198 results about "Height map" patented technology

The invention discloses an ultra-thin glass defect detection system and positioning method based on line scanning and phase deflection, and the system comprises a line scanning camera module, a distortionless phase deflection detection module, a high-magnification microscope module and a central processing unit. The line scanning camera module is used for collecting a 2D grayscale image of the ultrathin glass; the distortionless phase deflection technology detection module is used for collecting a surface height map of the ultra-thin glass; the high-magnification microscope module is used for carrying out depth positioning on the interlayer defect judged by the central processing unit; the central processing unit is used for receiving the 2D gray level image and the surface height map, executing an image registration and defect classification algorithm and sending a depth positioning control instruction to the high-magnification microscope module, a multi-module cooperative work detection system is constructed, the detection efficiency and the detection precision are both considered, and data support is provided for production process improvement.

The invention discloses a machine vision-based enzyme freeze-dried powder quality detection method, and belongs to the technical field of machine vision, and the method specifically comprises the following steps: in a closed optical cavity, synchronously collecting a multispectral polarization sequence of a sample through annular multispectral polarization illumination and microscopic imaging; performing short-time airflow pulse disturbance and continuous imaging on the sample, and constructing a polarization time sequence texture map by means of inter-frame polarization retention difference; according to Stokes parameters, calculating a deskewness graph and a specular reflection component graph, and carrying out joint normalization on the deskewness graph and the specular reflection component graph and the polarization time sequence texture graph to form a multi-channel feature stack; constructing a double-branch encoder, and performing comparative learning on the feature stack to generate an impurity prototype dictionary; using the dictionary to match the feature stack pixel by pixel, and tracking and inhibiting powder agglomeration false detection through a topological constraint connected domain; and recovering a height map by using an oblique incidence fringe phase shift method, performing pixel-level fusion with the candidate image, retaining targets with height abrupt change and consistent polarization characteristics, and outputting an impurity mask.

The invention discloses a frequency height map O / X wave trace intelligent extraction method based on a deep neural network, and belongs to the technical field of space environment detection and artificial intelligence. The method comprises the following steps: performing time-frequency feature analysis and data enhancement on an original frequency height map, and constructing a high-quality training set by adopting an expert labeling mode; a double-path attention mechanism network model is innovatively proposed, multi-scale features are captured through a spatial pyramid module, and feature weight distribution is optimized in combination with channel attention; a joint optimization strategy of cross entropy loss and Dice loss is adopted to effectively process the class imbalance problem; and finally, an extraction result is optimized through morphological post-processing. According to the method, the distance deviation at different frequency points is reduced by more than 30% compared with a traditional method, the method has strong anti-noise capability and weak feature capture capability, the ionosphere parameter inversion precision can be improved, and reliable technical support is provided for the fields of space environment monitoring, short-wave communication guarantee and the like.

The invention provides a sweet menu embryo pelleting seed breakage rate detection method based on machine vision, and relates to the technical field of seed detection.The method comprises the steps that firstly, visible light and near-infrared images of seeds under standard illumination and non-standard visible light images under multiple light sources are collected, and reflectivity calibration and polarization difference correction processing are carried out; features such as colors and textures are extracted based on visible light and near-infrared reflectivity images, and multispectral feature vectors are constructed; a surface normal vector is calculated by using a photometric stereoscopic vision technology and combining a plurality of non-standard visible light images, an initial three-dimensional height map is generated through integration, and a plurality of abnormal judgment features are extracted to mark a suspected damaged area. And further calculating an edge sharpness index, removing a mechanical damage area, and determining a final damage area and a three-dimensional contour thereof. And finally, combining the volume loss rate, the projection area ratio and other multi-dimensional features with the multispectral feature vector to construct a damage probability prediction model, thereby realizing accurate and efficient detection of the seed damage rate.

