2772 results about "Multiple image" patented technology

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.

A computer implemented method, system and non-transitory computer-readable device for a remote deposit environment. Upon receiving a user request, based on interactions with the UI, the method implements an electronic deposit of a financial instrument by activating a camera on the client device to generate a live stream of image data of a field of view of at least one camera, wherein the live stream includes imagery of at least a portion of the financial instrument. The method continues by extracting in real-time, based on the formation of byte array objects from the live stream of image data, data fields from a ranked sequence of imagery to be processed by an optical character recognition (OCR) program resident on the client device. The OCR process extracts one or more data fields from of the financial instrument that are communicated to a remote deposit server to complete the electronic deposit.

The invention provides a multi-image forgery detection method and system based on a cross-modal vision large language model, and relates to the technical field of image processing and computer vision, and the method comprises the steps: constructing a data set, and carrying out the preprocessing of the data set; according to the preprocessed data, respectively extracting visual features and language features through a pre-trained visual Transform and a language model so as to obtain cross-modal features; according to the cross-modal features, the visual features and the language features are clustered, cross-modal similarity is calculated, a matching relation is established, fusion is carried out, and fused multi-modal features are obtained; and according to the fused multi-modal features, performing adversarial training through a generator and a discriminator to generate an adversarial network. According to the method, high-precision detection and effective detection of multiple image counterfeiting types such as positioning splicing, copying and pasting, AIGC generation and the like are realized.

The invention relates to the technical field of road engineering detection, in particular to a highway pavement crack image intelligent detection system and method, and the system comprises an image acquisition module, a deep learning module, an image enhancement module, a crack measurement and calculation module, a crack development trend prediction module and an information visualization module. The core innovation of the invention lies in that a differential geometry theory is introduced to construct a crack development trend prediction module, and the module comprises a crack characterization model based on a differential manifold, a multi-scale crack evolution tensor field analysis model and a nonlinear space-time crack development prediction model. A pavement is regarded as a two-dimensional differential manifold, cracks are represented as singular curves on the manifold, a multi-scale tensor field analysis technology and a non-linear kinetic equation are combined, accurate prediction of the future development trend of the cracks is achieved, the system supports multiple image acquisition modes, transverse, longitudinal and net cracks can be accurately detected, and the detection precision is high. The method adapts to complex illumination and background conditions, and predicts the expansion rate and severity change of the crack.

The invention relates to the technical field of machine vision structure health monitoring, and discloses a steel structure weld fatigue cracking real-time monitoring system based on machine vision. The system comprises a space-time registration and fusion module, a multi-scale feature analysis module, a health monitoring module, a crack deduction calculation module and a regulation and control strategy generation module. Performing space-time registration and pixel-level fusion through the visual data of the plurality of image sensors to generate a synchronous multi-source image stream; a multi-level welding seam characteristic spectrum is constructed through multi-scale characteristic analysis, and a welding seam structure knowledge base is dynamically updated; the knowledge base and the real-time characteristic spectrum are used for monitoring the welding seam health state, and abnormity is recognized; deducing a crack initiation position and an evolution path in combination with historical damage data; and real-time load information is fused to pre-estimate the remaining service life, and a structural integrity regulation and control strategy is generated online. According to the invention, high-precision fusion of the multi-source visual data and active prediction of the crack trend are realized, and the monitoring accuracy and the early warning capability are improved.

The invention provides an industrial product defect automatic classification method and system, and the method comprises the steps: collecting original industrial product defect image data, and constructing a labeled image sample set and an unlabeled image sample set; constructing a training image sample set based on the labeled image sample set and the unlabeled image sample set in combination with a plurality of image synthesis strategies; based on the training image sample set, introducing a transfer learning strategy and fusing an attention mechanism, and constructing and optimizing an industrial product defect classification model; performing semi-supervised joint training and online learning based on the training image sample set and the real-time small-batch image sample set; and constructing an industrial product defect identification log based on the real-time image flow sample set, the classification model parameters and the corresponding classification prediction function. On the basis of multi-strategy image enhancement and semi-supervised training, transfer learning and a channel attention mechanism are fused, expansion of industrial product defect image samples and fine defect identification are achieved, and the method is suitable for an intelligent defect detection system in various industrial manufacturing fields.

