67 results about "Static image" patented technology

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.

A display panel and its display method and device are disclosed. The display panel includes a plurality of pixel units (P), each pixel unit (P) including a plurality of sub-pixels (P1, P2, P3, P4). Each sub-pixel (P1, P2, P3, P4) includes a pixel driving circuit, a detection compensation circuit, and a driven element. The display panel further includes a detection unit and a compensator. The pixel driving circuit is configured to drive the driven element during an effective display time. The detection compensation circuit is configured to detect the electrical characteristics of the driven element during a blank time. The detection unit is configured to detect whether the currently displayed image is a static image. When it is a static image, it sends a first notification to the compensator. When it is a non-static image, it sends a second notification to the compensator. The compensator is configured to, upon receiving the first notification, compensate the currently displayed image based on the detection result of the detection compensation circuit during the blank time of the currently displayed image. Upon receiving the second notification, it does not compensate the currently displayed image based on the detection result of the detection compensation circuit during the blank time.

The application provides a kind of sorting line visual supervision method, device and electronic equipment. Among them, the method comprises: listening to the running state of sorting line;When the running state is the unstart state, static image is displayed on the visual interface;When the running state is the start state, static image is replaced by dynamic image on the visual interface;The model of package is displayed on the visual interface;The position of package on the conveying belt is determined based on the data of sensor set by sorting line, and the model of package is adjusted based on the position of package on the conveying belt.In this way, an intuitive monitoring means can be provided, which simulates the running condition of the sorting line and the real-time position of the package based on the running data of the sorting line and the data of the sensor set by the sorting line, so that the operator can quickly understand the overall operation of the sorting system.

This invention relates to the field of defect detection technology in image recognition, specifically disclosing a method and system for detecting defects in mobile phone SIM card tray manufacturing based on image recognition. By acquiring multiple frames of time-series images and performing sub-pixel-level spatiotemporal registration, the influence of mechanical vibration and displacement deviation is eliminated. The optical flow field is calculated, and local strain tensors, optical flow vorticity, and motion consistency entropy are extracted to construct a time-varying feature matrix. Frequency domain decomposition and motion amplification are performed on the feature matrix to enhance the signal response of minute defects. Dynamic time-domain features and static image features are fused to establish a deep learning-based defect detection model. Finally, a defect probability cloud map is generated, and the defect type and location are output using a dynamic threshold mechanism. This invention achieves high-precision and high-efficiency automatic detection of minute defects in mobile phone SIM card trays, exhibiting good stability and adaptability.

This application relates to a method for generating simulated images. The method includes: acquiring a reference video stream; performing image recognition on each frame of the reference video stream to obtain a first data stream and a second data stream; acquiring image data of static image regions in multiple frames of reference images within the time range of a target timestamp from the first data stream, and rendering a target background image based on the image data of the static image regions in the multiple frames of reference images and the target viewpoint; acquiring target position information of a moving object corresponding to the target timestamp from the second data stream, and generating a simulated image corresponding to the target timestamp from the target viewpoint based on the target position information and the target background image. This method for generating simulated images can quickly and accurately generate simulated images in different scenarios, improving the generalization ability of simulated image generation.

The application provides a warehouse container appearance defect detection and stability analysis method and system based on AI vision and a medium. The method comprises the following steps: collecting static images and dynamic running images of a warehouse container under different illumination conditions to obtain an image set, preprocessing the image set, performing positioning analysis on the preprocessed images, and dividing the warehouse container into static targets and dynamic running targets; performing appearance defect detection on the static targets to obtain defect data; analyzing the dynamic running targets and the static targets, evaluating the stability of the dynamic running targets, and evaluating the stability of the static targets to obtain stability evaluation results; and transmitting the defect data and the stability evaluation results to a terminal in real time. Through the cooperative work of image acquisition, target detection, defect identification, and stability analysis, multiple key quality inspection tasks can be completed synchronously, and comprehensive and systematic monitoring of the safety state of the warehouse container is realized.

