1292 results about "Multiple image" patented technology

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 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 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.

The embodiment of the invention provides an ink line detection method and system, electronic equipment and a storage medium, and relates to the technical field of building construction, and the method comprises the steps: obtaining point cloud data corresponding to a building; generating a panorama according to the point cloud data; segmenting the panorama into a plurality of image blocks, and respectively inputting the image blocks into a pre-trained ink line detection model to obtain a corresponding detection result image; and obtaining the position of a target ink line in the building according to the point cloud data and the detection result graph corresponding to each image block. In this way, the panorama is generated through the point cloud data, the panorama is input into the ink line detection model for detection, the detection result graph is output, the position of the target ink line is restored according to the point cloud data corresponding to the ink line in the detection result graph, automatic and accurate detection of the ink line is achieved, manual detection is replaced, and the detection efficiency is improved. Error accumulation caused by ink line mixing under the conditions of cracks, recesses and the like is reduced, the measurement difficulty under the condition that the ink lines are abraded or shielded is reduced, and the detection efficiency and precision of the ink lines are improved.

A method of checking wing position in a CMS includes performing a calibration of a wing position supporting a camera relative to a vehicle to provide a desired field of view by capturing multiple images at different lighting conditions, extracting and storing a reference feature from each of the multiple images, triggering a wing position check, capturing a current image from the camera having a current position of the reference feature; sensing a current lighting condition at which the current image is captured, determining that one of the different lighting conditions is more similar to the current lighting condition, comparing the current position of the reference feature to the stored reference feature from the one of the multiple images generated under the one of the different lighting conditions, and outputting a result of the wing position check if a difference from the comparing step exceeds a threshold value.

The invention relates to the technical field of image data processing, in particular to an image adaptive optimization processing method and system for laser printing output, and the method comprises the steps: carrying out the multi-scale feature analysis and fusion of an input original scanning image, and obtaining a fused feature distribution mapping matrix; performing adaptive contrast enhancement based on the fused feature distribution mapping matrix to obtain an enhanced contrast image; performing edge feature extraction on the enhanced contrast image by using an improved multi-direction edge detection algorithm to obtain an edge feature image with an enhanced edge; performing adaptive local texture analysis and enhancement on the edge feature image to obtain a texture enhanced image; and carrying out printing adaptability optimization based on the texture enhanced image to obtain a final optimized output image. According to the technical scheme, the quality of the image printed and output by the laser printing equipment is comprehensively and remarkably improved from multiple image processing dimensions.

The invention discloses a real haze image defogging method based on a haze degradation model. The method comprises the following steps: constructing a haze degradation model fusing multiple scattering effects and multiple image degradation factors; using the haze degradation model to construct a training data set including the clear image and the corresponding pseudo haze image; constructing a defogging network for a real haze scene; training the defogging network by adopting the training data set until a preset loss function is converged; and inputting a to-be-defogged image into the trained defogging network to obtain a defogging result. According to the haze degradation model constructed by the invention, the difference between a synthetic domain and a real domain is effectively relieved; a designed space-frequency hybrid module improves the adaptability of the model to complex degradation characteristics; the prior-guided feed-forward network fully excavates and fuses dark channel prior information, and the sensing and modeling capability of the model to the haze area is effectively enhanced.

The invention relates to the technical field of image processing, in particular to a concrete member surface defect detection method and system based on image segmentation, and the method comprises the steps: obtaining a surface image of a concrete member, and dividing the surface image into a plurality of image blocks; and performing frequency domain transformation on any image block to obtain a power spectrum. According to the method, the feature space period of each image block is analyzed and calculated through frequency domain transformation, and adaptive weighted fusion is carried out on texture features at different distances by using Gaussian weight on the basis of the feature space period. According to the method, the feature extraction process can dynamically adapt to the physical scale of image local textures, namely, small distance analysis is automatically emphasized on fine textures and large distance analysis is automatically emphasized on rough defects, so that scale-perceived composite texture features are constructed; and the accuracy of identifying the concrete surface defects under the complex texture background is obviously improved.

