343 results about "Spatial image" patented technology

The invention discloses an intelligent fault diagnosis method fusing state monitoring and a multi-modal large model, and the method specifically comprises the steps: synchronously collecting time sequence data and a space image through a heterogeneous sensor group and monitoring equipment disposed in power grid equipment, and forming original data; based on the original data, a physical constraint feature vector is generated in combination with an equipment thermodynamic equation and a material deformation rule; performing health index prediction through the lightweight LSTM network based on the physical constraint feature vector; when detecting that the health indexes continuously decrease, clustering an HI time sequence curve by adopting a Gaussian mixture model, judging a degradation stage according to a clustering center distance, and obtaining a stage recognition result; and based on finite element simulation parameters, introducing a reinforcement learning model, optimizing the simulation parameters by taking maintenance cost minimization as a target, and outputting a predictive maintenance work order. According to the invention, intelligent fault diagnosis and accurate maintenance of the power grid equipment are realized, the fault processing efficiency and accuracy are improved, and the power failure loss is reduced.

The invention discloses a tunnel lining crack identification method and system based on panoramic space image splicing, and relates to the technical field of tunnel visual identification. The method comprises the following steps: acquiring a tunnel lining image array, and performing local splicing on tunnel lining images by using a splicing model based on cylindrical surface projection to obtain a local spliced image; fusing and splicing the local spliced images by using the global image space model to obtain a panoramic spliced data graph; and abnormal region detection is carried out on the panoramic splicing data graph according to the spatial local difference, and a tunnel lining crack identification result is obtained. According to the method, the lining panoramic space model based on image two-stage fusion is used for splicing, so that the visual coherence and naturalness of a global spliced image are improved.

The invention belongs to the technical field of image processing, and particularly relates to a cable shielding layer weaving density detection system based on image analysis, which comprises an image correction module, a frequency domain analysis module, a self-adaptive filtering module and a density defect detection module, obtaining the maximum peak point of the centralized complex frequency spectrum; constructing a band elimination filter according to the maximum peak point, filtering the centralized complex frequency spectrum through the band elimination filter to obtain a filtered complex frequency spectrum, executing inverse Fourier transform to obtain a filtered space image, and binarizing the filtered space image through a threshold segmentation algorithm to obtain a pore mask; the porosity is obtained according to the ratio of the non-pore pixel points to the total number of the pixel points; and judging the weaving defect of the shielding layer according to the porosity. According to the method, the strong periodic signals of the weaving textures are automatically identified and filtered in the frequency domain, so that non-periodic defects such as pores are effectively highlighted, the technical problem that periodic textures cover defect signals is solved, and the accuracy of defect detection is improved.

The invention relates to the field of intelligent city planning, and discloses a street space quality intelligent evaluation and image generation method and system based on deep learning, and the method specifically comprises the following steps: movably collecting a street space image, and recording the geographic position information; performing perspective correction and multi-view image segmentation on the acquired street image data, and performing spatial matching with geographical location information to obtain a fused image database; and performing semantic segmentation on the image in the fused image database based on a deep learning model, calculating objective and subjective visual perception indexes and facility function indexes of the image, and generating a street space quality evaluation result. The method solves the problems that in the prior art, evaluation dimensions are limited, and an automatic response generation mechanism is lacked, and has the advantages of being intelligent, efficient and comprehensive in subjective and objective evaluation.

The invention provides an underwater multi-color space image enhancement method based on depth information guidance, which is characterized in that a model of the method is based on a training model framework capable of utilizing physical priori knowledge and multi-color information, and comprises a depth information generation module, a multi-color space feature fusion module and a depth enhancement module, the relation between a depth image and an underwater scene is enhanced through attention estimation, scene reconstruction training and fusion of multi-color information, so that insensitivity of a single RGB color space to brightness and saturation image attributes is made up; the feature representation capability is improved, and the scene adaptability of the model is enhanced.

