293 results about "Visual Objects" patented technology

A composite pose-estimation algorithm includes a video-object segmentation sub-algorithm (311) configured to determine a mask of a visual object in an image, and an object-pose tracking sub-algorithm (312) configured to track a pose of a visual object over multiple depth-video frames, wherein the pose-estimation algorithm is configured to input a depth video, from which frames are extracted and fed to the video-object segmentation sub-algorithm, which determines respective object masks to be used by the object-pose tracking sub-algorithm alongside the depth video. A method of tracking a pose of a physical object comprises: obtaining a depth video depicting a physical object in a plurality of poses from an input interface (330); forming a storable data item representing the physical object by applying the pose-estimation algorithm to the depth video; and tracking the physical object or a copy thereof using an instance of the pose-estimation algorithm which has been initialized by means of the storable data item.

The invention provides a science and technology information deep analysis method and system based on cross-modal semantic enhancement, and relates to the technical field of science and technology information analys.The method comprises the steps that firstly, a cross-modal semantic anchor point set is constructed, and the cross-modal semantic anchor point set comprises text theme anchor points extracted from science and technology information texts, visual object anchor points extracted from images and the association mapping relation of the text theme anchor points and the visual object anchor points; constructing a semantic conduction path between anchor points based on the cross-modal semantic anchor point set, realizing bidirectional information transmission, generating a cross-modal semantic enhanced representation, performing hierarchical semantic analysis on the enhanced representation to obtain a topic association rule, a technical element dependency relationship and a concept evolution sequence, and integrating the topic association rule, the technical element dependency relationship and the concept evolution sequence into an analysis conclusion; the analysis conclusion is reversely mapped to adjust the association mapping relation strength, an updated set is obtained, finally, a structured science and technology information analysis report is generated based on the updated set, logic connection of all modules is achieved, and comprehensive and accurate science and technology information analysis is provided for users.

The invention relates to the technical field of text verification, in particular to an intelligent text verification method based on a hybrid model knowledge graph, which comprises the following steps of: analyzing a document, separating a text from a visual object, and generating semantics and visual vectors by using a bidirectional encoder and a hybrid visual model; performing form normalization verification by constructing a self-adaptive template matrix; judging the semantic homology of the image-text content by using a cross-modal gating arbiter; the text is converted into a semantic fact triple mapped to a unified space-time coordinate system, and logic irregularity is detected in a domain knowledge graph based on ontology constraint; and finally, summarizing all results to generate a structured verification report. According to the method, cross-modal semantic understanding and knowledge graph reasoning are effectively fused, full-dimension intelligent verification of content forms, image-text semantics and deep space-time causal logic is achieved, and the depth and accuracy of large-scale digital content verification are remarkably improved.

The invention provides a passable area reasoning method and system based on a visual language model, and the method comprises the steps: obtaining the multi-modal data of a vehicle and the current position information of the vehicle; analyzing the multi-modal data, and determining visual features and traffic symbol features; performing spatial position coding on the visual object and the traffic symbol elements, and determining aerial view angle coordinate information; performing semantic alignment on the visual features and the traffic symbol features, and determining a shared embedding representation; constructing a traffic semantic map by fusing, sharing and embedding representation based on a graph neural network and bird's-eye view coordinate information of a visual object and a traffic symbol element; and according to the current position information of the vehicle, the traffic semantic map and a preset traffic rule, generating a bird's-eye view semantic map including a passable area, a no-pass area and a semantic association relationship. According to the method and the device, semantic alignment and consistency expression of visual perception and traffic symbol recognition are realized, and further feasible region reasoning of a complex traffic scene is realized.

The present invention relates to a computer-implemented of controlling an electronic device with a display and a user input. The method includes the steps of: displaying a first visual object within a graphical user interface; wherein the first visual object comprises a plurality of first visual elements, and wherein the first visual elements are displayed in a first visual state in a ring within the graphical user interface; detecting a first user input event at one of the first visual elements; in response to the first user input event, displaying a second visual object within the graphical user interface; wherein the second visual object comprises a plurality of second visual elements, and wherein the second visual elements are displayed in a ring surrounding the first visual object within the graphical user interface; detecting a second user input event at one of the second visual elements; and, in response to the second user input event, controlling the electronic device to access functionality associated with the second visual element. An electronic device and a computer program are also disclosed.

