54 results about "Visual texture" patented technology

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 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 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.

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.

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.

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.

This invention discloses a velvet-textured stain-resistant ceramic tile and its preparation method, belonging to the field of ceramic tile manufacturing technology. It includes a body, a base glaze layer, a skeleton layer, a top glaze layer, and a protective glaze layer. This invention solves the problem of complex layering in the production of velvet-textured ceramic tiles by using the above materials and preparation method. By physically granulating the base glaze layer to form a skeleton with a micron-level protrusion array, it drives the top glaze layer to create undulations, directly shaping a velvet-like tactile and visual texture. The protective glaze layer achieves stain-resistant and slip-resistant functions. Simultaneously, this preparation method avoids the problem of insufficient precise control of microstructure and interlayer bonding strength caused by single firing or simple pre-firing. Through staged temperature-time control, it retains the micron-protrusion characteristics of the skeleton layer while ensuring the stability and functionality of the overall structure.

The application provides an automatic mapping method for complex terrain environment, and relates to the technical field of automatic mapping. The method comprises the following steps: firstly, acquiring multi-modal sensing data and performing pose calculation to construct an initial pose graph and a local mapping model; extracting spatial geometry and visual texture features to calculate an information matrix, and solving a spatial feature degradation index and a degradation type identifier; when the degradation index exceeds a preset threshold, determining a feature degradation area, calculating a dynamic observation weight matrix and extracting a local credibility topological boundary based on the feature degradation area; planning an active compensation survey trajectory based on the boundary, controlling a mapping platform to perform active compensation survey to acquire supplementary sensing data, and generating a compensation constraint edge by correlation; finally, injecting the weight matrix and the compensation constraint edge into the pose graph to perform global graph optimization solving, and outputting a globally consistent mapping product. The application solves the problems of divergence of pose solving caused by environmental feature degradation and lack of active compensation repair mechanism in the prior art.

The invention provides a visual texture navigation AGV moving track real-time monitoring method, which comprises the following steps: acquiring a real-time ground image frame and a current driving mileage from an image acquisition module and a position calculation module on an AGV chassis, and combining with pre-stored historical track coordinates for fusion to obtain initial positioning data; according to the initial positioning data, evaluating the similarity pattern repetition degree between the adjacent image frames and a preset threshold value, marking the position higher than the threshold value as a texture confusion high-risk section, and determining a potential misjudgment position; aiming at the candidate anchor points, dynamically adjusting a screening threshold value in combination with a travelled distance accumulative error, and reserving the anchor points of which the threshold value is lower than the average length of a texture period to obtain optimal anchor points; and selecting a texture section with the highest similarity between the texture feature of each anchor point and the gray level distribution of the current image frame from the preferred anchor points as a matching reference, evaluating the size of the deviation and the allowable range after coordinate correction, and determining the corrected track coordinates.

The application discloses a kind of intelligent coating paper coating defect identification method and system, method includes multidimensional data acquisition, data optimization processing, moire difference amplification, identification model construction and defect identification output.The original data is obtained by data acquisition;Adopt the data optimization processing method of image denoising filter, ROI cutting, size normalization, gray standardization and data set division;Ideal defect-free template is constructed and is carried out optical interference with the image to be measured sample, the difference amplification moire image is obtained, the abstract digital difference is converted into visual texture interference pattern, the lower limit and stability of defect identification are improved;Dual-path deep network model is used as identification model, by combining local and global feature extraction and introducing orthogonal decoupling attention mechanism, feature space is decomposed into defect main signal and mixed factor signal, can be stripped from complex surface anomaly mode Pure representation belonging to coating defect.

This invention discloses a deep learning-based visual inspection method and system for micro-defects, belonging to the field of visual inspection technology. By establishing a correspondence between material temperature and visual images, and mining temperature gradients and visual texture features, the concealment of hidden defects is overcome. By determining the boundaries of abnormal temperature regions, the location distribution of hidden defects is pinpointed. Furthermore, through local vector extraction and vector similarity measurement methods, the positioning accuracy of hidden defects is improved, achieving precise capture of hidden defects. By filtering and matching sets, and then precisely fusing minute temperature difference signals to the pixel layer of the visual image, high-precision matching between the temperature information of hidden defects and the visual image is achieved. Finally, by enhancing the fused image, the morphological parameters of the hidden defects are calculated, achieving precise positioning of material hidden defects and significantly improving the accuracy and reliability of micro-defect detection.

