33 results about "Image integration" patented technology

A federated distributed computational system enables secure biological data analysis and genomic medicine through hybrid simulation capabilities. The system implements a hybrid simulation orchestrator that coordinates classical numerical simulations with machine learning models for biological system analysis, while maintaining secure cross-institutional data exchange. The architecture coordinates multi-scale spatiotemporal synchronization across computational nodes, with each node containing local processing capabilities for biological data analysis and privacy preservation protocols. The system implements cellular machinery assembly analysis, real-time patient data integration, and multi-modal image integration with spatiotemporal health data annotation. Through a distributed graph architecture, the system enables cross-species genetic analysis, environmental response modeling, and multi-scale tensor-based data integration with adaptive dimensionality control. The system implements real-time therapeutic response prediction through multi-modal data analysis, enabling research institutions to collaborate on complex biological analyses while maintaining strict data privacy controls.

The invention belongs to the technical field of abnormal behavior person recognition, and particularly relates to an abnormal behavior person recognition method based on multi-source monitoring image integration. The method comprises the steps of obtaining original video data of a plurality of cameras in a target area; judging whether a person completely appears or partially disappears through an attitude estimation and semantic segmentation model, carrying out inter-frame sampling by taking the judgment as a starting point and a stopping point, and retaining time-space information; preprocessing the image frames, and extracting static posture and dynamic behavior characteristics; fusing dynamic behaviors, appearance and position information to realize cross-camera personnel identity association and construct a behavior track sequence; and matching the trajectory with a preset template, and calculating a deviation score in combination with time consistency, position path similarity, an action matching degree and an abnormal fragment confidence aggregation value to recognize abnormal personnel. The method breaks through the limitation of a single visual angle, improves the low-illumination recognition precision, solves the problem of cross-visual-angle identity breakage, and improves the recognition accuracy and traceability of abnormal behaviors.

The invention belongs to the technical field of machine learning, and provides a harmonic reducer fault diagnosis method combining a deep migration network and a fusion sample, which comprises the following steps: collecting dynamic characteristic signals of a harmonic reducer by using a multi-channel sensor, and carrying out full-period division; performing signal decomposition on the dynamic characteristic signals after the whole period division by using a dragonfly optimization algorithm, and extracting an intrinsic mode function set; carrying out Hilbert transform to obtain a Hilbert spectrum, obtaining time-frequency images in three axial directions, carrying out multichannel image fusion on the time-frequency images through an image integration method in a wavelet domain, constructing a fused image sample, dividing the fused image sample into a training set and a test set, and carrying out label calibration; training a CBAM-based fault diagnosis model by using the training set of the calibration label, and performing reverse parameter adjustment by considering domain migration loss and cross entropy loss; and inputting the test set into the trained CBAM-based fault diagnosis model, and outputting a fault diagnosis result. According to the invention, fault diagnosis under different working conditions can be accurately obtained.

The invention discloses a TBD and ISAR imaging integration method and device for a strong maneuvering target under a low signal-to-noise ratio. The method comprises the steps that an original echo signal is acquired and processed; dividing the processed echo signal to obtain a plurality of groups of sub-aperture signals; track inversion is carried out on each group of sub-aperture signals based on a track-before-detect idea, track fusion and polynomial fitting are carried out on all track inversion results, and an expression of a motion track of the target in the full aperture is obtained; determining a group of translation compensation parameters and the order of target motion based on the expression, and performing coarse compensation on the processed echo signal by adopting the group of translation compensation parameters to obtain a signal after coarse compensation; and taking the order of the target motion as prior information, performing estimation of another group of translation compensation parameters on the signal subjected to coarse compensation by using a PSO algorithm, and performing fine compensation on the signal subjected to coarse compensation by using the estimated group of translation compensation parameters to obtain an ISAR image of the target. According to the invention, the parameter estimation efficiency and accuracy can be improved.

