1832 results about "Image content" patented technology

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 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.

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 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 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.

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 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 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 relates to the technical field of natural language processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a document interpretation and report generation method, device, equipment and medium, which comprises the following steps: receiving an original document set to generate a structured document object, executing optical character recognition on an image content set to generate a recognition text set, the recognition text set and the text content set are combined into a unified text sequence, element item extraction is executed based on the interpretation template parameter set to generate an interpretation element set, a retrieval enhancement context is retrieved and generated from the domain knowledge base, and the unified text sequence, the interpretation template parameter set and the retrieval enhancement context are input into a language model to generate an interpretation result. And generating report content based on the historical report template set. According to the method, automatic closed loop of document interpretation and report generation is realized through multi-modal unified processing and semantic enhanced reasoning, the efficiency is improved, and the manual dependence and compliance risk are reduced.

The invention discloses a method and device for generating medical image question and answer thinking chain data in multiple stages. According to the method, by introducing a multi-stage model collaborative reasoning mechanism, extraction of key image content, enhanced generation of image description and construction of a thinking chain are completed in sequence, so that the accuracy, pertinence and interpretability in a medical image question and answer task are improved; the problems that an existing model is inaccurate in image detail recognition, opaque in reasoning process, generalized in question and answer content and the like are solved.

The invention discloses an image video super-resolution enhancement method based on a degradation generative adversarial network, and relates to the field of image processing, and the method comprises the steps: carrying out the image collection and preprocessing; building and training a super-resolution enhancement model; and carrying out super-resolution enhancement on the image based on the degradation generative adversarial network model. According to the method, an image content self-adaptive dynamic degradation kernel generation mechanism is adopted, the degradation process of the image under different equipment and organization structures is truly simulated, a dynamic up-sampling and residual error correction network guided by the degradation kernel is adopted, the detail reduction capability and the structure fidelity of the super-resolution image are remarkably improved, and the super-resolution image quality is improved. The image texture authenticity and key organization density consistency are effectively enhanced, the balance of training games between a generator and a discriminator is realized, and the model stability and convergence quality are improved.

The invention discloses a user non-inductive resource efficient scheduling method and device for a VR application and a storage medium, and belongs to the field of VR application. The method comprises the following steps: respectively extracting track time sequence features and video content features from user track data and image content data; respectively calculating attention weights of track time sequence features and video content features through a cross attention mechanism, adaptively and dynamically adjusting the attention weights of the features, and generating fusion feature information; inputting the fused feature information into a viewport prediction model, and outputting a viewport position prediction result in a future time window; dynamically selecting a multi-stream data source to perform resource scheduling by taking the viewport position prediction result and the current network state as resource scheduling conditions; and allocating a first code stream channel and network resources meeting the set QOS standard to a high-priority region concerned by the user in the viewport position prediction result. The dynamic change and scene switching of user behaviors are effectively handled, and the user experience quality is ensured.

The invention relates to the technical field of human resource management, and provides an intelligent paper marking system and method for talent selection and recruitment. The method comprises the following steps: cutting test paper according to a preset test paper template by using an image processing technology to obtain a plurality of plates corresponding to the test paper; converting the image content of each section of the cut test paper into a text format which can be edited and processed by adopting a character recognition technology; setting a preliminary scoring standard according to question type information and knowledge point information corresponding to the test paper, and optimizing the preliminary scoring standard by using a large language model; performing semantic understanding and logic analysis on the converted test paper text answers from a plurality of scoring dimensions including semantic accuracy, logic integrity and knowledge point coverage by using two preset scoring large models to give corresponding scoring information, and marking advantage information and defect information in the answers; and obtaining a comprehensive score corresponding to the test paper according to score information printed by each score big model for each question of each test paper.

The invention relates to the field of municipal communication pipelines, and discloses a municipal communication pipeline-based laying construction management system, which comprises the following steps of: acquiring images, position information and voice data of a construction site, preprocessing the acquired content by utilizing a lightweight edge calculation model, and judging whether data exception exists or not; on the basis of spatio-temporal information matching and an image content recognition algorithm, construction data collected on site are compared with the engineering plan model, and whether areas which are not constructed according to specifications are recognized or not is judged; uniformly converting the unstructured data into construction record items in a standard format by using a multi-modal feature fusion method, and judging whether the unstructured data is successfully converted or not; dynamically updating a construction progress state in combination with a project construction plan and real-time acquired data, and generating a visual Gantt chart display; and training the historical engineering data based on a machine learning model, and automatically pushing early warning information to a management terminal. The method has the advantage of improving the communication pipeline construction management capability.

