378 results about "Visual marking" patented technology

There is described a computer-implemented method for designing a stent, comprising: providing for display a first image of a 3D model of at least a portion of a body part comprising an aneurysm; receiving a position for each one of a plurality of reference points chosen along the body part; inserting in the first image a visual marker at the position of each reference point, thereby obtaining a second image; providing the second image for display; for each reference point, calculating a respective diameter; calculating at least one length using the positions of the reference points; and outputting the respective diameter and at least one length.

Techniques are generally described for controllable image-to-video generation. In various examples, a first image representing at least a first object may be received. First input data including a selection of the first object in the first image for animation may be received. Second input data including at least a first bounding box indicating a target location of the first object may be received. A latent diffusion text-to-image model and the first image may be used to generate a first plurality of visual tokens. One or more first grounding tokens may be generated representing a location of the first bounding box. The latent diffusion text-to-image model may be used to generate a video animating the first object based on the first plurality of visual tokens and the one or more first grounding tokens.

The invention provides a multi-modal knowledge graph completion model training method, a completion method and equipment, and relates to the field of computer systems based on specific calculation models. The method comprises the following steps of: extracting an image block feature vector, a word feature vector, a structure feature vector, a corresponding visual mark, a text mark and a structure mark by adopting a multi-modal knowledge graph completion model; based on each visual mark, the text mark and the structure mark, acquiring multi-modal fusion feature data of each entity by a graph attention mechanism; and complementing the original multi-modal knowledge graph according to each piece of multi-modal fusion feature data, and calculating the target loss of the complemented multi-modal knowledge graph to optimize the multi-modal knowledge graph complementing model. According to the method, fine-grained multi-modal feature extraction and application can be realized in the completion model training process, the reliability and effectiveness of the completion model training process can be effectively improved, and then the accuracy and reliability of multi-modal knowledge graph completion can be improved.

The invention relates to a memory enhanced vision-language-action submerged space dynamic fusion automatic driving method. Comprising the following steps: generating a bird's-eye view feature map; extracting scene, agent and map marks from the aerial view feature map, and fusing the mark at the current moment and the previous i historical marks to generate memory enhanced visual marks; the memory enhanced visual mark and the vehicle state information are converted into a submerged space, text input of a driver is marked and unified into the submerged space, the submerged space is represented and fused, and fusion representation is generated; an automatic driving instruction data set is introduced for adjustment, and a large language model adapting to an automatic driving task is obtained; according to the fusion representation, track planning is carried out in an autoregression mode by using a large language model, and path point coordinates are obtained; and designing a transverse controller and a longitudinal controller based on PID (Proportion Integration Differentiation) to track and control the coordinates of the path points. According to the invention, the visual representation capability of end-to-end driving is improved, and the visual-language-action fusion effect is improved.

The invention discloses a data processing method and system, electronic equipment, a storage medium and a computer program product, and relates to the technical field of large model technology and key value caching. The method comprises the following steps: acquiring a plurality of visual marks, a plurality of text marks and initial key value data corresponding to a visual language model; determining a key text mark in the plurality of text marks by utilizing the initial key value data; according to the initial key value data and the distribution positions of the key text marks in the multiple text marks, importance evaluation is conducted on the multiple visual marks, an evaluation result is obtained, and the evaluation result is used for representing cross-modal attention weight distribution between the multiple visual marks and the key text marks; and according to an evaluation result, performing cache compression processing on the initial key value data to obtain target key value data. According to the method and the device, the technical problem that the model reasoning efficiency is influenced due to high key value data caching overhead of a visual language model in related technologies is solved.

The invention discloses a light rail vehicle safety monitoring system based on multi-sensor fusion, and relates to the technical field of light rail vehicle safety monitoring, the system is composed of a plurality of functional modules, and the system comprises a multi-mode sensing module for capturing images of the surrounding environment of a light rail in real time and delimiting a visual mark area; performing three-dimensional scanning on the visual marking area, generating environment point cloud data in real time, and acquiring a dynamic object distance by using a Doppler compensation algorithm; the data processing module is used for calculating object deformation between adjacent frames by adopting an optical flow method and a CNN deformation detection network; carrying out object relative motion calculation according to object shape analysis; carrying out instance segmentation based on MaskR-CNN, and outputting an object contour; meanwhile, a dynamic target clustering algorithm is used for conducting multi-target tracking on the object contour, and three-dimensional coordinates and motion vectors of the object are output in combination with environment point cloud data; and the fusion and analysis module performs time synchronization based on the PTP protocol.

