2962 results about "Image content" patented technology

The present invention provides a screenshot content extraction method based on a multimodal large model, which is used to extract OTA hotel structured information, including the following steps: obtaining a screenshot image with hotel information; performing text recognition on the image through an optical character recognition model to obtain a text information box set; performing text area positioning on the image through an image positioning model, and extracting the text content of the text area to obtain a text content set; inputting the text content set into a text large model, extracting structured information through preset prompt words, and obtaining first structured information; inputting the image into a visual language model, extracting structured information through prompt words, and obtaining second structured information; comparing and fusing the first structured information and the second structured information to obtain hotel structured information. The present invention can understand the image content more comprehensively and accurately, significantly improve the accuracy of hotel structured information extraction, and reduce the error rate of information extraction.

The present disclosure provides a method for securing document content. A document security manager receives a digital document containing original text and image content. The original text content is processed to identify text portions by parsing annotations, analyzing proximate text, extracting content within the determined scope, and determining encryption attributes. The original image content is processed to identify image portions by detecting user-defined selections or employing a neural network to identify sensitive objects. Encrypt content requests are generated including the identified text and image portions with associated attributes and metadata. The requests are transmitted to a cryptographic engine, which returns encrypted content. A partially encrypted document is built by replacing original content with masking symbols, applying visual obfuscation, embedding encrypted content as metadata, and updating document properties to indicate encrypted content.

Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for generating images. The method includes acquiring a descriptive text for describing image content of a target image, determining position prior information, and generating the target image based on the descriptive text and position prior information. According to the method of the embodiments of the present disclosure, a target image that can be used for rare data simulation in rare scenarios can be generated by means of an input descriptive text. In addition, the method allows for position perception editing and operation, and can control, based on position prior information, a direction and a position of an object generated in the target image, thereby effectively and diversely generating images. Moreover, the method provided in the present disclosure is based on object types in each subdivided image block, thus making position perception more accurate.

The embodiment of the invention provides a project research and development data key information processing method and device. A research and development content recognition system and a self-adaptive research and development knowledge graph are constructed. Unified processing and time sequence alignment of text, voice and image contents are realized through multi-modal information decomposition and fusion. Semantic completion and error correction are carried out based on research and development of a semantic analysis model, a knowledge graph structure is dynamically constructed and optimized, and a structured document with a traceability relation is generated. The system adopts a deep neural network model to extract research and development key information, constructs a multi-level document framework, and realizes intelligent conversion from research and development data to a project application document. According to the method, the defects of the traditional technology in the aspects of multi-modal information processing and knowledge structure optimization are effectively overcome, and the research and development data management and project declaration efficiency is remarkably improved.

The invention belongs to the technical field of rail transit, and discloses a rail transit environment foreign matter intrusion detection method and system based on a visual model. The method comprises the following steps: firstly, extracting a suspicious region sub-graph in an original image through preprocessing, and respectively extracting a coarse-grained global feature vector and a fine-grained local feature vector by using an image encoder; meanwhile, text prompt information matched with the image content is generated, and a corresponding text feature vector is extracted; the multi-level similarity of the image feature vector and the text feature vector is calculated, and the respective weight fusion is combined to obtain a comprehensive abnormal score; and finally, dynamically modeling abnormal score distribution based on a Gaussian mixture model to realize foreign matter invasion judgment. According to the method, image and text multi-modal information is fused, the detection precision of foreign matters in a complex scene is improved through multi-scale feature association, the dynamic environment adaptability is enhanced by adopting an adaptive threshold strategy, and the problems that a traditional method is high in false detection rate and insufficient in generalization ability in a complex background of a railway are effectively solved.

