258 results about "Image type" patented technology

The invention discloses a multi-source remote sensing image zero sample change detection method, and relates to the technical field of remote sensing image processing, and the method comprises the following steps: obtaining remote sensing images collected by two or more remote sensing sensors at different time points in the same geographic area, the image types including optical images and radar images; preprocessing each source image, unifying the spatial resolution and the registration precision, and denoising and standardizing the image; according to the method, the cross-modal shared semantic embedding space is constructed and unsupervised comparative learning is introduced, so that the semantic consistency of the multi-source remote sensing image is effectively improved, and the change recognition capability of the model under the zero sample condition is enhanced; and meanwhile, a difference fusion calculation and structure consistency constraint module is adopted, so that the boundary judgment precision of a change region and the overall structure consistency are improved, and the accuracy and stability of a detection result are remarkably improved.

The invention provides a concrete bridge damage detection and residual life prediction method based on machine vision. The method comprises the following steps: acquiring a concrete bridge surface image and a corresponding structure coordinate thereof; the structure coordinates are explicitly embedded into the concrete bridge surface image, and a coordinate surface image is obtained; the coordinate surface image is input into an image recognition model, image types and corresponding structure coordinates are recognized, the image types comprise an extrinsic damage type image and a mark type image, and the mark type image is a mark image generated by manual testing; determining each first life influence parameter according to the external display damage image and the corresponding structure coordinate; determining each second life influence parameter according to each mark type image and the corresponding structure coordinate; and estimating the remaining life of the concrete bridge according to each first life influence parameter and each second life influence parameter. By implementing the method, the comprehensiveness of concrete bridge damage detection and the accuracy of residual life prediction can be improved.

Systems and techniques are provided for automatically analyzing and processing domain-specific image artifacts and document images. A process can include obtaining a plurality of document images comprising visual representations of structured text. An OCR-free machine learning model can be trained to automatically extract text data values from different types or classes of document image, based on using a corresponding region of interest (ROI) template corresponding to the structure of the document image type for at least initial rounds of annotations and training. The extracted information included in an inference prediction of the trained OCR-free machine learning model can be reviewed and validated or corrected correspondingly before being written to a database for use by one or more downstream analytical tasks.

Systems and methods are described for creating three dimensional models of building objects by creating a point cloud from a plurality of input images, defining edges of the building object's surfaces represented by the point cloud, creating simplified geometries of the building object's surfaces and constructing a building model based on the simplified geometries. Input images may include ground, orthographic, or oblique images. The resultant model may be scaled according to correlation with select image types and textured.

The invention relates to the technical field of medical report generation, and discloses a medical image automatic annotation and report generation system which comprises a preprocessing module, a hierarchical feature extraction module, a semantic annotation module, a guide generation module, a diagnosis reasoning module and a man-machine cooperation interface. According to the method, through standardized preprocessing, hierarchical feature extraction and automatic generation of the annotation and the report, the processing efficiency is greatly improved, and the manual load is reduced; secondly, on the basis of hierarchical feature extraction and semantic and visual embedding alignment, feature support and semantic mapping precision of annotation and reporting are enhanced, and result accuracy and cross-scene consistency are improved; meanwhile, multi-modal alignment processing and multi-dimensional information fusion enhance the adaptability of the system to different image types, generate a structured report conforming to the clinical process and improve the clinical applicability; in addition, a man-machine cooperation feedback mechanism forms a parameter optimization closed loop, so that the system dynamically adapts to new scenes and specifications, and continuous improvement of generalization ability is realized.

The invention discloses a transformer substation hidden danger comprehensive monitoring method, system and equipment integrating multiple sensors and a medium, and belongs to the technical field of transformer substation hidden danger monitoring. Cleaning and fusing the collected data, and constructing a unified data set; inputting the unified data set into an identification structure constructed by combining a convolutional neural network and a recurrent neural network, respectively processing image type and time sequence type data, and completing hidden danger type identification according to a preset training set proportion and training parameters; carrying out risk value calculation and grade judgment in combination with an identification result and hidden danger related characteristics; and early warning information is generated based on a judgment result, classified pushing and display are performed, early warning processing feedback information is received, and state updating and recording and archiving are completed. Fusion sensing and recognition of multi-source monitoring data are achieved, hidden danger early warning capacity and good environmental adaptability are achieved, and the operation safety and management efficiency of the transformer substation are improved.