The invention relates to a crack tracking and predicting method based on point cloud registration, belongs to the technical field of image processing, and solves the problem that an existing three-dimensional point cloud model is low in precision and lacks automatic crack tracking capability. The method comprises the following steps: after preprocessing a plurality of images regularly acquired by an unmanned aerial vehicle, identifying the plurality of images by using a deep learning model to obtain crack positions, and then extracting crack features; performing dynamic blocking and differential feature extraction and matching on the plurality of images, and constructing a point cloud model; mapping the crack position and the crack feature obtained in each period to a point cloud model constructed in a corresponding period, and storing the point cloud models in a point cloud model library according to a time sequence; the point cloud models of every two adjacent periods are registered, and a change area of crack characteristics is analyzed; and predicting the change trend of the crack in the future time according to the environmental data of the time sequence, the crack characteristics and the crack characteristic change rate. And improvement of the accuracy of the point cloud model and automatic tracking of the crack are realized.

The invention belongs to the technical field of image processing, and discloses a multi-image processing method based on multi-modal entity alignment, which comprises the following steps: acquiring a plurality of images; retrieving rich semantic information of the entity from an external knowledge base; selecting the most representative image for each entity in the multi-image scene by using semantic information; encoding the original input of each mode; performing first-layer fusion between a visual mode and a text mode by using cross-divergent attention, then performing second-layer interaction with a structured mode, and finally aligning an entity representation of an image by using contrast loss; and outputting multiple images fusing the text modality and the visual modality. According to the invention, hierarchical interaction fusion is utilized to enhance multi-modal interaction; enhancing entity text representation by integrating the external attribute value and the context information; and the most representative image is selected by using the semantic text, so that the influence of uncorrelated images is minimized.

The invention discloses a cradle head tracking method, system and device, a storage medium and a program product, and relates to the technical field of cradle heads, and the method comprises the steps: obtaining the target position information of a tracking target in continuous multi-frame image frames collected by an image sensor, and the size of a detection frame, determining a pan-tilt rotation angle of a target pan-tilt carrying an image sensor when multiple image frames are acquired; determining a target position offset between the target position and the center of the image based on the target position information, and mapping the size of the detection frame into a target distance between the tracking target and the image sensor; inputting the target position offset and the holder rotation angle corresponding to the multiple image frames into a motion estimation model to obtain a predicted position; and determining a pan-tilt angle value of the target pan-tilt based on the predicted position and the target distance, and driving the target pan-tilt to rotate based on the pan-tilt angle value. According to the invention, the cradle head keeps stable tracking while quickly responding to target movement, so that the stability and reliability of cradle head tracking control are improved.

The invention provides a frequency domain Mama multi-scale hyperspectral image classification method and device, and relates to the technical field of hyperspectral image classification. The method comprises the following steps: collecting hyperspectral image data, preprocessing the hyperspectral image data, and segmenting the hyperspectral image data into a plurality of image blocks; inputting the image blocks into a space spectrum interaction feature extraction module, and extracting interaction features through a space branch and a spectrum space fusion branch; inputting the interaction features into a frequency domain disturbance embedding module to obtain features after frequency domain processing; inputting the features after frequency domain processing into an adaptive bidirectional Mama module, and extracting forward and backward spatial features and spectral features; and performing multi-level integration on the spectral features and the spatial features in different stages to obtain global features, and realizing final classification. According to the hyperspectral image classification method, the accuracy and robustness of hyperspectral image classification can be remarkably improved, and higher generalization ability and stability are shown in different data sets and complex scenes.

