479 results about "Image type" patented technology

The invention discloses a dispatching automation fault intelligent processing system based on knowledge reasoning navigation. The dispatching automation fault intelligent processing system comprises a dispatching automation system environment sensing module which obtains machine room environment multi-source data in real time to serve as a data source; the intelligent analysis module performs preprocessing and labeling on the image type data and the text type data, then performs knowledge extraction to obtain entities and relationships among the entities, performs data fusion on the obtained entities, constructs an automatic duty knowledge graph, forms a scheduling automation system fault research and judgment mechanism reasoning model, and performs fault diagnosis on the scheduling automation system. A fault alarm event is generated, a knowledge reasoning environment is constructed, and a fault research and judgment conclusion is obtained through reasoning of a knowledge reasoning algorithm; an auxiliary decision-making module performs reasoning matching on the fault alarm event and the automatic duty knowledge graph through an RE2 model to obtain a fault handling auxiliary decision-making opinion; and constructing a fault exception handling flow navigation chart, and generating a fault handling coping strategy. The operation and maintenance management level of the dispatching automation machine room is improved.

The invention relates to the field of image segmentation, in particular to a multi-modal nuclear medicine image segmentation method and system and a storage medium. Comprising the steps that image type classification is carried out on multi-modal medical examination images, focus area positioning is carried out, and CT (computed tomography) focus images and PET (positron emission tomography) focus images are obtained respectively; carrying out lesion geometric mutation form prediction on the CT fault lesion image, and carrying out multi-modal image fusion boundary correction on the CT fault lesion image and the PET lesion image to obtain multi-modal lesion image fusion boundary correction data; according to the multi-modal lesion image fusion boundary correction data, performing multi-modal lesion region segmentation combination optimization on the CT image and the PET image to obtain a lesion region segmentation combination optimization strategy; and carrying out image segmentation model construction on the lesion region segmentation combination optimization strategy based on a strategy gradient algorithm to obtain a lesion image segmentation model. According to the method, the image segmentation technology is optimized, so that the image segmentation technology is more perfect.

The invention provides a deep counterfeit detection method based on self-mixing, and compared with the prior art, the method generates a mixed image containing counterfeit artifacts by using a self-mixing technology, and carries out the precise detection and recognition of an artifact region of an input image through combining a backbone network OfficientNet-b3 and a counterfeit artifact detection module. The method comprises the following steps of: firstly, performing framing processing on a real image, generating a source image and a target image by utilizing a self-mixing technology, and generating a mixed image with counterfeit artifacts by combining a mask generator with an alpha blowing model; and then, the backbone network extracts global features from the mixed image, and the fake artifact detection module reinforces local feature expression of a mixed region through an artifact space cooperation attention module, so that an artifact region is accurately positioned. And finally, fusing global and local features by using residual connection, and transmitting the fused features to a full connection layer to complete classification. According to the method, various forged image types can be effectively detected, and excellent generalization ability is shown in cross-dataset and unknown scenes.

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.

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 relates to a turbine starter appearance defect detection method based on multi-scale feature extraction, and the method comprises the steps: collecting appearance defect data of a turbine starter, and defining defect types to obtain an appearance defect feature map; the image is input into a data enhancement module, parameters are adjusted according to image types, and a multi-attribute defect feature map is output; constructing a feature extraction module, extracting defect data features in the multi-attribute defect feature map, converting the defect data features into multilayer feature mapping, and generating a multi-scale defect feature map; inputting the multi-scale defect feature map into an enhanced feature extraction network, obtaining and aggregating high-dimensional multi-scale defect features, and generating a multi-scale fusion defect feature map; constructing a defect detection module, and detecting bounding box prediction and classification results of multi-scale defects; and carrying out training and weight updating on the model by adopting similarity and union set intersection loss to obtain a final defect detection model. According to the invention, accurate positioning and classification of defects of various targets with different sizes on the appearance of the turbine starter are realized.

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.

The invention relates to the technical field of liquid crystal display driving control, and discloses a high-refresh-rate liquid crystal display driving method and system, and the method comprises the steps: a motion amount detection module calculates the motion intensity of a dynamic region through inter-frame comparison; the voltage excitation adjusting module applies voltage according to dynamic / static region differentiation, and the dynamic region adopts asymmetric voltage to increase the response speed; the gray scale adjustment module dynamically optimizes the voltage amplitude based on the local contrast so as to smooth the gray scale; the self-adaptive refresh rate module fuses the motion intensity and the image type to switch a refresh rate gear; and the charge balance management module intelligently triggers regional reverse voltage pulses in a static scene to eliminate ghosting. The system eliminates ghost shadows through multi-module cooperation and dynamic charge balance management, intelligently switches refresh rate gears based on motion intensity and cooperates with voltage regulation to reduce static power consumption, adopts regional gray scale compensation to optimize detail display, and combines asymmetric high-voltage driving to improve the response speed of liquid crystal molecules.