The invention belongs to the technical field of robot autonomous operation and reinforcement learning, and discloses an intelligent active perception and behavior interaction method in an unknown shielding scene. According to the method, a viewpoint planning strategy of an active sensing agent and a pushing strategy of a behavior interaction agent are closely fused, a 2.5 D occupancy height map is combined, and a pushing point screening mechanism of geometric features and task heuristic weights and a dense reward function are fused; the problems of insufficient perception information, low action efficiency and unstable strategy training in an unknown shielding environment are solved. According to the method, the pushing success rate, the environment entropy reduction efficiency and the strategy generalization ability are remarkably superior to those of a traditional method, efficient environment exploration can be achieved only through minimum invasive pushing of the scene, a good foundation is laid for tasks such as downstream grabbing and cleaning, and the method is suitable for robot autonomous operation of disordered shielding scenes such as bookshelves and storage cabinets.

The current invention relates to a method for determining a height map of a sample surface of a sample. The invention further relates to a method for determining a height map of a sample surface comprising a pre-scan step and a high-accuracy scan step. The invention further relates to an optical profilometry device configured for determining a height map of the sample surface. The invention further relates to a computer readable data carrier comprising a computer program.

An apparatus may be configured to perform a perception task based on features fused with height information. The apparatus configured may generate 3D sensor features from data from one or more sensors, generate a plurality of height maps from the 3D sensor features at a plurality of times, generate a height gradient map from the plurality of height maps, fuse the 3D sensor features and the height gradient map to generate height informed fused features, and perform the perception task using the height informed fused features.

A wave radar image inversion method based on a convolutional neural network, and the application thereof, which relate to the field of sea condition monitoring. A convolutional neural network autoencoder is used, and a training set generated by means of a simulation tool is input into the convolutional neural network autoencoder for supervised training, wherein an X-band radar image of an inversion region is an input of the training set, and a corresponding sea surface wave height map is an output of the training set, thereby obtaining a mapping relationship model therebetween. The convolutional neural network autoencoder comprises an encoder, fully connected layers and a decoder, wherein the encoder comprises five convolutional layers and five maximum pooling layers, the convolutional layers and the maximum pooling layers being alternately connected, the fully connected layers are two symmetric layers, and the decoder comprises five deconvolutional layers. Using a convolutional neural network deep learning algorithm achieves a better effect in restoring nonlinear components. Using an autoencoder technique can directly restore an X-band radar wave image to a real-time sea surface wave height map with a relatively small error, and can more comprehensively display sea condition information.

This invention relates to the field of forage seeds, disclosing an efficient and low-loss combined harvesting method and system for forage seeds based on the Internet of Things (IoT). The method includes: sending a detection laser from a combine harvester in the forward direction, and receiving the reflected laser in the combine harvester; constructing a density map and height map of forage plants in the forward direction; adjusting the height of the cutting module to obtain a cutting module with adjusted height; adjusting the perimeter of the conveying module using the height map to obtain a conveying module with adjusted perimeter; adjusting the size of the sieve aperture of the threshing module using the particle diameter to obtain a threshing module with adjusted sieve aperture; adjusting the wind speed of the separation module to obtain a separation module with adjusted wind speed; and completing the combined harvesting of forage seeds using the height-adjusted cutting module, the perimeter-adjusted conveying module, the sieve-aperture-adjusted threshing module, and the wind speed-adjusted separation module. This invention enables efficient and low-loss adjustment of the combine harvester through IoT technology.

The invention discloses a chip packaging method and a related device, and the method comprises the steps: determining an overlapping region, a reference region and a shielding boundary curve according to imaging data, and obtaining interconnection nodes and rigidity constraint data in the overlapping region; acquiring polarization intensity data in the overlapping region, and obtaining a surface slope field through a dual-band intensity ratio and multidirectional fusion conversion; taking the reference area as a Dirichlet boundary condition, taking a normal derivative of the shielding boundary curve as a Noiemann boundary condition, and carrying out Poisson integration on the surface slope field to obtain a relative height map of the upper chip; and solving low-order deformation parameters of the lower-layer chip through elastic constraint energy minimization according to displacement observation and interconnection rigidity constraint of the relative height map at interconnection nodes. According to the scheme, the technical problems that in a traditional method, geometric benchmark missing exists in an overlapping band, and a shielded layer cannot be observed can be solved.