The invention discloses an anti-halo interference fisheye image effective area detection method, which performs modeling simulation on halo interference possibly existing in a fisheye image shooting process aiming at halo intensity, fuzzy degree, color, position and size factors, and constructs a fisheye image effective area detection model based on a deep neural network. The input of the model is a halo interference fisheye image, the output is the center coordinate and size of an effective area, and the anti-halo interference fisheye image effective area detection model comprises a fisheye image type identification module, an effective area identification module and an effective area parameter extraction module. The fisheye image effective area detection method provided by the invention has stronger robustness and higher detection precision for various halo interferences, can effectively solve the problems existing in the traditional method, and further improves the distortion parameter estimation precision and the distortion correction effect of the fisheye image.

The invention relates to the technical field of computers, and discloses a document format conversion method and device for large model training, and the method comprises the steps: carrying out the classification of a PDF document based on a plurality of classification indexes, and obtaining a document type; when the document type is an image type, performing image conversion and preprocessing to obtain a preprocessed page image; analyzing the preprocessed page image to obtain multi-modal content, and processing the multi-modal content to obtain corresponding processing results; performing content reconstruction and optimization on the PDF document to obtain a first intermediate document; and performing content rearrangement on the first intermediate document to obtain a Markdown document. According to the method, the PDF documents are accurately classified by integrating multiple dimensions, different types of documents are subjected to differentiated format conversion, the format conversion efficiency and accuracy are improved, the generated Markdown document conforms to the original PDF document, semantic coherence and format standardization are achieved, the large model input requirement is met, and therefore the model training effect can be improved.

The invention relates to a document content customized extraction method and system based on a large model. The method comprises the following steps: inputting a document, and dividing the document into a text document or an image document; formulating a structured cue word according to the document and user requirements; calling a large model to process the document, and performing content extraction based on the structured cue word to generate a preliminary extraction result; comparing the preliminary extraction result with the extraction target, updating the structured cue word for iterative optimization, and recalling the large model until the extraction result meets a precision threshold value; and confirming that the extraction result meeting the precision threshold meets an extraction target and storing the extraction result as a final extraction result, and storing the final extraction result as an output format. According to the method, efficient extraction of multi-modal document content information is achieved through large model calling, customized extraction is achieved through structured cue words, the output precision is improved through an interactive process, a large amount of time cost is saved for a user, and remarkable achievement benefits are brought.

The invention provides a rumor detection method and system based on multi-modal evidence fusion, and relates to the technical field of data processing, and the method comprises the steps: obtaining N initial evidences and M associated declarations based on a to-be-detected declaration; calculating the quality score of the initial evidence according to the information density of the text type evidence and the image type evidence contained in each piece of initial evidence; according to the similarity between each piece of initial evidence and the to-be-tested statement and the correlation statement, calculating to obtain a similarity score, and according to the similarity score and the quality score, calculating to obtain a weight corresponding to each piece of initial evidence; calculating according to the initial evidence and the corresponding weight to obtain a target evidence; and obtaining a detection result according to the target evidence and the to-be-detected statement. According to the method, the evidence information density serves as an index for evaluating the evidence quality, the evidence weight is adjusted by combining the evidence quality, the similarity between the evidence and the to-be-detected declaration and the similarity between the evidence and the associated declaration, it is guaranteed that the high-quality evidence is obtained, and rumor detection is conducted according to the high-quality evidence.

For improving the efficiency of malware detection, a method is proposed that can handle computer files (1) of varying types and sizes and at the same time maintain a high detection performance by classifying a number of different types (A, B, C, D, E) of images (4), each calculated or derived from a particular computer file under test (1) (CFUT), using artificial intelligence methods such as machine learning, in particular deep learning, for example as provided by neuronal networks (24) or supervised deep learning algorithms. The different image types (A, B, C, D, E) are generated using different image conversion techniques and a number of approaches are presented for computing images (4) of uniform size Si that contain relevant information for classifying the CFUT (1).

The invention discloses a complex document recognition method based on layout analysis and OCR (optical character recognition) and a medium. Obtaining a to-be-recognized document image in real time, and performing preprocessing operation through a preprocessing module to obtain a to-be-recognized standard document image; processing the to-be-identified standard document image through a layout analysis module, and obtaining at least one target area image, a target area image type and an area vision matrix feature in combination with a layout analysis strategy method; performing text analysis on each target area image through a multi-mode OCR engine module according to the type of each target area image to obtain each text semantic analysis result and an area semantic matrix feature; obtaining a current visual text fusion feature through a multi-modal attention fusion module; and generating and feeding back a complex document identification output result according to the current visual text fusion feature. The problem that the accuracy is poor due to the fact that irregular layouts cannot be processed is solved, and the accuracy and flexibility of complex layout recognition are improved.