The invention relates to the field of image processing, in particular to a fire-fighting equipment fault automatic detection method and system based on image processing, and the method comprises the steps: collecting a plurality of images in a plurality of regions at the same time interval, and carrying out the graying of the images; pixel points are classified according to gradient distribution of gray level images in the area, and corrosion probability parameters of the area are obtained; obtaining a water leakage probability parameter according to the time sequence change of pixel points in the suspected corrosion area; and comparing the change conditions of the pixel points of the suspected corrosion area and the non-suspected corrosion area to obtain a water leakage correction parameter, obtaining a fault early warning parameter by combining the parameters, setting a threshold value, comparing the threshold value with the fault early warning parameter, judging whether a water leakage condition exists or not, and completing automatic fault detection of the fire-fighting equipment. The method can effectively distinguish environment condensate water interference and real leakage, and has anti-noise capability.

The invention provides an AGV natural scene positioning method and system based on a 3D laser radar, and relates to the technical field of automatic guided vehicle control. The method comprises the following steps: constructing matching sub-graph information, and providing a key reference for subsequent positioning; a visual sensor is used for capturing multiple image data, IMU data and odometer data of the current pose of the AGV are collected in real time at the same time, and the motion state of the AGV is accurately analyzed according to the IMU data and the odometer data. A 3D laser radar obtains scene point cloud data to construct a local map, the local map is registered with a matched sub-graph, initial pose estimation is obtained, finally, the initial pose, a motion state and image data are integrated, final pose information of the AGV is determined through complex operation, multi-source data cooperatively exerts force, and the positioning accuracy and reliability of the AGV in a natural scene are comprehensively improved.

The invention belongs to the technical field of Internet of Things information security, and particularly relates to a radio frequency fingerprint identification method based on multi-image feature fusion. According to the method, multiple images are extracted from original IQ data, the limitation of single image features is made up, different basic networks are selected according to different types of image data, the basic networks are optimized, SA-D-ResNet18 and SA-ConvNeXt-T networks are provided, a cross attention mechanism module is introduced between the networks, and image feature information is exchanged. In the final stage of feature extraction, a Concat mode is used to splice and fuse the features of a plurality of images. Therefore, the method can still maintain high recognition accuracy in a more complex environment. According to the invention, a common cross attention mechanism in multiple modes is applied to radio frequency image feature fusion, an image-based classification technology is also applied to the field of radio frequency fingerprint identification, and the radio frequency fingerprint identification method based on multi-image feature fusion is researched.

The invention relates to a glass flaw recognition method, system and equipment based on image enhancement and a medium. The method comprises the following steps: acquiring a multi-view image set of to-be-detected glass; respectively carrying out region-of-interest extraction on the vertical dark field image, the horizontal dark field image, the vertical bright field image and the horizontal bright field image to obtain multiple groups of image blocks; combining a plurality of image sub-blocks with the same position in each group of image blocks to obtain a standard image sub-block group; inputting the standard image sub-block group into a pre-trained glass flaw detection model to obtain a detection result and a classification probability corresponding to each image sub-block at the same position; and based on a preset channel fusion weight, performing weighted fusion on the detection results at the same position according to the corresponding classification probability to obtain a glass flaw recognition result at the corresponding position. According to the method, through region-of-interest extraction, channel separation feature extraction and fusion probability output under an independent view angle, the accuracy of glass flaw automatic identification is improved.

The invention discloses a detection method and device for intelligent visual detection of parts and a storage medium, belongs to the technical field of industrial quality detection, and aims to solve the problems that surface and internal defects of complex parts are difficult to identify synchronously and the identification precision is low. The method comprises the following steps: acquiring multi-modal image data obtained through structured light three-dimensional imaging and laser ultrasonic scanning; performing geometric registration and scale normalization on the image to generate a fused image; carrying out image preprocessing and edge analysis, and extracting a region of interest; dividing the candidate region of interest into a plurality of image blocks, and inputting the image blocks into an anomaly detection network; calculating a reconstruction error between the original image block and the reconstructed image block, and generating an abnormal scoring graph; and finally, extracting a defect area through image post-processing, and outputting information such as a defect type, a spatial position, a geometric dimension and a severity level. According to the method, the robustness and accuracy of multi-modal defect identification are improved, and the method is suitable for an online visual inspection task of an industrial production line.