This invention relates to the field of display control technology, specifically a method for reducing the power consumption of LED televisions. The method acquires illuminance using an ambient light sensor with integrated infrared suppression function, and generates effective ambient light feature values ​​through time-delay nonlinear hysteresis filtering; calculates the average image level and brightness histogram of video frames to generate image content features; calculates first and second backlight reference values ​​respectively, and uses a cross-state weight allocation strategy to dynamically map the target backlight adjustment value. Simultaneously, it extracts video motion vector information to bypass redundant calculations of static images; employs a nonlinear cosine gradient mathematical function to achieve a smooth transition of the physical backlight, and simultaneously uses a weighted piecewise linear enhancement curve for pixel-level visual lossless color compensation. This invention achieves a multi-dimensional dynamic balance between image quality protection, extreme energy saving, and computational optimization in complex scenarios.

This invention discloses a method for predicting the outcome of in vitro fertilization-embryo transfer (IVF-ET) based on static embryo images, used to distinguish between biochemical pregnancy and clinical pregnancy. It belongs to the field of medical artificial intelligence and assisted reproductive technology and is applicable to single embryo transfer (SET) and dual embryo transfer (DET) scenarios. The method includes: acquiring a static image of at least one embryo to be transferred; preprocessing and standardizing the embryo image, and then inputting it into a feature extraction model based on a convolutional neural network. The model uses DenseNet-169 as the backbone network and combines it with a Feature Pyramid Network (FPN) for multi-scale feature fusion. When the input is a dual embryo image, a Trophectoderm–Inner Cell Attention (TEIC-Attn) attention module is introduced to adaptively weight and fuse the features of the two embryos to characterize the contribution of different embryos to the final transfer outcome. When the input is a single embryo image, the prediction is completed based on the corresponding embryo features. The model ultimately outputs a probability prediction result of whether the IVF treatment outcome is a biochemical pregnancy or a clinical pregnancy. Furthermore, this invention combines a gradient-based class activation mapping method to generate interpretable visual heatmaps, highlighting key morphological regions closely related to embryo transfer outcomes, such as the inner cell mass and trophectoderm, thereby providing clinicians and patients with intuitive and reliable decision-making support. This invention enables unified prediction and interpretation of single and double embryo transfer outcomes without relying on time-series data, improving the accuracy and interpretability of in-vitro fertilization treatment decisions and demonstrating promising clinical application prospects.

This invention discloses a satellite image compression method under extremely low bandwidth conditions. The steps are as follows: collect a static image dataset; establish a variable bitrate image encoding / decoding network; train the encoding / decoding network; embed a generative adversarial network (GAN) at the decoder and retrain it; embed a super-resolution network after the encoder / decoder and retrain it; the user selects a region of interest (ROI), and the selected region is compressed; the decoder restores the bitstream to an image and displays it; the unselected regions are compressed; when bandwidth is sufficient, the unselected regions are transmitted, and the decoder stitches the images together; super-resolution processing is performed. This image transmission method, under extremely low bandwidth constraints, provides a superior subjective reconstruction effect compared to traditional image coding methods such as JPEG and deep learning-based image coding methods in a large number of test images.

The application discloses a shale sedimentary structure automatic division method, and comprises the following steps: S1, FMI static image is cropped and subjected to gray scale processing; S2, a gray scale coexistence matrix is used to calculate FMI gray scale image texture parameters; S3, texture parameter sedimentary structure sensitivity analysis is performed; S4, parameter optimization is performed based on a random forest classification model; and S5, shale sedimentary structure automatic division is performed based on the random forest. The FMI image is fully utilized in the aspect of sedimentary structure division, the restriction of conventional logging on shale sedimentary structure division is broken, a more convenient and effective shale sedimentary structure division method is provided, and important technical support is provided for accurate shale facies division.

Some embodiments include an X-ray imaging device comprising: a sensor array configured to convert incident X-rays into electrical signals; a memory configured to store static configuration data; a processor; a programmable logic device; and a communication interface, wherein the processor is configured to apply the static configuration data to the programmable logic device; and, when configured by the static configuration data, the programmable logic device comprises a static image processing module and a reconfigurable image processing module, the static image processing module and the reconfigurable image processing module being part of an image processing pipeline configured to receive electrical signals and output images.

