1814 results about "Camera image" patented technology

Disclosed are a high-performance loosely-coupled multi-modal data fusion system for a smart driving environmental perception system and a vehicle-mounted device, comprising: a fusion detection model based on a modality-independent feature interaction strategy, which is configured for converting a LiDAR point cloud, a camera image, and a millimeter-wave radar point cloud into a unified bird's-eye view representation, and performing multi-modal fusion; and a fusion tracking model based on a motion-appearance feature cascaded coupling data association strategy, which is configured for performing subsequent trajectory tracking and matching according to multi-modal fusion feature information. A VoD data set and a K-Radar data set are selected for training, verifying, and testing the comprehensive performance of the models, and a TensorRT accelerated inference model is applied, then quantized, and deployed to a vehicle-mounted computational testing platform. The present invention is compatible with mainstream sensor deployment solutions, and achieves the efficient complementary fusion of multi-source heterogeneous sensor information, significantly improving the reliability, accuracy, and adaptability of vehicle-mounted perception systems, thereby effectively responding to extreme operating conditions such as complex traffic scenarios and inclement weather.

The invention discloses a cradle head tracking method and device based on a binocular camera and a storage medium, and relates to the technical field of computer vision, and the method comprises the steps: processing image data based on a binocular parallax principle, and generating a three-dimensional coordinate of a center point of a tracking target; based on the three-dimensional coordinates of the camera coordinate system and the offset from the optical center of the camera to the rotation center of the holder, generating three-dimensional holder coordinates through coordinate transformation solution; determining a historical track based on the tracking target feature information and a historical target feature matching result, and outputting an identifier and a three-dimensional position observation value through correlation verification of a three-dimensional holder coordinate and the historical track; inputting an observation updating equation correction state through the identifier and the three-dimensional position observation value, and outputting a three-dimensional prediction position; based on the three-dimensional prediction position and a deviation formula, calculating the angle deviation with the camera image center under the holder coordinate system, and driving the holder to center the target in the picture center according to the angle deviation. The problem that the target tracking effect is poor is solved, and the robustness of target tracking in a complex scene is improved.

The invention relates to the technical field of image recognition, in particular to a license plate recognition system and method based on an image technology and a medium. The method comprises the following steps: acquiring area sensing data and a camera image set, and performing deformation effect compensation to obtain an environment compensation image set; performing image diffusion reverse enhancement on the environment compensation image set to obtain a license plate area enhanced image set; performing character region high-dimensional topological mapping based on the license plate region enhanced image set to obtain a character segmentation matrix; extracting character morphological characteristics according to the character segmentation matrix, and performing character recognition on the character morphological characteristics to obtain a character recognition result; and carrying out cross-character semantic compensation on the character recognition result to obtain a semantic compensation license plate character vector, and carrying out multi-target cross verification on the semantic compensation license plate character vector to obtain a license plate recognition result. According to the invention, the accuracy and robustness of license plate recognition can be improved.

The invention discloses a multi-modal information fusion odometer construction method for star catalogue positioning, and relates to the technical field of star catalogue patroller positioning. The method comprises the following steps: carrying out space joint calibration on a monocular camera, a laser radar and an inertial measurement unit, reconstructing a laser radar point cloud by using a timestamp of a camera image, and realizing time synchronization of the camera image and the laser radar point cloud; establishing an IMU pre-integration error model; and performing motion compensation distortion removal on the laser point cloud by using an IMU pre-integration result, and extracting geometric features of the distorted laser point cloud based on a neighbor region smoothness calculation method of a fixed measurement distance. By researching a multimodal information fusion odometer method, the accumulative error of motion measurement is reduced, the positioning precision and stability are improved, technical support is provided for design and development of a star catalogue navigation system, and the problems that a single-modal star catalogue positioning method is weak in environment adaptive capacity and poor in algorithm generalization are solved.