The invention belongs to the technical field of backlight module regulation and control, and particularly discloses a partition dynamic dimming control system and a partition dimming parameter self-learning method. A backlight control module; a sensor module; a power management module; through cooperation of the image signal processing module, the backlight control module, the sensor module and the power supply management module, backlight brightness of different areas can be accurately adjusted according to image content, and when a black or dark hue image is displayed, the backlight brightness of the corresponding area is reduced and even can be completely closed, so that the light leakage phenomenon is reduced, and the display quality is improved. According to the backlight module, the brightness of the image is adjusted, the purity of black is improved, the contrast ratio of the image is remarkably improved, specific brightness adjustment can be conducted on the bright part and dark part details in the image through partition dynamic dimming, the phenomenon of uneven brightness or flickering in different areas is reduced, and the dimming cost of the backlight module is reduced.

System, apparatus, article of manufacture, method and / or computer program embodiments are provided for generating synthetic video advertisements. An example method can include obtaining input data that includes one or more business attributes associated with a business; choosing, based on the one or more business attributes, a video advertisement template that includes a plurality of modular elements; producing, based on the one or more business attributes, textual content and image content for promoting the business; generating, based on the textual content, at least one audio track that is associated with one or more of the plurality of modular elements in the video advertisement template; and assembling a synthetic video advertisement by populating the plurality of modular elements in the video advertisement template with the at least one audio track and the image content.

The invention discloses a method for enhancing image operation positioning by generating a text prompt through a large language model, and the method comprises the steps: inputting an image and an instruction into the large language model (LLMs), and generating a prompt text related to an image tampering region; the prompt text is input into a text encoder (BERT), text features are extracted, and the text features are used for supplementing semantic information missing in image visual features; performing data enhancement processing on the image; inputting the image after data enhancement into an image encoder (PVTv2), and extracting tampering features of the image; by introducing the text prompt generated by a large language model (LLMs), the deep semantic relation and logic relation lacked in the image visual features are supplemented, the defect that a traditional image operation positioning (I ML) method only depends on visual clues is overcome, the model can better understand the semantic background of the image content, and the image quality is improved. Therefore, the positioning precision of a complex scene and a tampered area is obviously improved.

The invention provides a backlight dynamic refreshing method and system based on content change frequency, and is applied to the field of image data processing. According to the method, the inter-frame variable quantity of the current image content of the display screen is pre-collected, whether the image refreshing speed is matched with the backlight response or not is judged, the pixel change rate in unit time is calculated based on the frame difference method, the content change frequency index is reasonably divided, the backlight PWM parameters are dynamically adjusted in combination with user setting, and the display quality is improved. The response coordination of the display screen under the dynamic image is effectively improved, when the frame rate fluctuation is recognized, the gray scale compensation frame is introduced and the brightness change slope is limited, and the problems of smear, splash screen and the like caused by asynchronous refreshing can be relieved.

The invention belongs to the technical field of artificial intelligence and educational informatization, and relates to a multi-mode knowledge graph enhanced intelligent teaching-assistant question-answering system. According to the method, the knowledge graph construction technology, the multi-modal content analysis technology and the large language model reasoning enhancement technology are comprehensively applied, and the semantic understanding, knowledge integration and reasoning generation capabilities of the intelligent teaching assisting system in an education and teaching scene are improved. The related technology comprises layout analysis of textbook documents, semantic description generation of image content, entity and relation extraction of text content, multi-modal knowledge graph construction and question and answer reasoning and natural language generation combined with the knowledge graph. Through cooperative application of the technologies, semantic interconnection can be carried out on various modal information such as texts, images and tables in the textbook, and a searchable and traceable textbook-level knowledge network is formed.

One or more embodiments of the invention provide a digital human rendering method and device, a storage medium and a program product. The digital human rendering method comprises the following steps: analyzing a target voice stream matched with a to-be-rendered digital human to obtain a phoneme sequence and voice rhythm characteristics; a mouth shape control parameter sequence corresponding to the phoneme sequence is determined, all mouth shape control parameters in the mouth shape control parameter sequence are in one-to-one correspondence with all phonemes in the phoneme sequence, and a mouth shape control curve is generated based on the mouth shape control parameter sequence; performing rhythm alignment processing on the mouth shape control curve according to the voice rhythm characteristics, executing rasterization conversion, and generating a mouth shape image block sequence synchronized with the target voice stream; and synthesizing each mouth shape image block in the mouth shape image block sequence with other image contents of the digital human to generate a digital human image frame sequence.

