically optimize the path, solve the problems that traditional navigation depends on preoperative static images and lacks real-time adjustment, reduce operative complications and improve the treatment effect of patients.

The invention relates to the technical field of medical image intelligent analysis and artificial intelligence auxiliary diagnosis, in particular to a medical image disease course prediction system based on an industrial neural network, and the system comprises a reference generation module which is used for obtaining static image data; processing the static image data by using a physical perception neural network to generate a pure ideal state reference; a perturbation simulation module; the industrial kinetic parameters are used as perturbation terms to be superposed to a pure ideal state reference, and a theoretical damaged state is generated; the projection verification module is used for acquiring real multi-modal observation data; generating a real residual error; generating a theoretical residual error based on the theoretical damaged state and the pure ideal state reference; calculating a manifold coupling confidence coefficient; the closed-loop correction module is used for performing inversion optimization on the industrial kinetic parameters; outputting a disease course prediction result according to the manifold coupling confidence coefficient; according to the method, the problem that a traditional medical model lacks physical consistency explanation is solved, and the credibility of artificial intelligence auxiliary diagnosis is remarkably improved.

The invention provides an action recognition method and system based on double branches, and belongs to the field of action recognition, and the method comprises the steps: S1, obtaining an action video, and carrying out the preprocessing of the action video, and obtaining a preprocessed picture; s2, using a 2D branch and a 3D branch to perform feature extraction on the preprocessed picture to obtain spatial features and spatial-temporal features; s3, performing feature fusion on the spatial features and the spatial-temporal features by using a channel fusion and attention mechanism module to obtain fused features; and S4, detecting the fusion features to obtain an action category prediction result of each spatial position. According to the method, the spatial features and the spatial-temporal features are effectively extracted by combining the 2D branch and the 3D branch, the accuracy and efficiency of action recognition are remarkably improved through feature fusion and an attention mechanism, spatial information in a static image can be captured, the temporal dynamic features in a video sequence can be effectively processed, and the accuracy and efficiency of action recognition are improved. Therefore, more accurate action recognition can be realized in various complex scenes.

The invention discloses a high-fidelity dynamic scene video generation method based on a single static image, and relates to the technical field of computer vision and video generation, and the method comprises the steps: obtaining key elements and potential dynamic information in an image through deep understanding and semantic deconstruction of a static scene; through dynamic representation and modeling, spatial-temporal feature decoupling and complex dynamic scene modeling are realized; according to the method, the dynamic complexity can be processed and the visual high fidelity can be ensured at the same time, the problems of dynamic incoherence, detail loss and the like when a video is generated from a single static image are solved, the method can be widely applied to the fields of film and television production, virtual reality and the like, and the method is suitable for popularization and application. According to the method, the rich details and the sense of reality of the original image can be reserved to the greatest extent while the complex dynamic state is generated, the space-time consistency is kept in the whole video sequence, the image quality is prevented from being sacrificed due to dynamic state generation, and the method has high use value.

The embodiment of the invention provides a virtual human video generation method and device, a computer program product and electronic equipment. The method comprises the following steps: identifying corresponding text content according to original audio, and determining skeleton posture graphs of multiple groups of human body actions; processing the text content and the skeleton posture graph through a large language model, and outputting split mirror planning information corresponding to the text content; the sub-mirror planning information comprises an overall style, scene description, figure description and sub-mirror attribute information of each sub-mirror; generating a scene dynamic graph based on the overall style and the scene description, and generating a character static graph based on the overall style and the character description; and according to the character static graph bound to each sub-mirror and the sub-mirror attribute information, generating a character motion graph of each sub-mirror, and combining the scene motion graph and the character motion graphs of the plurality of sub-mirrors to generate a virtual human video in the same scene. According to the technical scheme of the embodiment of the invention, the efficiency of generating the virtual human video can be improved.

The invention discloses a dynamic video generation method and system based on a diffusion model, and belongs to the technical field of image processing, and the method comprises the steps: obtaining a plurality of role pictures and video clips to construct a data set; constructing an image dynamic generation model based on the diffusion model; extracting a posture sequence image of a role in the data set, rendering a posture sequence to obtain a posture skeleton sequence image, and inputting the data set and the posture skeleton sequence image into an image dynamic generation model for training to obtain a trained image dynamic generation model; and obtaining a static image to be generated and inputting the static image into the trained image dynamic generation model to obtain a dynamic video corresponding to the static image. According to the method, the stability of the time dimension in the dynamic video generation process and the consistency of video textures are ensured through the improved diffusion model.

