32782results about "Geometric image transformation" patented technology

A system and method are disclosed for generating hyperspectral images from multi-modal sensor data including RGB, LiDAR, thermal, and near-infrared inputs. Training data includes hyperspectral images and corresponding multi-modal measurements. Spectral band grouping is performed based on correlation coefficients. A multi-modal decomposition network with cross-modal attention mechanisms generate reconstructed hyperspectral images by fusing complementary sensor information. A fine-tuning network creates reconstructed RGB images. A comprehensive quality assurance system analyzes spectral consistency, cross-modal coherence, and fusion artifacts to generate quality metrics. Missing data compensation strategies handle corrupted sensor inputs using information from other modalities. The system includes temporal integration for video sequences and multi-resolution processing for different sensor resolutions. Quality metrics guide network weight adjustments to improve reconstruction accuracy while maintaining robustness to sensor failures and environmental variations.

Provided is a medical image segmentation method based on multi-scale feature fusion. The method includes acquiring medical image data of a same type, and sketching a region of interest (ROI); preprocessing sketched image data; constructing a multi-scale feature extraction module, a multi-scale feature fusion module, and an encoder-decoder deep learning network model; performing 5-fold cross-validation on the network model; and evaluating a medical image segmentation result output by the model with an evaluation index including a dice, an accuracy, a precision and a recall. This application extracts multi-scale features comprehensively from an encoder, a decoder, and connection between the encoder and the decoder in the network, and effectively learns multi-scale information in the image. The multi-scale features are fused by an interactive module, so this application relieves feature conformity caused by direct multi-scale fusion, thereby improving performance of the medical image segmentation.

The invention provides a circuit board welding fault identification method and system based on machine vision, and relates to the technical field of visual identification, and the method comprises the steps: obtaining an image of a circuit board welding region through employing a structured light projection system and a multi-camera imaging system; realizing accurate positioning of a defect region in the region of interest by utilizing three-dimensional shape information and through morphological operation and an edge detection algorithm; for the accurately positioned defect area, obtaining high-precision three-dimensional point cloud data of the defect area, extracting a multi-scale curvature feature descriptor, and fusing the multi-scale curvature feature descriptor with visual features to form a multi-feature descriptor; inputting the multi-feature descriptors into a defect classification and recognition module based on ensemble learning to obtain the types and severity of the welding defects; and according to a defect identification result, the three-dimensional space distribution and severity of the welding defects are visually displayed.

The invention discloses a high-precision image processing method and system based on illumination adaptive compensation, and relates to the technical field of computer vision and image processing, and the method comprises the steps: inputting an original image, and dividing the image into a high-frequency edge layer, an intermediate-frequency texture layer and a low-frequency illumination layer through a multi-scale residual network; acquiring illumination intensity, color temperature and scene categories in real time by using an ambient light sensor and a scene semantic segmentation model, and generating dynamic compensation parameters; carrying out dynamic range expansion on a low-frequency illumination layer based on a physical illumination model, and adjusting the weight of highlight suppression and dark area enhancement through a self-adaptive S-shaped exposure curve; a double-branch generative adversarial network is adopted, noise suppression and super-resolution reconstruction are carried out on the high-frequency layer, and texture detail enhancement is carried out on the intermediate-frequency layer; aligning the data of the depth camera and the infrared sensor with the visible light image through a cross-modal fusion module; and performing tone mapping on the fused image based on human visual characteristics, and outputting an enhanced image with a high dynamic range and reserved details.

The present invention provides a facial recognition system using deep learning methodologies while integrating privacy-preserving capabilities. This system employs convolutional neural networks (CNNs) to extract and classify facial features, ensuring high accuracy in recognition tasks. Moreover, the system addresses privacy concerns by incorporating techniques such as facial feature encryption and anonymization, thereby enhancing user privacy and data security. This invention is applicable across various domains, including security, surveillance, access control, and personalized services, where facial recognition is utilized while preserving individual privacy.