The invention discloses a deep transfer learning-based medical image classification model training method and system, and relates to the technical field of medical classification. Comprising the following steps: acquiring an intestinal medical image with known feature information and environmental parameters thereof, and performing image preprocessing to generate a training data set; the feature information comprises texture entropy, edge contour, color contrast, shape area and circularity. Normalization and sliding window filtering are performed on the environmental parameters to construct humidity, illumination and vibration influence functions. Thirdly, a first classification model and a second classification model are established, pre-training is carried out through deep transfer learning, and the learning rate is dynamically adjusted based on the environmental influence parameters; and finally, inputting the to-be-identified intestinal medical image and the environmental parameters thereof into the training model to obtain the corresponding image type and focus type, thereby improving the automation level of medical image analysis, and helping to assist doctors in more scientific and accurate diagnosis.

The invention provides a multi-modal large model-based screen content detection method and system and a storage medium, and the method comprises the steps: carrying out the image preprocessing of a screen content image, and obtaining a to-be-detected image and a to-be-detected text; inputting a to-be-detected image into the pre-trained visual detection large model for visual risk detection to obtain a visual risk value, and inputting a to-be-detected text into the pre-trained text detection large model for text risk detection to obtain a text risk value; performing weighted operation on the visual risk value and the text risk value according to the image type of the screen content image to obtain a target risk value; and if the target risk value is greater than the first risk threshold, controlling the target screen to start the virtual private network, and controlling the target screen to display preset content according to the virtual private network. According to the embodiment of the invention, a multi-modal content detection mode combining visual risk detection and text risk detection is adopted, so that the screen content detection accuracy is improved.

The invention relates to the technical field of graph generation, in particular to a self-adaptive topological plant station wiring diagram generation method, which comprises the following steps of: acquiring a wiring diagram image, extracting edge features, identifying an image type, positioning a line structure boundary, identifying text elements, classifying corresponding graph areas, and constructing a connection path to generate a topological wiring diagram. According to the method, clear recognition of the structure contour is realized through extraction of the edge features of the line structure of the image, the spatial position information of the graphic area is acquired in combination with a multi-scale primitive positioning technology, and then identity extraction and classification judgment are performed on the text elements contained in the image in a character recognition mode. And establishing a corresponding relationship between the equipment identifier and the graph region, constructing a complete path by combining the graph arrangement direction and the starting and ending coordinates of the connecting line, and determining a connection sequence and a relative position relationship between the equipment. Coordination and unification of spatial arrangement are realized through application of an attraction and repulsive force model between nodes, and clearness and coherence of a wiring diagram in the aspect of topological structure expression are improved.

The invention discloses a literature diffraction pattern quality evaluation and intelligent repair system and method, and belongs to the technical field of computers. The method comprises the following steps of: 1, inputting and processing data; step 2, image-text consistency alignment and consistency inspection are carried out; step 3, calculating quality parameters, constructing a quality evaluation system, and performing grading and report output on atlas data; step 4, repairing and warehousing the atlas; the system adopts a modular architecture design, is composed of an image processing module, a text analysis module, a signal analysis module, a quality evaluation module, a report generation and output module, a grading feedback module, an atlas restoration module and the like, and can flexibly adapt to different types of diffraction atlas quality evaluation tasks. According to the technical scheme, aiming at the limitation of literature image type XRD atlas quality evaluation, the evaluation system and method with intelligent repair, knowledge reasoning and self-learning capabilities are constructed, and the availability, credibility and structured processing efficiency of literature XRD atlas data are remarkably improved.

To provide a gaming machine that enhances the enjoyment of the game by utilizing presentations that suggest reliability based on the player's choices. [Solution] In the pre-performance, one of several candidate images is selected as the pre-selected image, and in the post-performance, one of several candidate images is selected as the post-selected image. When the combined performance is executed in state A, the reliability is higher when the pre-selected image and post-selected image are of the same type than when they are of different types. When the performance is executed in state B, which is different from state A, the reliability is higher when the pre-selected image and post-selected image are of different types than when they are of the same type.

