463 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 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 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 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.

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 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.

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 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.

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 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 system comprises a base, a power distribution box and a three-dimensional holder are arranged on the base, an infrared thermal imaging module and a camera are installed at the mobile end of the three-dimensional holder, and the power distribution box is provided with a Bluetooth module, a controller and a driver. The infrared thermal imaging module and the camera are respectively connected with the input end of the controller through network cables, the output end of the controller is connected and communicated with the computer through a network cable, the controller is connected with the three-dimensional holder through the driver, and the controller is connected with the IMU sensor mounted on the hoist through the Bluetooth module; the infrared thermal imaging module and the camera are used for synchronously collecting infrared thermal imaging data and visible light image data of a hydraulic hoist area. The system is used for solving the problems that the omission ratio of manual inspection is high, single infrared detection is easily interfered by the environment, fixed monitoring has a blind area, and static image analysis is poor in vibration resistance in oil leakage detection.

The invention relates to the technical field of defect detection of image recognition, and particularly discloses a mobile phone card holder processing defect detection method and system based on image recognition, and the method comprises the steps: collecting multiple frames of time sequence images, and carrying out the sub-pixel-level space-time registration, thereby eliminating the influence of mechanical vibration and displacement deviation; calculating an optical flow field, extracting a local strain tensor, an optical flow vorticity and a motion consistency entropy, and constructing a time-varying feature matrix; carrying out frequency domain decomposition and motion amplification on the characteristic matrix, and enhancing micro defect signal response; the dynamic time domain features and the static image features are fused, and a defect detection model based on deep learning is established; and finally generating a defect probability cloud picture, and outputting defect types and positions in combination with a dynamic threshold mechanism, so that high-precision and high-efficiency automatic detection of the small defects of the mobile phone card tray is realized, and the method has good stability and adaptability.

The invention provides a pulse neural network tracking method and system based on an event camera. Acquiring multi-time-step event data and a low-resolution grayscale image output by an event camera, and combining the event data into a multi-channel event tensor according to a time sequence and polarity; dynamic event features in the event tensor and static image features in the grayscale image are extracted respectively, and feature fusion is carried out through channel splicing and convolution operation; the fusion features are input into a super-resolution decoder, up-sampling of the image is achieved through transposition convolution operation, and a high-resolution moving target image is generated; and inputting the high-resolution image into a pulse neural network feature extraction module based on LIF neurons, extracting features of a target template and a search area, and determining a target position through cross-correlation operation to realize tracking. According to the invention, the spatial resolution and target positioning precision of the image output by the event camera can be effectively improved, and robust target tracking in a complex dynamic environment is realized on a low-power-consumption edge device.

Systems and methods for converting two-dimensional (2D) static images to three-dimensional (3D) animated images are provided. Such a method includes: receiving, by a server device, one or more 2D static images, each 2D static image of the one or more 2D static images depicting a respective environment; generating a 3D mesh based on a 2D static image of the one or more 2D static images; determining a visual perspective trajectory along the 3D mesh, the visual perspective trajectory indicative of simulated movement within a 3D animated image at least partially along an axis associated with depth in the respective environment depicted by the 2D static image; and generating the 3D animated image based on the 3D mesh and the visual perspective trajectory such that the 3D animated image replicates the simulated movement.

The invention provides a stock bin material volume detection method and device based on dynamic prompt and fusion verification, and the method comprises the steps: collecting and correcting a stock bin image, and building the coordinate mapping of the image and a real space; performing quality evaluation on the image and filtering dynamic interference to obtain a static scene image; based on the static image, performing primary segmentation through a clustering algorithm to position a material slope, and adaptively generating a dynamic prompt point or calling a static prompt point template according to a positioning result; inputting the image and the prompt point into a segmentation model to obtain a fine segmentation mask; calculating a fine-grained volume by three-dimensional reconstruction based on the fine mask; when the primary segmentation succeeds, calculating a coarse-grained volume, carrying out cross validation on the coarse-grained volume and the fine-grained volume, and outputting a final volume or judging failure according to an error range; according to the invention, through a dynamic prompt and coarse and fine granularity fusion verification mechanism, the precision of material segmentation and the robustness under a complex working condition are significantly improved.

The invention discloses a paper tray production quality detection method and system based on visual processing, and belongs to the technical field of quality detection, and the method comprises the steps: collecting image and motion data, carrying out the alignment according to a time sequence, carrying out the risk classification, marking a high-risk region, and constructing a stress prediction distribution diagram. Calculating an angle residual error according to an existing image and a stress prediction distribution diagram, calculating the angle residual error diagram to obtain a regional average residual error value, taking the regional average residual error value as a weighting coefficient and a deviation distribution index to generate speed and angular speed regulation factors, and finally generating a mold execution instruction of the next period based on an adjustment strategy. According to the detection method, process modeling and structure perception are combined, the defects of a traditional paper support defect detection means based on a static image in the aspects of time hysteresis, structure invisibility, control inaccessibility and the like are effectively overcome, and the intelligent level and the detection coverage rate of paper support quality detection are remarkably improved.

The invention relates to a neural puncture dynamic obstacle avoidance navigation method and system based on multi-modal image fusion, and belongs to the technical field of medical image navigation and surgical operations. A unified space-time reference model is constructed by fusing preoperative high-resolution static images and intraoperative real-time dynamic ultrasonic data; a three-dimensional model of a key nerve and blood vessel structure is automatically segmented and reconstructed by using an artificial intelligence technology, an optimal puncture path can be dynamically calculated based on real-time image data, a collision risk in a needle inserting process can be monitored in real time, and visual navigation guidance of multi-sensory fusion is provided for an operator through an augmented reality technology; by recording and analyzing data of the whole operation process, a big data platform is used for carrying out continuous iterative optimization on a segmentation and obstacle avoidance algorithm, and a self-perfect intelligent closed loop is formed. The problems that in a traditional nerve puncture operation, experience of an operator is relied on, real-time dynamic obstacle avoidance cannot be achieved, and the system lacks learning ability are effectively solved, and the accuracy, safety and intelligent level of the operation are remarkably improved.