The present invention relates to the field of image processing. Disclosed are a single-frame image super-resolution (SR) method and apparatus based on a hybrid feature interaction transformer. The method comprises: acquiring a low-resolution image to be reconstructed; constructing a single-frame image SR model based on a hybrid feature interaction transformer and training same, so as to obtain a trained single-frame image SR model, wherein the single-frame image SR model comprises a shallow feature extraction unit, a deep feature extraction unit and an up-sampling reconstruction unit which are sequentially connected, and the deep feature extraction unit comprises P hybrid feature interaction transformer modules which are sequentially connected; and inputting the low-resolution image into the trained single-frame image SR model, extracting shallow features by means of the shallow feature extraction unit, inputting the shallow features into the deep feature extraction unit for extraction, so as to obtain deep features, and inputting the deep features into the up-sampling reconstruction unit for reconstruction to obtain a reconstructed high-resolution image. The problem of the reconstruction performance being affected due to an SR method for a transformer ignoring latent correlations between features from different dimensions is solved.

A Convolutional Neural network (CNNs) based system and method based on the same for image analysis including for computer aided detection / diagnosis is provided, enabling an efficient and accurate delineation of diseased volumes in images / medical images. More particularly, the system of the present invention is adapted to incorporate deformable convolution (DC) module based processor means, along the down- and up-sampling pathways of the underlying U-Net architectural framework of said system for effective understanding and attending to deformable geometry of unknown transformations, considering deformable convolution (DC), unlike the basic convolution is advantageously not constrained by predefined geometrical structures of the kernels. Said system of the present invention advantageously also permits weighted combination of image feature components along the encoder and decoder arms of the deformable convolution (DC) processor module based system architectural network due to introduction of Weight Generation (WG) modules in said system, such that dynamic assignment of importance to relevant spatial locations of the corresponding image feature maps could be given so as to also boost overall accuracy while ensembling to enable reduction in module analytic error by simultaneously maintaining the generalization in performance in respect of robustness to noise to attain accurate and faster demarcation of the ROI (region of interest). Added to the aforesaid, preferred integration of Focal Asymmetric Similarity (F AS) loss function analytic based processor module / means in said system, allowed effective handling of class imbalance for further improved performance.

The invention discloses a building digital twinning three-dimensional reconstruction method and system based on a large model, and relates to the technical field of building digital twinning and three-dimensional modeling fusion, and the method comprises the steps: laying multi-source equipment in a building, collecting building multi-angle data, and carrying out type distinguishing and structured preprocessing. An adaptive deep network model is constructed and trained to identify building features, and the robustness of the model is improved by adopting a multi-type enhancement strategy. Point cloud splicing and coordinate unification are realized by using a fusion algorithm, and twin model updating and synchronization are realized based on a multi-dimensional threshold. According to the method, high-quality input is established for a depth model; building structure feature recognition has the advantages of robustness and precision; and the twinborn model has dynamic updatable capability. The three parts form a complete closed loop, the technical bottlenecks of a traditional reconstruction method in precision, stability and renewability are finally broken through, and a new building digital twinning three-dimensional modeling path facing a complex scene is achieved.

Some examples of the disclosure are directed to systems and methods for changing a level of zoom of displayed content. In some examples, an electronic device displays visual content. In some examples, in response to detecting a change in position and / or orientation of the user of the electronic device, in accordance with a determination that the position and / or orientation of the user satisfies the one or more criteria, the electronic device increases the size of the content and / or zooms in on the content.

The invention discloses a stainless steel surface defect detection method based on machine vision, and relates to the field of material defect detection.The stainless steel surface defect detection method comprises the steps that stainless steel surface images are collected, the collected multispectral surface images are subjected to image fusion, and a multispectral composite image is generated; performing detail enhancement on the low-resolution image through a super-resolution reconstruction algorithm to obtain a high-resolution image set; positioning a defect area position from the high-resolution image set through a defect detection algorithm, and obtaining a defect position data set; extracting a historical image defect feature set and a real-time image defect feature set from the defect position data set, and constructing a defect classification model according to the historical image defect feature set; inputting the real-time image defect feature set into a defect classification model, and performing defect classification and evaluation to obtain a defect classification data set; and generating and distributing a defect detection report. By combining the multispectral imaging algorithm, the super-resolution reconstruction algorithm and the defect classification model, the automation, precision and efficiency of stainless steel surface defect detection are improved.