The invention discloses a figure scene generation system based on AI interaction. The figure scene generation system comprises a user interaction design module, a figure image shooting module, a figure image analysis and processing module, a figure image processing module, an image integration and correction module, an image generation module and a management database. The figure image shooting module is used for acquiring a user image and a microstructure of a skin surface by using a high-definition camera, the microstructure comprises a skin glossiness reference value, a wrinkle level reference value, pore characteristics and pigmentation, and the user image contains a hair coverage rate; according to the method, an algorithm is used for calculating pore features, color conversion is carried out on a microstructure to obtain pigmentation, a user age evaluation correlation coefficient is obtained through calculation of skin glossiness, a wrinkle level, the pore features, the pigmentation and a hair coverage rate, and a more accurate age evaluation result is provided by improving an age prediction method. The features of skin texture, pigmentation and the like can be quantified more accurately, so that the accuracy of age assessment is improved.

The invention relates to an intelligent hospital management system based on clinical scientific research integration, and the system comprises a clinical scientific research integration platform which is provided with a special disease database data service module which is used for integrating patient clinical data with a patient as a center; the special disease database data service module comprises a special disease scientific research data center which is used for storing high-quality multi-dimensional structured patient data processed by an artificial intelligence technology; the regional decision application module is used for collecting multi-hospital-area data and carrying out hospital diagnosis and treatment and multi-dimensional analysis of medication data; the regional collaborative application module is used for realizing medical resource sharing of multiple hospital areas; and the regional multi-center-line image integration module is used for acquiring image system data of a plurality of courtyards and realizing sharing and mutual recognition. According to the invention, multi-center medical data integration and scientific research collaboration are realized, and the collaborative work and collaborative diagnosis and treatment efficiency is improved.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an inpainted image of a replacement individual in the place of a reference individual in a reference image using a metadata comparison. In one aspect, a system comprises receiving a first image comprising one or more reference individuals and a second image comprising one or more replacement individuals; obtaining replacement metadata and reference metadata; determining, based on the obtained replacement metadata and reference metadata, at least one portion of the first image for replacing corresponding with the one or more reference individuals; and generating a third image that comprises a modification of the first image wherein the at least one portion of the first image is replaced with replacement content, the replacement content being generated based on (i) the one or more replacement individuals, (ii) the replacement metadata, and (iii) the reference metadata.

The present disclosure provides a video image integrating device including an acquiring section acquiring video images in which work of a worker is captured; a detecting section; a judging section; and a generating section.

The invention discloses an intelligent inspection robot with an alarm function, and relates to the technical field of intelligent robots. Comprising a robot alarm mechanism, the robot alarm mechanism comprises a robot body, a mechanical arm is fixedly connected to the upper surface of the robot body, a fire extinguishing mechanism is arranged on the mechanical arm, cut-off tools are symmetrically installed on the mechanical arm, and an image integration box is fixedly connected to the upper surface of the robot body; the upper surface of the robot body is fixedly connected with a signal transmission integrator, and a base expansion unit is arranged below the robot body; the base expansion unit comprises a driving unit, and the driving unit is located below the robot body. According to the intelligent inspection robot with the alarm function, the fire extinguishing mechanism is arranged, the current of the cable is detected through a magnet, and a fire object can be judged quickly, so that the situation that when the cable is on fire, fire continues to spread due to the fact that a wrong fire extinguishing agent is used, and the robot is damaged is prevented.

This invention relates to the field of image management technology, specifically to an image data integration method based on multi-point image acquisition. It constructs an adaptive image integration reinforcement learning training framework, relying on a digital twin scene model to replicate the hardware characteristics and physical interference of the actual acquisition environment. Combined with the dynamic input / output design of the multi-point image integration agent, a priority experience playback mechanism, and stable training optimization logic, it improves training stability and policy generalization, avoiding fluctuations in integration effects caused by hardware parameter drift in real-world scenarios, and breaking the limitations of traditional fixed processes adapting to dynamic scenarios. Simultaneously, it designs an innovative reward system: a sharpness reward eliminates background interference and filters false gradients; a consistency reward focuses on overlapping areas and avoids extreme deviation interference; and a completeness reward distinguishes defect weights to ensure the retention of key defects. These three factors guide the agent strategy to meet industrial inspection requirements.