According to embodiments, a system for imaging a blood flow in a region of interest of a patient includes: a display configured to display an image; a processor configured to execute the instructions to: obtain ultrasound imaging data based on an imaging signal; select the region of interest in the ultrasound image data; determine at least one characteristic of a blood flow in the region of interest; generate an animation indicating the at least one characteristic of the blood flow in the region of interest, wherein the animation indicates the blood flow during a single time slot; and control the display to display a static image including the region of interest and the animation within the region of interest.

The invention relates to the technical field of magnetic resonance imaging, and discloses a motion estimation guided magnetic resonance four-dimensional dynamic imaging method and device, and the method comprises the steps: obtaining a three-dimensional single-frame static image of magnetic resonance and an under-acquired low-resolution four-dimensional dynamic image sequence, the low-resolution four-dimensional dynamic image sequence is a multi-frame low-resolution four-dimensional dynamic image obtained by carrying out undersampling on K space acquisition data; utilizing a motion encoder in the image sequence prediction model to extract motion estimation information in the low-resolution four-dimensional dynamic image sequence; inputting the three-dimensional single-frame static image into a structure encoder in the image sequence prediction model to obtain static anatomical structure information; and fusing and decoding the motion estimation information and the anatomical structure information through a decoder to obtain a high-resolution four-dimensional dynamic image sequence. The problems that an existing magnetic resonance four-dimensional imaging technology is long in scanning time, complex organ movement is difficult to capture, and time-space consistency is poor are solved.

The invention relates to the field of artificial intelligence, financial science and technology and digital medical treatment, and discloses an image-based video generation method and device, equipment and a storage medium. The method comprises the following steps: receiving and preprocessing an input static image to generate a multi-scale feature; based on the multi-scale features, determining an optimal space-time processing path through differentiable search of a space-time architecture generator, and generating output features fused with time sequence dynamic information; generating a video frame based on the time sequence recurrent neural network and the output features; and inputting the video frames into the video frame sequence, and generating a target video through the video frame sequence. According to the method, the optimal space-time processing path can be automatically determined through differential search, manual intervention is avoided, the time sequence coherence and detail authenticity of the video are ensured by dynamically generating the output characteristics of the fusion time sequence information and generating the video, the video generation quality and efficiency are improved, and the video quality is improved. The method is suitable for high-precision video generation such as video synthesis, content creation and virtual reality in the fields of finance and medical treatment.

A static-image augmented privacy display, mode-switchable privacy display system, and method provide a private image to a first view zone and a static image to a second view zone. The static-image augmented privacy display includes a privacy backlight configured to provide directional emitted light to the first view zone and an array of light valves configured to modulate the directional emitted light to provide a private image within the first view zone. The static-image augmented privacy display also includes a static display layer configured to provide a static image in a second view zone. The mode-switchable privacy display includes a broad-angle backlight configured to provide broad-angle emitted light to both a first view zone and a second view zone during a shared mode, a shared image being provided by modulation of the broad-angle emitted light using the light valve array.

The invention relates to a JPEG2000-based static image incremental coding and target area priority transmission method, which comprises the following steps of: S1, compressing a first frame image captured by a camera by adopting a standard JPEG2000 lossless coding mode to generate a complete initial image data packet, and directly carrying the complete initial image data packet back to a base in a copying mode; s2, calculating a difference image between a subsequent shot image frame and a previous frame, only performing JPEG2000 coding on a difference part to generate an incremental coding data packet, and transmitting the incremental coding data packet back to a base in a communication manner; and S3, the receiving end can perform image reconstruction according to the initial frame and the incremental data packet. And meanwhile, a key target area is endowed with higher JPEG2000 coding quality, and a progressive transmission function of JPEG2000 is adopted. According to the method, the high compression performance of JPEG2000 can be fully utilized, technologies such as incremental coding, target area priority coding and progressive transmission are combined, bandwidth resources needed by image remote transmission are greatly reduced, and the application requirement of high-definition image remote transmission in extreme environments such as the Antarctic pole is met.

The embodiment of the invention provides a widget display method and electronic equipment, and relates to the technical field of terminals. The method comprises the steps that a first trigger operation of a user is received, the first trigger operation is used for adding a first static widget, and the first static widget corresponds to a plurality of components; and rendering a plurality of components corresponding to the first static widget. A first static image can be generated and displayed according to the rendered components, and the first static image is used for displaying the first resource content corresponding to the first static widget. One static image is used for replacing a plurality of rendered components, so that on the basis of ensuring normal display of one static widget, continuous operation and refreshing of a plurality of components corresponding to the static widget can be avoided, the memory occupation of the widget is reduced, and the power consumption of the electronic equipment is reduced.

