68 results about "Visual texture" patented technology

The invention relates to the technical field of computer vision and three-dimensional reconstruction, and discloses an exhibition hall three-dimensional modeling intelligent optimization system based on multi-modal data fusion, and the system comprises a data collection module which is configured to synchronously obtain laser radar point cloud data, a multispectral image sequence and inertial measurement unit data; the preprocessing module is used for receiving the output of the data acquisition module, aligning a multi-source sensor coordinate system through a space-time calibration algorithm, and separating a static scene from a dynamic interference element by using a dynamic segmentation network; and the multi-modal fusion module is used for receiving the preprocessed data and carrying out adaptive weighted fusion on the geometric features of the laser radar and the visual texture features through a cross-modal attention mechanism. According to the invention, through multi-modal data fusion and a dynamic scene adaptive mechanism, the modeling precision and the real-time updating capability in a complex exhibition hall environment are significantly improved.

The invention relates to the technical field of intelligent traffic engineering, and provides a road guardrail intelligent inspection and maintenance planning method based on machine learning, which breaks through the limitation of a traditional single mode in a detection link, fuses laser radar three-dimensional geometrical characteristics and visual texture information, realizes high-precision identification of defects such as microcracks and hidden corrosion, and improves the inspection efficiency. The detection problem in a complex environment is solved, and the problem that a traditional method is high in omission ratio is solved. In the aspect of dynamic planning, a multi-dimensional state space is constructed based on real-time traffic, weather and defect levels, polling path dynamic optimization and multi-vehicle collaborative operation are achieved through a reinforcement learning algorithm, and the current situations that manual planning is low in efficiency and poor in flexibility are changed. In the aspect of maintenance decision, a time sequence data analysis model is used for accurately predicting guardrail maintenance requirements, a scientific maintenance plan is made in combination with an intelligent scheduling algorithm, and efficient utilization of resources is achieved. According to the method, a complete intelligent operation and maintenance closed loop is constructed, and road guardrail operation and maintenance are promoted to be converted from passive low efficiency to active intelligence.

The invention relates to the technical field of digital oil painting optimization, in particular to a multi-level visual texture feature optimization method and system for a digital oil painting. The method comprises the following steps: acquiring an original digital oil painting image, performing regional image semantic deep mining, performing illusion perception noise abstract injection, and constructing an illusion perception texture feature layer; performing local continuity texture visual detection on the illusion perception texture feature map layer, performing stroke tension transmission analysis, and constructing a stroke tension perception texture map; performing multi-scale semantic region division and inter-region cross shielding mining on the stroke tension sensing texture map, and constructing a multi-layer texture detail expression map; and carrying out layer-by-layer texture time sequence drawing speculation on the multi-layer texture detail expression map, and carrying out time sequence texture visual rendering optimization to generate a time sequence layered texture rendering effect. According to the method, through multi-layer texture reconstruction, oil painting textures of different time sequence levels are improved, and the stereoscopic visual effect is embodied.

The invention relates to the technical field of robot control, in particular to a robot motion control method, a robot and a system. The method comprises the following steps: acquiring depth information and color information of a current scene and visual textures and geometric features of a target object through an RGB-D camera, and generating a local environment map; introducing a motor current sampling value, correcting the material category of the target object, and determining an initial set value of the clamping force; the corrected joint torque estimated value is converted into a tail end force corrected value through a mechanical arm jacobian matrix; and on the basis of the force control weight and the position control weight, position control output and impedance force control output are calculated respectively, and a final control instruction is generated in combination with the clamping force initial set value and the tooth space compensation item. A harmonic compensation function based on the joint position, the speed and the current is constructed, periodic torque disturbance caused by the cogging effect of the servo motor is effectively restrained, the tail end force estimation is closer to the real acting force, and the clamping control stability is improved.