The present application belongs to the technical field of agricultural informatization, and particularly relates to a crop planting information monitoring method based on multi-source satellite remote sensing data, which comprises the following steps: multi-spatial resolution remote sensing satellite image integration; target attention strength evaluation and sorting; feature extraction based on a full connection neural network; feature fusion based on dense connection; design of an encoding-decoding structure, encoding features, final decoding and network output of crop Sanqi black shading shed analysis results; the present application studies a deep learning model for remote sensing image feature boundary extraction based on a full convolutional neural network with dense connection, designs a multi-feature feature extraction and analysis capability of crop Sanqi black shading shed, further enhances the feature transmission and cumulative integration characteristics of the crop Sanqi black shading shed through a dense attention mechanism, and realizes a new method for automatically extracting a multi-source satellite remote sensing crop black shed by automatically learning and mining while considering multi-level features of the crop Sanqi black shading shed.

The present application provides an image correction method based on infrared area array scanning equipment, which corrects the four surrounding dark corner problems caused by non-uniform of the infrared image collected by the equipment, and the method is also applicable to the image spot problem caused by dust on the detector. The method uses the imaging characteristics of the infrared area array search system: the dark corner distribution and the spot distribution between the continuous frames have consistency in space and continuity in time domain. After the normalized processing of the image integration of each circle, an adaptive matrix is obtained, which quickly solves the problems of infrared image dark corner and spot. It lays a good foundation for the following infrared panoramic stitching, target detection and identification.

The application discloses a kind of based on multi-dimensional game AR processing system, it is related to multi-dimensional game technical field, including reality background acquisition module, character movement data module and virtual-real combination module, the output end of reality background acquisition module is connected with image processing module, the output end of image greying unit is connected with image outlining unit, the output end of image integration unit is connected with background loading module, the output end of background loading module is connected with virtual-real combination module.This kind of based on multi-dimensional game AR processing system, by pursuit technology obtains the coping style of virtual character when walking and meets various obstacles, based on this, when using reality scene as game scene, virtual character can select corresponding response action according to the volume of obstacle to avoid the obstacle in display scene when moving, to deepen the link between virtual character and reality scene, to improve the game experience of playing AR game.

The embodiment of the invention discloses an adversarial attack method and device for improving digital human posture estimation, and the method comprises the steps: obtaining a to-be-processed image which is a monocular image in live broadcast; based on a dual heterogeneous noise generator, generating image-based noise beta1 and text-image integration-based noise beta2 corresponding to the to-be-processed image; optimizing the noise beta 1 based on the image and the noise beta 2 based on the text-image integration through an adversarial loss function and mean square error loss, and generating an adversarial noise beta; the adversarial noise beta is iteratively enhanced by adopting a PGD method to generate enhanced noise beta ', and an adversarial sample is generated based on the enhanced noise beta' and the image to be processed and is used for improving adversarial attack of digital human attitude estimation.

To provide a medical three-dimensional image measuring device in which the location and / or posture of a marker can be changed so as not to block the field of view for the marker of an imaging device by a patient fixing device, a surgical instrument, the medical three-dimensional image measuring device itself, and / or an operator, so that tracking of the marker by the external electronic device can be performed smoothly.SOLUTION: A medical three-dimensional image measuring device 100 includes a light source configured to output light, a camera configured to generate three-dimensional image information by receiving reflected light generated by reflecting the light from an object, a housing 162 including the camera disposed therein and forming an opening 162h through which the reflected light enters into the housing, and a marker 180 disposed on the housing 162 to be capable of changing at least one of a relative location or a relative posture to the opening 162h and including a tracking surface configured to be imaged by an external imaging device for tracking a location and a posture.SELECTED DRAWING: Figure 4