The invention provides an image fuzzy detection method based on frequency domain analysis and deep learning fusion. The method comprises the following steps: S1, frequency domain feature extraction and quantification; s2, spatial domain feature extraction and modeling; s3, carrying out multi-modal feature fusion; s4, joint optimization and post-treatment are carried out; s5, outputting and verifying; through complementarity design of frequency domain and deep learning, complex fuzzy detection requirements of static images and video streams are covered, high efficiency and reliability are verified in industrial quality inspection, video conferences and other scenes, energy attenuation characteristics caused by global blur are accurately captured through frequency domain analysis, motion blur and out-of-focus blur are effectively distinguished, and the method is suitable for large-scale popularization and application. According to the method, local texture degradation of deep learning network modeling, dynamic track abnormity analysis of an optical flow network and complex scenes covering static images and video streams are realized through a bidirectional feature fusion mechanism, the mAP of mixed fuzzy detection is effectively improved compared with a single-mode method, and dynamic fuzzy and static out-of-focus fuzzy are effectively distinguished.

The invention relates to a video memory power consumption optimization method and device, a storage medium and a program product. The method comprises the following steps: in response to a condition that a GPU enters a static image display state, downsampling an image frame of a first resolution rendered and generated by the GPU to a second resolution; writing the image frame with the second resolution into an on-chip cache of the GPU; controlling a display engine to read the image frame of the second resolution from the on-chip cache, upsampling the image frame of the second resolution to the first resolution and outputting the image frame of the second resolution to a display interface; and when the display engine reads the image frame from the on-chip cache, closing a data transmission path between a video memory and the display engine through a video memory controller, and reducing the working frequency of the video memory from a first frequency to a second frequency. The problem of power consumption waste caused by continuous high-frequency operation of the video memory is effectively solved, and the technical bottleneck that the frequency switching time window of the video memory is too short is broken through.

The invention relates to the field of remote quality control of kinesio, and provides a remote kinesio treatment guidance and quality control system. The image acquisition module is used for acquiring high-definition image data of a to-be-treated area through intelligent terminal equipment and marking anatomical feature points; wherein the high-definition image data comprises a static image and a dynamic muscle movement video stream; the AI analysis engine is used for carrying out feature extraction on the high-definition image data based on a muscle dynamics model of a to-be-treated region constructed by a convolutional neural network, identifying a muscle tension abnormal region and grading a nerve injury degree, and generating an initial sticking path recommendation scheme; the remote auditing interface transmits the recommended scheme generated by the AI to the doctor end interaction interface, obtains a target treatment scheme after the doctor end performs scheme correction based on the medical history data of the patient, and confirms a final treatment instruction through a digital signature; and the block chain quality control module is used for encrypting key operation data in the treatment process and then writing the key operation data into a distributed account book to generate a treatment process traceability chain which cannot be tampered.

A system and method for enhancing static photographs to simulate motion using machine learning models. The system comprises one or more processors and a non-transitory computer readable medium storing instructions that, when executed, cause the system to receive a static photograph, apply a machine learning model to generate a sequence of modified images creating an illusion of motion, and display the sequence to simulate a moving video. The machine learning model, trained on a dataset of static photographs and corresponding video sequences, extracts features using a convolutional neural network, processes the features with a recurrent neural network to generate motion vectors, and applies the vectors to create the modified images. The simulated motion may include tilting, vibrating, shaking, zooming, panning, and rotating. A user interface allows specifying the desired type or intensity of motion. The method enables creating video-like effects from static images, enhancing expressiveness and engagement of visual media.

The invention relates to the technical field of computers, and discloses an anthropomorphic sliding track generation method and system, and the method comprises the steps: obtaining video data containing human sliding operation, and carrying out the preprocessing, thereby obtaining a static image and scalar time; inputting the static image into an image encoder, inputting scalar time into a time encoder, respectively extracting space task and time constraint features, and fusing the space task and time constraint features into fusion condition features; the method comprises the following steps of: inputting a track point into a layered generator, outputting a current track point coordinate by a track generation head at each time step of the generator, and generating a track point sequence and a video frame sequence by a video generation head in combination with the coordinate, a fusion feature and a frame generated in a previous time step; and inputting the track and the video frame into a discriminator, and returning an authenticity result to adjust parameters of the generator after combined judgment by the discriminator until the output reaches the standard, thereby obtaining the anthropomorphic sliding track. According to the method, anthropomorphic similarity and sample diversity of track generation are improved, and a richer data set close to real human behaviors can be provided for downstream applications.

The present invention relates to the technical field of physiological state monitoring, and specifically provides a method for generating samples for non-contact heart rate monitoring, a heart rate monitoring method and a system. For the original samples, the overall brightness of the existing face image is dynamically adjusted through a randomly generated brightness waveform to simulate the light environment in which the driver's face brightens and darkens during actual driving, and the generated image is used as an augmented data set for training a monitoring model for detecting the driver's heart rate. After the model training is completed, the ROI region of the static image collected by the video acquisition module is located and partitioned through the positioning and partitioning module, and the static image is subjected to signal detection and processing through the signal quality analysis and processing module, and the obtained sample to be measured is input into the heart rate monitoring model to estimate the driver's heart rate value. The present invention effectively expands the training data set through fluctuating brightness, and greatly improves the anti-environmental light change ability of the model.