The invention discloses a pavement crack identification method and system based on YOLOv8-Seg, and belongs to the technical field of road engineering pavement maintenance. The method comprises the following steps: identifying a pavement crack slice image by using a trained YOLOv8-Seg segmentation model, and generating a mask area of a crack; extracting attribute information of the crack, wherein the attribute information comprises a center point coordinate, a bounding box, a crack area and a mask binary image; performing space-time continuity analysis on a plurality of images continuously acquired in the same road range, and judging whether cracks with similar forms and similar positions exist or not; and if cracks with similar forms and similar positions exist in the images of the at least two different point positions, determining that the cracks are real cracks, and outputting an identification result. According to the method, crack pixel-level segmentation and attribute extraction are realized, cross-image space-time continuity analysis is combined, crack authenticity is judged, the false detection rate is effectively reduced, and the identification accuracy and engineering applicability are improved.

The invention provides an image processing method, a training method of an image evaluation model and electronic equipment, and relates to the field of computer technology and artificial intelligence. The method comprises the steps that image data and inquiry text data are acquired, the image data are generated by an image generation model, and the inquiry text data are used for describing the requirement for quality evaluation of the image data by adopting a natural language; respectively inputting the image data and the inquiry text data into a plurality of image evaluation models, and respectively carrying out quality evaluation on the image data from corresponding evaluation dimensions by utilizing the plurality of image evaluation models to obtain a plurality of evaluation results; and integrating the plurality of evaluation results to obtain a quality evaluation result of the image data. According to the method and the device, the technical problems of relatively low accuracy and meticulousness of image quality evaluation in related technologies are solved.

The invention relates to the technical field of image processing, and discloses a pathological image classification method and system based on graph neural feature fusion, a terminal and a storage medium, and the method comprises the steps: obtaining multi-scale digital image data, carrying out the extraction of the multi-scale digital image data, obtaining a plurality of image features, fusing the plurality of image features through a pyramid-shaped multi-scale feature fusion model to obtain a multi-resolution feature; obtaining a slide level label, constructing an initial graph neural network model based on a dynamic graph construction mechanism, a self-attention mechanism, a channel reduction mechanism and multi-resolution features, and training the graph neural network model according to the slide level label to obtain a target graph neural network model; and obtaining to-be-detected pathological image features, inputting the to-be-detected pathological image features into the target image neural network model for classification, and outputting a classification result. According to the method, features of the to-be-detected pathological image are classified according to the target image neural network model, and efficient and accurate classification is realized.

The invention discloses an AI-based logistics document intelligent identification and filling system, and the system comprises an image collection module which is used for obtaining the image information of a logistics document, supporting a plurality of image input modes, such as scanner, mobile phone photographing, camera capturing and the like, carrying out the preprocessing of a collected image, including the operation of image enhancement, denoising, graying, binaryzation and the like, and obtaining the image information of the logistics document; the image quality is improved, and preparation is made for follow-up recognition; the document type identification module is used for carrying out document type judgment on the preprocessed document image based on a convolutional neural network (CNN) algorithm in deep learning, and can automatically identify common logistics document types; the invention relates to the technical field of logistics information, and the AI-based logistics document intelligent identification and filling system can efficiently and accurately identify various logistics documents, realize intelligent filling and improve the automation level and accuracy of logistics document processing.

The invention discloses a video anomaly detection method based on a traffic scene, and relates to the related field of computer vision, and the method comprises the steps: obtaining an input video clip which comprises a plurality of frames of images, traversing the plurality of frames of images, carrying out the selection through combining with a text prompt, determining a plurality of key frames, and enabling the text prompt to be input by a user; performing context generation based on the plurality of key frames to obtain position and time context information; the key frame, the position, the time context information and the text prompt are synchronized to a large visual language model for visual questions and answers of diversified traffic scenes, abnormal events are extracted, and the abnormal events comprise abnormal scores; and carrying out abnormal event detection analysis according to the abnormal score, and carrying out abnormal detection on the diversified traffic scenes. The technical problem that abnormal events in diversified traffic scenes are difficult to comprehensively and accurately detect in existing traffic scene-based video anomaly detection is solved, and the technical effect of improving the accuracy and generalization of traffic scene video anomaly detection is achieved.

