120 results about "Region selection" patented technology

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.

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.

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 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.

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.

Provided are a cloud application deployment method, a cloud application deployment apparatus and a computing device cluster. The cloud application deployment method is applied to a cloud management platform. The method comprises: a cloud management platform acquiring a resource request inputted by a user; on the basis of the resource request, determining a target region from among a plurality of regions, wherein cloud resources in the target region meet the resource request; and deploying a target cloud resource in the target region. The resource request may comprise a category, a specification and / or a service indicator of the target cloud resource. In the method, a user is not required to sense each region in a cloud platform, and a cloud management platform automatically performs region selection on the basis of a resource request of the user, such that resources of each region can be maximally utilized, and the resource utilization rate is thus improved. Moreover, the method shields the user from differences between regions in the cloud platform, thereby realizing regionlessness. In other words, the user is not required to select a region when deploying a cloud application, such that the difficulty for the user in deploying the cloud application can be reduced.

The images shown in "Image Image," "Image Image 1 Showing the Changed State," and "Image Image 2 Showing the Changed State" are images for content selection operations. These images are used to perform operations to select and display content related to the interior and exterior of a vehicle. Specifically, these images have display areas as shown in "Reference Image 1 Showing the Names of Each Part," "Reference Image 2 Showing the Names of Each Part," and "Reference Image 3 Showing the Names of Each Part." The display area shown in "Reference Image 1 Showing the Names of Each Part" is arranged with a text area and a content display area. The text area displays text indicating that the mode is for comparing content and text to guide the user in selecting a comparison target. The content display area is arranged with a content image area and a content text area. The content image area displays an image of content representing the vehicle model or name to be compared. The content text area displays the content text representing the vehicle model or name to be compared and a checkbox. The user selects the content to be compared by touching the content image area or the content text area. This touch operation changes the display from "Image Diagram" to "Image Diagram 1 Showing the Changed State (Reference Diagram 2 Showing the Names of Each Part)." Specifically, in "Reference Diagram 2 Showing the Names of Each Part," the checkbox in the content text area that was touched is highlighted in a color indicating its selected state. In addition, a selection target display area is displayed overlapping the bottom of the content display area. The selection target display area has a selection image area and a button area. Area A of the selection image area contains image area A displaying an image related to the first content selected by the touch operation, text area A displaying text related to that content, and button A. Area B of the selection image area contains image B' showing the silhouette of a car and text B'. In the button area, a comparison button is displayed, and when only the first content is selected, the comparison button is inactivated and cannot be touched. When button A is touched, the selection of the content displayed in area A is deselected.When the content image area or content text area of ​​the second content to be compared is touched, the display changes from "Image 1 Showing the Changed State" to "Image 2 Showing the Changed State (Reference Figure 3 Showing the Names of Each Part)." Specifically, in "Reference Figure 3 Showing the Names of Each Part," the checkbox in the content text area of ​​the second content that was touched is highlighted in a color indicating its selected state, just as with the first touch. Area B of the selection target image area contains Image Area B, which displays an image related to the second selected content; Text Area B, which displays text related to that content; and Button B. When the second content is selected, the comparison button is activated and displayed more highlighted than "Image 2 Showing the Changed State," enabling touch operation of the comparison button. Similarly to Button A, touching Button B deselects the content displayed in Area B. While selecting content to be compared in the content display area, the user can confirm the selected comparison target in the selection target display area displayed at the bottom of the content display area.

This invention proposes a video-based face forgery detection method and system that integrates artifact features and attention guidance to address the problems of existing methods failing to fully utilize artifact details in forged video frames and imprecise decision region selection. The method includes: data preprocessing, extracting a set of consecutive frames from the video and performing face recognition and alignment; artifact highlighting feature map generation, amplifying artifact details to enable the detection model to more sensitively capture forgery traces; attention feature extraction, guiding the network to focus on important regions for decision-making and discovering more valuable discriminative features; and feature map fusion, organically integrating artifact features and attention features to enable the backbone network to learn richer and more effective information. This method integrates artifact features and attention guidance mechanisms to perceive and enhance subtle differences in important facial regions in the video, providing a more accurate and reliable solution for video-based face forgery detection.

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 present disclosure relates to the technical field of target tracking, and proposes an adaptive region selection feature fusion method, a target tracking method and a system. Two sets of region predictors are designed for template features and search features respectively, the template foreground region features and the search target potential region features are extracted, and a feature extraction and adaptive region selection feature fusion network is proposed. In the feature extraction process, the complex attention mechanism is limited within the target region range of the two sets of feature maps, the tracking efficiency of the Transformer target tracking network is improved, and excellent tracking performance is maintained.

