189 results about "Region selection" patented technology

The invention discloses a texture perception state space modeling method for an image restoration task, and the method comprises the steps: 1, constructing a region selection mechanism based on texture complexity, and enabling the region selection mechanism to be used for distinguishing a flat region and a high-texture region in an image; 2, introducing a texture modulation mechanism, and performing explicit adjustment on a state transition matrix in the state space model; 3, enhancing the context modeling capability of the model through a multi-direction sensing module; and 4, by combining position embedding and a sequence modeling structure, the capability of the model in the aspects of image structure understanding and spatial information maintenance is improved. The method can effectively alleviate the problem of information loss when a traditional image restoration method processes texture details, improves the structure restoration capability of a complex region, gives consideration to the restoration quality and the calculation efficiency, is suitable for multiple image restoration scenes such as image super-resolution, image rain removal, low-light image enhancement and the like, and improves the image restoration efficiency. And the method has good engineering adaptability and actual deployment value.

The invention is suitable for the technical field of terminals, and particularly relates to a region selection method, electronic equipment and a computer readable storage medium. In the method, a first image feature of a target image and interaction information for the target image can be acquired, and the first image feature and the interaction information are input into a first area mask generation model for processing to obtain a first mask result corresponding to the interaction information. Besides, the target image and the first mask result can be input into a second area mask generation model for processing, a second mask result corresponding to the interaction information is obtained, and a target area corresponding to the interaction information is determined according to the second mask result. Wherein the second area mask generation model can optimize the first mask result according to the target image and perfect the edge of the first mask result and / or the missing area in the first mask result, so that the target area which the user wants to select can be accurately determined according to the second mask result, and the edge of the determined target area is smooth.

The invention discloses a power grid tower cross-domain adaptive type selection method, system and device and a medium. The method comprises the steps that tower data and multi-domain environment data are collected; inputting the tower data and the multi-region environment data into a domain adversarial neural network, and constructing a tower-region calculation model; based on the multi-region environment data, obtaining environment parameters of a target region through an LSTM prediction model, and obtaining tower parameters through a multi-objective evolutionary algorithm; and according to the environmental parameters and the tower parameters, generating a three-dimensional tower model, and performing safety analysis on the three-dimensional tower model to generate an optimal cross-domain adaptive type selection scheme. According to the method, multi-region data cross-domain sharing and privacy protection are realized through federated learning and homomorphic encryption, the domain adversarial neural network, LSTM prediction and a multi-objective evolutionary algorithm are combined, and a BIM modeling and knowledge graph reinforcement scheme is matched, so that the cross-region type selection suitability and precision of the power grid tower are improved, the type selection is safe and efficient, and the method is suitable for large-scale popularization and application. The problems that traditional model selection data are limited, and dynamic optimization is weak are solved.

The invention discloses a full-closed-loop control method for a humanoid robot joint module based on an FPGA (Field Programmable Gate Array), which relates to the technical field of robot control and comprises the following steps of: performing second-order active low-pass filtering and programmable gain amplification on analog signals of an encoder, a gyroscope and a force sensor to convert the analog signals into digital signals; a multi-dimensional feature sequence is constructed, temperature signals of a joint driver and an encoder are collected through a temperature sensor interface, a temperature drift model is constructed, and zero drift compensation parameters are obtained; further obtaining a joint elastic deformation error, and applying the joint elastic deformation error to an encoder signal for online offset correction; outputting a reconfiguration region selection signal based on the lightweight neural network; and triggering bit stream local refresh corresponding to the PR Region according to a region selection signal output by the lightweight neural network. The problem that a control loop is difficult to eliminate flexible errors due to the fact that a traditional rigid model or a constant parameter model is difficult to reflect deformation characteristics under the heat-lubrication-load combined action in real time is solved.