The invention relates to the field of liquid crystal display, and discloses a liquid crystal display screen dynamic contrast optimization method and system, and the method comprises the following steps: collecting ambient light intensity and backlight module temperature parameters in real time, and combining with image content entropy analysis to dynamically generate a non-uniform backlight partition grid; a brightness diffusion physical model is constructed based on hardware parameters such as the refractive index of the light guide plate and the LED spacing, and the optimal brightness of each partition is solved by adopting a temperature self-adaptive improved particle swarm algorithm; driving voltage is generated through piecewise nonlinear gamma mapping and inverse function iterative calculation, and the driving voltage is adapted to the nonlinear response characteristics of the backlight module; a hardware accelerator is used for executing a diffusion equation and optimization calculation in parallel, and model parameters are dynamically adjusted in combination with feedback of an optical sensor, so that a closed-loop control link is formed. According to the method, the contrast performance and the dynamic scene adaptability of the display screen are comprehensively improved through dynamic partition adaptive to the environment, an optimization algorithm driven by a physical model and real-time processing accelerated by hardware.

A system for generating highly personalized image content with generative artificial intelligence (AI) that incorporates brainwave data collected from a Brain-Computer Interface (BCI) device worn by the user while they are immersed in their experiences. The system can identify a mood (i.e., cognitive, mental, and emotional states) felt by the user during that time. The system develops a prompt that describes the target scene and modulates the weights assigned to each parameter in the prompt based on the mood identified. The generative AI engine can then to apply the mood to the target scene by adjusting image attributes such as hue, saturation, and lighting. The outputted image can offer a visual representation not only of the objects that were in the user's environment, but also the user's general perception and experience of the occasion. The process is passive and requires no interaction from the user to define their emotional affect.

The invention discloses an end-to-end underwater three-dimensional reconstruction method and system based on an underwater imaging model, and belongs to the technical field of underwater image processing. According to the method, deep learning pose estimation is combined with an underwater imaging model; firstly, a multi-frame underwater image sequence is collected as input, a deep neural network is constructed, and the network mainly comprises two core sub-modules: a pose estimation network; secondly, a three-dimensional reconstruction network is adopted, dense point cloud or voxel reconstruction is completed according to the predicted pose and image content, pose estimation and the three-dimensional reconstruction process are integrated in the same system, and overall joint optimization is achieved; and in combination with a self-adaptive underwater imaging model, modeling is performed on physical processes such as underwater illumination attenuation and scattering, so that the reality sense and the accuracy of a reconstruction result are improved. According to the method, high-quality three-dimensional reconstruction of images in a complex underwater environment is realized, and the method can be widely applied to ocean engineering, underwater robots, submarine topography surveying and mapping and underwater cultural relic protection.

A method, apparatus, non-transitory computer readable medium, and system for media processing includes obtaining a text prompt and a style input, where the text prompt describes image content and the style input describes an image style, generating a text embedding based on the text prompt, where the text embedding represents the image content, generating a style embedding based on the style input, where the style embedding represents the image style, and generating a synthetic image based on the text embedding and the style embedding, where the text embedding is provided to the image generation model at a first step and the style embedding is provided to the image generation model at a second step after the first step.

The present disclosure provides a method for securing document content. A document security manager receives a digital document containing original text and image content. The original text content is processed to identify text portions by parsing annotations, analyzing proximate text, extracting content within the determined scope, and determining encryption attributes. The original image content is processed to identify image portions by detecting user-defined selections or employing a neural network to identify sensitive objects. Encrypt content requests are generated including the identified text and image portions with associated attributes and metadata. The requests are transmitted to a cryptographic engine, which returns encrypted content. A partially encrypted document is built by replacing original content with masking symbols, applying visual obfuscation, embedding encrypted content as metadata, and updating document properties to indicate encrypted content.

The invention relates to the technical field of video coding, in particular to a video coding method and system based on multi-channel concurrent software and hardware mixing, and the method comprises the following steps: analyzing brightness distribution and edge structures, screening effective frames, calculating frame priorities, dividing image blocks and combining processing units, counting the number of frame streams, and analyzing frequency concentration. And evaluating image coherence and a load state, and outputting a processing channel adjustment record. According to the invention, through screening of image content features, occupation of redundant frames on coding resources is reduced, through dynamic configuration of frame priorities, timeliness and accuracy of target frame scheduling are improved, an aggregation strategy of a block structure is combined, picture consistency after image processing is enhanced, and task frequency trend identification is utilized, so that image processing efficiency is improved. According to the method, accurate resource matching of a high-load flow section is achieved, joint judgment of image boundary continuity and structure hopping frequency is adopted, an allocation strategy of a processing channel is optimized, an image structure and channel scheduling form linkage, and stability and adaptive capacity are improved.