The present application relates to the field of data processing, and specifically provides a road disaster early warning method and system based on Internet of Things sensing analysis, the method comprising: collecting real-time environmental data through a multi-modal sensing device deployed in a road monitoring area; obtaining vibration fluctuation features, deformation displacement vectors, humidity gradient features and visual texture features after feature extraction; fusing the features in multi-modal space-time features to generate road state fusion features inputting a disaster prediction model for disaster probability calculation to obtain the disaster occurrence probability of the current road area, and triggering an early warning signal matched with the disaster type when the disaster occurrence probability exceeds a disaster threshold. The present application can improve the accuracy, timeliness, comprehensiveness and model adaptability of road disaster early warning, effectively ensuring the safety and smoothness of the road.

This utility model belongs to the field of metal stamping technology and discloses a stamping die for bicycle tubing. The stamping die for bicycle tubing includes a punch assembly and a die assembly. Both the punch assembly and the die assembly include a pressing die and a retaining die. The retaining die is elastically disposed on the pressing die and protrudes relative to the pressing die. There is a cutting edge arc between the retaining die and the pressing die. The retaining die of the punch assembly abuts against the bottom wall and side wall of the tubing groove. The pressing die of the punch assembly abuts against one side of the tubing body, so that the cutting edge arc can reduce the R angle of the tubing groove. The retaining die of the die assembly abuts against the other side of the tubing body and the outer side wall of the tubing protrusion. The pressing die of the die assembly abuts against the top of the tubing protrusion, so that the cutting edge arc can reduce the R angle of the tubing protrusion. The convex or concave fonts or logos present sharp angles and clearer edges, meeting design requirements, improving visual texture, and are suitable for designs that pursue a tough style.

The application discloses a semantic segmentation method based on phase profile constraint and belongs to the technical field of computer vision. The method uses a calibrated camera-projector system to collect a two-dimensional texture image and a deformed fringe image of a to-be-measured scene under the same field of view; the deformed fringe image is solved through multi-step phase shifting and Gray code technology to obtain an absolute phase distribution and calculate a gradient field; according to a preset mutation condition, a physical edge profile reflecting the depth change of an object is extracted from the gradient field; the physical edge profile is mapped into the two-dimensional texture image based on the corresponding relationship of pixel coordinates to accurately define the semantic object area, the corresponding category label is given, and finally a semantic label mask image is generated. The application uses physical phase information to correct the uncertainty of visual texture edges, effectively solves the edge extraction problem under a complex light scene, realizes the automatic generation of semantic segmentation data, and improves the training precision and robustness of a semantic segmentation model.

This invention provides a method and system for dense 3D reconstruction by fusing imaging radar point cloud and visual image data, belonging to the field of 3D reconstruction technology. First, imaging radar point cloud data and visual image data are simultaneously acquired to form a dual-source data set. Next, a pixel-level depth correlation is established between the two, defining the pixel depth range. Then, visual texture detail information is extracted to fill in sparse areas of the imaging radar point cloud, forming intermediate dense point cloud data. Next, visual texture color information is mapped to the intermediate point cloud, and the spatial point depth positions are adjusted to generate dense point cloud data with coordinated depth and texture. Finally, all spatial point information is extracted to restore the target scene shape and texture, forming a color dense 3D reconstruction model. This invention effectively integrates the advantages of both types of data to achieve high-precision dense 3D reconstruction.

The invention provides a multi-texture vibration tactile rendering method and device and interaction equipment, and belongs to the technical field of tactile representation. Obtaining interaction action data of a plurality of different modes, and encoding the interaction action data to generate interaction features; obtaining visual data, and generating corresponding visual texture features according to the visual data codes of different textures; combining the interaction features and the visual texture features to generate fusion features; encoding the fused features to generate corresponding tactile features; decoding the tactile feature to generate predicted tactile data corresponding to the interactive action data; and training a rendering model according to the predicted tactile data and the real tactile data corresponding to the interactive action data. The method has the advantages that by obtaining any interactive action data and multiple different types of texture visual data, feature extraction and fusion are carried out on data of multiple modes, and high-reality tactile data corresponding to different types of textures are generated.

This utility model discloses a battery decorative cover, comprising: a fiberglass substrate; a gray base coat layer, screen-printed on the surface of the fiberglass substrate; a first color layer; a second color layer, used to supplement and strengthen the first color layer, thereby increasing color saturation and pattern complexity; a pearlescent ink layer, screen-printed on the second color layer; a first UV texture layer, transferred onto the pearlescent ink layer using a UV transfer device, providing a visual texture effect; a PVD coating layer, covering the surface of the first UV texture layer; a high-transmittance varnish layer, screen-printed on the PVD coating layer; and a second UV texture layer, transferred onto the high-transmittance varnish layer using a UV transfer device, providing a tactile texture effect. Through the above methods, this utility model can improve gloss and brightness, supporting the customization of complex decorative graphics.