347 results about "Static image" patented technology

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 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.

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.

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 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 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 invention relates to the technical field of computer vision, and discloses a dynamic four-dimensional content generation method and device based on a single image, equipment and a medium, and the method comprises the steps: generating a corresponding multi-view image set based on an input single static image; constructing a static three-dimensional scene representation model based on the multi-view image set; converting the static three-dimensional scene representation model into a dynamic four-dimensional scene representation model; performing time consistency optimization processing on the dynamic four-dimensional scene representation model to generate a dynamic frame sequence coherent in time dimension; and background illumination controllable editing processing is carried out on the dynamic frame sequence after time consistency optimization processing, and final editable dynamic four-dimensional content is generated. According to the method and the device, the three-dimensional model is constructed by utilizing the multi-view image set and is further converted into the dynamic four-dimensional model, so that the problems of insufficient multi-view image continuity and unstable dynamic details in time dimension in the prior art are solved, and the reality sense of dynamic contents and the user experience are improved.

The invention relates to the technical field of image target detection, and discloses an inland ship intelligent inspection method based on deep learning, and the method comprises the steps: collecting video image frame data containing a ship target in an inland waterway region, and constructing a ship detection data set and a ship compliance detection data set; on the basis of the YOLOv5s, a shallow high-resolution characteristic path is introduced, and an improved YOLOv5s model is constructed; respectively training a ship detection model and a compliance detection model based on the improved YOLOv5s by utilizing the ship detection data set and the ship compliance detection data set, and storing optimal model parameters; the ship detection model processes a real-time video, outputs ship position information and transmits the ship position information to a ByteTrack tracking algorithm, when the PTZ PTZ camera is regulated and controlled to meet a preset condition, the camera is controlled to shoot a static image and put the static image into the compliance detection model for detection, and a detection result is output; according to the method, the detection capability and the detection efficiency of small targets in ship compliance detection can be improved.

The invention discloses a physical examination suggestion intelligent adaptation and recommendation system based on a personalized health portrait. The method aims to solve the problems of static portraits, general suggestions, lack of continuous tracking, insufficient safety and the like in existing health management services. Through real-time acquisition and fusion of multi-source heterogeneous data, the incremental learning algorithm is constructed and adopted to dynamically update the health portrait of the user, and the timeliness of the portrait is ensured. Based on the dynamic portrait, the system carries out deep traceability and quantitative attribution on abnormal indexes through a multi-path reasoning decision tree, and carries out comprehensive health risk assessment. In a recommendation stage, the system comprehensively considers a user portrait, a risk assessment result, cost and feasibility preference, and performs strict medical suggestion conflict resolution by using a medical knowledge graph based on an OWL ontology and an SWRL rule, so that a highly personalized, safe and feasible physical examination suggestion and health management scheme is generated.

The invention discloses an equipment operation behavior identification method and system based on a relationship between a posture and a spatial position, and is used for solving the technical problem that the current equipment operation behavior identification method ignores the influence of environment context information and excessively depends on the precision of a posture detector, so that the identification precision is poor. The method comprises the following steps: acquiring equipment non-operation and operation video clips, and preprocessing to output equipment non-operation static images and equipment operation image frames; then detecting the device by using a YOLOv11 target detection model, and outputting a non-operation detection result of the device; generating a plurality of target human hand key point coordinates on the basis of an mmpose attitude estimation model and a regression function; determining a hand posture feature vector; and finally, a double-layer heterogeneous model stacking integrated structure is adopted, the hand posture feature vector and an equipment position feature vector in an equipment non-operation detection result are fused for recognition, and an equipment operation behavior recognition result is output.

The invention belongs to the technical field of quality detection, and particularly relates to an axle assembly quality detection method based on machine vision. The method comprises the following steps: firstly, fixing an axle to collect a static graph, and after adaptive filtering and edge sharpening processing, extracting static parameters by using improved YOLOv8 of a series gap feature attention module and comparing the static parameters; detecting the pre-tightening force of the fastener after the pre-tightening force is qualified, simulating working conditions to collect a dynamic graph and vibration data, performing motion fuzzy compensation on the dynamic graph and performing Kalman filtering denoising on the vibration data, and then improving YOLOv8 to extract and compare dynamic parameters; and if the detection result is completely qualified, the axle assembly quality is determined to be qualified. All working conditions are covered, the detection precision and reliability are improved, and the missed detection rate and the false detection rate are reduced.

The invention relates to the technical field of medical imaging, in particular to a pelvic stability dynamic detection method and stress analysis system based on dynamic DR. The method comprises the steps that on the basis of sign information of a user, parameters of a three-dimensional infrared motion capture system, a force measuring table and a pressure sensor are adapted to obtain static CT or MRI image data of a pelvis of a patient, and then the pelvic stability dynamic detection method and stress analysis system are obtained; establishing a pelvis coordinate system and defining geometric features of key points; collecting to-be-detected data of a user in the load bearing state; preprocessing the collected user data, and reconstructing a three-dimensional pelvis model of the user; performing biomechanical analysis based on the reconstructed three-dimensional pelvis model of the user to generate a pelvis stability index; and according to the pelvic stability index, automatically generating a pelvic stability risk index, and outputting a clinical decision suggestion. The problems that a traditional pelvis detection method still depends on a static image, dynamic stress distribution and stability evaluation cannot be effectively quantified, and an evaluation result is not accurate enough are solved.

