121 results about "Visual patterns" patented technology

A method, apparatus, non-transitory computer readable medium, and system for image processing include obtaining a pattern prompt and a text image, where the pattern prompt describes a visual pattern and the text image depicts text, generating a pattern image based on the pattern prompt, where the pattern image depicts the visual pattern, and generating a patterned text image based on the pattern image and the pattern prompt.

Techniques for automated tagging of visual content are described. A pairwise model is used to encode images and videos with a vision encoder, and encode keywords and phrases from an interest graph with a text encoder. Similarity layers compare these cross-modality embeddings by calculating distance in a shared embedding space. Scores indicate the association between visual features and text. A pairwise loss function brings together matched pairs while separating non-matches during training. Scores exceeding a threshold tag content with relevant keywords and phrases. The pairwise architecture relates images and text despite limited associated text. It leverages vision-to-text understanding for accurate tagging without per-class labels. Contrastive similarity techniques associate visual patterns with textual concepts. Automated tagging organizes user-generated content by topics using this scalable cross-modality approach.

The invention relates to the technical field of power equipment state monitoring, and discloses a cable state fault prediction method based on multi-source data fusion. According to the method, a digital twinborn model is constructed by acquiring multi-source data, and a theoretical health baseline changing along with working conditions is simulated and calculated in real time. And comparing the base line with the measured data to generate a thermoelectric coupling matrix with quantitative deviation, and diagnosing the degradation state according to the thermoelectric coupling matrix. And when the deviation exceeds a threshold value, performing attribution analysis by using a gradient boosting decision tree model, and generating a visual spectrogram associated with the partial discharge and the root cause. And finally, the defect type is judged by intelligently identifying a high-risk visual mode on the spectrogram, and the high-risk state is predicted. According to the method, working condition interference is filtered through dynamic reference, causal diagnosis is performed, the problem of high false alarm rate in the prior art is solved, and the prediction accuracy is remarkably improved.

The invention discloses a speech emotion recognition method based on multi-modal feature fusion, and the method comprises the steps: designing a multi-modal speech emotion recognition model, enabling an audio to serve as a main mode, employing the function of wav2vec2.0, and combining a text mode with a visual mode to enhance the performance of emotion recognition based on the audio; the self-attention mechanism, the GRU and the LVC are combined to extract global and local information from a visual mode, the global information is extracted only from text features, visual clues and text clues related to emotion can be comprehensively understood, the extracted text and visual features are integrated into a wav2vec2.0 model, multi-mode representation of multi-level audio information is formed, and the multi-mode representation of the multi-level audio information is realized. And meanwhile, the discrimination capability of the model is enhanced by utilizing multi-modal homogeneous feature difference learning, and the proposed model is evaluated on two data sets, so that the model is proved to have excellent robustness and generalization compared with the existing most advanced model.

Systems and methods for mounting a solar panel are provided. The method includes obtaining an image of the solar panel and the mounting structure during installation. The method further includes pre-processing the image by compensating for camera internal parameters or distortions, correcting the image, and / or determining depth information. The method further includes detecting the solar panel by inputting the image into a neural network. The method also includes a first post-processing to calculate a first panel pose based on the output of the neural network. The method further includes generating a control signal based on the first panel pose for operating a robot controller to install the solar panel. In some embodiments, the method further includes a homographic transformation to obtain a second panel pose based on the first panel pose and a visual pattern or reference on the solar panel, and further to generate a control signal based on the second panel pose.

The invention discloses an intelligent three-dimensional visual perception method based on a parking space scene, and the method comprises the following steps: S1, responding to the illumination change of a parking space environment, and automatically switching a passive or active visual mode; s2, in a passive vision mode, capturing a natural light image by using a binocular camera, and reconstructing a three-dimensional scene; s3, in the active visual mode, projecting a coding pattern, and reconstructing a three-dimensional scene through pattern change; s4, performing semantic segmentation on different objects in the passive visual mode or the three-dimensional parking space scene image in the passive visual mode, and endowing semantic tags; s5, mapping the target object in the two-dimensional image into the three-dimensional scene, and obtaining the position and posture of the target object; by automatically switching the passive vision mode and the active vision mode, the adaptability and the recognition precision of the parking space scene under different illumination conditions are remarkably improved; in a passive vision mode, the system utilizes natural light to capture images, a binocular camera system and a stereoscopic vision technology to accurately reconstruct a three-dimensional scene.