The invention relates to the technical field of computers, and particularly discloses a multi-modal large model training data acquisition method and system, and the method comprises the steps: carrying out the semantic feature extraction and cross-modal joint coding of image data and image content text description data, which are subjected to semantic alignment, through a data processing technology based on deep learning; the method comprises the following steps of: firstly, generating an image sample to capture semantic association between modalities, realizing cross-modal semantic information fusion, further generating the image sample on the basis, and further performing semantic offset measurement on the generated image sample and original image data to intelligently identify whether the generated image sample is a qualified enhanced sample or not. Through the mode, the multi-modal training data set can be effectively enriched, the data quality is ensured, and the problem of cross-modal semantic consistency in the multi-modal data enhancement process is solved, so that the training efficiency of the multi-modal large model is improved.

Systems and methods are provided for generating a flat garment pattern and / or 3D mesh representation of a garment from one or more images depicting the garment laid flat or hung up. A system may obtain both a front image depicting a front view of a garment and a back image depicting a back view of the garment. A front and back silhouette of the garment may then be generated, which may include segmenting the garment depiction from background image content. A parametric representation of the garment may then be generated based on the front and back silhouettes, which may be implemented by iteratively optimizing, using differentiable rendering techniques, a garment representation within a parametric garment space previously learned for the particular garment type. A 3D mesh garment representation may then be generated based on the parametric representation, from which a flat sewing pattern may subsequently be generated if desired.

The invention belongs to the related technical field of construction safety inspection, and particularly relates to a construction site safety inspection method based on a multi-modal large language model, which comprises the following steps of: introducing the multi-modal large language model, and applying the multi-modal large language model for multiple times in combination with a knowledge base containing safety standard specifications and evaluation rules to obtain a construction site safety inspection result. The method specifically comprises the following steps: obtaining image content description through an image and a preset visual perception system prompt text; intelligently decomposing user input query into a plurality of sub-query tasks through user input query, image content description and preset task decomposition system prompt; searching construction safety management standard data related to each sub-query from the construction safety knowledge base, and analyzing and reasoning the sub-query based on the image content description in combination with the searched standard data to obtain the reasoning and conclusion of the sub-query; and finally, obtaining a total reasoning and conclusion based on each sub-query data. The method can accurately, scientifically and comprehensively realize the safety inspection of the construction site.

The invention belongs to the technical field of natural language processing and multi-modal information extraction, and particularly relates to a multi-modal named entity recognition method based on semantic alignment and cross-modal graph fusion, which comprises the following steps: S1, acquiring a data sample containing a text sequence and image content; s2, encoding the text and the image into vectors respectively; s3, similarity is calculated through a trainable bilinear function, and optimization is carried out through loss comparison; s4, cross-modal attention is used to enhance association information between modals; s5, determining the proportion of reserved image information through a modal matching module; s6, introducing a gating mechanism to dynamically fuse visual and text features; s7, realizing local and global information complementation by a cross-modal graph fusion model; and S8, inputting the fused representation into the CRF layer to predict the entity type. According to the method, fine semantic alignment can be realized in a weak image-text correlation context, and balance between local entity recognition and global semantic understanding can be achieved.

The invention discloses a face anti-fraud model training method and device based on image-text comparison assisted learning, and belongs to the technical field of artificial intelligence. The method comprises the following steps: identifying the content of each sample image in an image set to obtain a description text corresponding to each sample image; generating text data corresponding to each sample image in combination with the description text and the label; and inputting each sample image in the image set into an image encoder in the multi-modal pre-training model, and inputting text data corresponding to each image into the text encoder in the multi-modal pre-training model, so as to train the multi-modal pre-training model and obtain a face anti-fraud model. According to the method, the image content description text and the face type label are integrated, the features are mixed, the central difference convolution adapter module is introduced to capture the fine features of the face, the features are subjected to semantic alignment through image-text comparison learning and matching, and the generalization ability of the model in different application scenes is improved.