This GUI is for a semiconductor device data analysis tool and is used to set the color and line type of sensor information displayed in charts (graphs). When the user clicks the "Change" icon in the upper left section of the display screen, selects a line on any chart that appears in the right-hand central section of the display screen, and right-clicks, a window for scale settings will pop up, allowing the user to change the color and line type of that chart. After selecting the chart color and line type in this window, the user can click the "Save" icon in the upper left section of the display screen to save the sensors and display settings displayed on the chart, and click the "Load" icon to display the chart reflecting the saved sensors and display content.

Sample regions of interest (ROIs) for use in autofocus procedures are identified based on a gradient image of the sample. ROIs with gradient values greater that a threshold are selected, and eigenvalues of the associated image matrices are determined. ROIs with suitable variation in eigenvalues such as at least two relatively large eigenvalues are associated with high contrast features orientated in multiple directions so that such ROIs are suitable for automatic focus and astigmatism correction. Suitable ROIs can also be identified based on a histogram of gradient orientations.

The invention relates to a method of generating an orthoimage, in particular to a computer-implemented method (100) of generating an orthoimage of a three-dimensional structure, the method comprising receiving (110) structure data comprising object data relating to a desired object and an undesired object, selecting (120) a mapping region comprising at least a portion of the three-dimensional structure based on the structure data, and generating an orthoimage of the three-dimensional structure based on the selected mapping region. Selecting (140) mapping content comprising one or more desired objects, and generating (150) an orthophoto displaying the mapping content, where selecting (140) the mapping content comprises at least partially automatically specifying (130) a three-dimensional mapping volume surrounding the mapping area (where the selected mapping content comprises objects located within the mapping volume), and / or performing a segmentation (135) on the surface data to identify desired objects and / or undesired objects.

The present application relates to a kind of feature matching and segmentation-based image similarity comparison method, equipment and medium, it includes: S1: obtaining image sample, construct comprehensive target feature;S2: using clustering segmenter to carry out superpixel segmentation to input image, in combination with similarity map generation and area selection mechanism realizes target area segmentation;S3: in target area, using SIFT to extract feature point, using FLANN matcher to carry out feature matching;S4: to feature point matching result removes abnormal matching point;S5: according to matching point set, calculate homography matrix, calculate the overlap area between images by perspective transformation, obtain overlap rate and overlap rate error;S6: according to the preset overlap rate threshold and overlap rate error threshold, the similarity of two images is automatically judged, and the similar image set is output.The accuracy of similarity judgment is significantly improved by the double optimization of target segmentation and abnormal point rejection.Experiments show that the overlap rate calculation accuracy on building image dataset reaches more than 85%.

This system provides the ability to identify a first region where a change has occurred and to identify a second region within that first region. [Solution] The system comprises at least one computer device. The system includes a first region identification means for identifying a first region where a change has occurred, based on a first image taken at a first time and a second image taken at a second time, which captures at least a portion of the first image; a selection acceptance means for accepting a selection of a second region within the first region, based on a third image capturing at least a portion of the first region, the first image, or the second image; and an address identification means for identifying an address corresponding to the second region. The first image, the second image, and / or the third image are images taken of the ground from above.

To provide a terminal which hardly causes misreading and has high character reading accuracy even in a health measuring machine having no back light.SOLUTION: This health measuring instrument 200 is provided with a photographing means for photographing the health measuring instrument 200, a model information recording means for recording model information of the health measuring instrument 200, a character area specifying means for specifying a plurality of character areas 311 to 333 from a photographed display part 211 based on the model information, a character selecting means for selecting a character having the highest degree of coincidence by template matching for each character area, a character deciding means for abandoning a character string when there is a character whose degree of coincidence by template matching is less than a prescribed value in the character area of at least one digit and deciding the character of the display part when all the degrees of coincidence of the character areas of at least one digit are equal to or more than a prescribed value, and a character deciding means for performing photographing of the display part a plurality of times and continuously deciding the same character twice or more.SELECTED DRAWING: Figure 1

The embodiment of the invention discloses a remote sensing in-orbit MTF calculation method based on a deep learning algorithm. The method comprises the following steps: acquiring a remote sensing image; performing target detection on the remote sensing image by using a preset deep learning model to obtain a target detection result; and processing the target detection result to obtain a modulation transfer function. According to the embodiment, the problems that automation cannot be achieved and subjectivity exists when the on-orbit MTF is calculated through the remote sensing image are effectively solved; automatic detection of a feature region in a remote sensing image is realized through deep learning, automatic calculation of MTF is completed based on a detection result, and the problems of low efficiency, strong subjectivity and the like caused by the fact that a traditional edge method and a pulse method depend on manual selection are solved; the accurate on-orbit remote sensor MTF can be automatically obtained through the remote sensing image, and the efficiency and accuracy of on-orbit satellite MTF calculation are improved.