The invention discloses a visual-text intelligent agent-based generative cross-modal retrieval method and system, and the method comprises the steps: obtaining image features, and constructing an image structured identifier; performing fine tuning on the multi-modal large language model, learning a semantic relationship between the image features and the structured identifiers, and learning a semantic relationship between the structured identifiers and query input of the user; generating a structured identifier based on the retrieval capability of the multi-modal large language model; based on the ability of the multi-modal large language model to understand multi-modal information, the most matched image is screened from the multiple images. According to the method, the two-stage query workflow is automatically constructed according to the query text input by the user, the same image identifier is allowed to correspond to a plurality of images in the coarse-grained retrieval stage, and the image which is most matched with the input of the user is screened from the plurality of images obtained by coarse-grained retrieval in the fine-grained retrieval stage.

The present application relates to the technical field of road engineering detection, in particular to a highway pavement crack image intelligent detection system and method thereof, comprising 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 present application lies in introducing differential geometry theory to construct the crack development trend prediction module, which includes a crack representation model based on differential manifold, a multi-scale crack evolution tensor field analysis and a nonlinear spatiotemporal crack development prediction model. By regarding the pavement as a two-dimensional differential manifold and the crack as a singular curve on the manifold, combined with multi-scale tensor field analysis technology and nonlinear dynamics equation, the accurate prediction of the future development trend of the crack is realized, the system supports multiple image acquisition methods, can accurately detect transverse, longitudinal and network cracks, adapts to complex lighting and background conditions, and predicts the expansion rate and severity change of the crack.

The invention discloses a cable defect detection method and system based on image processing, and the method comprises the following steps: S1, multi-view image collection: carrying out the image collection of a cable from different views at the same time through a plurality of image collection devices, obtaining a plurality of groups of images containing the surface and different depth level information of the cable, and meanwhile, the spatial position and attitude parameters of each acquisition device are recorded. According to the cable defect detection method and system based on image processing, comprehensive cable information is acquired through multi-view image acquisition, the image quality is improved in combination with adaptive image enhancement, and the defects are accurately identified by using multi-scale feature fusion and deep transfer learning, so that compared with the prior art, various defects of the cable can be more accurately detected, and the detection accuracy is improved. The defects include fine defects and hidden defects, and the detection precision is effectively improved. For example, in an actual test, tiny insulation layer damage defects which are difficult to detect by some traditional methods can be accurately identified and positioned by the detection system.

Systems, methods, software, and devices are disclosed herein for training a neural network using multiple images of a scene captured from different viewing directions. A method of training the network includes identifying pixels in multiple images of a scene captured from different viewing directions. and determining, for each of the pixels, at least a known color value, a known radiance value, and a spatial value. The training minimizes a loss function having multiple loss terms: a first loss term that is dependent upon at least the known color value and the known radiance value for each of the pixels; and one or more additional loss terms dependent upon the spatial value determined for each pixel, such that the loss function varies for at least some of the pixels.

The invention discloses a civil engineering foundation pit deformation monitoring system based on image analysis, and relates to the technical field of foundation pit deformation monitoring, and the system collects and enhances a multi-modal image, eliminates interference, corrects image distortion, tracks key points, constructs a panoramic image, generates a point cloud, extracts deformation features, and constructs a deformation map. The method comprises the following steps: identifying a construction disturbance event, constructing a deformation chain and performing causal reasoning, identifying a high-risk area, predicting a deformation trend and outputting an early warning, displaying a monitoring result, collecting user feedback and synchronizing system data. A deformation track is accurately extracted through image correction and key point tracking, a three-dimensional deformation map is constructed in combination with point cloud modeling, causal analysis is carried out based on construction disturbance and a deformation path, a high-risk area is predicted and identified by using a time sequence trend, an early warning is given out, user feedback collection and result synchronization are supported, and monitoring intelligence and response efficiency are improved.