The invention discloses an image-text processing method and device for marking a compression framework. The method comprises the following steps of: extracting visual features; a visual mark screening processing step; a text feature extraction step; and a multi-modal fusion and model processing step. The method has the beneficial effects that the inference efficiency of the MLLMs is remarkably improved under the condition that the visual mark compression frame does not need additional training; through global and local information fusion of the DVTS module and text guide supplement of the TGVC module, the number of visual marks is greatly reduced, meanwhile, key visual information is reserved, and visual-text alignment is enhanced; experiments show that in various image and video benchmark tests, compared with an existing method, the framework has the advantages that the calculation cost is greatly reduced, the model performance is maintained and even improved, and the framework has remarkable technical advantages and application potential.

A pallet loading system may comprise a processor, and memory with instructions stored thereon that, when executed by the processor, cause the processor to receive package loading data, the package loading data including characteristics of a package to be placed on the pallet and characteristics of a loading operator. A placement location for the package on the pallet may be determined using an artificial intelligence (AI) or machine learning (ML) algorithm based on the characteristics of the package and the characteristics of the loading operator and a visual marking (such as a visual projection) may be displayed at the placement location. The system may output an instruction to the loading operator to place the package at the displayed placement location.

Systems herein describe a secured transaction system for accessing a camera stream from a front-facing camera unit of a wearable computing device, identifying a visual marker in the camera feed associated with a payment application, in response to identifying the visual marker, transmitting a request to an operating system of the wearable computing device to isolate access of the camera stream to the payment application and in response to transmitting the request, initiating a transaction using the payment application.

The invention relates to the technical field of historical building protection, and discloses an intelligent identification and diagnosis method for historical building brick wall surface damage, which breaks through the limitations of low efficiency, strong subjectivity and easy detail omission of traditional manual exploration by fusing unmanned aerial vehicle high-precision image acquisition, deep learning semantic segmentation and multi-modal feature analysis technologies. And automatic rapid positioning and accurate classification of large-range wall surface damage are realized. Environmental distortion is eliminated by using a self-adaptive image registration algorithm, fine texture features are extracted in combination with a damage probability prediction model, and complex damage forms such as salting-out crystallization, plant root erosion and weathering spalling are effectively distinguished; through three-dimensional space clustering and visual marking technologies, the damage distribution density and the evolution trend are visually presented, and a reliable basis is provided for scientifically formulating a grading repair strategy. Besides, through establishment of a damage database and iterative optimization of an intelligent diagnosis model, the building health condition can be tracked for a long time, and technical support is provided for preventive protection and sustainable management of cultural heritage.

The invention discloses an automatic driving space planning enhancement method based on a visual marker. The method comprises the following steps: acquiring an original image and text input; processing the original image to obtain image features; processing the text input to obtain text features; generating a text output with a visual mark by using the image features and the text features; the text output with the visual marks is converted, and text output with coordinates is obtained; the accuracy and semantic consistency of spatial understanding in an automatic driving scene are remarkably improved, high synchronization of visual perception and semantic expression is achieved, and the problem of visual and language modal semantic segmentation in an existing method is effectively solved. The analysis precision of the object position, the motion state and the interaction relation in the automatic driving question and answer task is greatly improved, and the decision reliability and the planning naturalness in a complex driving scene can be remarkably enhanced.

The invention discloses a positioning control strategy for an automatic charging process of a Mecanum wheel mobile chassis based on an IMU and a camera, and belongs to the field of automatic control. The method comprises the following steps: firstly, marking a visual mark on a charging pile, adjusting a chassis to a specified angle by reading angle information of an inertial measurement unit (IMU), and adjusting the transverse displacement of the chassis to a specified position by reading a pixel coordinate of a central point of the visual mark (such as a color block) marked in a camera; and establishing a local coordinate system and a global coordinate system of the chassis according to the adjusted pose, further solving a local pose and a global pose by combining an IMU (Inertial Measurement Unit), then solving a chassis target speed according to the local pose and the global pose of the chassis and a chassis speed set value, and finally solving the rotating speed of each wheel according to a mobile chassis inverse kinematics model. Therefore, the vehicle is driven to move towards the target charging pile. The invention provides an automatic charging positioning control strategy based on the IMU and the camera, and the vehicle can be accurately moved to the position of the charging pile, so that the automatic charging function of the vehicle is realized.

