99 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.

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.

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 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 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.

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 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.

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.

The invention discloses an unmanned aerial vehicle building monitoring system and method based on binocular vision and deep learning, and the system is characterized in that an unmanned aerial vehicle carries a flight control system, an image collection module and a wireless communication module; the ground computing equipment comprises a task planning module, an image recognition and three-dimensional reconstruction module and a displacement analysis and alarm module; after the standardized visual pattern is arranged, the task planning module automatically generates a flight route according to the position of the pattern, the unmanned aerial vehicle executes the flight route and collects a synchronous image, the image is sent into the deep learning model to recognize the pattern, a three-dimensional coordinate is reconstructed, displacement analysis is performed after comparison with an initial state, and an alarm is triggered. According to the invention, the unmanned aerial vehicle platform carries the high-resolution camera for dynamic acquisition, and the deep learning algorithm is used for analysis, so that the depth perception capability of binocular stereo vision, the dynamic flexibility of the unmanned aerial vehicle platform and the environment robustness of deep learning are fused, and non-contact, full-dimension and high-economy building deformation monitoring is realized.

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.

A digital image radial pattern decoding system is described. In one example, an unfolded digital image is formed by the radial pattern decoding system by unfolding a radial pattern in a digital image. An inflated digital image is then generated by the radial pattern decoding system by upsampling the unfolded radial pattern. A grid pattern is determined by the radial pattern decoding system based on the inflated digital image. A radial pattern cell is then generated based on a reverse transform of the grid pattern. A visual pattern is generated by the radial pattern decoding system based on the radial pattern cell.

An eye exam can be performed using an electronic device in a virtual environment to determine vision corrective measures based on vision correction simulation. The electronic device can execute a visual assessment application for displaying a user interface to create a 3D virtual environment corresponding to a field of view of a user associated with the electronic device. The electronic device can render a visual pattern in the field of view and apply a vision correction filter to the visual pattern. The electronic device can obtain a set of user response data captured by a plurality of sensors in response to the visual pattern and determine whether the set of user response data satisfy a response quality criterion. Filter parameters of the vision correction filter can be dynamically adjusted based on the set of user response data until the set of user response data satisfy the response quality criterion.

A patient's visual health can be evaluated via a virtual reality (VR) system, which can include a VR headset in electronic communication with a computing device. The computing device can cause a phenomenon (such as a visual test or an image) to be displayed on the screens of the VR headset. Using varying combinations of sensors, cameras, probes, and microphones, the VR headset collects data about the patient as she perceives and responds to the phenomenon. Optionally, the computing device can alter the phenomenon and analyze the patient's perception and responses to evaluate the patient for macular degeneration. Optionally, the computing device can alter the phenomenon based on the patient's perception and responses.

Real time pattern preview generation and capture techniques are described. In an example, a live stream of digital images is displayed in a user interface by a computing device. A real time preview of visual patterns in the user interface is then generated and displayed based on the digital images. The visual patterns, for instance, are generated by the computing device in real time using a combination of shape extraction and pattern generation. An option is also made available to convert the real time preview into a vector image, such as a vector pattern tile.

The invention discloses automatic packaging equipment which comprises a belt lifting conveyor, a vertical film rolling bag-making packaging machine and an intelligent counting and feeding device. The intelligent counting and feeding device comprises a first belt conveyor, a second belt conveyor, a quantitative transferring mechanism used for receiving plastic parts output by the first belt conveyor and transferring the plastic parts to the second belt conveyor, and a visual figure counting system used for shooting and calculating the plastic parts conveyed by the second belt conveyor. The visual pattern counting system comprises image acquisition equipment, an industrial computer for storing and processing acquired images, and a PLC (programmable logic controller) communicated with the industrial computer, a software program built in the industrial computer can identify and calculate the number of plastic parts of the images, and control instructions are sent to the PLC according to the number of the plastic parts. The PLC controls starting, stopping and the working speed of the first belt conveyor, the second belt conveyor and the quantitative transfer mechanism according to the received control instruction. The plastic part counting and packaging device has the advantages that the effect of automatically and accurately counting and packaging plastic parts is achieved.

The utility model provides a small-size folding light path visual training device which comprises a front shell, a middle frame and a rear shell which are connected in sequence, the front shell is provided with a lens device, and a visual training module, a left darkroom, a right darkroom, a flexible FPC and a reflection lens are arranged between the middle frame and the rear shell; the visual training module, the reflecting lens, the left darkroom, the right darkroom and the lens device jointly form a light path system. According to the visual training device with the small-size folding light path, the problems that an existing device is large in size, close in imaging position and poor in experience are solved, the visual training device is heavy to wear, the left eye and the right eye can see the same visual pattern through the spirally-arranged LED light sources, the left eye and the right eye share one visual cylinder, the size is saved, and the imaging distance is increased.

A system for producing an image to design a product can include a processor and a memory. The memory can store a regularizing module, a blending module, a denoising module, and a communications module. The regularizing module can produce a regularized image of a denoised image of an interpolation of a first diffused image and a second diffused image. The regularized image can be regularized with respect to a visual pattern. The blending module can: (1) determine a blending weight and (2) produce, based on the blending weight, a blended image of the denoised image and the regularized image. The denoising module can denoise the blended image to produce the image to design the product. The communications module can cause the image to be sent to a computer-aided design system to design the product.