According to an embodiment, a wearable device may display, based on contact on a second surface identified using a touch sensor, a visual object indicating a first position of the touch input on the second surface in a screen through a display. The wearable device may identify, in response to the touch input, a second position in the screen of a gaze identified based on an image obtained through a camera exposed outside at a portion of a first surface. The wearable device may provide, in response to the second position identified within a specified distance from the first position of the visual object, feedback with respect to the touch input. The wearable device may cease to provide the feedback in response to the second position identified outside from the first position by the specified distance.

An interactive computer-implemented system manages a virtual deck of digital cards whose attributes, such as value, status, score, or color, refresh continuously in response to live external data. A server-side ingestion pipeline normalizes event feeds and maps them to a programmable card object model executed on one or more processors. A rendering engine delivers sub-five-second visual updates, while a synchronization layer broadcasts state changes to all connected clients to maintain uniform gameplay. A rules-based constraint engine recalculates permissible user actions as card states evolve, preventing pre-event optimization and preserving competitive fairness. The architecture is device-agnostic, supports accessibility overlays, and extends beyond fantasy sports to any domain where real-time data drives interactive visual objects.

The invention discloses a visual target detection method and device based on vector quantization uncertainty perception, and the method comprises the steps: collecting image data in an open scene as original data, marking the collected original data according to the category, and taking the marked original data as an initial task data set; training a target detection model on the initial task data set, wherein the target detection model comprises a target detection module, a vector quantization module and an uncertainty perception label distribution module; a new category of interest is screened from the explored unknown objects, and image data is collected and marked to serve as a new task data set; meanwhile, selecting a part of samples from the old task data set as a playback sample set; and finely adjusting the target detection model on the new task training set and the old task playback set to realize continuous expansion and evolution of visual target detection. The device comprises a processor and a memory.

An electronic device may include: a display; a camera; a speaker, a micro array comprising a plurality of microphones; and a processor. The processor may identify a position of an external object shown through the display on the basis of an image obtained by the camera. The processor may control the microphone array on the basis of the identified position and obtain an acoustic signal generated by the external object. The processor may interlock with the external within the display on the basis of the acoustic. signal and display a visual object for adjusting a volume of the acoustic signal. The processor may output an audio signal associated with the acoustic signal through the speaker in response to an input received on the basis of the visual object and ensuring adjustment of the volume. An electronic device according to an embodiment may comprise: a display; a camera; a speaker, a micro array comprising a plurality of microphones; and a processor. The processor may identify a position of an external object shown through the display on the basis of an image obtained by the camera. The processor may control the microphone array on the basis of the identified position and obtain an acoustic signal generated by the external object. The processor may interlock with the external within the display on the basis of the acoustic signal and display a visual object for adjusting a volume of the acoustic signal. The processor may output an audio signal associated with the acoustic signal through the speaker in response to an input received on the basis of the visual object and ensuring adjustment of the volume.

Exemplary embodiments of the present disclosure are directed towards a method for detecting and identifying container number in real-time. Monitoring vehicle carrying containers and triggering first camera, second camera, third camera, fourth camera, fifth camera, and laser sensors to capture container views by pre-processing module. Transmitting containers image data to computing device by the pre-processing module. Detecting container number region in container image frames by visual object detection module. Cropping container number region by visual object detection module. Applying two-dimensional Fast Fourier Transform on cropped container number region. Segmenting each character situated in container number region by segmentation and character classification module. Classifying each character situated in container number region by segmentation and character classification module. Arranging characters in order based on relative positions of characters to obtain container number information by segmentation and character classification module. Aggregating container image frames and generating container number by post-processing module.