To provide a gaming machine that enhances the enjoyment of the game by utilizing presentations that suggest reliability based on the player's choices. [Solution] In the pre-performance, one of several candidate images is selected as the pre-selected image, and in the post-performance, one of several candidate images is selected as the post-selected image. When the combined performance is executed in state A, the reliability is higher when the pre-selected image and post-selected image are of the same type than when they are of different types. When the performance is executed in state B, which is different from state A, the reliability is higher when the pre-selected image and post-selected image are of different types than when they are of the same type.

To increase interest of a game.SOLUTION: In a game machine having display means capable of displaying multiple types of images, one of the multiple types of images is a decorative symbol. A first performance can be executed in which a decorative symbol is newly displayed from a state in which no decorative symbol is displayed on the display means, and the first performance is executed while a big winning game is executed.SELECTED DRAWING: Figure 25

The invention discloses a pixel extraction and reconstruction method for large-size SAR image simulation of an unmanned aerial vehicle, and the method comprises the steps: obtaining a path and an image type of a to-be-processed image, and creating a storage directory; reading the original width and the original height of the to-be-processed image; calculating the width and height of the filled image according to a preset sub-block size; adjusting the width and height of the to-be-processed image to the filled width and height, and storing the filled image in a storage directory; segmenting the filled image into a plurality of sub-block images by adopting an array slicing mode; creating a null array consistent with the filled image in size as a reconstruction canvas; according to the number of rows and the number of columns of the sub-blocks, the sub-block images are read in sequence, and the sub-block images are written into corresponding positions of the reconstructed canvas in an array slicing mode; the reconstructed canvas is converted into an image, the image is adjusted to the width and height of the original image, and a reconstructed image is obtained and stored; and deleting the storage directory of the sub-block image. According to the method, the segmentation and reconstruction efficiency is improved, the reconstruction precision is ensured, and the robustness is enhanced.

This invention belongs to the field of image detection technology, specifically a method for detecting power transmission line equipment based on a lossless data augmentation strategy. The method includes the following steps: establishing an image detection model for detecting various types of power transmission line equipment based on different image types in the original image set in the database; preprocessing the images of the power transmission line equipment to be detected, converting the COCO format target bounding box annotations to YOLO format to meet the needs of the target detection task; setting training parameters and training the established image detection model to obtain an improved YOLOv10 model; inputting the preprocessed power transmission line equipment image into the improved YOLOv10 model to obtain a data-augmented image, and then detecting it to determine the type of power transmission line equipment. This invention exhibits excellent detection performance in complex power transmission environments, effectively improving the monitoring efficiency of power transmission equipment and providing a reliable solution for ensuring power supply.

1. The name of the design product: image type fire detector (SKU4). 2. The use of the design product: the design product is used for fire detector. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view.

The present application relates to the technical field of industrial surface defect detection, and particularly relates to a phase deflection detection method based on asymmetric sampling and multi-scale gradient fusion, which comprises the following steps: firstly, extracting several phase shift phase images in horizontal and vertical directions, and using a region interpolation algorithm to down-sample and reduce dimensions to reduce calculation amount and suppress noise; based on a four-step phase shift method, demodulating wrapped phase and calculating multi-class reflection component images, and completing phase unwrapping through vectorization one-dimensional path accumulation; for different image types, using bicubic, nearest neighbor and bilinear differential up-sampling schemes to restore original resolution; performing small, medium and large three-scale gradient extraction on unwrapped phase, introducing weight coefficients to fuse features through a weighted maximum absolute value method; finally, generating two types of defect detection maps of gradient amplitude and gradient direction; the present application can greatly reduce calculation cost, and simultaneously consider different scale defect detection.

The application discloses an adaptive analysis method of unstructured geological reports and relates to the technical field of tunnel and underground engineering informatization, and the method comprises the following steps: automatically identifying the type of a report image based on a multi-dimensional characteristic fingerprint library; calling a dedicated preprocessing pipeline to extract a pure data area according to the identified type; constructing a coordinate mapping system from pixels to physical quantities; extracting structured geological parameters by adopting a color block type or a curve type strategy; and realizing parameter self-optimization through a feedback-driven quality checking closed loop, so that the automatic analysis difficulty of multi-style geological report images is solved.