Systems and methods are described for generating image content. The systems and methods may include, in response to receiving a request to cause a sensor of a computing device to identify image content associated with optical data captured by the sensor, detecting a first sensor data stream having a first image resolution, and detecting a second sensor data stream having a second image resolution. The systems and method may also include identifying, by processing circuitry of the computing device, at least one region of interest in the first sensor data stream, determining cropping coordinates that define a first plurality of pixels in the at least one region of interest in the first sensor data stream, and generating a cropped image representing the at least one region of interest.

Techniques for generating modified images using content information and style information are disclosed. First image data comprising image content information is received, and a content encoder generates a first embedding by extracting the image content information from the first image data. A second embedding generated by a style encoder is received, the second embedding comprising style information of second image data. The style information comprises color information and texture information. A decoder generates a modified image using the first embedding and the second embedding, the modified image comprising the image content information of the first image data and the style information of the second image data.

The invention discloses a fine-grained costume image retrieval method and a fine-grained costume image retrieval device based on large language model common knowledge injection. According to the method, firstly, fine-grained visual features of an input image are extracted through an image encoder, and optimization is carried out in combination with a low-rank adapter, so that the representation capability of an image patch level is improved; thirdly, generating attribute-enhanced common knowledge context through a pre-trained large language model, and enriching image attribute representation, thereby helping the model to understand and infer unknown attribute information in an open scene; according to the method, a switchable mode prompt and interpolation mechanism is introduced, and it is guaranteed that agent embedding can be dynamically supplemented when attributes or texts are missing. In the retrieval process, fine-grained image content matching is carried out based on the relation between image features and attribute enhancement contexts through an attribute-guided cross-modal attention mechanism. According to the method, through multi-modal feature alignment and optimization, the accuracy and robustness of clothing image retrieval in an open world scene are improved.

The present invention relates to the technical field of computer image processing, and in particular to an agent-based image analysis and processing method and system, and a storage medium. Image quality analysis and estimation is performed by means of a pre-trained image quality analysis model, and a corresponding processing strategy and a corresponding image processing sequence are generated, thereby achieving automatic image quality analysis and evaluation and facilitating selection of different processing strategies based on different image content, so as to satisfy processing requirements of various application scenarios. A corresponding image processing model is retrieved on the basis of the result of matching between the processing strategy and basic information of each image processing model in a knowledge base, and then called according to the image processing sequence, and an automatic image processing flow control model automatically controls the image processing model to execute image processing according to the image processing sequence and performs monitoring, thereby achieving automatic image processing, reducing the requirement of manual intervention, and saving the time and cost.

According to the sorting control system of the flow cytometry sorting instrument, liquid drop images are shot through a camera, and liquid drop states are quantitatively evaluated and monitored on the basis of image content information. In order to determine the liquid drop breaking state, a liquid drop image stroboscopic shooting mode is designed to observe the liquid drop state, and the STM32H7 provides a synchronous control signal. When the droplet generation state is stable, the droplet breaking image is close to a stationary state. The stroboscope lamp is controlled to be turned on at different moments in a period, so that different states of liquid drop generation can be observed. The time difference between the detection time and the charging time is quantitatively determined through liquid drop delay control, and accurate delay control over sorting is achieved. And finally, a full-automatic algorithm is adopted to calibrate the liquid drop time delay, the liquid drop time delay is continuously tried to be changed, and when the shooting brightness of the sorted CCD camera under a certain liquid drop time delay is the highest, the time delay is considered to be the current liquid drop time delay, so that full-automatic calibration of the liquid drop time delay is realized.

The invention discloses a question answering system and method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: recognizing an answering demand of a question consultation text through a task decision module, generating an answering task corresponding to the answering demand, and sending the answering task to a server; and at least one of the image answering model and the text answering model is called to answer the answering task to obtain the answering result, so that the combined answering demand of the text and the image or the processing of the image answering demand is realized, the information loss caused by transferring the image content into the text by the user is reduced, and the user experience is improved. And the task verification module is used for carrying out exception verification on the answering result, so that the answering accuracy of the question answering system is improved. Therefore, the technical problem that the answering accuracy of the question answering system is low can be solved, and the technical effect of improving the answering accuracy of the question answering system is achieved.