The present application relates to the technical field of image processing, and discloses a method and apparatus for correcting a label of an image sample, an electronic device, and a storage medium. The method comprises: acquiring multiple image samples having annotation error rates lower than a set value; dividing the multiple image samples into N parts; executing N cycles: using N-1 parts of image samples to perform model training, and using a trained model to predict an unused part of image samples to obtain predicted labels, wherein an unused part of image samples in each cycle is different; and if it is determined on the basis of the number of image samples of which predicted labels are inconsistent with annotated labels that a correction stopping condition is not met, recording the inconsistent image samples to correct the annotated labels of the inconsistent image samples. Thus, image samples needing label correction are found by means of the inconsistency between predicted labels and annotated labels and are corrected, without the need to correct all image samples, so that human resources can be saved, and the correction efficiency can be improved.

Example computer-implemented methods, media, and systems for identification and characterization of geologic features in carbonate reservoir are disclosed. One example computer-implemented method includes obtaining multiple core sample images of a carbonate reservoir. The multiple core sample images are labeled using multiple feature classes, where the multiple feature classes include at least one of a vug or fracture. Multiple image patches are generated using the labeled plurality of core sample images. A machine learning model is applied to the multiple image patches to identify one or more vugs or fractures in the multiple core sample images. At least one of porosity or permeability of the carbonate reservoir is predicted using the identified one or more vugs or fractures in the multiple core sample images.

The invention provides a slope crack detection method and device based on super-resolution processing, and relates to the technical field of image analysis. The method comprises the following steps: performing synchronous data acquisition on a target slope area through multiple image acquisition devices to obtain a multi-modal slope image; performing super-resolution processing and image detail enhancement on the slope image to obtain a high-resolution image; inputting the high-resolution image into a feature extraction model, extracting image features, and optimizing the image features through multi-scale feature enhancement; and inputting the optimized image features into a target detection model, and identifying and positioning the crack. According to the technical scheme, the precision and efficiency of slope crack detection can be improved.

Disclosed are various techniques for generating image specific global tone curves and residual gain maps based on multiple images. A gain map is generated based on a comparison of a first image to a second image. A gain map scatter plot is constructed based on the gain map values and channel values of either the first image or the second image. An image specific global tone curve is determined by curve fitting to the gain map scatter plot. A residual gain map is generated based on a comparison of the gain map to a reconstructed gain map derived from the image specific global tone curve. An approximation of the second image can be constructed using the image specific global tone curve and the first image. A replica of the second image can be constructed using the image specific global tone curve, the residual gain map, and the first image.

A computer-implemented method for automatically determining subsurface reservoir boundaries of a wellbore subsurface reservoir in a drilling system that include receiving an inversion image that forms part of an electromagnetic inversion result profile that indicates subsurface measurements captured by a downhole resistivity sensor. The method also includes generating an image mask for the inversion image using an ensemble image model that enables initial image masks outputted from multiple image-to-image machine-learning models into the image mask. The method additionally includes augmenting the inversion image with subsurface boundaries based on the image mask to generate an augmented inversion image. The method further includes generating at least one of: one-dimensional, two-dimensional, and three-dimensional representations of a subsurface area which indicate geological features and properties of the wellbore subsurface reservoir and adjusting a drilling parameter of a downhole drill within the wellbore subsurface reservoir based on the augmented inversion image.