The application discloses a kind of based on the improved U-net's ultrasonic image's disassembly method, it is related to ultrasonic image technical field, by combining depth learning technique and image post-processing algorithm, can efficiently handle ultrasonic image type (single figure, double jigsaw, four jigsaw) The identification of operation, ultrasonic regional map contour segmentation and jigsaw disassembly, through the depth learning framework of U-net network, in combination with the feature extraction capability of ResNet50, efficient classification and segmentation of jigsaw image are realized, compared with traditional image processing method, the present application can process large-scale different organ tissue ultrasonic image in short time;Through accurate classification and segmentation, the contour of ultrasonic region in ultrasonic image can be accurately extracted, and jigsaw is effectively disassembled, the integrity of ultrasonic image is restored, avoids the case of misremoval or loss of information, and has wide clinical application prospect.

Disclosed herein are innovations for bitstreams having clean random access (CRA) pictures and / or other types of random access point (RAP) pictures. New type definitions and strategic constraints on types of RAP pictures can simplify mapping of units of elementary video stream data to a container format. Such innovations can help improve the ability for video coding systems to more flexibly perform adaptive video delivery, production editing, commercial insertion, and the like.

The utility model discloses a multi-source target detection device, and relates to the field of spectral detectors, a hyperspectral imaging module comprises a movable imaging lens protruding out of a mounting shell, an imaging spectrometer communicated with the imaging lens, and a detector mounted at the output part of the imaging spectrometer; the RGB camera and the imaging lens are located in the same acquisition direction, and a lens of the RGB camera is embedded into the surface of the shell to acquire an RGB image; the push-sweeping module is mounted on the inner wall of the mounting shell and comprises a driving motor, a sliding rail and a push-sweeping platform; the push-broom platform is mounted on the slide rail, the imaging lens is embedded in a mounting hole of the push-broom platform, and light enters through a slit at the entrance of the spectrometer; the push-scan platform is controlled based on the driving motor and moves along the sliding rail, and a hyperspectral image is obtained through push-scan. According to the scheme, the problems of poor precision and difficulty in correction of aircraft push-scan imaging are solved through the built-in push-scan module, and image type diversification is realized through the built-in multifunctional camera module.

The invention relates to the technical field of document and image processing, and provides a format conversion method and system based on multi-modal fusion and generative adversarial networks, and the method comprises the steps: obtaining document layout information of a document format original file through a deep learning model according to the extracted document multi-modal information of the original file, obtaining document conversion style information by utilizing a document generative adversarial network model according to the document layout information and the first target style requirement, and converting the original file into a PDF format file according to the document conversion style information; for the image format original file, obtaining image understanding information by using an image analysis model according to the extracted image multi-modal information of the original file, and obtaining image conversion style information by using an image generative adversarial network model according to the image understanding information, a target image type requirement and a second target format requirement, and converting the original file into a PDF format file according to the image conversion style information. And PDF files meeting specific requirements are efficiently and accurately formed.

The invention relates to the technical field of fusion devices, in particular to a vacuum inner structure of an imaging type neutral particle analyzer. The vacuum inner structure comprises a first connecting shell which is of a hollow structure, and one end of the first connecting shell is used for being connected with the vacuum chamber side wall of the fusion device; the second connecting shell is of a hollow structure, and one end of the second connecting shell is connected to the other end of the first connecting shell; the first mounting base plate covers one end of the second connecting shell, and a particle channel is formed in the middle of the first mounting base plate; the scintillator screen is mounted on the side, right facing the second connecting shell, of the first mounting substrate; the stripping film covers the end part of one end of the particle channel; and the second mounting substrate is mounted on the first mounting substrate and is provided with a collimation channel, one end of the collimation channel directly faces the stripping film, the other end of the collimation channel is provided with a collimation slit, the collimation slit can be used for ion injection, and the probability of deformation of the whole structure in the vacuum baking process is reduced.

This application discloses a multimedia material management method, apparatus, device, and storage medium, comprising: upon receiving a selection operation based on a material type selection interface, displaying a material list corresponding to the material type, wherein the material list includes a material tag for each material content and a corresponding first operation control, and the material type includes one or more of image type, video type, and text type; upon receiving a click operation on the first control, obtaining the corresponding basic material content according to the material tag corresponding to the first control; and forwarding the basic material content to the target application. This solution can solve the technical problem of low efficiency in multimedia material management, allowing users to quickly find the basic material content of the corresponding material type through selection on the material type selection interface, significantly improving the efficiency of multimedia material search, and thus improving the overall efficiency of multimedia material management.