The invention discloses a multi-agent automatic image retouching system based on content analysis, and the method comprises the steps: 1, receiving the input of an original image, calling a finely-adjusted multi-mode large model to carry out the combined semantic-visual analysis of the image, extracting key semantic elements in the image, and carrying out the recognition of the key semantic elements; comprising but not limited to background coordination degree, illumination condition, figure hair style definition, weather quality and composition layout rationality content information. Based on these information, the system performs comprehensive image quality scoring on the image, which combines image style, aesthetic level, definition and subjective quality multi-dimensional evaluation criteria. Meanwhile, based on a multi-dimensional image quality scoring result and understanding of image semantic content, the system automatically generates a repair and optimization strategy set for specific defects so as to guide a subsequent image quality enhancement process. The invention relates to the field of multi-modal model and multi-agent cooperation, and can meet the increasing requirements of rapid optimization and high-quality propagation of image contents.

The invention discloses an image encryption system and method in an intelligent power grid environment, and aims to solve the problem that an encryption strategy is static and lacks self-adaptive capability in the prior art. The image encryption system comprises terminal equipment, edge equipment and central equipment, and a dynamic key generation unit on the terminal equipment generates a dynamic key by cooperatively utilizing a physical unclonable feature (PUF) of the equipment and a real-time load of a power grid; the hierarchical encryption unit performs encryption of different intensities based on image content complexity. A topology sensing transmission module of the system analyzes power grid topology by using a graph neural network (GNN) and dynamically optimizes transmission. In addition, the image encryption system further integrates low-density parity check code encoding and decoding and a self-adaptive decryption strategy so as to enhance the anti-interference capability. According to the invention, by constructing a sensing, decision-making and execution integrated adaptive security system, the security, high efficiency and robustness of smart grid image data transmission are significantly improved.

Mechanisms are provided for generating audio descriptions of visual elements of video content. A plurality of extracted video features for the video content are received from an image recognition and analysis system. Based on the extracted video features, one or more audio descriptions are generated that describe the extracted video features audibly. At least one temporal location within the video content is determined, in which to place the one or more audio descriptions so as to minimize overlap with other audio features of the video content and other audio descriptions. An audio description data structure is generated that specifies the temporal location(s) within the video content for the one or more audio descriptions. The audio description data structure is provided to a client computing device for playback of the video content including output of the one or more audio descriptions.

The invention relates to a computer-implemented method for automatically detecting anatomical structures (3) in a medical image (1) of a subject, the method comprising applying an object detector function (4) to the medical image, wherein the object detector function performs the steps of: (A) applying a first neural network (40) to the medical image, wherein the first neural network is trained to detect a first plurality of classes of larger-sized anatomical structures (3a), thereby generating as output the coordinates of at least one first bounding box (51) and the confidence score of it containing a larger-sized anatomical structure; (B) cropping (42) the medical image to the first bounding box, thereby generating a cropped image (11) containing the image content within the first bounding box (51); and (C) applying a second neural network (44) to the cropped medical image, wherein the second neural network is trained to detect at least one second class of smaller-sized anatomical structures (3b), thereby generating as output the coordinates of at least one second bounding box (54) and the confidence score of it containing a smaller-sized anatomical structure.

The invention discloses an AI intelligent image-text situation content accurate layout full-marketing generation method, and particularly relates to the technical field of image-text recognition. The method comprises the following steps: performing semantic analysis on a text input by a user, extracting a deliberate graph item, reverse semantics, an auxiliary emotion item and a tonality keyword, and constructing a multi-semantic field group; the field groups are injected into a collaborative generation engine composed of an image generation sub-model and a semantic prediction sub-model, and the semantic prediction sub-model predicts graph attribute tags and guides the image generation sub-model to conduct regional attention intervention; after the image is generated, the matching degree of the image content and the field group structure is evaluated through a structure-level semantic consistency judgment mechanism, and regeneration is triggered if the image content and the field group structure are inconsistent; finally, the image content with the aligned structure is output and subjected to image-text combination typesetting with the original text, and image-text situation content meeting the multi-platform marketing application requirement is generated. According to the method, the semantic accuracy, the structural consistency and the visual expression quality are remarkably improved while the generation efficiency is ensured.