A system for guiding a user with heart rate variability feedback may include a computing device comprising a heart rate sensor, one or more processors, and memory having stored thereon computer program code that, when executed by the one or more processors, is configured to cause the one or more processors to receive user input, responsive to receiving the user input, cause an audio output device to output an audio content associated with a default breathing pattern, dynamically receive interbeat interval data from the heart rate sensor, dynamically extract one or more characteristics from the interbeat interval data, cause the display to indicate a dynamic visual pattern based on the one or more characteristics, dynamically determine a second breathing pattern based on the one or more characteristics, and dynamically cause the audio content to change based on the second breathing pattern.

A monocular and / or a binocular system may be used to generate one or more images, or videos, of a subject's retina by scanning in one or two directions in one or two dimensions to, for example, measure and / or report fixation, smooth pursuit, and / or saccadic responses of the subject's eye. The scanning and / or image generation process may be optimized to correct for distortions in the subject's eye and / or improve SNR among one or a plurality of retinal images. In some cases, the retinal images may be used to train a retinal feature detection model and / or algorithm that may be used to make predictions and / or inferences regarding a visual pattern showing features of subsequently received retinal images. These models and / or predicted patterns present within the models may be used by the retinal feature detection model and / or algorithm to monitor features of the retina and, in some instances, track voluntary and / or involuntary eye motion.

A tamper-evident item authentication label may include a substrate layer that includes a first substrate side and a second substrate side, a first adhesive layer, a second adhesive layer, a first unique indicium, a second unique indicium, and a plurality of cuts across the first unique indicium and the second unique indicium extending through at least a portion of a thickness of the substrate layer. The adhesive layers may be deposited on the substrate sides and arranged to fuse the tamper-evident item authentication label with a first and second surface. The first unique indicium may be deposited on the substrate layer and encoding a first unique identifier that links the tamper-evident item authentication label to one or more records in a remote database system. The second unique indicium may be deposited on the substrate layer. The second unique indicium may include a stochastically created visual pattern.

The invention provides a generalizable large model-oriented diversity induction bias learning method. The method comprises the following steps of: constructing an input pair image of complete prompt description according to a training sample set; extracting image features and text features of the input pair by using the large generalization model; and for the same pair of inputs, performing supervised training by using cosine similarity loss. In order to meet the semantic gap between a language and a visual mode, the invention provides a text-level inductive deviation, and detailed information of each category is provided by supplementing a plurality of descriptions generated by LLM in a prompt text; in order to enable the model to well capture inductive deviation, designing a phrase adapter for a text encoder to explicitly explore connection between adjacent words; designing a space adapter for the image encoder to allow the model to see more local relationships and details; according to the method, overfitting is reduced through optimization level inductive bias; this is accomplished by a dynamic training strategy that enables the model to learn fitting states of varying degrees.

The invention relates to the technical field of image recognition, in particular to a multi-resolution fusion image recognition method based on a human vision mode, and the method comprises the steps: obtaining a target image through image collection equipment, carrying out the preprocessing of the image, carrying out the processing of the preprocessed image according to different resolutions, and generating images of a plurality of resolutions; the method comprises the following steps of: extracting features of images with different resolutions through a convolutional neural network, performing weighted fusion on the extracted multi-resolution image features, and then introducing a human visual attention mechanism to optimize the fused features, thereby improving the precision of image recognition. And finally, inputting the optimized multi-resolution features into a support vector machine model, performing image recognition and classification, further removing noise and enhancing classification confidence through a post-processing step, and outputting a final image recognition result. According to the method, the image recognition precision in a complex scene can be effectively improved, and the method has relatively high robustness and is suitable for various image classification tasks.

The invention provides a full-slice image classification method and system based on multi-branch attention and random instance masks and a medium, and the method comprises the steps: obtaining a full-slice digital pathological image, carrying out the image segmentation, and obtaining a plurality of instances; performing feature extraction based on a feature extraction network to obtain an instance feature sequence; carrying out parallel analysis on the instance feature sequence based on a multi-branch attention mechanism, executing a random Top-K instance mask operation, and carrying out weighted summation on the instance feature sequence based on the attention distribution of each attention branch; aggregating the packet level feature representations of all the attention branches, generating a comprehensive feature representation of the full-slice digital pathological image, and obtaining a classification prediction result; by setting a plurality of parallel attention branches, different branches are promoted to actively learn and capture a plurality of different visual modes existing in the full-slice digital pathological image, so that tumor heterogeneity can be effectively represented, and the generalization ability of full-slice digital pathological image classification is improved.