The present invention belongs to the fields of machine learning and visual object tracking, and provides a visual single-object tracking method assisted by motion information. The present invention models the camera motion and the target motion during the tracking process respectively. For the camera motion modeling method, the present invention uses a feature point matching algorithm to calculate the transformation matrix between adjacent frames, and gives the target offset caused by the camera motion. For the target motion modeling method, the present invention uses a convolutional long short-term memory network to estimate the future speed and position of the target through the target historical motion information. After introducing motion information for assisted tracking, the present invention can significantly improve the ability of the tracking algorithm to cope with challenges such as illumination changes and occlusions, enhance the robustness of the tracking algorithm, and has a low computational amount, capable of meeting the real-time tracking requirements.

The electronic device according to various embodiments include a memory configured to store instructions and at least one processor configured, when executing the instructions, to obtain a first video sequence including a visual object corresponding to a body; obtain a local posture sequence of the visual object that indicates postures of at least one visual element of the visual object in the first video sequence, the at least one visual element of the visual object corresponding to at least one joint of the body; obtain a global motion sequence of the visual object, based on a difference between the postures of the at least one visual element, the postures of the at least one visual object obtained by using the local posture sequence; and obtain a second video sequence, based on the local posture sequence and the global motion sequence.

The invention discloses a visual target tracking method based on natural language and target state information. The method comprises the following steps: (1) constructing a training sample set; (2) constructing a visual target tracking model based on a natural language and target state information; step (3), adjusting parameters of an image-text encoder and loading a pre-training weight to obtain a feature after the text and the first template are fused, a feature of the second template and a feature of the search image; (4) fusing the position information of the target in the sample set and the bounding box information of the target into the features of the second template; step (5), obtaining features after joint modeling; step (6), acquiring a token containing target position information after query; (7) obtaining a predicted target bounding box regression result; and (8) obtaining a final tracking result. According to the invention, the tracking accuracy of the visual tracker based on the natural language is effectively improved.

According to various embodiments, an electronic device include a display, an input circuit, at least one memory and at least one processor configured to obtain a first image; display, in response to cropping an area comprising a visual object corresponding to a potential vehicle appearance from the first image, fields for inputting an attribute for the area, wherein, the fields include a first field for inputting a vehicle type as the attribute and a second field for inputting a positional relationship between a subject corresponding to the potential vehicle appearance and a camera obtained the first image as the attribute; obtain information about the attribute, by receiving a user input for each of the fields including the first field and the second field through the input circuit; store a second image configured of the area in a data set for training a computer vision model for vehicle detection.

A method of a wearable device, includes: in a first state for displaying a first image of a camera of the wearable device: identifying types and positions of external objects included in the first image, displaying, on a display, a second image comprising visual objects corresponding to the identified types and arranged with respect to the external objects, identifying an input for changing to a second state for providing a virtual reality, and in the second state for displaying, on the display, at least a portion of a virtual space, changed from the first state based on the input: maintaining displaying of a first visual object corresponding to a first external object from among the visual objects, based on identifying the first external object that is spaced apart below a threshold distance from the wearable device from among the external objects.

An electronic device displays, in an unfolded state of the electronic device, a screen on a flexible display, based on data detected by one or more sensors. The unfolded state is distinguished by an angle between a first side and a second side of a foldable housing in which the flexible display is disposed. A visual object is displayed for selecting at least a portion of the screen to be displayed on a cover display, in a sub-folded state of the electronic device that is switched from the unfolded state, based on the data. An input selecting at least a portion of the screen is received based on the visual object. In the sub-folded state, within the cover display, the at least a portion of the screen selected by the input is displayed, in response to an input identified based on the received input.