The invention discloses an image recognition method and system for stone slab crack detection, and the method comprises the steps: carrying out the preprocessing of a color image of a stone slab, and obtaining a gray image; calculating the gradient amplitude of the grayscale image, determining an optimal threshold value, and obtaining a preliminary edge image; calculating the first order gradient and direction of the preliminary edge image, constructing a Hessian matrix and generating a crack response image; performing edge detection on the crack response image to generate an initial edge image; performing connected domain analysis on the initial edge image to generate a final edge image; and extracting coordinates of crack points from the final edge image, mapping the crack points to the stone slab color image, and performing visual marking to generate a marked image. According to the method, high-precision extraction and fine distinguishing of the cracks under the background of complex stone textures are achieved, the limitation that the cracks and the patterns are difficult to separate in a traditional method is effectively overcome, meanwhile, crack marking is directly carried out on the stone slab original drawing, and the accuracy and practicability of stone slab quality detection are remarkably improved.

System, method and interface for generating data visualizations are provided. The system receives a user input to specify a natural language command directed to a data source. The system also generates a prompt for generating a data visualization based on relevant data fields and data values, rules that characterize the data visualization, and a context free grammar. The system also prompts a trained large language model using the prompt to generate a structured document following a domain-specific schema based on a shorthand notation. The system also uses a parser that uses the context free grammar to map the structured document to a visual specification. The visual specification specifies the data source, visual variables, and data fields from the data source. The system also generates and displaying a data visualization based on the visual specification, including displaying visual marks representing data, retrieved from the data source, for the data fields.

The invention relates to a railway tunnel construction crack detection method based on machine vision, which is applied to the field of tunnel detection, and comprises the following steps: marking a crack by using the flight coordinate of an inspection unmanned aerial vehicle body on the basis of the existing unmanned aerial vehicle detection, and then controlling the inspection unmanned aerial vehicle body to approach and confirm; then, the marking bomb is ejected to the crack in a manual control mode, so that visual marking is carried out, researchers can know crack information through data and can rapidly and accurately find the crack through marking to carry out field research and analysis, and therefore the detection efficiency and accuracy are effectively improved; in addition, the method can effectively avoid defects of inspection robots for top lining detection in the prior art, and effectively reduce interference of factors such as distance, dust and vibration.

The invention discloses a customer service information generation method and system based on multi-modal intention recognition, and the method comprises the steps: obtaining customer service session data, and carrying out the heterogeneous data classification operation of the session data; performing feature extraction operation on each piece of parting modal information; establishing a semantic bridging matrix among the modal features, performing hierarchical attention routing, and outputting an intention label; a knowledge graph is injected, the knowledge graph comprises a static knowledge base, a real-time service flow and a user historical portrait, and three-source dynamic knowledge is output; constructing a decision tree based on the intention label, the three-source dynamic knowledge and the space-time identifier; and executing a corresponding text generation operation, a visual mark generation operation or a voice synthesis operation based on a response action corresponding to the leaf node of the decision tree, and generating customer service information. According to the method and the system, the buyer intention recognition accuracy of the multi-modal session data can be greatly improved, and the response delay time of generating the customer service information is shortened.

The embodiment of the invention provides an aircraft positioning method and device, an aircraft, a medium and a program product, and relates to the technical field of flight positioning. The method comprises the following steps: acquiring a to-be-identified image, and identifying a visual marking object; matching and associating the two-dimensional coordinate information corresponding to each visual marking object with the actual three-dimensional coordinate information; wherein each piece of actual three-dimensional coordinate information is obtained based on a preset coordinate transformation rule and is represented by a unified transformation matrix parameter; and constructing and solving an objective function about the two-dimensional coordinate information, the actual three-dimensional coordinate information and the to-be-solved aircraft pose, and obtaining the aircraft pose information according to a solving result. According to the embodiment of the invention, the position information of each mark is represented through the transformation matrix based on the unified coordinate system, so that different marks can be arbitrarily arranged in different planes, and the application flexibility of a visual mark positioning technology in a complex scene is effectively improved.