An example operation may include one or more of generating a unique image based on execution of an image generating model and displaying the unique image via a user interface of a software application, assigning a segment of the unique image to a goal at a first point in time based on user inputs via a user interface of the software application, monitoring account activity of a user within the software application, determining, at a second point in time, that the user is closer to the goal based on the monitored account activity of the user within the software application, and in response to the determination, increasing a size of the segment of the unique image assigned to the goal of the user.

The present application discloses a face image restoration method based on a state space model, which includes inputting the to-be-restored face image to a restoration model to obtain a restored face image; the restoration model includes an encoder and a decoder; the encoder sequentially includes a first image fusion module, a first multi-scale state space module, a second image fusion module, a second multi-scale state space module, a third image fusion module, and a third multi-scale state space module; the decoder sequentially includes a fourth multi-scale state space module, a first multi-scale attention fusion module, a fifth multi-scale state space module, a second multi-scale attention fusion module, a sixth multi-scale state space module, and a third multi-scale attention fusion module. This method ensures the consistency of face semantic information while restoring the detailed texture, especially low quality face images in real degraded scenes can achieve better restoration results.

The invention discloses a historical block scene three-dimensional reconstruction method and system based on Gaussian sputtering, and the method comprises the steps: collecting a historical block video sequence through a lightweight panorama camera, extracting a multi-frame panorama, and generating an image data set through a projection converter; monocular depth and normal estimation is carried out through a pre-training visual model, and a prior depth and normal graph data set of a historical block scene is constructed; sparse reconstruction is carried out on the multi-view image data set based on the SfM technology, initial point cloud and camera pose information are acquired, and a Gaussian ellipsoid is optimized in combination with prior depth and normal information; dynamically calculating the geometric width estimation value of the street, and guiding and adjusting the adaptive density of the Gaussian ellipsoids of the vertical surfaces on the two sides; rendering the optimized Gaussian ellipsoid through an improved rasterization renderer; and designing a multi-modal loss function and a regularization mechanism to optimize reconstruction and rendering results. According to the method, high-fidelity three-dimensional reconstruction of the historical block scene is realized, and the adaptability of the Gaussian sputtering method to the complex block scene is enhanced.

The invention discloses a video super-resolution reconstruction method and system, and relates to the technical field of video restoration processing, and the method comprises the steps: collecting a to-be-reconstructed video frame sequence and a reference frame sequence; extracting a dense optical flow field between the reference frame and the target frame; performing geometric alignment on the target frame to the reference frame according to the dense optical flow field to obtain a motion consistency feature; performing feature extraction on the video frame sequence through a parameterized residual scaling module; inputting the extracted features into a bidirectional propagation module to process forward and backward image sequences respectively, extracting time sequence forward features and time sequence reverse features, and fusing the features to obtain detail features; carrying out adaptive fusion on the motion consistency features and the detail features, and sending the fused features into a reconstruction network to obtain deep visual features; pixel-level adaptive reconstruction is realized according to the deep visual features, and a super-resolution video sequence is obtained; according to the method, the feature space consistency is kept, and meanwhile, the feature matching precision in a complex motion scene is remarkably improved.

The invention relates to the technical field of image processing, and particularly discloses a remote sensing image super-resolution reconstruction method and system based on a prior diffusion model, and the method comprises the steps: carrying out the shallow feature information extraction of a low-resolution image, and obtaining a shallow feature map; the method comprises the following steps: performing multi-scale down-sampling and convolution fusion processing on a shallow feature map and a high-resolution image, and performing high-frequency information extraction and adaptive high-frequency enhancement processing on the processed feature map to obtain condition information; according to the method, deep mining of high-frequency information is carried out on a low-resolution image input as condition information, so that the high-frequency information in the low-resolution image gradually approaches a high-resolution image, and the reconstruction precision is improved; the noise prediction network model guides step-by-step noise removal in the back diffusion process, and the detail reduction capability and precision of natural disaster remote sensing image super-resolution reconstruction are improved.