The invention provides a three-dimensional target marking method and device based on a point cloud data-assisted two-dimensional image, and the method comprises the steps: fusing precise three-dimensional point cloud information obtained by a laser radar and two-dimensional image data collected by a camera based on a point cloud data and image data cooperation three-dimensional marking method; and a dynamic high-precision three-dimensional target marking system is constructed. Specifically, a space-image relationship is established through three-dimensional point cloud information and a two-dimensional image plane, and three-dimensional information of a target is mapped into a two-dimensional image, so that geometric and semantic information labeling is carried out on the target in the two-dimensional image, information loss of two-dimensional visual data in dimensions such as depth, attitude and space structure is made up, and the accuracy of the three-dimensional information labeling is improved. And meanwhile, when the three-dimensional point cloud is unmeasurable, a three-dimensional target labeling mode is dynamically adjusted in combination with two-dimensional image labeling information. According to the method, through dynamic high-precision three-dimensional target labeling, the obtained image-label can be used for training and reasoning of a three-dimensional target detection network, and the method has a wide application prospect.

The application provides a camera module and an electronic device. The camera module comprises a lens, an SMA motor, a substrate and an image sensor. The SMA motor comprises a carrier, a seat and an SMA driving assembly. The lens is fixed to the carrier. The SMA driving assembly connects the carrier and the seat. The SMA driving assembly is used to drive the carrier to move relative to the seat. The shell of the SMA motor comprises a top plate and a bottom plate arranged oppositely. The bottom plate comprises a first part and four second parts. The four second parts are arranged at the outer circumferential side of the first part at intervals. The distance between the second part and the top plate is greater than the distance between the first part and the top plate. The four corners of the substrate are provided with recessed avoiding spaces. The first part is fixed to the substrate. The second part is at least partially located in the avoiding space. The image sensor is fixed to the middle part of the substrate and electrically connected to the substrate. The image sensor is arranged to face the lens. The above-mentioned camera module can have a smaller module shoulder height while ensuring the shooting quality.

Systems and methods for fast reconstruction of motion-resolved magnetic resonance images using space-time-coil convolutional networks are disclosed. The system can receive a plurality of k-space data sets. The system can detect a motion signal therefrom. The system can classify the k-space data sets according to states of the motion signals. The system can resolve the k-space data set to Euclidean space images. The system can resolve the Euclidean space images to a combined Euclidian space image. For example, the system can use a convolutional network that exploits spatial, temporal and coil correlations without k-space data consistency to minimize computation time.

The invention discloses a multi-view target image fusion method, device and equipment and a readable storage medium, and the method comprises the steps: carrying out the spatial position coding of the azimuth angle, pitch angle and distance of each image relative to a shooting position, and splicing the obtained spatial position code of each image with a corresponding image block embedding vector; forming a spatial image block embedding sequence by using all the spatial image block embedding vectors, and adding a learnable classification mark at the foremost end of the spatial image block embedding sequence to obtain a new spatial image block embedding sequence; inputting the new space image block embedding sequence into a Transform model, extracting global information in all space image block embedding vectors based on a proxy attention mechanism to update learnable classification marks, and obtaining a fused classification mark; and performing category prediction on the target through a multi-layer perceptron by using the fused classification mark. According to the invention, the target identification accuracy of the model can be improved.

Apparatuses and methods for using Raman Resonance Spectroscopy to evaluate metabolic and oxygenation status of the eye are disclosed herein. In some embodiments, metabolic mapping of the eye may be performed by aligning a Raman spectrum and a recorded spatial image of the eye.

It is possible to achieve both a high-resolution real-space image used in, for example, a video see-through type of AR device or MR device, and reduced system load. By an image acquisition unit, a first image captured at a first angle of view and a second image captured at a second angle of view narrower than the first angle of view within the first angle of view are acquired. By an image generation unit, a display image is generated by synthesizing an image of a high-resolution region based on the second image and an image of a peripheral region around the high-resolution region based on the first image. For example, based on gaze information of a user, the movement of an imaging direction of an image capturing unit for obtaining the second image is controlled, and the movement of a position of the high-resolution region is also controlled.