The invention relates to a wharf sensing method and system based on data fusion of a laser radar and a camera, and the method comprises the steps: firstly employing a fisheye camera to shoot a checkerboard calibration plate at multiple angles, employing a Zhang's calibration method to calculate an internal reference matrix and a distortion coefficient of the fisheye camera, and synchronously collecting the data of the laser radar and the camera in real time; the method comprises the following steps: performing distortion removal processing on a camera image, calculating an external parameter matrix between a laser radar and a camera through a feature extraction algorithm, a feature point matching algorithm and a nonlinear optimization algorithm, and performing data fusion through an internal parameter matrix, the external parameter matrix and a bilinear interpolation method to generate fusion point cloud data with color information; and finally, identifying a key target of the wharf by marking and training a CNN model, and calculating three-dimensional parameters of the key target by adopting a specific calculation method, so that a sailor can quickly distinguish the target in the fused point cloud data, the accuracy and efficiency of wharf environment perception are remarkably improved, and accurate environment perception and decision support are provided for ship berthing and departing operation.

The invention discloses an intelligent car coupler identification method and system of a car dumper based on a picking and re-hooking robot, and relates to the technical field of intelligent identification, the method comprises the following steps: multi-modal data is collected and preprocessed, and the multi-modal data comprises laser radar point cloud data, camera images and infrared data; fusing the processed multi-modal data, identifying coupler information by adopting a convolutional neural network and an attitude estimation algorithm, and generating a coupler identification result; a path planning algorithm is adopted, a grabbing point path of the car coupler is calculated, interferents are avoided, the grabbing path is optimized, and an optimal grabbing path is generated; and the robot moves to the position of the car coupler according to the optimal grabbing path, the grabbing posture is corrected in real time through image perception, and a mechanical arm tail end executor is driven to clamp the car coupler for grabbing. Through multi-modal data fusion and path planning, the coupler is accurately recognized, the grabbing path is optimized, and the grabbing precision and stability of the robot in a complex environment are improved.

Methods and processors for rendering a 3D object are disclosed. The method includes acquiring multi-camera image input including first image frames of the 3D object generated by a first camera and second image frames of the 3D object generated by a second camera, acquiring an initial 3D Gaussian Splatting (3DGS) model having a plurality of initial parameters including an initial frame-wise GS parameter and an initial camera-wise GS parameter, generating an adjusted 3DGS model by adjusting, based on the multi-camera image input, at least one of: the initial frame-wise GS parameter, the initial camera-wise GS parameter, generating, by the adjusted 3DGS model, a 3DGS output and rendering a 2D image of the 3D object using the 3DGS output.

The invention belongs to the technical field of data processing, and particularly discloses a dynamic path planning method and system for an inspection robot, and the method comprises the steps: collecting field data through a sensor, processing a laser scanning point cloud and a camera image through a positioning mapping algorithm, and fusing multi-source information to generate a real-time map. Obtaining a three-dimensional environment model containing a dynamic obstacle position; according to the three-dimensional environment model, obstacle features are extracted, a deep learning model is adopted to identify moving personnel and randomly placed goods, obstacle types and movement tracks are judged, and a classified obstacle data set is obtained; through the classified obstacle data set, the distance and the relative speed between the obstacle and the current position of the robot are calculated, and if the distance is smaller than a preset threshold value and the speed is larger than zero, high-priority interference is marked; according to the invention, the problem of inaccurate dynamic obstacle identification and obstacle avoidance in a complex scene in the prior art is solved.

The invention relates to the technical field of security and protection management, in particular to a campus security and protection management system based on deep learning, which improves the accuracy and robustness of identity recognition by acquiring access control card numbers, face images or fingerprint features and generating standardized identity authentication data. And on the basis of a comparison result of the identity authentication data and the campus database, a behavior chain initialization identifier is generated, and accurate identity binding of the school entering personnel is realized. Furthermore, by collecting multi-camera image stream data, pedestrian re-identification and similarity calculation are executed by using a deep feature matching network, and a cross-camera continuous trajectory data set is generated. And matching the behavior track data set with the conventional path template to generate a behavior offset feature vector. And carrying out joint modeling on the behavior offset characteristics and the identity information through a graph neural network model containing an attention mechanism, and outputting a behavior purpose label and a risk grade score. And a graded security response instruction is generated based on the risk score, so that the missing report rate and the false report rate are effectively reduced.