The invention provides a framing image acquisition device and a framing image acquisition method based on flight time, which are used for solving the technical problems that a system is difficult to install and collimate when an existing traveling wave gating image intensifier based on a micro-strip is used for acquiring a multi-framing image, and the imaging quality of the traveling wave gating image intensifier is relatively low. Or the image quality is poor when a multi-framing image is obtained by an ultra-fast compression imaging technology. A plurality of scintillators are arranged on a ray passing path at intervals, rays emit light when passing through the scintillators, the light emitting light sequentially passes through a reflection unit, an image integration unit and an image coupling structure, multiple paths of light signals are integrated into an integrated light image, and the integrated light image is imaged to different areas of an image input surface of an image intensifier type camera. A plurality of images of ray space distribution in different time periods can be obtained through single exposure of a single camera.

The method for operating a medical image processing device using augmented reality according to the present disclosure includes the steps of: acquiring a first image including vascular information and bone tissue information obtained by tomography from a first external device; acquiring a second image including facial structures and facial curvature obtained by 3D photography from a second external device; extracting facial feature points from facial specific information including the vascular information, the bone tissue information, the facial structures, and the facial curvature; aligning the first image to the second image based on the facial feature points; generating a composite image by 3D modeling the first image aligned with the second image; projecting the composite image onto a patient's face; and displaying a first layer corresponding to the vascular information and a second layer including the bone tissue information on the projected image.

A method for text-image integration is provided. The method may include receiving a question related to pairable data comprising text data and image data. Embeddings are generated from the text tokens and image encodings. Embeddings are generated from the text tokens and image encodings. The embeddings include text embeddings and image embeddings. A spectral conversion of the text embeddings and the image embeddings is performed to generate spectral data. The spectral data is processed to extract text-image features. The text-image features are processed to generate inferred answers to the question.

The present disclosure relates to a railway vehicle autonomous driving technology, and provides an apparatus and a method for detecting an obstacle by using multiple camera sensors, the apparatus comprising: a reception unit for receiving two or more pieces of image information generated at the same time from two or more camera sensors, respectively; an image integration unit for converting the two or more pieces of image information into one integrated image; a region of interest setting unit for setting a region of interest in the integrated image, by using location information and control information of a railway vehicle; and an obstacle detection unit for detecting an obstacle in the region of interest.

The present invention discloses a multi-source night-time remote sensing image integration method based on land use data, including: selecting DMSP / OLS and NPP / VIIRS night-time remote sensing images covering the study area in 2013 and land use status grid data; performing coordinate transformation, spatial resampling, and projection transformation to make the coordinate systems, spatial resolutions, and projection methods consistent; registering the DMSP / OLS and NPP / VIIRS night-time remote sensing images; cropping the land use status grid data, DMSP / OLS, and NPP / VIIRS night-time remote sensing images; classifying the two night-time remote sensing images according to land categories to obtain night-time remote sensing images of different land use categories respectively; establishing regression models for the DMSP / OLS and NPP / VIIRS night-time remote sensing images of each land use category respectively and combining them into a regression model group; and based on this regression model group, using the current data to simulate and generate the NPP / VIIRS image of that year. The present invention effectively integrates the DMSP / OLS night-time remote sensing image and the NPP / VIIRS night-time remote sensing image, and better weakens the difference in light intensity between the two night-time light remote sensing images.