The invention discloses a backlight source partition dimming control method, and belongs to the field of data security, and the control method comprises the following specific steps: I, analyzing the data features of an input image, recognizing a highlight motion region, and predicting and optimizing a current backlight partition dimming strategy; iI, adjusting a backlight area in real time according to the brightness, contrast and color change of the input content, and constructing a multi-level light diffusion coupling model after partition adjustment; the backlight partition can be adaptively adjusted according to the image content, the local contrast ratio and energy efficiency are improved, more uniform light distribution is achieved, the problems of halo and uneven brightness are reduced, the dimming precision is improved, the service life of a light source is prolonged, the light overflow effect of the partition edge is effectively reduced, and the picture layering sense is improved; the subjective image quality experience is effectively improved, the light efficiency utilization rate is improved, meanwhile, image quality and power consumption optimization can be considered, brightness adjustment better conforms to human eye visual characteristics, visual fatigue is reduced, and it is ensured that the optimal image quality is maintained under the lowest power consumption.

The invention relates to the technical field of computer graphic interactive processing, in particular to a real-time interactive image generation system based on a multi-point touch canvas. The input acquisition unit is used for acquiring original touch data from an operating system and preprocessing the original touch data; the gesture recognition and analysis unit is used for performing high-level semantic behavior analysis on the contact data sequence processed by the preprocessing module; the interaction control and parameter mapping unit is used for receiving the semantic event output by the gesture recognition and analysis unit, analyzing the semantic event into an image control command and generating an executable command sequence; and the image generation unit generates interactive image content dynamically responded in real time based on an internal graph state management mechanism. By introducing the adaptive Kalman filtering and trajectory prediction auxiliary mechanism, the filtering intensity of the contact data can be dynamically adjusted, the efficient suppression of finger jitter and the consistent reconstruction of the contact ID are realized, and the input stability and data continuity under the multi-point touch operation are remarkably improved.

The invention discloses a multi-modal driving test scene generation method and system, and the method comprises the steps: obtaining a plurality of groups of model training samples, each group of model training samples comprising a user natural language description, a driving scene visual image, a risk point thermodynamic diagram, and a safety key scene description code; constructing a driving test scene generation framework, and training the driving test scene generation framework by using the multiple groups of model training samples to obtain a trained driving test scene generation framework; wherein the driving test scene generation framework comprises an image content coding model, a risk point mining model, a thermodynamic diagram coding model and a large language model, the risk point mining model and the thermodynamic diagram coding model are connected in series, and the output end of the image content coding model and the output end of the thermodynamic diagram coding model are connected with the input end of the large language model; and generating a multi-modal driving test scene according to the input user natural language description and the driving scene visual image by using the trained driving test scene generation architecture.

The invention relates to the technical field of image-text recognition, in particular to a big data image-text recognition system and method based on artificial intelligence, and the system comprises an element identification module, a text keyword extraction module, a mapping network construction module, an image-text perception adjustment module, a decision diagram construction module and a format adaptability adjustment module. In the invention, through deep pixel analysis and visual feature matching, accurate analysis of the relationship between the object in the image and the visual label is realized, the accuracy of image-text data identification is improved, the specific association between the image content and the related text data is accurately understood, and the constructed image network is subjected to semantic association calculation, so that the accuracy of image-text data identification is improved. According to the method, the semantic model can dynamically adapt to new data input, the decision path of the image-text content is optimized, the image-text recognition efficiency is effectively improved, the classification decision is more accurate, the image-text recognition quality and speed in the big data environment are optimized, and the processing performance and the application flexibility are remarkably improved.