The application discloses a fine-grained multi-modal video behavior recognition method and system guided by motion saliency, and the method comprises the following steps: selecting the first K image blocks with the most motion saliency from all image blocks corresponding to the DRDI image of each time segment as dynamic image blocks; selecting the corresponding K image blocks from the RGB image as static image blocks; splicing the dynamic and static image blocks under each time segment, and inputting the spliced image blocks into a visual encoder for fine-grained multi-modal interactive learning to obtain the feature representation corresponding to the current time segment; processing the feature representations of all time segments to obtain a video global feature representation; extracting a text feature representation from a text description of a behavior category; calculating the similarity between the video global feature representation and the text feature representation, and determining a video behavior recognition result according to the similarity. The application can improve the accuracy of video behavior recognition.

The application provides an image reasoning method, device and equipment and a storage medium. The method comprises the following steps: inputting a user-inputted static image to be reasoned and a natural language prompt into a trained task routing module, identifying at least one task category corresponding to the natural language prompt, and activating a trained expert agent corresponding to each task category; inputting the static image to be reasoned and the natural language prompt into each trained expert agent for first reasoning, obtaining a shared semantic representation of each task category, and inputting the shared semantic representation into a collaborative memory pool for graph fusion to generate a consensus representation of the static image to be reasoned; and inputting the consensus representation of the static image to be reasoned and the natural language prompt into a trained target agent for second reasoning to obtain a final answer corresponding to each task category of the static image to be reasoned. The application avoids interference between tasks and improves the generality, reasoning efficiency, reliability and stability.

This invention belongs to the technical field of breast cancer molecular subtyping, and more specifically, relates to a method for identifying HER2 expression status in breast ultrasound videos based on spatiotemporal feature interaction. The method includes: using raw breast ultrasound videos as input data and performing multi-level data preprocessing operations; then performing data balancing on the training data to obtain a standardized training set; sequentially constructing a feature module, a multi-stage feature fusion module, and a classification module based on the UniFormerV2 network, and training the resulting model; and inputting the test set into the model to obtain the final prediction result of HER2 expression status. This invention solves the problems of existing HER2 detection methods being invasive and unable to dynamically monitor, and existing deep learning methods based on static images ignoring the spatiotemporal dynamic information in ultrasound videos, leading to insufficient accuracy in predicting HER2 expression status, especially the HER2-Low subtype.

This application relates to the field of computer vision technology and discloses a method and system for detecting pain expressions. The method includes: acquiring a facial video stream, segmenting muscle regions and extracting texture features after facial key point localization and inter-frame alignment; calculating motion energy based on these features, extracting enhanced micro-expression temporal segments and multi-scale spatiotemporal features, constructing a muscle dynamics model and completing state estimation; generating an adaptive candidate spatiotemporal window set through spatiotemporal attention weighted fusion; performing temporal modeling for dynamic feature fusion, and selecting the optimal window by fusing micro-expression features; and outputting the final pain level after double consistency verification and loop closure optimization. This application overcomes the limitations of static images, accurately captures facial dynamics and temporal changes, strengthens feature correlation, and significantly improves detection accuracy.

A system, method and non-transitory computer readable medium for generating validated remote sensing change images that includes a user input device for selecting high-resolution satellite images, and processing circuitry to generate a depth map and a semantic map from a static image. A change simulator determines candidate areas for change simulation and generates a change depth map and change mask focusing on objects removed from the static image. An image diffusion neural network applies a control network and stable diffusion to generate pre-change and post-change image tiles. Validation processing circuitry iterates through a validation process to validate the pair of change tiles to obtain a validated pair of change tiles and a validated change mask.