The invention discloses a multi-modal document structured processing method and device, equipment and a medium. For other different data types extracted from the to-be-processed document, the data of the other data types can be subjected to structured processing according to the preset structured processing steps corresponding to the other data types, so that the information carried by the data of the other data types is deeply mined, and the processing efficiency of the to-be-processed document is improved. And the information carried by the data of other data types in the to-be-processed document is presented in a unified and structured form through effective integration and utilization of the multi-modal data. For the image data, the character image type and the non-character image type are distinguished, and different processing modes are adopted, so that the information carried in the image data is extracted more accurately. For the table data, the table data is converted into an information set with rich semantics from a simple numerical matrix by obtaining a header name and a line name and generating a descriptive text for each data item.

The invention discloses a multi-modal fashion trend prediction and evaluation method, which comprises the following steps of: marking a reference target picture according to a full-amount trend characteristic parameter to obtain a full-amount characteristic parameter; carrying out dimension reduction processing by utilizing a Transform model, extracting strong correlation characteristic parameters, and forming a trend characteristic template; performing CLIP fine adjustment according to the trend feature template, enhancing the cross-modal understanding ability, and realizing image-text semantic alignment; performing image annotation on the reference target image data set by using the finely adjusted CLIP model to obtain an image-text data pair; fine adjustment of a text graph and a graph type is carried out on the diffusion model, and image generation is carried out to obtain a clothing trend image; and introducing an evaluation mechanism to perform effect evaluation on the generated garment trend image. According to the invention, strong correlation characteristic parameters can be flexibly, rapidly and accurately screened, more accurate data annotations are obtained, and then a clothing image which is more in line with fashion trend is generated.

The invention provides a PDF (Portable Document Format) document structured loading method based on image recognition, which relates to the technical field of image data processing, and comprises the following steps: setting a predetermined extraction scale of a document image based on margin information of a PDF document; obtaining an initial image of the PDF document with a predetermined coarse scale in predetermined extraction scales; introducing a preprocessing strategy to process the initial image to obtain a target image; a target feature parameter set of the target image is collected in a multi-dimensional mode, the loading engine classifier is activated to analyze the target feature parameter set, and a target engine category is determined; and carrying out structured loading on the initial image through the target engine category. The technical problem that in the prior art, due to the fact that a loading engine cannot be adaptively selected according to semantic features and layout forms of images in a PDF document, the image type PDF structure restoration effect is poor is solved, and the technical effect of improving complex PDF document structure reconstruction accuracy and data loading quality is achieved.

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.

The invention discloses a railway CORS station selection method. At present, an experiential method is mostly adopted during site selection of the CORS. The method comprises the following steps: collecting DEM data of a target area, and calculating gradient, slope direction and surface roughness information of a research area; generating a DOM according to the remote sensing image type, and calculating a vegetation index; processing the SAR data through InSAR to obtain a deformation graph of the target area; communication network coverage conditions are collected and marked on a map, and a network signal coverage range map of the area is obtained; preprocessing the site selection factor of the CORS station; intelligent site selection is carried out according to the determined multiple factors; and according to the calculated site selection suitability plane graph, selecting a most suitable position as a CORS station deployment position every 50km along the railway, and guiding the construction of the CORS station. According to the method, the defects of a traditional on-site reconnaissance site selection method are overcome, and the arrangement position of the CORS station can be optimized.

Described herein is a computer implemented method for generating a plurality of synthesised designs based on a selected design that includes a first element. The method includes accessing design generation data that includes source data and field association data. The source data defines a first field and a first design generation record that defines a first content item that is an image-type content item and that is associated with the first field. The field association data defines a first association between the first field and the first element. The method further includes generating a first synthesised design that is based on the selected design and that is generated such that the first element of the selected design is updated in the first synthesised design using the first content item.

The invention belongs to the field of digital images, and relates to a data processing technology, in particular to a digital image intelligent processing system adaptive to urinary surgery, which comprises an image acquisition module, an image segmentation module, a processing evaluation module and a processing optimization module, the image acquisition module, the image segmentation module, the processing evaluation module and the processing optimization module are sequentially in communication connection, and the image acquisition module is further in communication connection with the processing optimization module; the urinary surgery digital image is collected, the analysis image is classified according to the image type and shooting purpose of the digital image, data support is provided for processing and optimizing the analysis process, the urinary surgery digital image is subjected to segmentation processing through the image segmentation module, the analysis image is segmented into a plurality of image sub-areas, and the image sub-areas are divided into a plurality of image sub-areas. And image analysis is facilitated.