The invention discloses a cross-modal eye fundus image generation method and system based on a generative adversarial network, relates to the technical field of medical image processing, and constructs an eye fundus focus perception and edge consistency generative adversarial network by taking a cyclic consistency generative adversarial network as a baseline. The core of the method is that a lesion perception mixed attention module is embedded in a bottleneck layer of a generator so as to strengthen the extraction capability of fine features of a lesion area; an edge information extraction module is designed, and key edge features are accurately extracted in combination with Roberts edge detection, wavelet transform and non-local mean denoising; and a joint loss function containing edge consistency loss is constructed, and the semantic consistency of a focus structure during cross-modal generation is ensured by minimizing the feature difference between the source image and the generated image. According to the method, the problems of disordered content, inconsistent structure and unstable training of the generated image in the prior art are effectively solved, and the simulation degree and clinical availability of the generated image are remarkably improved.

The invention belongs to the technical field of data processing, and particularly relates to a potential safety hazard real-time identification method and system based on a multi-modal large model, the system comprises the multi-modal large model, a field knowledge base and a multi-modal inference engine, the multi-modal large model is responsible for visual feature extraction and scene semantic understanding of an input field image, and the field knowledge base is responsible for field knowledge base analysis; processing a natural language query provided by a user; the domain knowledge base stores construction safety related laws and regulations, guidelines and historical cases, and factual basis is provided for the system through a structured storage and efficient retrieval mechanism; the multi-modal reasoning engine coordinates the whole process of visual understanding, task decomposition, knowledge retrieval and report generation, and is a core control module for realizing multi-modal reasoning and decision making. And refined understanding of entities and hidden dangers in a construction scene is realized. According to the system and the method thereof, the image content and the text specification can be dynamically fused, and missing detection or misjudgment caused by modal splitting in a traditional method is avoided.

The invention relates to the technical field of medical image processing, and discloses a method for intelligently describing liver space-occupying lesion ultrasonic image content by using LLM. A liver ultrasonic image sequence, a patient historical medical record text and a blood biochemical index vector are obtained through a multi-modal data acquisition module, and features are extracted through a cross-modal contrast learning network to generate embedded vectors and align the embedded vectors. And inputting the aligned image embedding vector into a dynamic context sensing decoder, and generating a description text semantic mark sequence by using a layered multi-head attention mechanism. The confidence coefficient is evaluated through an uncertainty calibration module, and the text is optimized through a post-processing reordering mechanism when the confidence coefficient is lower than a threshold value. A real-time interaction optimization mechanism is further arranged, and the model is updated according to feedback of doctors. According to the method, multi-modal data are fused, description accuracy and reliability are improved, text quality is optimized, clinical requirements are met, and liver disease diagnosis is assisted.

The invention discloses a robot control method and system, electronic equipment and a storage medium, and belongs to the technical field of artificial intelligence technologies. The robot control method comprises the steps that a robot control instruction is received, and semantic features in the robot control instruction are extracted; acquiring a color image of an environment where the robot is located, performing image segmentation on the color image to obtain a mask corresponding to image content, and determining a semantic category of the mask; fusing the mask, the semantic category of the mask and the semantic feature to obtain a fused feature; inputting the fusion features and preset features into a multi-modal large model to obtain control information; and controlling the robot according to the control information so as to complete a task corresponding to the robot control instruction. The user intention can be accurately recognized, and the flexibility and accuracy of robot control are improved.

The invention provides a large model illusion relieving method and device and a storage medium, and the method comprises the steps: receiving a given image and a prompt text as input, and generating a plurality of text samples through a large-scale visual language model; comparing inconsistencies in the plurality of text samples, searching out contradiction points, and determining illusion factors; generating a problem for the illusion factor; performing question and answer according to the question, analyzing the image content by using a visual question and answer model, outputting an answer for the question, and correcting the prompt text by using the answer; and regenerating an optimized text sample by using the corrected prompt text. The method can effectively relieve illusion generated by a large-scale visual language model and improve the effect of large model generation.