This disclosure relates to methods, systems, and techniques for visualizing external audio captured by a microphone of the vehicle. Using the techniques described herein, external audio signals may be interpreted and converted into clear visual indicium that can be understood by individuals associated with the vehicle, such as passengers inside the vehicle or individuals awaiting pickup.

The invention belongs to the field of computer vision, and particularly relates to a video space-time understanding method and device based on a multi-modal large model and a medium. A multi-modal large language model is connected with a mask segmentation model, and video features are encoded by using a multi-modal encoder; the time tasks and the space tasks are represented by adopting different numbers of sampling frames and visual mark forms. The visual mark is aligned to the text space and then input into the large language model together with the text mark, and a corresponding text answer is obtained through decoding. For a time task, a timestamp is directly extracted from a text answer, and spatial information passes through lt; sEGgt, SEGgt; and the mark code is embedded as a prompt input to the mask decoder, so that mask generation of the sampling frames and mask propagation of the whole video are realized. Compared with the prior art, the method has the advantages that joint training of fine-grained video time-space understanding is realized, and more accurate event time-space positioning can be realized besides overall understanding of the video.

The invention belongs to the technical field of optical observation equipment, and relates to a space observation optical technology, in particular to a space telescope on-orbit assembly method, a computer program product and a system. A space telescope on-orbit assembly method comprises the steps that a target sub-lens module is determined based on an installation sequence, and the target sub-lens module is locked in a storage module; after a mechanical arm module is controlled to move to a first set area where the target sub-mirror module can be grabbed, a visual mark on the target sub-mirror module is recognized, and the visual mark comprises pose information and identity information of the target sub-mirror module; calculating a first relative pose of a butt joint interface fixed between the target sub-mirror module and the mechanical arm module relative to the mechanical arm module based on the visual mark; and after the mechanical arm module is controlled to grab the target sub-mirror module based on the first relative pose, the target sub-mirror module and the storage module are unlocked. The technical problem that the precision requirement of a visual camera is high is solved.

The present disclosure describes techniques for generating video descriptions using a machine learning model. A plurality of sets of visual tokens corresponding to a plurality of frames of a video is generated. A first type of tokens is generated by implementing temporal pooling on the plurality of sets of visual tokens corresponding to the plurality of frames. A second type of tokens is generated by compressing each of the plurality of sets of visual tokens corresponding to each of the plurality of frames. A third type of tokens is generated by applying cross-attention between each of the plurality of sets of visual tokens and a fourth type of tokens including text tokens generated based on an input text query. A text description of the video is generated based on the first type of tokens, the second type of tokens, the third type of tokens, and the fourth type of tokens.

Systems and methods herein describe a real-time scan system that identifies a visual marker in an image frame, identifies key points from the image frame, compares the image frame with a database of visual marker images using the identified key points, identifies a set of matched images in the database of visual marker images, filters the set of matched images using a neural network trained to analyze the image frame and set of matched images, detects the visual marker in an image of the filtered set of matched images, causes presentation of a notification on a display unit of the computing device, receives a selection of the notification, and in response to receiving the selection of the notification, causes presentation of multi-media content associated with the visual marker on the display unit of the computing device.

The invention discloses a man-machine cooperation ship maintenance system, and relates to the technical field of robots, and the system comprises a scene perception interaction module, at least one multi-degree-of-freedom mechanical arm, a plurality of ship cabin base adapters, a plurality of ship cabin base adapters with visual marks, and a plurality of operation tools. The two ends of each multi-degree-of-freedom mechanical arm are each provided with a modularized mechanical arm end executor. Each modular mechanical arm end effector is provided with a quick-change interface and a first visual sensor, the quick-change interface is used for locking a ship cabin base adapter or an operation tool, and the first visual sensor is used for identifying a visual mark of the ship cabin base adapter; the scene perception interaction module is used for identifying a control instruction of an operator and controlling the multi-degree-of-freedom mechanical arm to execute a maintenance task according to the control instruction; the control instruction is a voice instruction, a gesture instruction or a bioelectricity information instruction. According to the invention, the efficiency and safety of maintenance work can be improved.