The invention relates to a virtual reality and reality image fusion navigation method and system for temporal bone surgery, and the method comprises the steps: obtaining medical image data of a temporal bone region, and carrying out the three-dimensional reconstruction, thereby obtaining a temporal bone dynamic virtual model; optical tracking data and instrument space pose data are obtained, dynamic registration mapping is conducted on the temporal bone dynamic virtual model, and a real-time space fusion relation is obtained; performing navigation view construction on the instrument space pose data and the real-time space fusion relationship to obtain an augmented reality navigation view; performing structural distance analysis on the augmented reality navigation view to obtain dynamic early warning parameters; and performing display regulation and control according to the augmented reality navigation view and the dynamic early warning parameters to obtain a real-time navigation strategy. The problem of traditional static image registration lag can be effectively solved, and it is ensured that the virtual model and the actual anatomical structure are always kept in high-precision matching.

The invention relates to the technical field of child emotion recognition, in particular to an emotion calculation method and device for child expressions, and the method comprises the steps: collecting and marking the multi-modal dynamic expression data of a plurality of Asian children, and constructing an enhanced mixed expression database according to the multi-modal dynamic expression data of the Asian children; performing iterative training on a pre-constructed deep convolutional neural network model by using the enhanced mixed expression database until a preset iterative period is reached, so as to obtain a deep time sequence emotion calculation model; and inputting the multi-modal dynamic expression of the child to be recognized into the depth time sequence emotion calculation model to calculate the current emotion of the child. Therefore, the problems that an existing child emotion recognition model is mostly based on European and American adult database training and mostly adopts single-frame static image analysis, and the complete dynamic process of expressions cannot be captured, so that the accuracy is remarkably reduced, the model robustness is poor, and effective application in real and continuous interaction scenes is difficult are solved.

Systems and methods for generating and providing multiple, independent 3D objects in an augmented reality (AR) environment are described. One aspect of some embodiments relate to an augmented reality technology activation platform that enables the creation of interactive message 3D objects (e.g., static images, animations, product information and offers, etc.) and product 3D objects (e.g., product images, descriptions, etc.). These two types of 3D objects can be displayed at a user device so that customized, interactive messages associated with the product are displayed with the product, where the interactive messages are rendered (e.g., as a 3D object) along with the product 3D object in the same scene (e.g., as opposed to being layered on top of the 3D environment or augmented reality image).

The present application relates to the technical field of image recognition, and particularly relates to a living mite detection method, device and equipment and a storage medium, through a large field of view technology, static images of mites at different time points are captured for mite samples to obtain a continuous mite image sequence; a multi-target tracking model is used to accurately identify the specific position of each frame of mite in the image, the moving path, speed and behavior pattern of the mite can be observed, and the identification precision of the activity of the mite is improved; the gradient direction histogram of a local area of the image is calculated to describe the texture and shape features of the image, the main components and edge information in the image are extracted, the system can more deeply understand the internal features of the mite image, and the similarity of the mite image can be evaluated; by comparing the similarity between different frames, the system can determine whether the mites are the same individual or whether there is a significant morphological change, and whether the mites are living bodies is determined according to the similarity result.

This invention discloses a high-precision phase recovery method for tooth surface fringe images during gear rotation. The method first establishes an N-step static phase-shift image dataset of the gear at subdivided rotational positions. When constructing network training data pairs, three image combinations with different position indices and phases satisfying the standard three-step phase shift are selected to generate an input sequence simulating unidirectional rotation and motion errors. The high-precision sine and cosine terms calculated using the N-step static images are used as labels. Secondly, an encoder-decoder deep neural network with a bottleneck layer dual attention mechanism and global residual connections is constructed and trained using an adaptive loss function. Finally, the dynamic image to be tested is input into the network to directly predict the sine and cosine terms, calculating the wrap-around phase without motion artifacts. This invention effectively suppresses motion errors caused by gear rotation, contributing to the high-precision reconstruction of the three-dimensional morphology of the tooth surface.

The invention discloses a shooting method and device. The shooting method comprises the steps that in response to first input, N times of shooting are carried out, N image data sets are obtained, each image data set comprises a first static image and a first video associated with the first static image, and N is a positive integer larger than 1; and in response to the second input, performing synthesis processing on the first video in the N image data sets to generate a second video, performing synthesis processing on the first static image in the N image data sets to generate a second static image, the second video comprising a content image layer and a dynamic special effect image layer superposed with the content image layer, the content image layer comprises N first videos arranged in a grid layout, the dynamic special effect image layer comprises dynamic elements in a grid special effect template, and the second static image comprises N first static images arranged in a grid layout; and associating the second video with the second static image to generate a dynamic photo of the grid layout.