Disclosed is a method for constructing a bionic binocular active visual simultaneous localization and mapping (SLAM) system. The method includes: S1, constructing a binocular active visual system, peripheral vision is simulated using a panoramic camera, a panoramic value map is constructed based on the panoramic camera, and a binocular bionic eye gaze control strategy based on the panoramic value map is proposed; S2, designing a SLAM algorithm framework which is suitable for motion of the two bionic eyes, where relative pose optimization of left and right cameras based on a Perspective-n-Point algorithm and depth calculation for binocular matching points based on triangulation are adopted in a SLAM tracking thread; and S3, with the assistance of camera poses estimated by the SLAM system and sparse landmark point coordinates, training a self-supervised depth estimation network, to provide dense depth estimation for a mapping module of the SLAM system, achieving dense mapping.

A computer-implemented method for reconstructing a scene in three dimensions from a plurality of images of one or more viewpoints of the scene acquired using an imaging device includes: receiving the plurality of images without receiving extrinsic or intrinsic properties of the imaging device; and processing the plurality of images using a neural network to produce a plurality of pointmaps of the scene that correspond to the plurality of images and that are aligned in a common coordinate frame, where each pointmap is a one-to-one mapping between pixels of one of the plurality of images and three-dimensional points of the scene.

The invention discloses a multi-view panoramic point cloud splicing method, which comprises the steps of performing joint calibration on a binocular camera and a laser radar through a calibration algorithm, and aligning a coordinate system; a binocular camera is used for collecting a two-dimensional image and carrying out distortion correction, a depth map is generated based on parallax calculation, and three-dimensional point cloud reconstruction is carried out in combination with a triangulation principle; performing deep learning target detection on the two-dimensional image, and screening a line rod assembly area through non-maximum suppression; multi-view point cloud data are collected through a laser radar, point cloud segmentation processing is carried out, and a telegraph pole assembly point cloud subset is reserved; matching the two-dimensional pixel area of the telegraph pole assembly with a laser radar point cloud projection result, and screening point cloud data belonging to the telegraph pole assembly; and carrying out alignment and fusion on the multi-view point clouds through initial registration and fine registration by adopting an improved point cloud splicing algorithm to generate a panoramic point cloud image. According to the method, the point cloud splicing processing time is shortened, and the robustness, precision and integrity of point cloud splicing are improved.

The invention relates to the technical field of construction progress monitoring, and discloses a construction progress monitoring method and system based on big data. The method comprises the following steps: forming a space-time alignment data set through multi-source data acquisition, filtering and quality evaluation; performing feature extraction and registration to generate a digital model; target detection classification is performed to form a completion state table; progress evaluation is achieved through component-task mapping; trend analysis and risk identification are performed to generate a prediction result; decision reference is provided for personalized information screening and augmented reality display. Through multi-source data acquisition, fusion and intelligent analysis, accurate perception, objective evaluation, scientific prediction and visual presentation of the actual state of the construction site are realized, so that a comprehensive, accurate and prospective construction progress monitoring method is provided, the construction period delay risk is effectively reduced, and the construction management efficiency is improved.

The invention relates to the technical field of infrared thermal imaging, and discloses a feature analysis method and system for an infrared thermal imager, and the method comprises the steps: obtaining an infrared thermal image of a multi-focal plane, and employing a self-adaptive filtering and enhancement algorithm to remove noise and improve the contrast ratio. And determining a heat source area by using an edge detection algorithm and obtaining accurate positioning data. Then, a heat source influence area is extracted based on an improved optical flow algorithm, a preliminary heat flow vector field is calculated, and abnormal vectors are removed through a preset noise filtering mechanism; and then, inputting the filtered heat flow vector field into a thermodynamic model for correction so as to improve the precision of the heat flow trend. And finally, generating a complete heat distribution image according to the corrected heat flow vector field, and performing heat flow feature reconstruction to obtain a high-precision heat flow vector field. The method can dynamically reflect the heat change.