The invention relates to a two-stage road traffic abnormal event identification method and system based on a visual large model, and the method comprises the steps: collecting the monitoring image and video data of an expressway and an urban expressway, and building a static image semantic data set and a dynamic video traffic semantic data set; utilizing the static image semantic data set to train a visual large model to obtain a first visual large model; constructing a same-preference data pair, and performing direct preference optimization training of the first visual large model by using the same-preference data pair to obtain a second visual large model; intercepting an abnormal video key frame based on the dynamic video traffic semantic data set, and performing parameter fine tuning on the second visual large model based on the abnormal video key frame to obtain a road traffic abnormal event recognition model; and collecting a monitoring image or video sequence in real time, and performing abnormal event identification by using the road traffic abnormal event identification model. Compared with the prior art, the traffic abnormal event identification method provided by the invention can effectively combine dynamic and static characteristics of data and is efficient.

The invention relates to the technical field of image generation, in particular to a content generation method and device based on a user image and a medium, and the method comprises the steps: collecting a user face image and environment features, and extracting face key points, hair style features, expression intensity parameters and posture features of a user in the face image; generating an image interaction state vector in combination with the environment features; presenting a stimulus image sequence containing different tones and colors to the user, collecting facial micro-expressions of the user, establishing a facial expression response curve, and further generating a color physiological response curve; extracting specified characteristic parameters based on the color physiological response curve, and constructing a visual image personality vector in combination with the image interaction state vector; and inputting the personality vector of the visual image into an image style parameter mapping network to obtain an image style control parameter set, processing the three-dimensional reconstruction model of the user face by an image generation and rendering module according to the image style control parameter set, and outputting a static image or a dynamic image.

Approaches presented herein may be used to generate animated images using two-dimensional (2D) static images. An input image may be used to generate a first image corresponding to a foreground of the input image and at least a second image corresponding to a background of the input image. The second image may include an inpainted region based on a mask generated from the first image. The first and second images may be provided with configuration settings for rendering on a client device.

The invention relates to the technical field of power transmission lines, in particular to a power transmission line risk prediction method based on multi-modal fusion, which comprises the following steps of: acquiring a static image and a dynamic video of a power transmission line, extracting geographic position information embedded in file names of the image and the video, and comparing the extracted geographic position information with geographic coordinates in the geographic information, and matching the image and the video to the corresponding geographic coordinates. According to the invention, by integrating the static image and dynamic video data of the power transmission line, the analysis and risk prediction of the environmental characteristics of the power transmission line are realized. And meanwhile, geographic position information extraction and image data fusion are introduced, so that the accuracy of risk area positioning and the reliability of prediction are improved. Potential threats of environmental changes to the line are monitored in real time by analyzing relative positions and morphological parameters of the power transmission line and surrounding objects. The timeliness of fault prediction is improved, and the prediction capability of the future risk trend is enhanced.

The invention belongs to the technical field of image processing, and provides a facial paralysis grading method, system and equipment fused with multi-modal data and a medium in order to solve the problem that existing facial paralysis grading is inaccurate. Carrying out joint modeling on the handmade facial paralysis features based on prior knowledge and the depth visual features containing spatio-temporal information; extracting multi-scale features based on static image features and key point features extracted from a single-frame face image of a target individual, gradually transmitting small-scale features representing local asymmetry to medium-scale features and large-scale features, and adaptively performing feature fusion through dynamic weight distribution to obtain static symmetric features; and a bidirectional information interaction channel between the dynamic facial features and the static symmetric features is constructed, so that the generated fusion features simultaneously contain complete pathological information of spatial structure asymmetry and motion abnormality, and diagnosis grading is more accurate.

The invention relates to the technical field related to data processing, in particular to an intelligent face recognition identity verification system and method for an examination environment, and the system comprises the steps that a face recognition device collects face data of examinees, and establishes an image set after preprocessing; the authentication database calls examinee information, performs feature extraction and comparison on the preprocessed image, and generates authentication feedback; the technical problem that a single static image comparison mode is poor in adaptability in a complex illumination environment and a dynamic face scene and cannot adapt to complex environments and dynamic changes is solved, face data of the examinees are collected through a high-precision linkage camera, and the face data of the examinees can be accurately compared with the face data of the examinees. And in combination with illumination compensation and a dynamic feature extraction mechanism, the method is suitable for a large-scale examination environment with high security requirements, and the security and reliability of identity verification are enhanced.