The invention relates to the technical field of disinfection devices, and discloses a disinfection device control method, a disinfection device and a computer readable storage medium, the disinfection device control method comprises the following steps: receiving an area selection instruction, and determining a target processing area according to the area selection instruction; receiving a mode selection instruction, and determining a target processing mode according to the mode selection instruction; according to the invention, a user can autonomously select the target area according to actual requirements, so that energy waste caused by idling of a chamber which does not need to be treated is avoided, and the treatment requirements of different spaces can be met in a targeted manner; the target processing mode is accurately matched through the mode selection instruction, so that the disinfection device only executes the corresponding function in the selected area, it is ensured that the processing effect meets the actual requirement, the operation process is simplified, and on-demand processing can be achieved without complex setting. And the disinfection device can be flexibly adapted to diversified use scenes through the control logic of double autonomous selection.

The application relates to the technical field of data processing, and discloses a radar real-time data transmission processing classification identification method and system.The method comprises the following steps: performing track mode identification on narrowband data of a target radar to obtain a real-time motion mode category of the narrowband data; performing directional region selection on synchronous wideband echo data of the target radar based on the real-time motion mode category to obtain a signal segment of the synchronous wideband echo data; performing sparse decomposition on the signal segment to obtain compact features of the signal segment; performing motion mode compatibility quantification on the compact features based on the real-time motion mode category to obtain category scores of the compact features; performing feedback adjustment on the real-time motion mode category based on the category scores and the compact features to obtain an optimized motion mode of the real-time motion mode category; and performing confidence fusion judgment on the optimized motion mode to obtain a classification identification of the real-time motion mode category; and the application can improve the efficiency of radar real-time data transmission processing classification identification.

The invention discloses a wide-angle video correction method and device based on area selection, relates to the technical field of network wide-angle cameras, and mainly aims to improve the definition of key details in a video so as to enhance the safety of remote operation. According to the main technical scheme, an area video viewing request is obtained, the viewing request comprises view angle configuration parameters of an area, and the view angle configuration parameters specify the coordinates and the size of the area to be intercepted in an original video stream of a wide-angle camera; intercepting a to-be-intercepted area from an original video stream output by the wide-angle camera according to coordinates and sizes in the view angle configuration parameters to obtain a to-be-corrected video area; extracting a local mapping table corresponding to the to-be-corrected video area from a preset global distortion correction mapping table according to the view angle configuration parameters; performing real-time distortion correction on the to-be-corrected video region by using the local mapping table to generate a corrected video stream; and outputting the corrected video stream through a network interface of the wide-angle camera.

The present disclosure provides an image processing method, a system, an electronic device, a storage medium, and a program product. A method comprises: in combination with image content of a target image, performing point sampling on the target image to obtain sampling point information; performing full image segmentation on the target image on the basis of the sampling point information; displaying, on a client interface, the target image which has undergone full image segmentation, the target image which has undergone full image segmentation comprising multiple segmented selectable regions; and in response to a region selection operation triggered by a user for the multiple selectable regions, performing image matting of the target image on the basis of at least one selected region, to obtain a matted image. By using the present solution, the segmented target image can be adaptively changed on the basis of the image content of the target image, which can improve the full image region division effect of the target image, and balance the time consumption of functions; moreover, interaction between the target image which has undergone the full image segmentation and the user can be used to clarify an actual intention of the user on the basis of the region selected by the user, so that excessive matting or missing matting is avoided, and better image matting service can be provided for the user.

The embodiment of the invention discloses a virtual object control method and device, a terminal and a storage medium, and belongs to the technical field of computers. The method comprises the following steps: displaying a master control virtual object in a first orientation in a virtual scene; in response to a first trigger operation on the perspective skill control, displaying a perspective area selection control; in response to an area selection operation on the perspective area selection control, controlling the master control virtual object to change from the first orientation to a second orientation, the second orientation being the direction of the perspective area indicated by the area selection operation; and controlling the master control virtual object to release a perspective skill in the perspective area in the virtual scene, wherein the enemy virtual object in an invisible state in the perspective area has a perspective display effect. By adopting the scheme provided by the embodiment of the invention, the perspective skill release flexibility can be improved, and the display form of the virtual object in the virtual game is enriched.