The application belongs to the technical field of image processing, and particularly relates to a cable shielding layer weaving density detection system based on image analysis, which comprises an image correction module, a frequency domain analysis module, an adaptive filtering module and a density defect detection module; a maximum peak point of a centralized complex frequency spectrum is obtained; a band-stop filter is constructed according to the maximum peak point, and the centralized complex frequency spectrum is filtered through the band-stop filter to obtain a filtered complex frequency spectrum and perform inverse Fourier transform to obtain a filtered spatial image, and the filtered spatial image is binarized through a threshold segmentation algorithm to obtain a pore mask; a porosity is obtained according to the ratio of non-pore pixel points to the total number of pixel points; and the shielding layer weaving defect is judged according to the porosity. The application automatically identifies and filters the strong periodicity signal of the weaving texture in the frequency domain, effectively highlights the non-periodic defects such as pores, solves the technical problem that the periodic texture covers the defect signal, and improves the accuracy of defect detection.

The invention discloses an unmanned aerial vehicle positioning method and system based on a total station measurement network and visual collaboration. The method comprises the following steps: determining a space reference of the total station based on a ground control point; determining the spatial position of the unmanned aerial vehicle by using the total station after determining the spatial reference; capturing a space image corresponding to the unmanned aerial vehicle by using a camera; and projecting a positioning mark corresponding to the unmanned aerial vehicle on the captured space image based on the determined space position of the unmanned aerial vehicle. Therefore, efficient labeling, excellent consistency, cross-device / cross-camera unified output, precision verifiability, traceability, flexible deployment and excellent expandability can be realized.

The invention discloses a spectral space coding imaging system and method of a programmable diffractive optical element. The method comprises the following steps: S10, receiving a target scene light signal through a light field model and forming an optical image; s20, performing spatial spectrum joint modulation on the optical image by adopting a programmable diffractive optical element to obtain a coded light field; s30, projecting the coding light field to a detector and obtaining a two-dimensional coding image of primary exposure; and S40, inputting the two-dimensional coding image into a reconstruction module, combining modulation information of the programmable diffractive optical element, and performing decoding reconstruction by using a compressed sensing algorithm to obtain a high-resolution space image and spectral information of the scene. According to the invention, the limitation of the existing system on spatial / spectral resolution is broken through, the system structure is simplified, and the hardware programmability is improved; the imaging capability in a dynamic scene is improved; the signal-to-noise ratio and the penetrating power are improved; and balancing a physical model and a data driving method.

The invention discloses a forestry seedling culture data acquisition and analysis system, which relates to the technical field of data processing, and is characterized in that backscattered light of a seedling lignified tissue is acquired, an initial speckle interference video stream is generated, and after spatial image stabilization registration is performed on the initial speckle interference video stream, the speckle interference video stream is output; the correction module is used for obtaining a speckle interference video stream, extracting pixel time sequence gray information of the speckle interference video stream, mapping the pixel time sequence gray information into a time history speckle pattern, calculating a texture statistical characteristic value of the time history speckle pattern and outputting the texture statistical characteristic value as a first cell chaos degree index, and the compensation module is used for obtaining a metabolic rate compensation coefficient through a correction model. Temperature correction is conducted on the first cell chaos degree index according to the metabolic rate compensation coefficient, a second cell chaos degree index is output, the deviation degree of the second cell chaos degree index of the to-be-detected nursery stock and the dead wood reference value is calculated, the dormant state of the nursery stock is judged according to the deviation degree, and instant and accurate regulation and control over key nodes of seedling warehouse-out and recovery are achieved.

The invention relates to a method for detecting defects in a photolithography mask, the method comprising: i. Acquiring an aerial image of the photolithography mask; ii. Obtaining an underlying design of the photolithography mask; iii. Generating a plausible design of the acquired aerial image by solving an optimization problem that minimizes the deviation of a simulated aerial image of the plausible design from the acquired aerial image; and iv. Detecting defects in the photolithography mask by comparing the underlying design to the plausible design. The invention also relates to a corresponding system for detecting defects.