The invention provides a differentiated service classification and multi-path traffic scheduling method and system, and belongs to the technical field of network communication, the quintuple, packet size and timestamp characteristics of each data packet are captured in real time, a timestamp is mapped to an image horizontal axis, the packet size is mapped to a longitudinal axis after standardization, and discrete data is converted into a continuous visual mode; classifying the traffic in the network by using a pre-trained classification model, and distributing corresponding service requirements and priorities for each traffic type; distributing an INT data packet for each path, and detecting and monitoring the time delay, throughput and packet loss rate of the path in real time; and selecting an optimal path by adopting a CMAB algorithm in combination with the priority of the flow, the service demand, the real-time performance of the path and historical scheduling feedback. According to the invention, comprehensive path quality evaluation is realized; the high-priority service is ensured to obtain stable and high-quality transmission quality; and the accuracy and robustness of path selection are improved.

The embodiment of the invention provides an agent-based task interaction method and device, equipment and a storage medium. The method comprises the steps that in response to a received request for indicating a target task, an execution plan of the target task is determined through an intelligent agent, and the execution plan indicates at least one subtask; during execution of the target task according to the execution plan, presenting a content object representing an intermediate execution result of the target task, the content object having an at least partially structured visual pattern, the intermediate execution result comprising at least a portion of a corresponding execution result of the at least one sub-task; in response to receiving feedback for at least a portion of the content object, a response to the feedback is provided with the agent.

A flotation froth video characterization method based on bubble cluster visual pattern statistical modeling comprises the following steps: firstly, performing feature extraction on continuous T frames of froth images by using an OfficientNet encoder to generate a feature map corresponding to each frame; the method comprises the following steps of: firstly, dividing each frame of feature map into a cluster mark sequence with the length of K by using a Patchy module, dynamically grouping cluster marks by using a mask with a U-Net structure to generate a T * K grouping index matrix, and finally introducing a double-branch additive attention Transform module, and the global branch is used for extracting an interactive dependence and overall evolution mode between the bubble clusters. According to the dynamic additive attention strategy provided by the invention, the modeling capability for the long-distance dependency relationship is effectively reserved while the calculation complexity is remarkably reduced, and more accurate visual analysis is provided for monitoring of the flotation process.

The invention discloses a traffic flow prediction method based on cross-modal interactive geographic image coding, and relates to the technical field of intelligent traffic systems and data processing. The method comprises the following steps: firstly, acquiring historical traffic data and a road network geographic image, and respectively constructing an embedded representation containing space-time periodicity and extracting node-level visual features; secondly, through a hybrid cross attention mechanism, utilizing a learnable global visual token as an abstract agent, compressing visual features and performing cross-modal alignment with the dynamic space-time representation to generate an enhanced space-time representation; meanwhile, a visual relation mode is constructed based on local visual patches between the nodes, and refining is carried out through a general relation matrix; and finally, dynamically fusing time, space and visual relation characteristics by using an adaptive gating mechanism, and predicting future traffic flow. Visual modes such as road geometry can be effectively captured, the problem of dynamic and static heterogeneous mode alignment is solved, and the accuracy of traffic prediction is remarkably improved while the calculation efficiency is guaranteed.

The invention discloses a visual language model generation type semantic detection method based on hierarchical feature optimization. Cross-modal semantic alignment and high-quality image synthesis of a text and an image are achieved. Image features are decomposed into multi-scale representation, the decomposition rule is constructed based on a multi-scale token graph, the features are mapped to different resolutions through adjustment operation, and finally a layered feature set matched with the resolution of an original feature graph is formed; the optimization process is limited in a semantically meaningful feature subspace through quantization loss, the feature is prevented from exceeding an effective potential space of a decoder, the quantization loss is realized by calculating a distance mean value between the feature and the quantized feature, and gradient propagation to the quantization step is avoided by adopting a stop gradient operation in the quantization process; the gradient of an image quality evaluation model is introduced, the optimization process is guided to approach a visual mode conforming to human aesthetic appreciation, and the quality evaluation loss is calculated through the mean value of evaluation results of a plurality of quality evaluation models.