The invention discloses a line laser 3D contourgraph working method integrating various stray light suppression algorithms, and the method comprises the steps: constructing a stray light sensitivity weight map based on a multi-exposure image sequence through the combination of a deep convolutional neural network and a point spread function; guiding Gaussian Laplacian pyramid adaptive filtering by using the weight map, and adjusting the filtering intensity by using a residual stability index to generate a purification intensity image; performing one-dimensional profile analysis based on the purified image and the original height map, calculating a morphological distortion index, extracting an energy concentration ratio through three-level wavelet packet decomposition, and triggering morphological restoration; performing center line extraction and motion blur correlation degree calculation on the repaired image, and performing anisotropic smoothing on the height map when a threshold value is exceeded; and registering the processed multi-frame height map, constructing geometric fidelity as a feedback driving genetic algorithm to optimize an initial parameter, and restarting a whole process until the fidelity meets a precision requirement. According to the invention, the precision of 3D imaging is improved through various stray light suppression algorithms.

Methods and systems for generating and modifying three-dimensional occlusograms. One method includes calculating a height map and / or a distance field of a tooth of an upper jaw or a lower jaw. An occlusogram for the tooth may be generated based on a space and / or a collision depth between the tooth and a tooth on an opposite jaw. The occlusogram may be displayed over a three-dimensional (3D) model of the tooth. The occlusogram may be dynamically changed in response to a change in one or more input parameters associated with one or more dental treatments on the tooth.

The invention discloses a plate cutting method based on a 3D laser line scanning device, and the method comprises the following steps: arranging the 3D laser line scanning device above a to-be-cut plate, enabling the scanning visual field of the 3D laser line scanning device to cover the to-be-cut plate, and enabling the scanning visual field to cover the thickness of the to-be-cut plate; scanning the point cloud of the to-be-cut plate, then converting the point cloud into a height map, and then converting the height map into a grey-scale map; extracting edges of cut-off parts in the point cloud on the grey-scale map, independently establishing an X array and a Y array for each cut-off part, and storing the X array and the Y array into the arrays according to the edge sequence; the multiple arrays X and Y obtained in the third step are converted into dxf format files, the dxf format files are transmitted to a control system, graphic software is used for opening the dxf format files so that a user can determine an effective area by observing the collected height map, and further cut graphs are determined through the graphic software; and the control system stores the graph as coordinates and imports the coordinates into a cutting device for cutting.

This invention discloses a method for 3D mesh reconstruction of buildings from multispectral LiDAR point clouds, belonging to the field of multispectral LiDAR point cloud processing technology. The method includes: extracting building instances from the input point cloud using a multispectral LiDAR point cloud classification method; extracting geometric features from the height map obtained from the building point cloud, and then extracting spectral features from the spectral map using a spectral adaptive convolution module; inputting the data into a spectral-geometric fusion module to fuse the features of the spectral map and the geometric map to achieve accurate prediction of 2D corner points; projecting the 2D corner points onto the height index of the height map to obtain 3D corner points, and using an attention mechanism jointly guided by multispectral and geometric features to predict the boundary structure of the 3D corner points, generating a wireframe model; finally, achieving seamless and watertight 3D mesh reconstruction of the building under the guidance of the topological relationships contained in the wireframe model. This invention can effectively reduce artifacts and planar misalignment problems during the reconstruction process.

To improve measurement accuracy of a pin even when a tip shape of the pin is different. A setting unit of a setting support device for a measurement device includes a tool for measuring a predetermined measurement object as a measurement item, receives designation of a measurement region, and sets a tool including a measurement region for measuring the measurement object and a filter parameter according to the designation of the measurement region and a size of the target region and / or a representative height in the target region. A symbol corresponding to the target region is displayed on the height image, a measurement object corresponding to the filter parameter in the measurement region is specified on the basis of the setting of the tool, and the measurement of the tool is executed based on the specified measurement value of the measurement object.