The invention discloses a space-time correlation visual target tracking algorithm based on multi-level feature aggregation, which comprises the following steps of: inputting a video clip consisting of a time token, a reference frame and a search frame into a feature extraction network taking ViT-Base as a basic structure to capture a long-distance global context in a video, and extracting multi-level time token and search frame features; for a multi-level time token, features of different levels are aggregated by adopting a bidirectional strategy, then a learnable time token combined with Fourier transform is introduced, and noise interference caused by multi-level aggregation is suppressed and target features are enhanced by emphatically paying attention to multi-level frequency characteristics of the time token from a shallow layer to a deep layer. Attention features of different levels and axial directions are calculated and fused by using a multi-level cross-axis attention mechanism for search frame features, so that richer target feature representation is obtained. And finally, aggregating the multi-level time tokens and the search frame features by using self-attention, and sending the aggregated features to a prediction head network to realize target tracking.

A method executed by a wearable device including a display system including a first display and a second display facing eyes of a user wearing the wearable device, and a plurality of cameras configured to obtain an image including the eyes, includes: displaying objects at different time points on a screen of the display system, based on the image, identifying gazes looking at the objects, identifying, based on the identified gazes, errors associated with the gazes, wherein the errors indicate differences between display positions of the objects and focal positions of the gazes, and wherein the focal positions have a one-to-one correspondence with the objects, displaying a visual object on a background screen on the display system to move the visual object through partial display positions of the display positions, which are selected based on the errors, and based on another gaze looking at the visual object, correcting the errors.

The invention discloses a method and a system for identifying a multi-modal named entity, and belongs to the technical field of digital data processing. In order to solve the technical problems that in the prior art, when images and text information are processed, shared information and private information are sequentially connected in series, and the shared information and the private information of visual objects in the images are directly connected in series, so that feature information confusion is caused, fine-grained alignment in visual modes is influenced, and cross-modal understanding of a GMNER system is influenced. The shared visual features and the private visual features of the image are extracted respectively, the features of the visual objects in the image and the relation features between the visual objects are distinguished, and then the images are dynamically integrated and projected to the text embedding space, so that the corresponding relation between the visual object entities and the text entities is clearer, and the text embedding efficiency is improved. And the accuracy of fine granularity alignment is improved, so that the comprehensive cross-modal understanding capability of the GMNER system is improved. The method is mainly used for multi-modal named entity recognition.

A method of a wearable device, includes: establishing a communication link with an external electronic device viewable through a display of the wearable device; obtaining information with respect to a gaze toward a first portion of the display; displaying, based on identifying the gaze being adjacent to the external electronic device and based on the information with respect to the gaze toward the first portion of the display, a screen for controlling the external electronic device; displaying, in the screen, a visual object associated with at least one function selected among a plurality of functions based on a position of the gaze with respect to the external electronic device; and transmitting, based on an input with respect to the visual object, a signal to control the at least one function.

This foldable electronic device comprises: at least one processor including processing circuitry; a housing including a first housing part and a second housing part; a flexible display including a first display area corresponding to the first housing part and a second display area corresponding to the second housing part; at least one sensor; an NFC circuit in the first housing part; and a memory which stores one or more programs configured to be individually or collectively executed by the at least one processor, and includes one or more storage media, wherein the one or more programs may include instructions for causing the foldable electronic device to: receive a signal from an external electronic device by using the NFC circuit; and display a visual object associated with the external electronic device and located in the first display area, on the basis of the signal received through the at least one sensor while identifying that the first housing part and the second housing part are partially folded.

This electronic device may be configured to: receive, through a first screen, a first input for trading any one of virtual items of a PC at a specified timing; transmit, to a server, a first signal for registering the virtual item corresponding to the first input; receive, and display, in response to a second input, a second screen including a list of the virtual items of the PC and a visual object indicating that the virtual item corresponding to the first input can be sold on the basis of the specified timing.

The invention discloses a visual target tracking dynamic calculation distribution method based on scene complexity perception, and relates to the technical field of computer vision and artificial intelligence, and the method comprises the steps: firstly constructing a multi-layer visual Transform backbone network comprising a fixed layer and a dynamic layer; a scene complexity analyzer is activated behind the first dynamic layer, pooling, similarity calculation and enhancement processing are carried out on the template and search area features, and the exit score of each dynamic layer is predicted; and then, according to comparison between the score and a preset threshold value, dynamically determining whether to terminate reasoning in advance. And meanwhile, the teacher model knowledge is migrated to a plurality of dynamic layers of the student model by adopting a layered distillation method, so that the prediction precision of the early layer is improved. According to the method, adaptive perception of scene complexity and dynamic allocation of computing resources are realized, the balance of high precision and high real-time performance is achieved on resource-constrained equipment, and the reasoning efficiency of visual target tracking is remarkably improved.