The invention discloses an aurora and substorm identification method and device based on a visual eye movement mode and deep learning, and mainly solves the problems that an existing aurora and substorm identification method is not high in accuracy and has no basis for model design. According to the implementation scheme, the method comprises the following steps: collecting eye movement data of a plurality of spatial physicians to generate expert eye movement fixation mode patterns of various types of aurora substorms; establishing an aurora sub-storm eye movement data set; a visual eye movement mode prediction module is further designed to learn a visual gazing mode of an expert, a visual mode embedded sub-storm sequence identification module is arranged, spatial physical knowledge and the visual gazing mode of the expert are integrated in the module, and finally a deep learning model suitable for aurora sub-storm sequence identification is constructed and trained. And identifying the aurora sequence by using the trained model. The method not only enhances the representation of the aurora and substorm features, but also improves the recognition accuracy of the aurora and substorm image sequence and the reasonability of model design, and can be used for automatic spatial physical event recognition.

The invention provides a calibration method, a calibration board, a storage medium, electronic equipment and a program product, and relates to the technical field of sensor calibration. The calibration method comprises the following steps: acquiring a target image of a calibration plate shot by a camera and scanning data of a laser radar on the calibration plate, wherein the calibration plate comprises a visual pattern used for extracting camera feature points and a scanning structure used for extracting laser radar feature points; acquiring a relative position relationship between the visual pattern and the scanning structure in the calibration plate; determining coordinates of the camera feature points in a camera coordinate system; coordinates of the laser radar feature points in a laser radar coordinate system are determined; and determining external parameter data of the camera and the laser radar based on the relative position relationship, the coordinates of the camera feature points in the camera coordinate system and the coordinates of the laser radar feature points in the laser radar coordinate system. According to the invention, joint calibration of the camera and the laser radar is realized, and the applicability and flexibility of calibration are improved.

A tamper-evident item authentication label may include a substrate layer that includes a first substrate side and a second substrate side, a first adhesive layer, a second adhesive layer, a first unique indicium, a second unique indicium, and a plurality of cuts across the first unique indicium and the second unique indicium extending through at least a portion of a thickness of the substrate layer. The adhesive layers may be deposited on the first and second substrate sides and arranged to fuse the tamper-evident item authentication label with a first and second surface. The first unique indicium may be deposited on the substrate layer and encoding a first unique identifier that links the tamper-evident item authentication label to one or more records in a remote database system. The second unique indicium may be deposited on the substrate layer. The second unique indicium may include a stochastically created visual pattern.

The invention relates to a next-generation device using far-UV for targeted decontamination, the device comprising: - a module (1) for projecting a visual pattern onto a localised site (5) identifying a target zone (50) to be irradiated; - a device (2) for generating a UVC beam in the form of a housing (20), the device comprising a front face (200) that defines an opening (201) from which the UVC beam escapes, and incorporating a UVC radiation light source (21), a reflector module (22), an adjustment module (23) for adjusting the intensity of the UVC beam, and a grating (25) that generates a substantially collimated UVC beam; - a telemetry module (3) that measures the distance between the opening (201) in the housing and the target zone (50); - a control module (4) that determines the intensity of a UVC beam to be generated at the output of the housing according to a predefined irradiation dose, and controls the adjustment module according to the power.

A surveying system includes a first subsystem having a GNSS base station and an optical base station. The optical base station includes an optical sensor, a laser module, and one or more motors configured to reposition the optical sensor. The GNSS base station and the optical base station are configured to be coupleable in a first predefined configuration. The system further includes a second subsystem comprising a GNSS rover communicatively coupled to the GNSS base station and an optical rover comprising a visual pattern. The GNSS rover and the optical rover are configured to be coupleable in a second predefined configuration.

The invention discloses an interactive hypnotherapeutic computer-implemented system based on artificial intelligence, comprising a microphone for receiving voice commands from a user; a display device for displaying dynamic pictorial representations generated by the system; a computing device operatively connected to the display device and the microphone. The computing device is configured to execute a computer program implementing a hypnotherapeutic interaction with the user based on received voice commands, whereby the computing device comprises: a word recognition module configured to recognize predefined induction words or phrases from the received voice commands; an artificial intelligence, AI, response generation module trained to respond to the recognized induction words or phrases by generating corresponding dynamic pictorial representations, created by moving particles, on the display device, and configured to record interactions and optimize responses based on user-specific feedback. The system transitions through three stages represented by distinct visual patterns of dynamic pictorial representations, induced by the predefined induction commands, wherein the dynamic pictorial representations vary in parameters defining the behavior of pictorial representation's particles, in particular vary in color and / or speed and / or movement pattern and / or scattering of representations' particles; and wherein the visual representations generated by the system are semiotically inconsistent and do not match the semantic signification of the user's voice commands. The invention also discloses a computer program product comprising instructions that cause the computer system to: a. recognize specific induction words or phrases from voice commands received via a microphone; b. in response to the recognized induction words or phrases, generate corresponding dynamic pictorial representations, comprising particles, on a display device using an artificial intelligence response generation module, the pictorial representations being distinct for three stages of the system and varying in color, speed, movement pattern and scattering of representations' particles; c. record interactions and optimize responses based on user-specific feedback; wherein the visual representations are semiotically inconsistent and do not match the semantic signification of the user's voice commands.