The invention provides a three-dimensional online boxing method considering a buffer area based on hierarchical reinforcement learning, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: obtaining a size information list of a plurality of to-be-boxed boxes in the buffer area, and a three-dimensional height map of a current loading state in a container; extracting buffer area state feature vectors, and splicing the state feature vectors to obtain a global state feature vector; inputting the global state feature vector into a high-level strategy network in a trained hierarchical reinforcement learning network, outputting selection probability distribution of each box body in a buffer area, and selecting a target box body to be placed from the buffer area according to the probability distribution; and inputting the size information and the three-dimensional height map of the target box body into the trained low-layer strategy network, outputting probability distribution of each candidate placement position of the bottom surface of the container, generating an effective mask in combination with a preset physical constraint condition, and carrying out masking and renormalization processing on the probability distribution so as to determine a final placement position of the target box body.

The presently disclosed subject matter includes a computer system and a computer-implemented method of generating synthetic examination output images, including synthetic fault images and synthetic fault-free images. The synthetic images comprise artificially generated 3D defects. A machine learning model is trained for transforming examination output images to height maps. The height maps are modified to include certain 3D features, and a second machine learning model is used for transforming the height maps to modified examination output images to exhibit the 3D features.

An imaging system is disclosed herein. The imaging system includes an imaging apparatus and a computing system. The imaging apparatus includes a plurality of light sources positioned at a plurality of positions and a plurality of angles relative to a stage configured to support a specimen. The imaging apparatus is configured to capture a plurality of images of a surface of the specimen. The computing system in communication with the imaging apparatus. The computing system configured to generate a 3D-reconstruction of the surface of the specimen by receiving, from the imaging apparatus, the plurality of images of the surface of the specimen, generating, by the imaging apparatus via a deep learning model, a height map of the surface of the specimen based on the plurality of images, and outputting a 3D-reconstruction of the surface of the specimen based on the height map generated by the deep learning model.

The invention relates to the technical field of digital recognition and intelligent classification, in particular to an intelligent recognition and classification method for non-missing patterns, which comprises the following steps of: constructing a multi-dimensional embroidery pattern knowledge base, calculating a disparity map and a height map to obtain a stereoscopic strength coefficient, and calculating a stitch complexity index as a physical vector by combining a histogram of oriented gradients; meanwhile, the pattern recognition model is used for outputting the category probability of each independent pattern part as a semantic vector, then an embroidery method is determined, graph nodes are created based on physical and semantic vectors, a spatial relation edge and a cultural relation edge are established, then a multi-modal heterogeneous graph is generated, the graph is input into a graph neural network for message passing and global pooling, and the multi-modal heterogeneous graph is obtained. According to the method, a global feature vector is obtained, an embroidery theme is matched through a theme classifier, finally, the comprehensive credibility is calculated, and through multi-modal data fusion and heterogeneous graph analysis, the accuracy and automation level of pattern recognition are effectively improved, and the method is particularly suitable for intelligent classification of complex non-missing embroidery.

The invention relates to the technical field of carbon sequestration and geological sequestration, and particularly discloses a slice CO2 water-rock reaction corrosion identification and pore evaluation method based on machine learning, and the method comprises the steps: firstly collecting multi-focal-plane images of the same view field of a slice before and after a reaction on a scanning electron microscope platform, and carrying out the registration; utilizing the trained U-Net instance segmentation model to automatically identify a corrosion area and extract a contour; generating a relative height map through a depth-of-field reconstruction and calibration technology in combination with a multi-focal-plane sequence, and obtaining the effective corrosion depth of each corrosion area; and carrying out volume normalization calculation based on area-depth information, and directly outputting porosity increment and spatial distribution thereof. According to the method, destructive detection is not needed, quantitative volume characterization of corrosion characteristics is achieved, the analysis efficiency and accuracy are remarkably improved, batch processing and mineral phase layering statistics are supported, and reliable data support is provided for CO2 geological storage reaction mechanism research and reservoir simulation.