The invention relates to the technical field of robot control, and discloses an industrial robot adaptive control method and system based on multi-modal sensor fusion, and the method comprises the steps: collecting multi-modal original data, and carrying out the time-space alignment; capturing space-time semantic association of visual textures, tactile pressure distribution and force sense fluctuation in the multi-modal data through a multi-head attention mechanism guided by a physical model, and performing space-time registration; a CNN-LSTM hybrid model is adopted to extract visual texture features and time sequence tactile features in the physical information enhanced multi-modal feature matrix; and carrying out dynamic weight distribution on the fusion feature vectors with physical consistency by utilizing a weight distribution model driven by element reinforcement learning to generate dynamic weighted fusion features. According to the method, the spatial positioning precision of the industrial robot in a precise assembly scene is greatly improved, the contact force control stability is greatly improved, and the control robustness in a complex operation scene is remarkably enhanced.

The invention relates to the technical field of image data processing, and discloses a laser point cloud and visible light fused plot boundary identification method, which comprises the following steps of: processing laser point cloud and visible light image data streams in parallel, and respectively detecting geometric elevation mutation and visual texture transition to extract a modal specific change event and a timestamp thereof; identifying a cross-modal co-reality example based on a time sequence weak synchronization window, and marking the spatial position of the associated event as a boundary candidate point; according to the method, through a coupling mechanism of independent mode change perception and time sequence co-occurrence judgment, dependence on precise space alignment of a sensor is eliminated fundamentally, and the robustness and environmental adaptability of boundary recognition in a complex working environment are remarkably improved while the high efficiency of edge calculation is guaranteed.

The invention relates to the field of data processing, and particularly provides a road disaster early warning method and system based on Internet of Things sensing analysis, and the method comprises the steps: collecting real-time environment data through a multi-mode sensing device disposed in a road monitoring area; performing feature extraction to obtain a vibration fluctuation feature, a deformation displacement vector, a humidity gradient feature and a visual texture feature; and performing multi-modal spatial-temporal feature fusion on each feature, generating a road state fusion feature, inputting the road state fusion feature into a disaster prediction model to perform disaster probability calculation, obtaining a disaster occurrence probability of the current road region, and triggering an early warning signal matched with a disaster type when the disaster occurrence probability exceeds a disaster threshold value. According to the method, the accuracy, timeliness, comprehensiveness and model adaptability of road disaster early warning can be improved, and the safety and smoothness of a road are effectively guaranteed.

The invention discloses a semantic segmentation method based on phase contour constraint, and belongs to the technical field of computer vision. The method comprises the following steps of: acquiring a two-dimensional texture image and a deformed stripe image of a scene to be detected in the same view field by using a calibrated camera-projector system; resolving the deformed fringe image through a multi-step phase shift and Gray code technology to obtain absolute phase distribution, and calculating a gradient field; according to a preset sudden change condition, extracting a physical edge contour reflecting the depth change of the object from the gradient field; and mapping the physical edge contour into the two-dimensional texture image based on a pixel coordinate corresponding relation to accurately define a semantic object region, endowing a corresponding category label, and finally generating a semantic label mask image. According to the method, the uncertainty of the visual texture edge is corrected by utilizing the physical phase information, the problem of edge extraction in a complex illumination scene is effectively solved, automatic generation of semantic segmentation data is realized, and the training precision and robustness of a semantic segmentation model are improved.

The invention provides an AGV (Automatic Guided Vehicle) visual texture navigation path map real-time generation method, which comprises the following steps: collecting ground glossiness and a gray scale saturation region area, comparing the brightness difference between the gray scale saturation region and a normal region, and identifying the distribution position and the coverage range of a high-reflection region; according to the distribution position and the coverage range of the high-reflection area, extracting a texture missing part covered by the specular reflection patches, and determining boundary coordinates of the texture missing part; according to the optimized light incident angle distribution, the gray jump amplitude when the AGV drives into the polished tile area from the rough cement ground is collected, and the transition intensity of material change is evaluated; and filling a texture missing region in the path map by using the reconstructed complete texture features, and integrating the texture information of the path map to generate a complete AGV navigation path map.