The invention relates to a multi-spectral target detection method and device based on feature fusion and electronic equipment. The method comprises the following steps: respectively extracting visible light features of an RGB image and thermal radiation features of a thermal imaging image; carrying out bimodal feature fusion on the visible light features and the thermal radiation features by adopting a double-cross attention mechanism to obtain a fusion feature map; and inputting the fused feature map into a detection network, and carrying out target classification and positioning to obtain a detection result. By comprehensively utilizing the advantages of each image type, the limitation of another mode is compensated by the strong point of one mode, so that the fuzzy degree of the color image under a severe weather condition or a changing illumination environment is effectively solved, abundant extra information is further provided, and the robustness and accuracy of target detection are enhanced.

The invention relates to a cross-modal heart image segmentation method and system based on space-time causal. The method comprises the following steps: acquiring a clinical heart image; the clinical heart image comprises a magnetic resonance heart image, a computed tomography heart image and an ultrasonic heart image; identifying the image type of the clinical heart image, and obtaining annotation information corresponding to the image type; the image type comprises a time sequence image, a 3D image and a 3D time sequence image; inputting the clinical heart image and the annotation information into the space-time causal diffusion network model to obtain a heart image segmentation result; the space-time causal diffusion network model is used for performing image segmentation on the clinical heart image according to a set mode and a corresponding image type to obtain a heart image segmentation result; the heart image segmentation result comprises a heart segmentation image and annotation data. By adopting the method, the dynamic change characteristics of the heart in different time and space can be captured according to different types of clinical heart images, and a heart image segmentation result is obtained.

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 embodiment of the invention provides an information retrieval method and device, electronic equipment and a storage medium, belongs to the technical field of artificial intelligence, and is suitable for financial science and technology and medical science and technology scenes. The method comprises the steps that image conversion is carried out on an original document to obtain an initial document, and the document content type of the original document comprises at least one of the following types: an image class and a table; determining a target information extraction model based on the document content type; performing content extraction on the initial document through a target information extraction model to obtain target document content features; performing information query on the target document content features based on the target query problem to obtain preliminary query content; screening from the original document based on the preliminary query content to obtain a hit document position; generating retrieval basis information based on the hit document position; and performing content optimization based on the historical query context information, the preliminary query content and the retrieval basis information to obtain target retrieval content. According to the embodiment of the invention, the accuracy of information retrieval can be improved.

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 AI-based agricultural disease and insect pest identification method. The disease and insect pest identification method comprises the following specific steps: S100, transmitting image data of various diseases and insect pests to a disease and insect pest standard database of identification equipment; s200, carrying out image acquisition on the crops by using identification equipment, carrying out feature extraction on the shot image by using the identification equipment, and processing the extracted image data to enable the image data type to be the same as the image type in the pest and disease damage standard database; s300, comparing the acquired image features with data in a pest and disease damage standard database to determine the type of pest and disease damage; and S400, transmitting the determined pest and disease damage type to terminal equipment in a wireless transmission mode. According to the method, crops are monitored in real time through recognition equipment, the recognition equipment can collect images of the crops around the recognition equipment, and the collected data are compared with data in a database.

The invention provides a femoral head necrosis recognition system and method based on deep learning, and the method comprises the steps: obtaining a multi-mode magnetic resonance image of a hip joint of an examined person, and the multi-mode magnetic resonance image comprises a magnetic resonance image formed by a plurality of magnetic resonance scanning sequences; loading a pre-trained image segmentation model to identify a femoral head and an acetabulum in the hip joint of the multi-modal magnetic resonance image, and segmenting the femoral head and the acetabulum to obtain a femoral head segmentation image of the magnetic resonance image of each scanning sequence; performing enhancement processing on each femoral head segmentation image; extracting femoral head feature data of each femoral head segmentation image type and forming splicing feature data; and loading a pre-trained necrosis recognition model to process the spliced feature data to obtain femoral head necrosis recognition results under each magnetic resonance scanning sequence and output the femoral head necrosis recognition results. The diagnosis accuracy can be improved, and the misdiagnosis rate is reduced.