The invention relates to the technical field of image recognition, in particular to an exterior wall panel falling monitoring system and method based on visual image recognition, and the system comprises an image integration module, a pixel potential energy calculation module, a tension gradient analysis module, a falling risk recognition module and a monitoring and early warning module. According to the method, the spatial calibration relation between the images is constructed through continuous frame gray extraction and texture direction angle measurement and calculation, so that the surface texture keeps pixel-level matching in a dynamic scene, the response sensitivity of microstructure deformation is improved through combined calculation of the gray change rate and the reflection intensity deviation, and pixel potential energy distribution is established to quantify the local stress state; the continuous tracking of the strain trend is realized, the joint judgment of the potential energy deviation of adjacent frames and the direction consistency deviation can accurately position a potential crack or loose area, misjudgment caused by external illumination, reflection and view angle difference is avoided, and the early recognition and trend evaluation of the multi-period outer wall falling risk are realized. And the stability of the monitoring process and the result credibility are obviously improved.

The invention discloses a BIM-based construction three-dimensional visualization drawing intelligent generation system and method. The system comprises a data access and processing module, a rule base management module, an intelligent view processing engine, an automatic information labeling engine, an image-text integration and typesetting engine and an output and safety management module. The method comprises the following steps: preparing a BIM model and data; configuring a three-dimensional drawing rule through the rule base management module; generating an intelligent view through an intelligent view processing engine; automatically labeling the information through an automatic information labeling engine; integrating a construction scheme through image-text integration and a typesetting engine; and automatically typesetting and outputting drawings through the output and safety management module. The invention relates to the technical field of building informatization, and can solve the problems that in the prior art, three-dimensional drawings are low in drawing efficiency, low in standardization degree, insufficient in information integration level and difficult to update and maintain.

The invention relates to the technical field of image acquisition, in particular to an image acquisition system for wheat phenological period identification, which comprises a data acquisition unit, a self-adaptive adjustment unit, a trigger acquisition unit, an intelligent analysis unit and an image integration and feedback unit. The trigger acquisition unit controls a shooting rhythm and acquires a wheat phenological period image, then the intelligent analysis unit analyzes the wheat phenological period image and diagnoses a phenological state, and finally the image integration and feedback unit summarizes image data, environmental parameters and analysis results. According to the invention, the environment data processing module collects and inputs original illumination, temperature and humidity, outputs normalized data and a comprehensive score, and then inputs the normalized data and the comprehensive score through the parameter decision and control module; and outputting the exposure time, the gain upper limit and the white balance gain to reduce the conditions of image overexposure and noisy point increase which trigger the acquisition unit due to strong light or cloudy and rainy days as much as possible.

The present application relates to the field of environmental monitoring and solid waste treatment, in particular to a landfill microplastic migration path visualization detection method based on fluorescence tracing, comprising the following steps: obtaining polyethylene particle samples and screening, screening target particles, laying particles, setting threshold value checking layout and imaging, applying leachate to collect fluorescence images, extracting signals to construct three-dimensional trajectories, and outputting dynamic image integration graph, in the present application, through polyethylene particle screening and fluorescence coating treatment, the particle signal stability and image recognition accuracy are improved, the particles are uniformly laid in the model with layered structure and observation window, and the layout is checked in combination with the coverage area and path connectivity, the trajectory resolvability is enhanced, the fluorescence images are collected at regular time intervals, the light signal area is extracted, and image stacking and three-dimensional positioning are performed, the continuous reconstruction and structural layer position comparison of the particle migration path are realized, and the spatiotemporal recognition ability of the migration behavior and the clarity of the image expression are improved.

The present invention belongs to the field of machine learning technology and proposes a harmonic reducer fault diagnosis method that combines a deep migration network with fusion samples. The method comprises: using a multi-channel sensor to collect the dynamic characteristic signal of the harmonic reducer and divide it into full cycles; using the dragonfly optimization algorithm to decompose the dynamic characteristic signal after full cycle division and extract a set of intrinsic mode functions; performing a Hilbert transform to obtain the Hilbert spectrum to obtain three axial time-frequency images, and then performing multi-channel image fusion on them using an image integration method in the wavelet domain to construct fused image samples. The fused image samples are divided into training and test sets and then labeled; using the labeled training set to train a CBAM-based fault diagnosis model, while considering domain migration loss and cross entropy loss for reverse parameter adjustment; inputting the test set into the trained CBAM-based fault diagnosis model and outputting the fault diagnosis results. The present invention can accurately obtain fault diagnosis under different working conditions.