The invention relates to the technical field of computer vision, and discloses a precise medicine delivery method and system, and the method effectively strips illumination artifacts through the construction of a local gradient structure tensor field and an anisotropic screening mechanism, remarkably improves the robustness of a visual front end in a light and dark alternating environment, and improves the accuracy of medicine delivery. Rigid body motion constraint and Lie algebra continuous modeling are utilized, high-precision space-time alignment between heterogeneous sensors is realized on the premise that scene depth does not need to be recovered, hardware delay errors are eliminated, a physical-driven probability weight dynamic allocation mechanism is constructed by introducing a multipath scattering index and a structural information entropy flux, and a dynamic space-time alignment algorithm is established. Environment degradation is sensed in real time, the observation weight is adjusted in a self-adaptive mode, the influence of the non-line-of-sight multipath effect and visual texture missing is effectively restrained, high-precision inertial dead reckoning can still be maintained in a blind area where a sensor is in full failure in combination with momentum prior information generated through Schel complement marginalization operation, and the method has the advantages of being high in precision and high in precision. And the navigation continuity and reliability of the distribution robot in a complex scene are ensured.

The invention belongs to the technical field of unmanned aerial vehicle gate maintenance scheduling, and provides an unmanned aerial vehicle gate maintenance scheduling method and system based on multi-sensor physical correction. Texture, depth and thickness are re-projected to the same graph plane, and material codes, a sand content mean value and a coating thickness form a scene vector; mapping the scene vectors into residual bias and adding the residual bias with the primary features to form fusion features containing physical semantics; according to the fusion features, determining a corrosion-crack confidence map; aligning the corrosion-crack confidence map with a preset determined correction depth and recess map to obtain a pixel-level corrosion level and spray gun accessibility; according to the defect semantic graph, the corrosion grade and the spray gun accessibility; a segmented adaptive corrosion gradient operator is adopted to complete block segmentation, reordering is carried out according to block material requirements and residual material vectors, and a nozzle wheel disc parameter library is inquired to determine a nozzle model, a target pipe diameter and a target flow velocity; and the consistency of thickness measurement and visual texture can be considered.

The present application belongs to the technical field of mobile robot navigation, and particularly relates to a SLAM navigation method and system for a mobile intelligent cabinet, which comprises the following steps: acquiring a laser point cloud sequence and a grayscale image of the mobile intelligent cabinet and performing data cleaning; obtaining a geometric feature index based on the spatial jump distribution of the laser point cloud sequence; obtaining a visual texture index based on the local dispersion of the image gradient; calculating laser dynamic weight and visual dynamic weight by using the geometric feature index and the visual texture index, weighting and fusing the pose change quantity calculated by the single-line laser radar odometry and the visual odometry to obtain a fused pose quantity; and updating the global state based on the fused pose quantity and driving autonomous navigation. The present application can adjust the sensor weight in real time according to the environmental characteristics, solves the problem of positioning divergence in the long corridor of a shopping mall or a high-reflectivity environment, and improves the robustness of navigation.

The application provides a display device and a subtitle language recognition method. The method can acquire a subtitle corresponding to a media video in response to a play instruction of the media video, extract visual texture features of the subtitle in a case where the subtitle is an image subtitle, perform language recognition according to the visual texture features to obtain a language recognition result, extract syntax topology features of the subtitle in a case where the subtitle is a text subtitle, perform language recognition according to the syntax topology features to obtain a language recognition result, write the language recognition result into metadata corresponding to the media video, control a display to play the media video and the subtitle, and display a language identifier corresponding to the subtitle according to the language recognition result in the metadata corresponding to the media video. The method recognizes a language by analyzing features of different languages in physical forms and syntax structures, so that the language identifier of the subtitle can be correctly displayed when the media video is played.

The invention discloses a multi-task learning-based driver driving-suitable credibility identification method and system, and relates to the technical field of multi-task learning, and the method comprises the steps: determining the multi-modal data of a driver based on the feature positions of a plurality of key face features, the corresponding feature forms and the real-time face image of the driver; the multi-modal data simultaneously contains the accurate head posture of the driver, the dynamic geometric information of the eyes and the mouth and the visual texture and expression details of fatigue, distraction or specific emotion on the face of the driver, and a preset credibility value is determined based on the previous driving event of the driver, a multi-task learning system and the current state of the driver; the corresponding driving early warning item is triggered based on the real-time comparison of the preset credibility value and the driving-suitable credibility value, so that the real-time performance and the accuracy of the driving early warning item are ensured.