An electronic device may include: a memory storing instructions, a processor(s), and a display. The instructions, when executed by the processor(s), may cause the electronic device to: display a first visual object at a first spot via the display, the first visual object being the topmost visual object among a plurality of visual objects stacked according to an arrangement sequence; display one or more visual objects other than the first visual object among the plurality of visual objects via the display while the first visual object is being displayed to move in response to a user input to the first visual object; and after the user input, display the first visual object at a second spot, dependent on the user input, via the display. The one or more visual objects may be displayed to sequentially follow the movement path of the first visual object according to the arrangement sequence while the first visual object is being displayed to move.

Certain aspects of the disclosure provide a method for enhanced object detection. The method includes providing, to a machine learning (ML) model, a first prompt comprising a first image and a first instruction to output a first description; obtaining, from the ML model, the first description comprising an identification of an unidentified visual object; generating an embedding of the unidentified visual object; obtaining, from a retrieval augmented generation (RAG) database, an embedding associated with a known visual object and satisfying a similarity threshold; retrieving information associated with the known visual object; generating an enhanced context comprising the information associated with the known visual object; providing, to the ML model, a second prompt comprising the enhanced context and a second instruction to output a second description of the first image; and obtaining, from the ML model, the second description including an identification of a visual object associated with the unidentified visual object.

A system and method of identifying objects is provided. In one aspect, the system and method includes a hand-held device with a display, camera and processor. As the camera captures images and displays them on the display, the processor compares the information retrieved in connection with one image with information retrieved in connection with subsequent images. The processor uses the result of such comparison to determine the object that is likely to be of greatest interest to the user. The display simultaneously displays the images the images as they are captured, the location of the object in an image, and information retrieved for the object.

According to an embodiment, at least one processor of a wearable device may display, based on an input for entering a virtual space, on a display the virtual space. The at least processor may display within the virtual space a first avatar which is a current representation of a user and has a first appearance. The at least processor may display within the virtual space a first avatar together with a visual object for a second avatar which is a previous representation of the user and has a second appearance different from the first appearance of the first avatar. For example, the metaverse service is provided through a network based on 5G (fifth generation), and / or 6G (sixth generation).

A computing system can be configured for low-photon-count visual object classification. The computing system can include a photon-detection system that includes one or more cells. Each of the one or more cells can include one or more photon detectors. Each of the one or more photon detectors can be configured to output a photon signature in response to a photon incident on the one or more photon detectors. The computing system can include one or more processors and one or more storage devices storing computer-readable data. The data can include a low-photon-count classification model and one or more instructions that, when implemented, cause the one or more processors to perform operations for low-photon-count visual object recognition. The operations can include obtaining a photon signature from the photon-detection system. The operations can include providing the photon signature to the low-photon-count classification model. The operations can include determining, by the low-photon-count classification model, a classification of a visual object placed in a field of view of the photon-detection system based at least in part on the photon signature. The operations can include providing the classification as an output of the low-photon-count classification model.

The present invention may comprise: a memory for storing instructions and including one or more storage media; one or more cameras; a display assembly including a display; and at least one processor including processing circuitry, wherein the instructions, when individually or collectively executed by the processor, cause the wearable device to: display, on the display assembly, an avatar representing a user; obtain images by using the one or more cameras while displaying the avatar; detect, using at least a portion of the images, an external object gripped by the user's hand; identify a first size of the user's hand by using the at least a portion of the images on the basis of the detection; identify a second size of a visual object corresponding to the external object according to the first size; and display, on the display assembly, the avatar gripping the visual object having the second size.