The invention discloses a three-dimensional environment reconstruction optimization method based on multi-sensor fusion data, and relates to the field of three-dimensional environment reconstruction optimization, and the three-dimensional environment reconstruction optimization method based on the multi-sensor fusion data comprises the following steps: S1, collecting multi-source sensor data, and constructing a data set under a unified coordinate system; s2, generating dense visual point cloud, and extracting laser point cloud features to construct a model; s3, establishing a local three-dimensional model, and generating a local environment image; s4, shadow parameters are extracted through shadow geometric analysis, and time sequence optimization is carried out; s5, consistency verification and correction are carried out, and three-dimensional reconstruction data are output; and S6, comparing the reconstruction data with the navigation map database, and carrying out map optimization updating. According to the method, time synchronization and space calibration are carried out on data acquired by the depth camera and the laser radar, complete and accurate three-dimensional information modeling of the target environment is realized, and the geometric precision of environment reconstruction and the image detail reduction capability are improved.

The invention discloses a power transmission line multi-mode warning system and a power transmission line multi-mode expelling method, belongs to the technical field of power transmission line safety monitoring, and aims at solving the problems that power transmission line invasion target monitoring is not accurate, and the warning and expelling effect is poor. An environment image is acquired through an acquisition unit, and redundancy compensation is carried out on a fault unit. In the aspect of image splicing, a database is constructed by using structural feature points of a power transmission line, rapid projection transformation of a fixed area is realized, incremental feature matching is adopted for a dynamic area, and an environment panoramic image is generated. The method comprises the following steps: establishing a background coordinate system based on an environment panoramic image by aligning a fixed structure region, extracting multi-modal features of an intrusion target, constructing a dynamic trajectory parameter set, completing species classification and behavior recognition, constructing a multi-dimensional evaluation index system, dividing threat levels, and generating a thermodynamic diagram. And finally, according to data such as threat levels, a multi-mode grading warning system is constructed, warning equipment is dynamically adjusted, a target track is tracked, a warning effect is evaluated, and accurate and efficient invasion target expelling is realized.

Methods and systems for detecting and geolocating features in satellite-captured images include an edge device (e.g., satellite, etc.) receiving from a mission operations center (MOC) an acquisition context and a transformation for mapping pixel coordinates to geographic coordinates and storing the acquisition context and transformation in onboard memory. The edge device may capture one or more images, detect one or more features in the one or more captured images, process the detected features by filtering noise and clustering contiguous features, apply the transformation to the pixel coordinates of the processed detected features to determine geographic coordinates of the detected features, generate geolocation data for the detected features based on the geographic coordinates, and transmit the geolocation data and associated metadata over a low-bandwidth link.

The present disclosure generally describes user interfaces related to time. In accordance with embodiments, user interfaces for displaying and enabling an adjustment of a displayed time zone are described. In accordance with embodiments, user interfaces for initiating a measurement of time are described. In accordance with embodiments, user interfaces for enabling and displaying a user interface using a character are described. In accordance with embodiments, user interfaces for enabling and displaying a user interface that includes an indication of a current time are described. In accordance with embodiments, user interfaces for enabling configuration of a background for a user interface are described. In accordance with embodiments, user interfaces for enabling configuration of displayed applications on a user interface are described.

The invention relates to an image processing technology, and discloses an efficient panoramic image splicing method and system based on multi-view fusion, and the method comprises the steps: collecting a plurality of images from different views; performing multi-scale feature extraction on each image, generating a feature descriptor for each extracted feature point, and matching a corresponding feature point pair; performing multi-view geometric constraint screening on the feature point pairs; according to the screened feature point pairs, estimating a homography matrix between adjacent images, and carrying out global optimization on the homography matrix; aligning all the images into the same coordinate system; determining an overlapping region between adjacent images; and according to the pixel information in the overlapping areas, fusing the overlapping areas by adopting a self-adaptive weighted fusion algorithm so as to splice the plurality of images into a panoramic image. The invention further discloses a control device and a computer readable storage medium. The invention aims to improve the efficiency and accuracy of generating the multi-view fused panoramic image.