The invention discloses a physiological signal extraction method based on self-adaptive ROI and ViM dual paths, which comprises the following steps: constructing a first quality perception space-time diagram of a first user and a second quality perception space-time diagram of a second user based on a self-adaptive region-of-interest selection mechanism and a low-rank tensor completion method; extracting a first time sequence prediction signal of the first quality perception space-time diagram and a second time sequence prediction signal of the second quality perception space-time diagram through a time path of the ViM dual-path network structure, and extracting a first frequency domain prediction signal of the first quality perception space-time diagram and a second frequency domain prediction signal of the second quality perception space-time diagram through a frequency path; and fusing the first time sequence prediction signal and the first frequency domain prediction signal to obtain a first physiological signal of the first user, and fusing the second time sequence prediction signal and the second frequency domain prediction signal to obtain a second physiological prediction signal of the second user. According to the method, the robustness, precision and efficiency of physiological signal extraction are improved, and different scene requirements can be considered.

The present disclosure provides a region of interest selection method and device based on vehicle navigation, equipment, medium and product, relating to the technical field of artificial intelligence, especially in the field of autonomous driving. The specific implementation scheme is: determining the standard recognition result corresponding to the road video collected by the vehicle-mounted camera; using a region selection strategy to select a candidate region of interest for the road video to obtain at least one candidate region of interest; performing lane line recognition on the candidate region of interest to obtain a to-be-compared recognition result, and determining the selection result of the corresponding candidate region of interest according to the to-be-compared recognition result and the standard recognition result; determining the target recognition result that meets the result selection condition according to the selection result corresponding to each of the at least one candidate region of interest, and determining the candidate region of interest corresponding to the target recognition result as the target region of interest. The embodiment of the present disclosure realizes efficient and accurate selection of the target region of interest.

System and method for automatic selection of training region selection for deep learning model is disclosed. An identification module 110 identifies equipment shutdowns and excludes buffer region data. A segmentation module 120 estimates operational regions and divides data into predefined operating regions using K-means clustering. A feature analysis module 130 determines top contributing tags and identifies low and high values within operational regions. A domain comparison module 140 compares contributing tags with domain knowledge, excluding regions associated with mapped failures. A data cleaning module 150 ensures data integrity by recalculating statistical measures and eliminating outliers. A filtering module 160 refines data and consolidates regions to reduce training ranges. This integrated approach enhances equipment monitoring and predictive analytics, enabling proactive maintenance strategies and optimizing operational efficiency.

The invention discloses a multi-label classification method based on key region selection and cross-layer feature fusion. Comprising the following steps: carrying out standard preprocessing operation on a chest radiograph image, and carrying out multi-label coding processing on an image label; inputting the preprocessed image into an OfficientNet-B0 network to extract features, performing dimensionality reduction to obtain an original feature sequence, and splicing the original feature sequence with learnable category embedding and position codes to form an embedding sequence; inputting the embedded sequence into a Transform encoder, and extracting deep features of the image through a multi-head attention mechanism and a multi-layer perceptron; constructing a key area selection module, selecting a discriminative area by using an attention mechanism of an encoder and filtering irrelevant information; constructing a cross-layer feature fusion module, fusing features of different levels in the encoder, and inputting the features into a classifier to obtain a multi-label classification result; and designing adaptive class balance loss, dynamically adjusting the attention on different classes of positive and negative samples, and optimizing model parameters through back propagation. The method is an optimization method for effectively improving image multi-label classification performance.

The video creation method includes a setting step of setting, in an imaging region of a first video having a first angle of view, regions having a second angle of view smaller than the first angle of view, a selection step of selecting a selection region which is an imaging region of a recorded video from among the regions, a switching step of reselecting the selection region from among the regions to switch the selection region after the selection step, a first recording step of recording a video of the selection region in a first period, in which the first recording step is executed between a time when the switching step is executed and a time when the first period elapses, a second recording step of recording a video of the selection region in a second period other than the first period, in which the second recording step is executed the second period, and an adjustment step of adjusting at least one condition of an exposure amount of the selection region, white balance of the video of the selection region, or focus of the imaging lens in each of the first period and the second period, where in the adjustment step executed in the first period, an adjustment speed of the condition is faster than in the adjustment step executed in the second period.