A server (10) comprises: a surface information database (111) that stores, as surface characteristic data, a plurality of pieces of surface information, including texture information indicating visual texture, tactile sensation information indicating tactile sensations by a plurality of numerically expressed metrics, physical property information indicating the properties of material related to surface finishes that affect texture and tactile sensations, and processing information for realizing a surface characteristic; a presentation unit (12) that presents, to a user, the surface information in such a manner that the surface information is selectable on the basis of the texture information, the tactile sensation information, or the physical property information; and a processing information provision unit (16) that presents the processing information of the selected surface information to the user. Further, the server (10) has: a texture examination mode that presents, to the user, the surface information in such a manner that the surface information is selectable on the basis of a texture image as the texture information and the physical property information; and a tactile sensation examination mode that presents, to the user, the surface information in such a manner that the surface information is selectable on the basis of the tactile sensation information and the physical property information.

The invention discloses a display panel, a preparation method of the display panel and a display device. Comprising an array substrate, a pixel definition layer, a first electrode layer and a covering layer, the first electrode layer is located on the array substrate, and the pixel definition layer is arranged on the side, away from the array substrate, of the first electrode layer; the pixel definition layer is provided with a plurality of pixel openings at intervals; the covering layer is arranged on the side, away from the array substrate, of the pixel defining layer, the covering layer is made of a black material, and the surface, away from the array substrate, of the covering layer is provided with an uneven interface structure, so that the influence of ambient light on the display panel is reduced, and the visual texture and the display contrast ratio of a display surface are improved.

This invention discloses an adaptive end-to-end network learning-driven polarization descattering depth estimation method. It proposes a physics-driven end-to-end network to simultaneously acquire clear reconstructed images and high-precision depth images in scattering environments. The method first acquires linearly polarized images of the scene in four directions, extracting light intensity and linear polarization feature maps through decoupling calculations. Then, a dual-task network including a polarization-guided fusion module is constructed. Utilizing the physical sensitivity of DOLP to scattering particle concentration and optical path length, it transforms these into a spatial attention mask, physically constraining and enhancing the shallow texture of the intensity map in the feature space. Finally, dehazing and depth estimation branches are encoded and decoded in parallel. This invention overcomes the intensity-depth ambiguity of traditional RGB images, effectively suppressing the interference of non-uniform fog on geometric perception and visual texture, and achieving high-precision scene depth estimation and high-fidelity dehazing imaging under adverse weather conditions.

The invention relates to the technical field of AR (Augmented Reality), and particularly discloses an intelligent painting display method based on AR, which comprises the following steps of: acquiring humidity data of a current environment through an environment sensor; generating texture adjustment parameters of the painting work based on the humidity data, wherein the texture adjustment parameters are used for quantifying the influence degree of humidity on the texture of the painting work; dynamically modifying the digital texture of the painting work by using the texture adjustment parameters; and fusing the modified digital texture with the augmented reality scene, and displaying the fused painting on augmented reality equipment in real time. According to the invention, through the dynamic adjustment of the digital texture of the painting work and the fusion of the augmented reality scene, the intelligent self-adaptive display effect of the visual texture of the painting work is realized.

The invention discloses a harmonic texture and sequence reasoning fused audio-to-MIDI method and system, and relates to the technical field of audio processing. Aiming at the problem of insufficient transcription accuracy of the existing polyphonic music, the method comprises the following steps: carrying out time-frequency transformation on the polyphonic music to generate a spectrogram; constructing a time-frequency target detection network, detecting notes based on harmonic comb texture features, and outputting a time-frequency bounding box; constructing a sequence inference network to extract time sequence context features; carrying out local alignment and weighted fusion on the time sequence context features by using a time-frequency bounding box by adopting an ROI-guided gating fusion mechanism; and inputting the fusion features into a decoder to generate an MIDI sequence, and correcting the note time value by using a time-frequency bounding box. According to the method, the visual texture features and the music sequence logic are effectively fused, and the time precision and robustness of polyphonic music transcription are remarkably improved.