The present disclosure provides a video image integrating device including an acquiring section acquiring video images in which work of a worker is captured; a detecting section; a judging section; and a generating section.

The invention relates to an intelligent inspection analysis system for distribution network equipment, which is characterized in that an electronic map can be generated by utilizing a GIS (Geographic Information System) map module, and marking division of an inspection area and compiling of an inspection plan can be realized by utilizing an inspection area division module and an inspection plan compiling module; the inspection execution module can be used for guiding and controlling the inspection unmanned aerial vehicle, so that the inspection unmanned aerial vehicle performs inspection operation according to the inspection plan; the image integration processing module and the three-dimensional imaging module are used for carrying out preprocessing and integration operation on image data shot by the image shooting unit and then forming a three-dimensional visual image through virtual reality and three-dimensional map technologies, and the fault analysis module is used for recognizing and marking an abnormal area in a polling area. The fault processing module is used for carrying out hidden danger analysis on an abnormal area and generating a hidden danger processing report, and the control and operation module is used for generating and distributing an overhaul work sheet; the method has the advantages of safety, high efficiency, accuracy and convenience.

A satellite-ground combined rapid tuning method for an optical remote sensing satellite comprises the following steps: (1) selecting a sky area through imaging simulation according to the star observation capability of a satellite camera and the number of fixed stars; (2) when a satellite initially enters an orbit, completing initial calibration of a star sensor-camera included angle; (3) selecting a sky area according to the current direction of the camera for fixed star imaging, and selecting multiple groups of imaging integration time; (4) keeping the direction of the camera immobile, adjusting a secondary mirror and a focal plane adjusting mechanism, and performing fixed star imaging again; repeating the step for a plurality of times; (5) performing imaging data transmission; (6) interpreting imaging data, and determining the adjustment amount of a secondary mirror and a focal plane; (7) multiple rounds of fine focusing are carried out, and the final adjustment amount of the focal plane is determined; (8) performing an imaging quality index test, if the requirements are met, curing the positions of the secondary mirror and the focal plane, otherwise, repeating the steps (2)-(7); and (9) performing imaging quality interpretation at regular intervals, and if degradation occurs, selecting an opportunity to repeat the steps (2)-(8) to perform adjustment and optimization again.

A portable three-dimensional image measuring apparatus according to various embodiments of the present disclosure includes a light source that outputs pattern light, a camera that receives reflected light generated by the pattern light reflected from an object body to generate a light field image of the object body, and a light path control element that reflects the pattern light so that the pattern light output from the light source is irradiated to the object body and transmits the reflected light so that the reflected light reflected from the object body reaches the camera, a light path of the pattern light irradiated to the object body from the light source and a light path of the reflected light reaching the camera from the object body are coaxial and overlap in an interval between the light path control element and the object body.

The application discloses a kind of sparse ISAR translation compensation and imaging integration method based on super network, mainly solve the precision of prior art under low signal-to-noise ratio condition translation compensation and the quality of ISAR imaging is poor, echo signal-to-noise ratio is not robust, spatial complexity is high and the problem of low efficiency.Its implementation scheme includes: the establishment of sparse observation model under echo defect condition;According to sparse observation model, construct target function based on L1 norm optimization;Iterative solution target function, construct translation compensation and ISAR imaging integration network based on super network;Scattering point is used to generate training set and train integration network;Real-time data is input into trained integration network, and the final ISAR image is obtained by network forward propagation.The application significantly improves the precision of translation compensation and the quality of ISAR imaging and the robustness of network to echo signal-to-noise ratio, reduces time and spatial complexity, and can be used for ship-borne, airborne ISAR system to extract target important information.