The invention relates to a patient pain degree identification method and device and a pain degree identification system. The method for identifying the pain degree of the patient comprises the following steps: judging the data type of pain data to be detected: if the data type is a physiological signal type, carrying out feature extraction by adopting a multi-scale convolution mechanism, a compression-excitation residual network and a variable-voltage encoder, and carrying out pain grading on the extracted signal features by adopting a support vector machine, obtaining the pain degree of the patient; and if the data type is an image type, carrying out multi-scale feature extraction on the facial expression image by adopting a double-branch feature extraction mechanism, alternately carrying out feature fusion by combining a residual error and an attention mechanism, and carrying out feature flattening and pain degree mapping on a fused feature map to obtain the pain degree of the patient. The method for identifying the pain degree of the patient has the advantages of high accuracy of pain grading based on physiological signals and high accuracy of pain grading based on images.

The invention provides a breast lump ultrasonic image classification method and system based on deep learning, relates to the technical field of ultrasonic image classification, realizes integrated processing of various ultrasonic image types through a unified feature extraction model, improves the diagnosis efficiency, and improves the diagnosis accuracy. Meanwhile, the influence of uneven probe pressure, equipment noise interference and motion artifacts on the imaging quality is quantified based on an image quality score generated based on the physical basic characteristics, and the knowledge enhancement intensity is dynamically adjusted according to the influence, so that the pathological sensitive characteristics are accurately compensated under the clamping of a medical knowledge graph; a hierarchical decision-making mechanism is established through a two-stage cascade classification network, subtype deep analysis is started for malignant lesions on the premise that rapid diagnosis is ensured, and the recognition rate is improved; and a two-factor dynamic calibration system is formed by combining model cognitive factors based on decision logic self-consistency to provide support for efficient and accurate classification of breast lump ultrasonic images.

The invention provides a test case generation method, a test method, an electronic device, a readable storage medium and a computer program product.The test case generation method comprises the steps that firstly, test requirement information of a to-be-tested function is obtained, and the obtained test requirement information comprises multiple types of test requirements; the multiple types comprise a text type and an image type, then classifying the test demand information according to the types of the test demands, and adding different type identifiers to the obtained different types of test demands to obtain multi-modal demand data carrying multiple different type identifiers; wherein the multi-modal demand data comprises text data and image data, then generating a cue word of the model according to a specified case generation rule and the multi-modal demand data, and finally generating a test case of a function to be tested based on the cue word.

Methods, systems, and apparatus for selecting an input mode are described. In one aspect, a method includes receiving request data specifying a request to launch a virtual assistant application from a lock screen of a mobile device. In response to receiving the request data, input signals are obtained. A selection of an input mode for the virtual assistant application is made, from candidate input modes, based on the input signals. Each candidate input mode is of an input type different from each other input type of each other candidate input mode. The input types include an image type and an audio type. The input mode of the image type receives pixel data for input to the virtual assistant application. The input mode of the audio type receives audio input for the virtual assistant application. The virtual assistant application presents content selected based on input signals received using the selected input mode.

The invention discloses an image printing data generation method, device and system and a storage medium, and relates to the technical field of electronic digital data processing.The image printing data generation method comprises the steps that a to-be-printed image is obtained, and the image type of the to-be-printed image is recognized; based on the image type, determining a rendering mode and a halftone mode matched with the to-be-printed image; and analyzing the to-be-printed image according to the rendering mode and the halftone mode matched with the to-be-printed image to obtain printing data of the to-be-printed image. The printing data of the image can be adaptively recommended, so that the technical effect of improving the overall quality of printing output is achieved.

Embodiments of the present disclosure relate to a method, apparatus, device, and medium for image special effect processing, wherein the method including: in response to a first generation request for a first target node in a main image, generating a first sub-container node connected to a main container node associated with the main image; the first generation request including a first image type corresponding to the first target node, generating a first sub-image associated with the first sub-container node based on the first image type, the first sub-image including at least one first operation node; acquiring a command for image special effect processing based on a main operation node of the main image and the first operation node of the first sub-image; and processing and displaying a target image based on the command for image special effect processing.

The invention provides a multi-modal video information monitoring method and device, computer equipment and a medium, and belongs to the field of information recognizing.The method comprises the steps that a multi-modal sensitive information recognition model is constructed; obtaining a target video and historical sensitive information of a user corresponding to the target video; determining an image type of the sensitive information, and extracting a first image frame of the target video according to the image type; extracting the text information and the audio information of the target video, and adjusting the modal weight in the SE through the historical sensitive information; and inputting the text information, the first image frame and the audio information into a multi-mode sensitive information identification model to obtain an identification result of the sensitive information. According to the method, firstly, image frames related to sensitive information are extracted, invalid information in a target video is reduced, secondly, modal weights are adjusted through historical sensitive information records, the accuracy of sensitive information recognition is improved, then features of various modal information are fused, and recognition of multi-modal sensitive information is further improved.