This invention relates to an intelligent tumor image classification method based on three-dimensional medical images, belonging to the interdisciplinary field of artificial intelligence-assisted medical image analysis and computer simulation technology. Addressing the problem that existing technologies heavily rely on physical elasticity measurement equipment when extracting tumor biomechanical properties, this method, based solely on static raw medical images, maps voxel grayscale to virtual tissue mass density and constructs a virtual extrapolation vector using the tumor surface mesh as a reference. Combining the local curvature of the mesh and the density spatial gradient, a three-dimensional virtual solid stress tensor field is generated through tensor analysis. This tensor is then transformed into a graph structure and input into a graph network to extract physical topological features, which are then used as query vectors input into a cross-attention mechanism to guide visual features to complete weight allocation and classification. This invention relies purely on computing power to inversely construct a virtual physical force field on static images, eliminating the limitations of peripheral hardware and significantly improving classification accuracy.

A video display method, a video display device, and a non-volatile storage medium. Considering that factors such as the brightness, display area, and display shape of a static image also affect the surface temperature of a panel, a video display method for effectively mitigating burn-in is provided. The video display method comprises: splitting a frame corresponding to an input video signal into multiple regions, detecting static image regions, calculating grayscale values of the static image regions, classifying adjacent regions of each grayscale into a same group, changing the weighting of coefficients on the basis of the area and / or shape of the grouped regions, and on the basis of the weighted coefficients, controlling the level of a video signal displayed as a static image.

The application discloses a kind of lightweight flag flying video generation method and system, method includes: to single static image is analyzed and target area segmentation, obtains target area binary mask;Confirm the optical flow vector corresponding to the pixel in source frame, generate target frame;Based on the image morphing driven by optical flow, obtain the motion candidate field that meets target motion law;The control signal mapping weight of user output is carried out to the weighting of motion candidate field, constructs controlled motion field;Based on controlled motion field, source frame, target area binary mask, intermediate target frame, image morphing and fusion are carried out to target area, generate continuous frame and output as target video.The application generates time sequence consistent, controllable target motion video from single static picture, to meet the application needs of space science popularization display, interactive teaching and virtual simulation.

Computing platforms and methods for dynamic presentation of graphical and video content are disclosed. Exemplary implementations may: provide, in a static browsing state, a static image associated with underlying video content; in response to an extended focus on the static image for a first threshold amount of time, expand a visual tile to a widescreen aspect ratio while remaining in the static browsing state; after a second threshold amount of time, initiate a dwell browsing state in which dwell tile content is provided in the expanded visual tile, while other content remains in the static browsing state; in response to a determination that the playback device supports a dwell video mode, play a video asset within the expanded visual tile; and in response to a determination that the playback device does not support the dwell video mode, provide an alternate non-video visual asset within the expanded visual tile.

This invention relates to the field of cell image analysis and biological cell screening technology, and discloses a method for screening liver stem cells. The method includes: acquiring original images; performing effective image screening; performing background correction, noise reduction and enhancement, and contrast compensation; performing single-cell instance segmentation; performing single-cell feature extraction and trajectory association; determining target cells using a neural network model; and outputting the liver stem cell determination result. Compared to existing screening methods that rely on manual observation or single-frame static image discrimination, especially in the case of primary hepatocyte suspensions containing mature hepatocytes, bile duct epithelial cells, stromal cells, and cell debris, this method addresses the technical problem of unstable and automated identification of liver stem cells. This application improves the accuracy and automation of liver stem cell screening by constructing a continuous processing flow of image quality gating, instance segmentation, and dual-branch feature fusion judgment.

This invention provides an image classification method and system based on the VIT model. Based on real-time shooting information from a monitoring terminal, the method preprocesses the dynamic image stream generated by the monitoring terminal to obtain an effective dynamic image stream of the target area. This effective dynamic image stream is then framed into a static image sequence, and the original dynamic image stream is used for image restoration. Based on the VIT model, all static images within the static image sequence are identified to obtain the individual image features of each static image. This divides all static images into several static image sets, ensuring that all static images within each set have high similarity and correlation on the global screen. Based on the image data requirements of the client's task, at least a portion of the static image sets are selected and sent to the client. This fully utilizes the VIT model to perform refined identification and classification of several images generated in spatial and temporal proximity, improving the scalability and usability of the image data.