The invention discloses an unmanned aerial vehicle visual positioning method and system based on satellite image map matching, and belongs to the technical field of unmanned aerial vehicle positioning and image processing. A plurality of image pairs are formed by a plurality of obtained satellite image maps and unmanned aerial vehicle images to be matched; determining a matching number corresponding to the feature points in each image pair, and obtaining a satellite image map corresponding to the maximum matching number; obtaining a matching error of the feature points of the satellite image map corresponding to the maximum matching number, and when the obtained matching error of the feature points of the satellite image map corresponding to the maximum matching number is smaller than a preset matching error threshold value, determining the satellite image map after matching error screening; and according to the satellite image map and the unmanned aerial vehicle image after matching error screening, obtaining corresponding pixel coordinates and latitude and longitude coordinates of the unmanned aerial vehicle image in the satellite image map by determining a homography matrix between the two images. Through the method, accurate positioning of the unmanned aerial vehicle can be realized.

Techniques are described for using data capture devices at a building to automatically generate a building floor plan and to determine associated absolute location data for the generated floor plan, such as by associating separately captured GPS data or other absolute location data with the floor plan. In some situations, a building floor plan is automatically generated by analyzing visual data of images captured at multiple image acquisition locations by a camera device to determine room shapes of surrounding rooms, and GPS absolute location data is associated with the generated floor plan using additional data captured at other data capture locations at the building by a separate mobile device that moves independently from the camera device, such as by extending the absolute location data from the mobile device to the camera device's image acquisition location and its surrounding room shape.

The invention discloses a multi-degraded image recovery method based on frequency domain decomposition, and aims to solve the problems that a single model is difficult to deal with various image degradation and recovery processes of different frequency domains are mutually coupled in the prior art. According to the method, a degraded image is decomposed into a high-frequency space and a low-frequency space through fast Fourier transform, and a double-branch network architecture is adopted for targeted processing: for the high-frequency part, a high-frequency feature adaptive processing module HFPM is designed, and detail texture features are effectively extracted and interference is suppressed through feature enhancement and cross-layer fusion technologies; and for the low-frequency part, constructing a low-frequency feature conversion enhancement module LTEM, and capturing global context information by using cyclic convolution to improve the integrity of the structure contour. According to the method, decoupling processing of frequency domain features is realized, and the image restoration performance of the model in various degradation scenes such as rain removal, noise removal and defogging is remarkably improved through the synergistic effect of high-frequency detail enhancement and low-frequency structure optimization. Experimental results show that the method has excellent recovery effect and robustness when a plurality of image degradation tasks are processed at the same time, and can be effectively applied to visual tasks such as traffic accidents with high image quality requirements.

The invention aims to provide a multi-condition diffusion model-based Shengtuo calligraphy rubbing fusion restoration method, which comprises the following steps of: firstly, acquiring a plurality of Shengtuo calligraphy rubbing images from different sources, preprocessing the images, and constructing a standardized training data set; designing a multi-image feature extraction and alignment module to obtain a fused structural feature tensor; constructing a core network structure of the multi-conditional diffusion model, and supporting guidance of a plurality of conditional vectors; jointly inputting the obtained fusion structure feature tensor and a plurality of condition guide items into a diffusion model to generate a fused and repaired potential image; and finally, restoring the potential image obtained by reconstruction into a final output image through a reverse mapping module. According to the method, the problems of blurring, pen lacking, breakage and non-uniform styles during fusion and restoration of the rubbing image in the prior art are solved.

Methods and systems for providing multi-plane viewing of a digital image of a subject matter which do not require viewing multiple images taken at multiple image planes. Pseudo-focus images are generated from a flattened, merged image of a three-dimensional subject matter. The merged image is generated by merging a plurality of images taken at different focal planes of the subject matter which form a through-focus image stack. The merged image comprises the best focus pixels originating from different respective image planes. A depth map is also generated from the multiple images which associates each best focus pixel with a pixel depth value. A plane of interest (POI) is selected from the image planes. A pseudo-focus image is then generated using the merged image and pixel depth map by generating a blurred focus value for each best focus pixel based on the focus offset of each pixel from the POI.