A natural scene text recognition method based on geometric prior and knowledge graph, which collects domain scene text images, detects and crops text line images, and performs feature extraction and columnar deformation correction through a deformation correction model based on geometric prior; for the corrected image, a visual recognition module based on the attention mechanism is used to perceive each character to obtain character-level aligned visual texture features; then a global semantic reasoning module based on the domain knowledge graph introduces scene domain knowledge, perceives context information, and encodes high-level semantic features; finally, the output of the visual and semantic modules is integrated to obtain the text recognition result. The present invention can be transferred and applied to natural scene text recognition in different application fields such as automatic control instruments, equipment manufacturing, numerical control machine tools, automobile manufacturing, and rail transit, solves the problem that the traditional text recognition technology has low recognition accuracy due to columnar text deformation and lack of relevant dictionaries in natural scenes, and realizes more accurate recognition of domain text.

The invention provides a method and device for testing the obstacle recognition function of a vehicle-mounted intelligent driving system, and relates to the technical field of automatic driving, and the method comprises the steps: employing a wave-transparent material to make a three-dimensional base, and the three-dimensional base comprises a plurality of surfaces; a mapping relation between the aging degree and physical attributes is established, the physical attributes of the different surfaces are determined based on the mapping relation, and the physical attributes comprise radar cross-sectional areas and contrast ratios; according to the physical attributes, radar reflecting layers with different radar scattering sectional areas and visual texture layers with different visual textures are arranged on all the surfaces of the three-dimensional base, so that obstacles with different aging degrees on the different surfaces are generated; the obstacle recognition function of the vehicle-mounted intelligent driving system is tested by using the obstacle. By adopting the method and the device for testing the obstacle recognition function of the vehicle-mounted intelligent driving system, the function test efficiency and the test scene coverage when the vehicle-mounted intelligent driving system is tested are improved.

The invention provides an underwater structure biological attachment grade evaluation method, which comprises the steps of performing sharpening processing on an edge blurred image region, extracting attachment contour features of the edge blurred image region, performing multipath suppression processing on a reflection waveform of a corresponding region according to the attachment contour features, and obtaining attachment texture information and depth contour data; analyzing attachment texture information, extracting surface roughness and texture directivity, distinguishing hardness in combination with depth contour data, and if the roughness is high and the depth contour changes drastically, judging that the hardness is high, and determining an attachment type through clustering analysis; attachment visual texture features and sonar depth features are extracted from attachment types, fusion weights of the texture features and the depth features are obtained through weighted calculation, and a target feature weight ratio is determined according to the fusion weights.

This application discloses a lane selection method, electronic device, and computer-readable storage medium, relating to the field of intelligent driving technology. The method involves acquiring the real-time smoothness score of the current lane while meeting safety and navigation requirements, and estimating the smoothness score of candidate lanes using observations of the movement of the preceding vehicle, historical data, or visual texture information. By comparing the differences between the two scores and based on a preset smoothness difference threshold, a decision is made on whether to switch lanes to a candidate lane with better smoothness. This invention solves the technical problems of related intelligent driving systems being unable to perceive smoothness differences between multiple lanes in the same direction and being unable to actively avoid bumpy lanes by introducing a cross-lane smoothness comparison perception and multi-source confidence weighted fusion mechanism. It achieves the technical effects of automatically selecting smooth lanes, improving driving comfort, reducing abnormal wear of vehicle suspension and tires, and suppressing ineffective lane changes under the premise of safety and compliance.

The application discloses a kind of unmanned aerial vehicle monocular vision inertial SLAM loop detection methods based on undergrowth trunk geometry, the method includes the following steps: step 1, trunk region segmentation and three-dimensional point cloud generation;Step 2, trunk clustering detection and position positioning;Step 3, triangular geometric feature construction and screening;Step 4, multidimensional similarity calculation;Step 5, geometric and visual joint decision and loop confirmation.This method extracts stable geometric information of trunk by driving depth data, constructs triangular features with invariance with trunk position as vertex to break through the dependence of visual texture, realizes double-path independent detection and joint decision by combining traditional visual bag-of-words model, balances detection accuracy and real-time through adaptive matching strategy, thereby effectively improves the accuracy, robustness and environmental adaptability of loop detection in undergrowth GNSS-free environment.