This invention belongs to the field of image visual recognition technology and provides a neural network-based method for detecting defects in textile fabrics. The method includes performing neural network detection and analysis based on fabric visual state data to obtain a defect judgment confidence value and generate an initial defect judgment result. Based on this, the defect judgment confidence threshold is corrected according to the fabric's environmental coupling state type to obtain an updated corrected defect judgment result. Finally, based on the initial and corrected defect judgment results, a final defect judgment result is obtained to characterize whether a defect exists in the target detection area. The neural network-based textile fabric defect detection method provided by this invention avoids the problem of environmental artifacts interfering with the recognition results caused by judging solely based on static image features, reduces the false alarm probability caused by complex working conditions such as vibration and temperature differences, and improves the stability and reliability of fabric defect detection results under complex environmental conditions.

This invention discloses a method and system for monitoring the progress of power engineering based on image recognition, relating to the technical field of image recognition. It classifies the construction status of key power equipment in image data using image recognition technology to obtain a preliminary identification list. A project construction network is constructed based on the identification list and the target equipment installation plan. Dynamic weight matrices for different categories of key power equipment are calculated based on the project construction network. A systematic error feature library is identified based on the construction benchmark confusion matrix. The preliminary identification list is summarized to construct an original identification matrix. The data in the original identification matrix is ​​adjusted based on the systematic error feature library to obtain a deviation correction matrix. This application systematically integrates the originally isolated and static image recognition technology into dynamic, logic-driven project management science, solving the fundamental problems of traditional methods' inability to self-correct and distinguish between primary and secondary contradictions.

The application discloses an X-ray flaw detection intelligent detection method, which comprises the following steps: using an X-ray detection device to perform real-time dynamic detection on a steel pipe; a computer receives a dynamic image in real time, and sets a window position and a window width of the dynamic image; the detected dynamic image is saved in real time; an intelligent defect recognition model is established based on an artificial intelligence deep learning method; real-time dynamic image reasoning is performed through the dynamic recognition model, an alarm is given after a defect is detected, and a stop signal is sent to stop the steel pipe from moving; static image reasoning is performed on the static steel pipe through a static recognition model, a defect image is recognized and uploaded, and dynamic detection of the steel pipe is continued; after the detection of the whole steel pipe is completed, the detection work is stopped and the steel pipe is exited; and the beneficial effects are that a complete set of X-ray flaw detection solutions are provided by aiming at a weld X-ray flaw detection image, and the actual problems of users on site are well solved.

The application provides a dynamic anterior segment image processing method, device and equipment and a storage medium, and belongs to the technical field of image processing. The method comprises the following steps: acquiring anterior segment image data of a target eye; the anterior segment image data comprises a static image in a dark room state and a dynamic image sequence under light stimulation conditions; extracting an anterior segment structure boundary based on the static image, and extracting a spatial dynamic change amount of the anterior segment structure based on the dynamic image sequence; and performing quantitative calculation of a preset physiological index based on the anterior segment structure boundary and the spatial dynamic change amount to obtain quantized anterior segment dynamic parameters. The technical scheme can realize quantitative and standardized evaluation of changes in the iris and the anterior chamber structure, and provides a unified reference basis for diagnosis, classification and efficacy research of anterior segment-related diseases.

The application discloses an orange sphaeranthine raw material microscopic image recognition method, relates to the technical field of intelligent microscopic detection, and comprises the following steps: S1, extracting orange sphaeranthine raw material microscopic basic features; S2, using microscopic basic features for parameter mapping; S3, using a microscopic structure parameter set for microscopic partition recognition; S4, comparing impurity structures according to a target region set; and S5, using a pure microscopic region data set for texture consistency determination. Through the setting of an abnormal disturbance recognition mechanism based on an electromagnetic reference benchmark, the system can recognize potential impurity regions from dynamic texture changes of the microscopic image, and realizes the transformation from static image recognition to dynamic microscopic disturbance feature recognition. The method not only significantly improves the recognition sensitivity of abnormal regions in the orange sphaeranthine raw material microscopic image, but also improves the recognition stability under complex illumination and texture interference conditions, thereby improving the accuracy of the overall microscopic analysis.