The invention provides a visual information fusion method, device and equipment, a medium and a computer program product, and the method comprises the steps: carrying out the coding of an input image and an input text, and obtaining a target mark sequence; the target marking sequence comprises a visual marking sequence and a language marking sequence; determining a fused visual context based on the attention of the language marker sequence to the visual marker sequence; based on the fused visual context, determining a modulation parameter of each target mark sequence; and based on the adjustment parameter, determining a semantic understanding result of the input text. According to the method, a dynamic feature modulation mechanism is introduced into each layer of the large language model, so that visual information can adaptively adjust text representation, and the ability of the large language model to understand multi-modal information is enhanced.

The invention relates to the technical field of engineering construction measurement and control, and discloses a calibration method for positioning and attitude determination of a heading machine, which comprises the following steps: constructing a data acquisition module, a route making module, a real-time dynamic monitoring module, an execution module, a feedback module and an optimization module; the data acquisition module is used for completing comprehensive acquisition of multi-source data of the heading machine, a route, an environment and an obstacle, the route making module is used for fusing the multi-source data to carry out path planning and error compensation, and the real-time dynamic monitoring module is used for realizing heading machine pose tracking and emergency correction through visual marking points and a laser scanner. The execution module is used for converting an optimal tunneling path into a driving motor control instruction, the feedback module is used for collecting deviation data of an actual operation pose and a planned path in real time and returning the deviation data, the optimization module realizes long-term self-adaptive optimization of the system, and finally, precision, safety and high efficiency of the tunneling operation of the tunneling machine in a complex underground environment are realized.

A control system manages a plurality of mobile machines each equipped with a visual perception system to capture perception data. The control system via an onboard controller applies an object detection operation to the perception data to detect a detected marker position corresponding to a visual marker. The onboard controller assesses an assessed marker heath status with respect to the detected marker position and transmits that to a central worksite server. The central worksite server aggregates the assessed marker health statuses from a plurality of mobile machines to determine an aggregate marker health status for the visual marker.

The present disclosure relates to a method and apparatus for generating a video, an electronic device, and a computer program product. The method includes obtaining a visual token for generating an image frame in the video. The method further includes obtaining a control token for constraining position information of an object in the image frame. In addition, the method also includes generating the image frame in the video based on the visual token and the control token, where the object in the image frame satisfies the position information.

Described herein are techniques for generating captions for augmented reality (AR) effects in videos using an adapted multimodal large language model (MLLM). The technique involves sampling frames from base and AR-applied videos, combining them into concatenated frames, and processing them with a hybrid vision encoder. Visual tokens are projected into a language model token space, reshaped, downsampled, and interleaved with text tokens. A fine-tuned large language model processes this input sequence to generate AR effect captions. Optical character recognition extracts text from AR frames, which is combined with generated captions and metadata to produce merged captions and content tags. This approach enables accurate description of temporal AR effects and facilitates downstream applications like search and ranking.

The present application relates to a method for controlling a lighting effect of a spliced lamp, as well as a device, an apparatus, and a medium. The method comprises: displaying a spatial topological graph of the spliced lamp in an effect editing region of a graphical user interface; displaying a visual marker of a motion base point of the spliced lamp in the effect editing region in an initialized manner; updating the motion base point according to the latest position of the visual marker relative to the spatial topological graph; driving the spliced lamp to play a lighting effect by using a lighting effect playing instruction that is generated on the basis of the motion base point, such that a mapping position of the motion base point in the physical space serves as a benchmark reference point of a motion process of the lighting effect.

The invention provides a VLA-based agaricus bisporus autonomous picking method and system, and relates to the technical field of cultivation, and the method comprises the steps: obtaining picking task description information and visual observation data, and coding the visual observation data to obtain a visual feature sequence; constructing a multi-scale visual feature map based on the visual feature sequence, and generating an object-level visual mark sequence; encoding the picking task description information into a text marking sequence, mapping the text marking sequence and the object-level visual marking sequence to a unified semantic space, and splicing the text marking sequence and the object-level visual marking sequence to form a joint semantic sequence; and inputting the joint semantic sequence into a pre-trained VLA model, performing autoregression prediction to generate an action mark sequence, decoding according to the action mark sequence to obtain a picking control instruction, and driving a picking device to complete picking, so as to improve the key visual information extraction capability and enhance the accuracy and robustness of action generation in a complex unstructured cultivation scene.