A camera calibration method based on an integrated dynamic dispersion-enhanced particle swarm optimization algorithm includes: acquiring multiple images of a calibration board of different angles and converting them into grayscale images, detecting Harris corner points, and solving sub-pixel coordinate; estimating, by using the sub-pixel coordinates and a distortion camera model, initial values of camera intrinsic parameters through Zhang's camera calibration method; calibrating the camera intrinsic parameters, and calculating fitness values of particles; determining whether iteration termination condition is met, whether the fitness values of the particles have reached a convergence condition, and whether algorithm is trapped in a local optimum, to thereby determine whether a maximum number of iterations is reached or a specific fitness threshold is met; and outputting camera parameters corresponding to a global optimal solution of the particles when the maximum number of iterations is reached or the specific fitness threshold is met.

The invention relates to the technical field of image processing, discloses an image meaning analysis scene consistency evaluation system based on a visual model, and aims to solve the problem of insufficient recognition stability in complex environments such as illumination variation, angle deviation and local shielding in the prior art. The system comprises an image input module used for receiving and preprocessing a plurality of images; the visual large model analysis module is used for carrying out multi-dimensional semantic feature extraction on the preprocessed image; the structured description generation module is used for converting the semantic features into structured text description in a unified format; the scene consistency evaluation module is used for performing logic consistency analysis on the structured text descriptions of the plurality of images; and the result output module is used for generating and outputting a final consistency evaluation report. According to the technical scheme, the method can effectively improve the recognition stability of the system in a complex environment, achieves the multi-dimensional semantic understanding of the image content, and remarkably reduces the misjudgment rate of multi-view image evaluation.

A multi-camera, multimodal gesture detection system for augmented reality devices combines outputs from multiple image sensors, using different modalities, to generate fused gesture detection trigger signals and gesture detection release signals. The system processes fused trigger signals using a trigger suspension component, which suppresses false triggers based on hand velocity, position of a detected hand within a field of view of an image sensor, and visibility of key hand landmarks. The system processes fused release signals using a release suspension component, which suppresses false release signals based on hand velocity, position of a detected hand within a field of view of an image sensor, and visibility of key hand landmarks. This approach enhances gesture detection accuracy and reliability in challenging environments.

Methods, systems, and apparatus, including medium-encoded computer program products, for camera detection of projectile point of impact include: processing at least one of multiple images of a physical target in a field of view of a camera to determine an orientation of the physical target, correct spatial perspective distortion for the physical target, establish a metric for the physical target that relates a pixel-to-pixel distance with a real-world distance, or a combination thereof; comparing respective images in a sequence of images from the multiple images to identify image data representing a projectile having hit the physical target; determining a point of impact of the projectile on the physical target based on the image data representing the projectile having hit the physical target; and providing the determined point of impact for scoring and presentation on a display device.

A hybrid checkout system that enables in-motion, multi-signal, bulk item identification is disclosed. The hybrid checkout system includes a computer vision apparatus that includes a plurality of cameras fixed at different locations relative to a conveyor belt. The conveyor belt includes markings that assist a user with item placement. RFID sensors are provided with the hybrid checkout apparatus. The cameras capture multiple images of items placed on the belt as the items are in motion. Each camera captures multiple images of the items captured from different vantage points as the items are in motion. In addition, the RFID sensors gather RFID data from RFID tags affixed to the items, as the items are in motion. The image data and other sensor data is provided as a multi-signal input to a machine learning model that is trained to recognize items and output item identifiers for the items. Pricing information corresponding to the item identifiers received from the model is determined and the items and their respective prices are added to a transaction record.

The invention provides a PDF analysis method and system based on image-text semantic alignment, and belongs to the technical field of document processing. The method comprises the following steps: reading PDF or picture file byte data, and converting pictures into PDF byte data in a unified format; creating an image and a Markdown directory according to the output directory, the PDF name and an analysis method; extracting byte data of a specified page of the PDF according to starting and ending page numbers; selecting a back end to analyze PDF byte data to obtain a reasoning result, an image list and the like; generating an intermediate JSON containing PDF detailed information according to an analysis result; analyzing the positions of the text and the picture, and associating information to realize semantic alignment of the picture and the text; and generating multiple types of machine readable files according to the intermediate JSON and storing the files in a specified directory. According to the method, the PDF multi-mode content can be accurately extracted and subjected to semantic alignment, the PDF multi-mode content is efficiently converted into a machine readable format, the analysis accuracy and usability are improved, and the method is suitable for PDF analysis of multiple image-text tables such as scientific and technical literatures.