The invention relates to the technical field of crane control, in particular to an unmanned crane control method and system, which comprises the following steps: acquiring a running space, establishing a basic simulation space through simulation, acquiring a real-time space image, establishing an execution state space, and marking a starting point and an ending point to determine a track space; a dangerous object is identified, a safety space signal is determined, and after the safety space signal is detected, the gravity center of a transported object is obtained, a grabbing point is set, the transported object is hoisted, stress analysis is conducted, longitudinal inertia force is set, tipping and anti-tipping moments are calculated, and a deviation angle is adjusted to obtain a normal operation signal; and finally, carrying out stress analysis again according to the deviation angle, setting a transverse inertia force to calculate a horizontal direction moment, determining an acceleration critical value and setting a real-time operation acceleration. According to the method, shutdown is reduced through unmanned control, parameters are optimized, efficiency is improved, the method is adaptive to multiple scenes and multiple models, and technology upgrading is promoted.

In some examples, a first electronic device is in communication with multiple displays while also interfacing with two external cameras, each capturing distinct viewpoints. In some examples, the first external camera captures first image data concurrently with the second external camera capturing second image data, with both contributing to generating spatial image data. In some examples, the first electronic device obtains the spatial image data from both external cameras or generates the spatial image data based on the first image data and the second image data. In some examples, when one or more first criteria are met, the first electronic device renders the spatial image data on one or more displays.

The application discloses an image clustering method based on a composite subspace, and is implemented according to the following steps: step 1, pre-training a convolutional autoencoder; step 2, training a composite subspace image clustering network; and step 3, obtaining a clustering result by applying spectral clustering. The application realizes image clustering in a manner of unsupervised learning, obtains an explicit nonlinear mapping by simultaneously learning self-expression coefficients in an input space and a latent space, embeds input samples into corresponding deep representations, and thus better captures a subspace structure and improves clustering precision.

An information processing device acquires an image of a virtual space, detects a first position that is a position of a real object in a first captured image of a real space, detects a second position that is either a position of the real object in a second captured image referred to when generating the image of the virtual space or a position of a virtual object associated with the real object in the image of the virtual space, determines a motion vector indicating a direction and an amount of movement of the virtual object in the image of the virtual space based on the first position and the second position, and corrects the position of the virtual object in the image of the virtual space based on the motion vector.

According to the short-term wind and light output prediction method based on the double-branch deep learning model provided by the invention, the meteorological time sequence, the meteorological space image and the historical output are acquired, and then the wavelet transform processing is carried out on the meteorological time sequence, so that data noise is effectively removed and signals are stabilized; and then time scale features and space scale features are obtained from a meteorological time sequence and a meteorological space image based on a double-branch deep learning model, and then fusion is carried out, so that rapid output change caused by a complex space weather phenomenon can be captured more accurately, the robustness and generalization ability are enhanced, and the final prediction precision is effectively ensured.

To improve user experience. [Solution] The program causes the computer to function as a control means that operates operation objects and user objects placed in a virtual space based on the user's operations in a predetermined area, and an image processing means that displays a virtual space image, which is an image of the virtual space based on a virtual camera placed in the virtual space. When the control means detects the user's first operation outside the predetermined area, it places a position designation object in the virtual space that specifies the destination of the user object, and moves the user object to the destination in a specific manner that is faster than normal movement.

An image processing apparatus includes a generating unit that generate a virtual space image, which is an image of a virtual space, an output unit that outputs a range to be captured in the virtual space and a marker indicating a position in the range to be captured where focusing is performed to a display device, and a control unit that controls the virtual space image so that, in response to an operation of an operation device for performing a movement operation of a viewpoint, which is a position where shooting in the virtual space is performed, in the virtual space, at least one of movement of the viewpoint in a direction indicated by the marker and movement of the viewpoint a distance that the focusing is performed is performed.