The invention relates to the technical field of image processing, in particular to a method for transmitting real-time pictures of ultrasonic equipment, which comprises the following steps of: acquiring an ultrasonic image through the ultrasonic equipment, dividing the acquired image into block images, and analyzing each block image by using a spatial information analysis model; the method comprises the following steps: identifying a key region and a non-key region in an image through a deep learning model, setting different compression strategies, performing time sequence analysis on acquired bandwidth data by using a bandwidth prediction model, and adjusting a compression ratio, a resolution ratio and a frame rate of the images of the key region and the non-key region according to a prediction result. According to the method, compressed image data is split into a plurality of data streams, a path with high transmission quality is preferentially selected for transmission, multi-stage reconstruction is performed on ultrasonic image data at a receiving end, an image of a key area is processed by using a super-resolution reconstruction algorithm, and high-frequency noise is removed by using a hierarchical denoising algorithm and a convolutional neural network. According to the invention, the real-time performance and reliability of image transmission are improved.

The invention provides a virtual reality building roaming method and system based on scene rendering, and relates to the technical field of virtual reality, and the method comprises the steps: dividing building three-dimensional model data through an octree space segmentation algorithm, and determining a detail level threshold according to a viewpoint distance, and constructing a multi-level detail model. And baking the basic illumination chartlet by using a pre-calculation irradiance transmission technology, and performing real-time synthesis in combination with the multi-resolution shadow chartlet. And calculating illumination by adopting a physics-based rendering method, and fusing the illumination with the scene light and shadow synthesis data to generate dynamic illumination rendering data. And determining a current viewpoint based on the spatial position and attitude data of the virtual reality head-mounted display, performing view cone elimination and shielding elimination, and performing GPU instantiation rendering on visible building data. And the rendered image is optimized through high dynamic range processing and a time domain anti-aliasing technology, and a final scene image is generated in combination with an atmospheric scattering algorithm. According to the invention, the rendering efficiency and scene reality sense of virtual reality building roaming can be effectively improved.

The invention provides a trajectory control video generation method and device based on depth information and time-frequency optimization, and relates to the technical field of image processing, and the method comprises the steps: optimizing a 3D trajectory through multi-entity segmentation, depth estimation and time-frequency decomposition in combination with a user instruction, and generating a control signal through a multi-scale fusion network; finally, the signals and original images are input into an improved Stable Video Diffusion model to generate a video potential representation sequence, the problems that an existing video generation method is insufficient in dynamic entity motion control precision and poor in cross-frame consistency are solved, and through 3D trajectory modeling guided by depth information and a time-frequency joint optimization mechanism, the video potential representation sequence is generated. And the motion smoothness, the space authenticity and the time-frequency stability of the generated video are obviously improved.

A system to detect, classify and track small objects in an image frame. The system includes an image data system and image analytic system. The image data system receives real-time image data from an image capturing system and inputs the image data to image analytic system. The image analytic system performs the object detection, classification and tracking of objects in an image frame using a combination of techniques.

A cognitive computing-based system and a method for non-invasive analysis of one or more physiological indicators using assisted transdermal optical imaging are disclosed. The cognitive computing-based system is configured to obtain multi-modal data from a plurality of sources to preprocess the multi-modal data that includes colored image data, thermal image data, physiological data, and user-provided contextual data. The preprocessed data is used for generating one or more multi-modal features. The cognitive computing-based system is configured to generate unified features representation data for machine learning (ML) models analysis using the extracted one or more multi-modal features. Further, the cognitive computing-based system is configured to perform non-invasive analysis on the unified features representation data to detect temporal trends in the unified features representation data, and generate numerical predictive insights, visual representations data, categorical predictive insights, and recommendation data of the physiological indicators based on the non-invasive analysis of the physiological indicators.