The present invention discloses an AFM scanning wafer structure analysis method based on image and data processing, comprising the following steps: S1, performing Gaussian fitting and denoising on the height map data row by row; S2, performing least squares polynomial fitting on the height map data row by row, and obtaining leveling data using the new data; S3, normalizing the leveling data to form a grayscale image; finding all circular regions and excluding them to the edge; S4, cutting out three-dimensional data clusters in the leveling data and performing clustering processing to separate each corresponding bottom hole data cluster and upper surface data cluster; obtaining the corresponding bottom hole cloud diameter based on the bottom hole data cluster, averaging each data in the bottom hole data cluster to obtain the hole bottom height. The present invention processes the AFM data through an improved flattening method, converts it into a two-dimensional image, and then cuts out the three-dimensional data for clustering and denoising, which can effectively extract the actual contour data and analyze the circular structure size of the wafer, improve the calculation process, and enhance data stability and calculation accuracy.

The application provides a surface feature-based anti-counterfeiting information generation method, device, equipment and medium, which comprises the following steps: firstly, a microstructure intensity map is obtained by extracting surface micro-relief features through a phase consistency model; a first height map is obtained by reconstructing an initial surface height field by using a MiDaS depth estimation network; a local displacement field caused by embedding a preset mark is quantified by using a RAFT optical flow model; an initial stress disturbance distribution is constructed by fusing the height field and the displacement field as physical input of a coupling prediction model, and then the surface morphology of a concrete sample after solidification is predicted; finally, the microstructure intensity map, the local displacement field and the first height map are taken as initial surface features at the sampling time, and the initial surface features and target surface features are encoded as anti-counterfeiting information, so that the reliability of sample identification is effectively improved.

A UAV control method, a device, a UAV, and a storage medium are provided. The method includes: obtaining a UAV's take-off position; obtaining a height map of an area around the take-off position; determining height information of a highest target in the area around the take-off position based on the height map; determining a relative height between the highest target and the take-off position based on the height information of the highest target; determining a restricted flight altitude of the UAV relative to the take-off position based on the relative height, and controlling the UAV based on the restricted flight altitude; where the restricted flight altitude is greater than or equal to the relative height. It thus can ensure flight safety while offering more reasonable and user-friendly flight flexibility and freedom.

The invention discloses a virtual scene editing method, a virtual scene editing device, a computer readable storage medium, electronic equipment and a computer program product, and the method comprises the steps: generating a terrain editing object in a virtual scene in response to a placement operation for the terrain editing object in the virtual scene, and in response to an editing operation for the terrain editing object, determining a terrain editing area corresponding to the terrain editing object, generating a target height map associated with the terrain editing object, and editing a target display style of the terrain of the terrain editing area in the virtual scene according to the target height map, therefore, a user can generate the height map corresponding to the terrain editing area by placing and editing the terrain editing object, so that flexible generation of the height map is realized, matching between the height map and user requirements can be guaranteed to a certain extent, matching between the terrain editing effect and user expectations can be guaranteed to a certain extent, and the user experience is improved. Therefore, the terrain editing efficiency of the user is improved.

An object interference check technique using height maps for machine tool operations and other applications. A CAD model of the workpiece or other obstacle is converted into a height map that defines, for each cell of the grid, an obstacle height above a plane of the reference frame. The machine tool center point path is then converted to the same reference frame. Interference checks are performed by comparing the height coordinates of each converted tool path checkpoint to the height map values of the corresponding grid cells. Any interference detected is addressed by defining new waypoints with increased height coordinates to replace interference critical waypoints. By creating a bottom-to-top height map of the tool and comparing each pixel of the tool height map to a corresponding grid cell of the obstacle height map, an interference check can be performed on the entire tool.