The application discloses a robust online learning ship tracking method based on a twin network, which firstly extracts a series of candidate target frames by using an anchor frame-based method, then extracts features of templates and detection frames respectively by using a twin network, uses a region selection network for classification and anchor frame regression, introduces an adaptive online update network UpdateNet, fuses SiamRPN classification scores and online classifier scores, and establishes a shielding judgment mechanism according to the classification fusion scores, finally tests on an inland ship dataset and evaluates tracking performance. The application improves the SiamRPN tracking model, so that it is suitable for inland water scenarios, thereby improving the target tracking drift problem under the mutual shielding condition of inland ships. The application applies the twin method to inland ship tracking, and the performance is better than that of existing methods, and the application shows strong robustness under the shielding scene.

The invention discloses a multilayer semantic information region selection method, system and device and a storage medium, and belongs to the technical field of deep learning adversarial attacks, and the method comprises the steps: for all convolution layers in a convolution layer set, firstly obtaining the gradient of a channel, then adding all gradients along the channel direction, and obtaining Gi; the Gi is normalized to be between [0, 1], up-sampling to an input image size is obtained, and a semantic mask mi to be output through a binary function delta (x) is obtained; and performing element-by-element product on the obtained mi of all the convolution layers to obtain a final mask M '. According to the method, the disturbance applying area is controlled through the mask, and multi-round iterative optimization is performed in combination with the gradient direction, so that the generated confrontation sample has attack effectiveness and also has good sparsity, imperceptibility and mobility. Experiments show that the method is superior to existing methods (such as DAG, FGSM and the like) in white-box and black-box scenes.

1. Name of the product in this design: Map Merging Graphical User Interface for Electronic Devices. 2. Intended use of this design: for use in an electronic device. 3. The key design feature of this product is its graphical user interface. 4. The picture or photo that best illustrates the key design points: Design 1 front view. 5. Design 1 is designated as the basic design. 6. Purpose of the graphical user interface: This graphical user interface is used for map merging. Design 1's main view shows the interface where one area of ​​the map is selected. When the user clicks on another area of ​​the map, the "Confirm" button is activated, as shown in Design 1's interface state change diagram 1. When the user clicks the "Confirm" button, a reminder pop-up appears, reminding the user that non-adjacent areas cannot be merged, as shown in Design 1's interface state change diagram 2. Design 2's main view is a region selection interface with a pop-up reminder window; Design 3's main view is an interface for selecting one region on the map; Design 4's main view is an interface for selecting all regions on the map. Users can achieve corresponding product functions by operating the corresponding controls.

The invention relates to a multi-agent collaborative sensing method based on spatio-temporal joint selection. The method comprises the following steps: acquiring original sensing data of a plurality of agents; constructing a spatial sparse region selection mask; generating a joint sparse feature map; sparse features of the intelligent agent are obtained; obtaining a fused feature map; and carrying out intermediate-stage three-dimensional target detection, and carrying out later-stage fusion on the intermediate-stage three-dimensional target detection and an independent detection result of the collaborative agent to obtain a final collaborative perception detection result. According to the method, the space sparse region is constructed to select the mask, so that the relatively high sensing performance can still be kept in a scene with extremely limited bandwidth; the communication burden is reduced, the transmission content is more focused on a dynamic target which really contributes to collaborative perception, and the information utilization rate is improved; the detection precision is improved by using multi-view complementary information, the missed detection and false detection risks caused by time delay or shielding in intermediate fusion are reduced through a later compensation mechanism, and finally a more accurate and more complete 3D target detection result is obtained.

A method and an apparatus for selecting regions used for film grain modeling are provided. A reconstructed image is obtained from an encoding of an input image. At least one region of the reconstructed image is selected based on at least one coding parameter of the region when encoding the input image, and film grain parameters are determined from the at least one region selected and the input image.

Image processing device (204) comprising the following: an object detection unit (241) that detects an object from an image; a region separation unit (242) that separates an image area in which the object was detected into several sub-areas; a range selection unit (243) that selects a sub-range to be used to calculate a magnification ratio from the several separate sub-ranges; and a TTC calculation unit (244) that calculates the time until collision with the target object from the magnification ratio calculated using the selected sub-area, wherein the image processing device is characterized in that The area selection unit (243) selects an area in which the time to collision is matched and stores the extracted area as a small deformation area if multiple means can calculate the time to collision simultaneously, and selects the stored small deformation area as a sub-area to be used for magnification ratio calculation if only the calculation of the time to collision based on the magnification ratio is possible.