The invention discloses a texture perception state space modeling method for an image restoration task, and the method comprises the steps: 1, constructing a region selection mechanism based on texture complexity, and enabling the region selection mechanism to be used for distinguishing a flat region and a high-texture region in an image; 2, introducing a texture modulation mechanism, and performing explicit adjustment on a state transition matrix in the state space model; 3, enhancing the context modeling capability of the model through a multi-direction sensing module; and 4, by combining position embedding and a sequence modeling structure, the capability of the model in the aspects of image structure understanding and spatial information maintenance is improved. The method can effectively alleviate the problem of information loss when a traditional image restoration method processes texture details, improves the structure restoration capability of a complex region, gives consideration to the restoration quality and the calculation efficiency, is suitable for multiple image restoration scenes such as image super-resolution, image rain removal, low-light image enhancement and the like, and improves the image restoration efficiency. And the method has good engineering adaptability and actual deployment value.

The invention provides a three-dimensional reconstruction method, a data processing method, a rendering method and a device applied to the field of artificial intelligence, which are used for dividing a plurality of images in a scene into a plurality of areas in the scene and performing scene reconstruction based on at least one image of each area so as to realize block scene reconstruction. The method comprises the following steps: acquiring a plurality of images for a scene, wherein the scene comprises a plurality of areas; dividing the plurality of images into the plurality of areas according to the poses of the shooting equipment corresponding to the plurality of images to obtain at least one image of each area; obtaining at least one three-dimensional Gaussian body of each region according to the at least one image of each region, wherein the at least one three-dimensional Gaussian body is used for representing the region in the scene; and combining the at least one three-dimensional Gaussian body of each region to obtain a combined three-dimensional Gaussian body, and representing the scene by the combined three-dimensional Gaussian body.

The invention relates to a defect identification method based on a point cloud model, belongs to the technical field of image processing, and solves the problem that an existing three-dimensional point cloud model is low in precision and lacks dynamic defect identification capability. The method comprises the following steps: carrying out image dynamic partitioning and differentiation feature extraction and matching on a plurality of images acquired by an unmanned aerial vehicle, constructing a point cloud model, and storing the point cloud model in a point cloud model library according to a time sequence; obtaining a difference region by comparing the newest point cloud model with a historical point cloud model in a point cloud model library; and acquiring a local image of the difference region by using an unmanned aerial vehicle, preprocessing, and identifying a plurality of defects in the local image by using a deep learning model. The improvement of the accuracy of the point cloud model and the automatic identification of defects are realized.

The invention relates to the technical field of three-dimensional reconstruction, and provides a three-dimensional reconstruction method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a first image frame set of an oral cavity in a first state, and the oral cavity in the first state represents that at least one actual scanning rod is installed in the oral cavity; identifying a scanning rod area of each image frame in the first image frame set; and based on the feature information in the scanning rod region, splicing the plurality of image frames in the first image frame set, and reconstructing to obtain target three-dimensional data corresponding to an actual scanning rod in the oral cavity. According to the method, splicing is carried out based on the rigid structure, the influence of anatomical structures (such as gingiva, lip and tongue) in the image frame on the splicing precision can be reduced, and the overall precision of three-dimensional reconstruction can be improved.

Systems described herein use either multiple image sensors or one image sensor and a complementary sensor to provide underwater depth perception. The systems include a computing system that provides the underwater depth perception based on data sensed by the multiple image sensors or the one image sensor and the complementary sensor. The systems can include a submersible device (such as a submersible mobile machine) that includes a holder configured to hold the one image sensor. The holder can be configured to hold the computing system in addition to the one image sensor. And, in some embodiments, the holder is configured to hold the complementary sensor in addition to the computing system and the one image sensor. Alternatively, in some embodiments, the holder is configured to hold the multiple image sensors. And, the holder can be configured to hold the computing system in addition to the multiple image sensors.