The invention relates to an oil painting image style feature expression method, system and device based on comparative learning and a medium. The method comprises the following steps: performing double-branch feature extraction on an original oil painting image by using a double-branch structure style recognition model to obtain an emotion semantic feature vector and a visual texture feature vector; based on the emotional semantic feature vector and the visual texture feature vector, constructing and optimizing a double-branch structure style recognition model according to comparison learning loss to obtain an optimized style recognition model; and based on the optimized style recognition model, performing double-branch feature extraction on the target oil painting image, obtaining and splicing a target emotional semantic feature vector and a target visual texture feature vector, and obtaining a target structured style feature expression. According to the method, through double-branch structure style recognition model construction and contrast learning training, the characterization capability and interpretability of oil painting image style features are remarkably improved, and the independence and structured output capability of the features are enhanced.

Disclosed herein are systems, methods, and software for providing a virtual environment with enhanced visual textures and haptic detail. In some embodiments, a texture atlas and UV mapping is used to render virtual objects having multiple textures that can be manipulated in real time. In some cases, UV coordinates are used to provide enhanced haptic detail.

This invention relates to a method (100) for transferring emotional state and facial texture between two human face photographs using optimization techniques, without requiring any dataset. It can be utilized in forensic investigations, the entertainment and social media sectors, pre-procedural studies in medical aesthetics, synthetic dataset generation for 0 artificial intelligence software, and identity anonymization in images.

The invention discloses a preparation method of a multi-scale skin-friendly wood grain texture mold, which comprises the steps of wood grain feature acquisition and digital modeling, preparation of a micron-sized main grain glass female mold, mold transfer printing, construction of a skin-friendly nano wrinkle layer and the like. The mold not only can reproduce visual textures of natural materials, but also can present soft and fine experience in touch. According to the multi-scale structural design, the surface performance is closer to a natural state, attractiveness and practicability are taken into consideration, the composite material is suitable for being applied to decoration, electronic shells, interiors and other occasions needing comfortable touch feeling, meanwhile, laser direct writing, wet etching, nanoimprint and the like are combined in the technological method, high-fidelity copying of complex textures can be effectively achieved, and high-fidelity copying of the textures is achieved. And certain stability and repeatability are realized in subsequent use. The process route gives consideration to the requirements of experimental feasibility and large-scale production, and can be suitable for different application scenes.

The application provides an industrial-grade image recognition and text matching method and a method thereof, and the application obtains an enhanced identification plate image through preprocessing of an obtained industrial equipment identification plate image to be queried; and performs text detection and recognition by using OCR; removes the duplicate of a cropped identification subject region image by using a double-layer hash value; and extracts visual texture features and semantic features by using a double-branch model; obtains a candidate result through coarse screening, checks the candidate result by using a double-layer hash value, calculates a text matching score of a query text of the query image and a candidate text of the checking result, and judges a matching result based on a final matching score and the text matching score. The application realizes super-high-precision and super-low-delay recognition and matching of industrial identification through lightweight model depth optimization, pure CPU inference heterogeneous acceleration and multi-scene adaptive matching, and the text recognition accuracy is not less than 99.2%, and the overall matching accuracy is not less than 96.5%.

The application provides a kind of vehicle-mounted intelligent driving system obstacle identification function test method and device, related to automatic driving technical field, which comprises: adopting wave-transparent material to make three-dimensional pedestal, three-dimensional pedestal includes multiple surfaces;Establish the mapping relationship between aging degree and physical properties, determine the physical properties of different surfaces based on the mapping relationship, physical properties include radar cross section and contrast;According to physical properties, radar reflection layer with different radar cross sections and visual texture layer with different visual textures are respectively arranged on each surface of three-dimensional pedestal to generate obstacles with different aging degrees on different surfaces;Using obstacles, the obstacle identification function of vehicle-mounted intelligent driving system is tested. By using the above vehicle-mounted intelligent driving system obstacle identification function test method and device, the functional test efficiency and test scene coverage when testing the vehicle-mounted intelligent driving system are improved.