Video frames of a video may be marked with visual patterns to identify individual video frames. The video may be changed by applying one or more effects to the video. The accuracy with which the changes were made to the video by the effect(s) may be determined using the visual patterns marked on the video frames.

This invention discloses a method and apparatus for identifying auroral substorms based on visual eye-tracking patterns and deep learning, primarily addressing the problems of low accuracy and lack of basis for model design in existing auroral substorm identification methods. The implementation scheme includes: collecting eye-tracking data from multiple space physicists to generate expert eye-tracking gaze pattern maps for various types of auroral substorms; establishing an auroral substorm eye-tracking dataset; further designing a visual eye-tracking pattern prediction module to learn the experts' visual gaze patterns; and setting up a substorm sequence recognition module embedded with visual patterns, incorporating space physics knowledge and expert visual gaze patterns. Finally, a deep learning model suitable for auroral substorm sequence recognition is constructed and trained, and the trained model is used to identify auroral sequences. This invention not only enhances the representation of auroral substorm features but also improves the accuracy and rationality of auroral substorm image sequence recognition, and can be used for automated space physics event recognition.

The application discloses a kind of unmanned plane vision enhancement system and method based on binocular rotating polarizer, it is related to computer vision and optical imaging technical field, system includes binocular camera module, rotatable polarizer assembly that can be integrally moved into / out of optical path, mechanism for driving its lifting and rotation and main control unit.The method comprises: after moving polarizer into optical path, in a single acquisition cycle, it is driven to rotate at high speed to multiple preset angles and synchronously acquires binocular image sequence;Process image sequence to calculate Stokes parameters and construct three-dimensional polarization feature space in combination with depth information;The feature space is input into multimodal fusion neural network with original image, and enhanced image, semantic segmentation map and optimized depth map are output.The application realizes the quick and flexible switching of polarization mode and ordinary vision mode, and significantly improves the real-time vision perception and enhancement capability of unmanned plane in underwater, strong reflection and other complex environments through software and hardware depth collaboration.

Systems and methods for visualizations of music may include one or more processors which receive an audio input, and compute a simulation of a human auditory periphery using the audio input. The processor(s) may generate one or more visual patterns on a visual display, according to the simulation, the one or visual patterns synchronized to the audio input.

A system comprising an output device, a brain oscillation monitor, and one or more processors. The output device is configured to output an audio signal for audio stimulation of the patient, and a visual pattern for video stimulation of the patient. The brain oscillation monitor is configured to generate feedback indicative of a response to the audio stimulation and the visual stimulation. The one or more processors are configured to determine a target frequency for the audio stimulation and the visual stimulation. The one or more processors are further configured to cause constructive interference of the audio stimulation and the visual stimulation output by the output device at the target frequency, by modifying at least one of a phase of the visual pattern or an amplitude of onsets for the visual pattern, relative to the audio signal, according to the feedback from the brain oscillation monitor.

The invention discloses a vehicle visual mode switching method, device and equipment and a computer program product. The method comprises the steps that the real-time vehicle distance, the threat fuzzy coefficient, the real-time vehicle speed, the traffic jam condition and the dynamic balance level of a current vehicle and the driving habit style of a driver are obtained; inputting the real-time vehicle distance, the threat fuzzy coefficient, the real-time vehicle speed, the traffic jam condition, the dynamic balance level and the driving habit style into a pre-trained visual mode decision model to obtain a target visual mode; switching the visual mode of the current vehicle to a target visual mode, and controlling a corresponding target visual sensor to operate; wherein the target visual mode is one of a vector-level visual mode, a contour-level visual mode and a high-precision visual mode. On the premise of ensuring the driving safety of the vehicle, the computing power resource consumption of the vehicle is optimized, so that the performance of vehicle hardware is improved, the service life of the vehicle hardware is prolonged, and the method can be applied to the technical field of intelligent driving.