A head-worn device system includes multiple image sensors (e.g., cameras), one or more display devices and one or more processors. The system also includes a memory storing instructions that, when executed by the one or more processors, configure the system to obtain a first image captured by a first image sensor of the device; generate, based on obtaining the first image, a respective predicted skeleton corresponding to a respective view from each of the first image sensor and the one or more second image sensors; select, based on generating the respective predicted skeletons, an image sensor from among the first image sensor and the one or more second image sensors, the selected image sensor being used for classifying the first image; and determine, based on the selected image sensor, a classification for the first image.

The invention discloses a text-guided multi-dimensional multi-modal image clustering method and system, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a target clustering dimension and a plurality of to-be-clustered images, and generating a description text and two answer texts for the target clustering dimension for each image; performing feature coding on each image and the corresponding description text and answer text to obtain image features, description text features and answer text features; calculating similarities between the image features and the latter two, and performing weighted fusion based on the similarities to obtain fused text features; performing cross attention calculation on the image features by taking the fused text features as query to obtain image representation focused on a target clustering dimension after text guidance, and clustering according to the image representation to obtain a clustering result; the method has the advantages that the problems of text and image content disjunction and semantic dimension conflict in image clustering can be relieved, and semantic interpretability and accuracy of clustering results are improved.

The invention relates to the technical field of image recognition, in particular to a disconnecting link opening and closing video image recognition system and method. The disconnecting link opening and closing video image recognition system comprises an image acquisition unit which is used for acquiring image data of a disconnecting link in real time in various environments. By adopting advanced image processing and mode recognition technologies and combining various image enhancement algorithms, high-precision recognition of the opening and closing states of the disconnecting link under complex illumination conditions and different visual angles can be achieved, meanwhile, the system has the real-time image processing capacity, response can be made in time when the state change of the disconnecting link is detected, and the detection accuracy of the disconnecting link is improved. By means of the system, potential safety hazards can be quickly found and processed by operators, the safety performance of the system is enhanced, and in addition, the system not only provides real-time identification results, but also can store and analyze historical data, presents the operation condition of the disconnecting link in a data report form, and supports subsequent decision making.

Aspects concern a method for generating a visual representation of an environment, the method comprising receiving, for each device pose of a plurality of device poses within the environment, a lidar point cloud generated from lidar data and multiple images, wherein the lidar data and the multiple images are recorded by a device having the device pose in the environment, and an estimate of the device pose and optimizing an objective function over model parameters of a visual representation model modelling an environment shown by the received images and over, for each device pose, device pose compensations of the device pose, wherein, one or more learning rates are used for the device pose compensations which depend on the sensitivity of the lidar point cloud with respect to the device pose compensations.

An information processing apparatus includes: an obtainment unit configured to obtain a captured image obtained by each of multiple image capturing apparatuses; a detection unit configured to detect a position of a predetermined part in the object from the captured image of each of the multiple image capturing apparatuses; an estimation unit configured to estimate a camera parameter indicating a position and an orientation of each of the multiple image capturing apparatuses by using the detected position of the predetermined part; an update unit configured to update the camera parameter of each of the multiple image capturing apparatuses by using the estimated camera parameter as an initial value; and a determination unit configured to determine the camera parameter of each of the multiple image capturing apparatuses based on a result of performing three-dimensional reconstruction of the object based on the updated camera parameter.

A computer-implemented method and an apparatus for automatic modulation recognition that enables the detection and identification of modulation schemes in received raw signals with a signal receiving unit (70) without prior information about the raw signal detail, characterized by; comprising at least one computing unit (10) configured to transforming received raw signals from time domain to frequency domain including the noise in the signal with segmenting the signal and computing its modulation in multiple image with the spectrogram extraction process in order to capture temporal dependencies and sequential information by treating the raw signals received from signal receiving unit (70) as images; augmentation of the data for increasing the dataset size for increasing the accuracy with the limited data; training the data for enabling the network to learn spatiotemporal relationships based on a predefined or a precalculated signal-to-noise ratio (SNR) level; applying the data to one algorithm of two, which are convolutional neural network (CNN) and convolutional neural network (CNN) long short-term memory network (LSTM) hybrid algorithm.