To provide an object interference check technique using a height map for a machine tool and other applications.SOLUTION: The CAD model of the workpiece or other obstacle is converted into a height map that defines the obstacle height above the plane of the reference coordinate system for each cell of the grid. The tool center point path is then transformed into the same reference coordinate system. The interference check is performed by comparing the height coordinates of each transformed tool path check point with the values of the height map of the corresponding grid cell. The detected interference is handled by defining a new path point with an increased height coordinate to replace the interference critical path point. The interference of the entire tool can be checked by creating a bottom-to-top height map of the tool and comparing each pixel of the tool height map with the corresponding grid cell of the obstacle height map.SELECTED DRAWING: Figure 2

A method of convolutional neural network (CNN) based inversion of wave radar images and use thereof pertain to the field of sea-state monitoring, in which a CNN autoencoder is employed. A training dataset produced by a simulation tool is input to the CNN autoencoder for supervised training, with X-band radar images of an inversion region being taken as input to the training dataset and with corresponding ocean surface wave height maps being taken as output from the training dataset, thereby deriving a model defining a mapping of them. The CNN autoencoder includes an encoder, fully connected layers and a decoder. The encoder includes five convolutional layers and five max-pooling layers, which are connected alternately. There are two symmetric fully connected layers, and the decoder includes five deconvolutional layers. A CNN-based deep learning algorithm is used to provide good reconstruction of the nonlinear components. The autoencoder technology is employed, which can directly reconstruct real-time ocean surface wave height maps from X-band radar wave images with minor errors, which provide more comprehensive sea-state information.

The invention provides a robot grabbing method for a multi-shielding complex scene, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting a cross view angle image of a working space of a robot through double fixedly-installed RGB-D cameras, carrying out the segmentation and orthogonal projection processing of the cross view angle image, and obtaining a multi-shielding complex scene; obtaining an RGB image, a depth image and a visible mask height image; constructing a mask estimation network model, extracting features in the RGB image and the depth image, and performing complete mask estimation of the target grabbed object through an attention mechanism in combination with the visible mask height image; constructing an action generation network model, inputting the complete mask of the target object into the action generation network model, and outputting candidate grabbing actions of the robot; an action evaluation network model is constructed, the candidate grabbing actions are graded and scored through the action evaluation network model, and the optimal grabbing action is selected and executed by adopting a condition greedy strategy; and the robot is controlled to execute the selected grabbing action so as to complete grabbing of the shielded target object.

The present application relates to the technical field of image data processing, in particular to a kind of urban building three-dimensional reconstruction method and device, method includes: obtaining from the same scene Multi-View Satellite Ortho Image, target area's spaceborne laser height measurement data, digital terrain model and two-dimensional building vector data.Accurate geometric registration is carried out to Multi-View Satellite Ortho Image, and dense matching is executed, and initial parallax point cloud is generated.Combined with the azimuth information of Multi-View Ortho Image, parallax map with accurate plane positioning is constructed.Using spaceborne laser height measurement data and digital terrain model, the elevation value above ground is calculated.Combining elevation value and parallax value, parallax elevation proportion coefficient is estimated, to convert parallax map into height map.Combined with two-dimensional building vector data, the three-dimensional building vector data of target area is generated.Therefore, the problem that the cost is high and data acquisition is limited based on surveying and mapping level remote sensing data in the related art cannot meet the global urban three-dimensional reconstruction demand.

To improve the measurement accuracy of a pin even when the tip shape of the pin is different.SOLUTION: A setting part 31c of a setting support device 1 of a measuring instrument has a tool for measuring a predetermined measuring object as a measuring item, receives designation of a measuring area and designation of an object area for specifying the measuring object in the measuring area in response to selection of the tool, and sets the tool including the measuring area and a filter parameter for measuring the measuring object according to the designation of the measuring area and a size of the object area and / or a representative height in the object area. The symbol corresponding to the target region is displayed on the height image, the measurement target corresponding to the filter parameter in the measurement region is specified based on the setting of the tool, and the measurement of the tool is executed based on the measurement value of the specified measurement target.SELECTED DRAWING: Figure 3