This invention relates to the field of industrial equipment diagnostic technology and discloses a method for fault diagnosis and tracing of industrial equipment based on multi-source heterogeneous parameter fusion and deep learning networks. The method includes: acquiring a multi-dimensional heterogeneous signal sequence representing the operating state of the controlled object; mapping the signal sequence into a two-dimensional spatial image tensor using the Gram angle field algorithm; constructing a dual-stream feature extraction network containing graph convolution branches and cyclic unit branches to extract spatial topological features and temporal evolution features respectively; determining the logical offset matrix based on the spatial flow feature gradient divergence, and resampling the temporal flow features accordingly to achieve spatiotemporal causal phase alignment, and outputting diagnostic analysis results. This invention compensates for the physical system response hysteresis through asynchronous resampling operators in the computational domain, eliminates feature phase contamination caused by conventional rigid alignment, enhances the model's ability to capture weak abnormal signals, and achieves causal consistency in fault root cause localization.

An embodiment of the present disclosure provides a method of controlling a robot based on a simplified map and a natural-language command. The method may include acquiring an image of a space, acquiring a simplified map of the space, the simplified map including a current position of a robot and a destination position of the robot and not including at least some elements describing the space, acquiring a natural-language command specifying compliance requirements that the robot is to comply with during an operation of the robot, generating input tokens based on at least one of the image, the simplified map, and the natural-language command, and generating waypoint tokens by processing the input tokens with a trained vision-language-action model.

The invention provides an AI-based text generation method and system, and the method comprises the steps: extracting a global semantic text vector sequence from text data, and extracting a spatial correlation image vector sequence from image data; obtaining a text and image attention weight matrix and an image and text attention weight matrix based on the two; further acquiring a global text and graph vector sequence and a spatial text and graph vector sequence, and acquiring multi-modal fusion features based on the global text and graph vector sequence and the spatial text and graph vector sequence; and taking the multi-modal fusion features as condition information of a language model, outputting a plurality of query texts through the language model, and outputting confidence coefficients corresponding to the query texts. Through fine-grained alignment, the relevance between image-text features is ensured, the problem of modal difference is solved, and the semantic relevance between different modals is improved; the dependency degree on texts or images in the image-text fusion process is dynamically calculated based on the gating weight, and deep semantic association between different modes is further mined.

A method of detecting presentation attacks based on a finger or face image in a first color space captured by an image capture device. The method includes receiving a first color space image in the first color space, converting the first color space image into a number of additional color space images, wherein each additional color space image is in a color space other than the first color space, and providing the first color space image and the number of additional color space images to a trained attention-leveraged data fusion-based classification system that is configured to determine whether the original image is live or a spoof. Also, a facial or finger video-based biometric authentication system and method employed combined losses during training to train a backbone network for performing vide-based identity verification.

The claims generally describe an apparatus for spatial image and video capture comprising a micro-electromechanical system (MEMS) scanning mirror. The apparatus comprises at least one adaptive MEMS micromirror, a first optical subsystem configured to: receive light from a first portion of an environment to direct light to the at least one adaptive MEMS micromirror; and a second optical subsystem configured to: receive light from a second portion of the environment to direct light to the at least one adaptive MEMS micromirror. The apparatus further comprises control circuitry that positions the MEMS micromirror to direct light from the first optical subsystem and light from the second optical subsystem to the at least one sensor. The apparatus generates image data based at least in part on the received light.

To provide an information processing system capable of reproducing a video image photographed in a virtual space in matching with a characteristic of a real space photographing section.SOLUTION: An information processing system comprising: a virtual space display unit configured to display a virtual space; a virtual space image generation unit configured to generate an image by imaging a part of the virtual space; and a real space imaging unit including an image processing unit capable of developing the image, wherein the image processing unit develops the image based on imaging information in the virtual space image generation unit and characteristic information of the real space imaging unit.SELECTED DRAWING: Figure 1

Systems and techniques are provided for processing image data. A process can include obtaining a pair of images with a first zoom level and including first and second image data of a scene obtained using a first and second camera, respectively. Information indicative of a second zoom level different from the first zoom level can be obtained, and a rectification matrix corresponding to the second camera can be determined based on a scale factor corresponding to the second zoom level. Zoomed second image data can be generated based on using the rectification matrix to warp a portion of the second image data determined based on the second zoom level. A zoomed pair of images associated with the second zoom level can be outputted to include the zoomed second image data and zoomed first image data comprising a portion of the first image data corresponding to the second zoom level.