The present application relates to the technical field of pattern recognition, in particular to a blood glucose detection region selection method based on blood perfusion imaging, comprising collecting continuous laser speckle original images of a to-be-detected skin region and a peripheral pulse wave signal of a subject, and constructing a bottom layer data matrix. The image sequence is converted into a perfusion contrast evolution sequence by calculating the ratio of the standard discrete degree and the average brightness of the pixel intensity in the three-dimensional data block. Frequency locking operation is performed using the main frequency of the pulse wave to separate out the perfusion fluctuation component synchronized with the heart beat rhythm, and the phase lag distribution map of each pixel point is determined through cross-correlation time delay analysis. The tissue damping coefficient of each pixel point is obtained by combining the tissue viscoelasticity kinetic damping model inversion, and the pixel set in the target response interval is screened and clustered according to the mapping relationship between the blood glucose concentration and the damping coefficient to determine the blood glucose detection target region. The present application realizes the locking of the detection region through tissue damping characteristic inversion and clustering screening.

The embodiment of the invention discloses a quantum chip wiring method and device based on path template multiplexing. A specific embodiment of the method comprises the following steps: extracting quantum chip geometric information from a quantum chip design file; performing safety area expansion processing on each obstacle area in the obstacle area set to generate an obstacle expansion area; selecting a first quantum bit as a template to generate an optimal path from a first external pin coordinate to a corresponding quantum bit control point position; regarding a wired path as a dynamic obstacle, and sequentially performing sequential path planning on the remaining quantum bits to generate each planned path; performing geometric cross detection on each planned path to generate a cross detection result set; and performing geometric optimization and smoothing processing on each planned path to generate an optimized wiring path. According to the embodiment, full-automatic non-cross wiring of multiple signal paths in the quantum chip can be achieved, and path cross conflicts frequently occurring in traditional manual wiring are eliminated.

The invention relates to a method for diagnosing a malfunction of a slicing machine (100), the method comprising the following steps: Displaying, by means of a display device (140a) of a user interface (140) of the slicing machine (100), a malfunction area selection view, which includes a plurality of possible malfunction areas for the malfunction and a prompt to select at least one malfunction area from the plurality of possible malfunction areas; Selecting, by means of an input device (140b) of the user interface (140), the at least one malfunction area; Displaying, by means of the display device (140a), a malfunction type selection view, which includes a plurality of possible malfunction types for the malfunction and a prompt to select at least one malfunction type from the plurality of possible malfunction types; Selecting,by means of the input device (140b), which indicates at least one type of malfunction, and displays, by means of the display device (140a), at least one malfunction correction proposal based on the at least one malfunction area and / or the at least one type of malfunction.

The present disclosure provides a gene expression analysis method and device, electronic device and storage medium. The gene expression analysis method includes: clustering multiple capture sites according to the spatial transcriptome data of the target slice to obtain biological functional areas; generating a regional distribution map according to the spatial position information of the biological functional areas and each capture site; receiving a region selection operation to determine the target analysis area; determining the biological gradient direction, and dividing the target analysis area into sub-areas according to the biological gradient direction; obtaining the regional expression amount of the target gene in each sub-area according to the spatial transcriptome data; performing gene expression analysis according to the biological gradient direction and the regional expression amount corresponding to each sub-area; gene expression analysis is used to describe the changing trend of the gene expression of the target gene along the biological gradient direction in the target analysis area. The embodiment of the present application can realize gene expression analysis based on the gradient direction within the target analysis area.

This invention discloses a method, system, device, and storage medium for document element recognition and extraction. The method includes: identifying electronic documents based on a region selection network to obtain multiple target candidate regions; determining the text content and text box position corresponding to each target candidate region, and obtaining a first fusion feature corresponding to each target candidate region based on the text content and text box position; performing spatial distance transformation on the first fusion feature and the first visual feature corresponding to each target candidate region to obtain a second fusion feature and a second visual feature corresponding to each target candidate region; and fusing the second fusion feature and the second visual feature corresponding to each target candidate region to obtain document elements. This improves the generalization ability of document element recognition and extraction.

This invention proposes a rock thin section image segmentation method based on feature collaboration and parameter control. The method first performs color space transformation and principal component dimensionality reduction on the image to construct a single-channel structural representation suitable for segmentation. Then, it fuses global semantic features extracted by a deep network with local statistical features of the image. A parameter prediction module generates dynamic weight parameters for the level set energy term to guide the differentiated evolution of the contour in different structural regions. The proposed dynamic parameters are incorporated into a generalized local fuzzy fitting level set model to achieve stable segmentation of multi-scale particles, weak contrast boundaries, and complex texture regions. Finally, region selection and structural consistency constraints suppress noise pseudo-regions. This method significantly improves the adaptability and detail integrity of segmentation under complex lithological conditions, providing a robust and efficient solution for automated rock thin section analysis.

The invention discloses a refined government affair data sharing method and system based on a block chain technology, and belongs to the technical field of big data information processing, and the implementation of the method comprises the following steps: constructing a cross-department and cross-region shared domain circle which is created by a responsible department; configuring related member departments in the shared domain, supporting cross-region selection departments, supporting selection of a plurality of departments, and forming a cross-department and cross-region shared domain; member departments configure directory resources shared in the domain; the member department configures a pre-authorization rule of resources in the domain and stores an evidence on a chain; the intra-domain department uses the pre-authorized resource, configures an application using the resource, and obtains a resource authorization code; performing shared domain thematic statistics: performing statistics on conditions of participating departments in the domain, conditions of resource directories in the domain and use conditions; shared domain thematic display is carried out on a shared portal, and only member departments have the authority to view a shared resource directory in the domain. According to the invention, small circle sharing can be provided, the approval efficiency is improved, and the approval process is simplified.

Provide a system. 【Solution means】 Means for automatically correcting address information, Means for identifying an area based on the corrected address information, Means for selecting a fee plan according to the identified area, Means for generating an estimate based on the fee plan, Means for transmitting the generated estimate to the outside using communication means, Means for immediately examining the information entered by the customer and presenting amendments in real time, Means for selecting and presenting the optimal delivery method and shipping fee, A system including the above.

This invention discloses a hierarchical multi-agent reinforcement learning-based trip recommendation method and system, belonging to the field of trip recommendation technology. The hierarchical multi-agent reinforcement learning-based trip recommendation method includes: acquiring basic POI data, user historical behavior data, and contextual information; constructing a dynamic heterogeneous POI graph and generating POI embedding representations; constructing multi-type user preference representations and calculating the Bayesian user preference posterior distribution, and calculating candidate POI preference scores; constructing a hierarchical multi-agent reinforcement learning model, including, from high to low, a region selection agent, an agent action set generation unit, a POI selection agent, a time scheduling agent, and a path optimization agent; and outputting the final personalized trip route based on the hierarchical multi-agent reinforcement learning model. This invention can efficiently generate executable trips that balance personalization, path quality, and hard constraint satisfaction in large-scale POI scenarios, significantly improving recommendation accuracy and user experience.

The application provides a kind of overlay accuracy correction method based on bearing table, comprising: obtaining the historical warping data of bearing table surface and the historical overlay accuracy data of substrate after lithography on bearing table corresponding thereto;Mapping relationship of historical warping data and historical overlay accuracy data is constructed, and the mapping relationship includes that different overlay accuracy is obtained by the warping of bearing table in different areas of substrate;The warping of bearing table is detected, and according to the mapping relationship, the predicted overlay accuracy of the different areas of substrate placed on bearing table is obtained, and the corresponding overlay accuracy compensation is selected based on the different areas of preset overlay accuracy on substrate.The application, by detecting the warping of bearing table, uses the mapping relationship to compensate the overlay accuracy of different areas of substrate during lithography, not only improves the overlay accuracy during lithography process, but also prolongs the service life of the object table relatively, so as to reduce the cost and improve the utilization rate of machine.