The invention discloses an external parameter calibration method for a laser radar and camera system, and the method specifically comprises the steps: synchronously collecting the laser radar point cloud and camera image data of a circular plane calibration plate through employing the circular plane calibration plate with a two-dimensional coding pattern printed at the center; segmenting laser radar point cloud subsets belonging to the calibration plate from the laser radar point cloud through a plane extraction algorithm, and calculating centroid coordinates of the point cloud subsets as three-dimensional feature points; detecting angular points of the two-dimensional coding pattern in the camera image, and calculating geometric centers of the angular points as two-dimensional feature points; multiple groups of three-dimensional-two-dimensional feature point corresponding relations are established through multiple times of scanning, and initial external parameters are solved by using a PnP algorithm; and minimizing a re-projection error through a nonlinear optimization algorithm to obtain a final external parameter. The method provided by the invention is suitable for application scenes such as robots, automatic driving or three-dimensional reconstruction, and accurate, efficient and automatic external parameter calibration can be realized.

The invention provides a high-altitude operation risk early warning method and system based on camera image recognition, and relates to the technical field of computer vision, and the method comprises the steps: firstly collecting a video image sequence of a high-altitude operation scene, and generating a fusion feature map containing environment and operation main body features through multi-level feature extraction; performing spatial dimension segmentation and regional feature comparative analysis on the fusion feature map to obtain a spatial risk distribution map containing risk region identification information, processing the spatial risk distribution map of continuous frames based on a time sequence feature fusion rule to generate a dynamic risk evolution map, and calling a risk decision model to perform mode recognition to obtain a dynamic risk evolution map; and generating a risk level classification result and a risk position coordinate set according to the risk level classification result and the risk position coordinate set, and finally generating a risk early warning signal and sending the risk early warning signal to the monitoring terminal, thereby comprehensively, accurately and dynamically monitoring the high-altitude operation risk, and improving the accuracy and timeliness of risk early warning.

The invention relates to the technical field of autonomous navigation of inspection robots, and provides a field inspection robot autonomous navigation and path planning method based on multi-modal sensing. The method comprises the following steps: collecting a laser radar point cloud, a camera image, inertial measurement and positioning data, obtaining an environment semantic feature set through spatio-temporal feature fusion, and generating a semantic occupation map; inputting the current position of the robot, the target point and the semantic occupation map into a trajectory generation network to obtain candidate trajectories meeting obstacle avoidance and path smoothness constraints, and completing task sorting and trajectory splicing in combination with inspection task points to form a global path; in the operation process, the reinforcement learning control model adjusts the linear speed and the angular speed in real time, and path tracking and dynamic obstacle avoidance are achieved. The navigation precision and the operation safety of the inspection robot in the complex field area are improved.

The invention provides a touch and visual perception integrated micro-nano robot magnetic field control system and method, and the system comprises a camera which collects the image of a micro-nano robot in a pipeline in real time; the magnetic field generating device generates a gradient magnetic field for driving the micro-nano robot to move through a permanent magnet; the tail end of the mechanical arm is connected with the permanent magnet and adjusts the three-dimensional pose to change the magnetic field distribution; the force feedback equipment is used for providing a touch signal for the micro-nano robot to be in contact with the pipeline boundary; and the computer is configured to identify the aggregation state of the micro-nano robot according to the camera image, increase the resistance of the force feedback equipment and reduce the rotation speed of the permanent magnet when the dispersion state is detected, and reduce the resistance and recover the conventional magnetic field control after the aggregation state is recovered. According to the system, accurate control and abnormal state self-adaptive response of the micro-nano robot in a complex experiment scene are realized through touch-vision collaborative perception and dynamic magnetic field regulation and control.

The embodiment of the invention provides a multi-camera video fusion method and system for a robot with a body and a storage medium, and belongs to the field of image processing. The method comprises the following steps: performing time and space alignment on images based on a vision and inertia alignment model to generate a unified aligned image sequence; extracting multi-scale image features based on the uniformly aligned image sequence, and constructing a cross-view residual image for guiding attention fusion operation to generate a fusion image; performing privacy area identification on the fused image, and performing shielding processing on a privacy area to obtain a privacy protection image; and performing semantic compression on the privacy protection image, adjusting an image frame scheduling sequence according to network link state information, and outputting a video signal for controlling a decision. The method is oriented to whole-process optimization of space-time alignment, cross-view fusion and privacy protection processing in the multi-shot image acquisition process of the robot with the body, and video signals for control are output in a self-adaptive mode based on the link state.

The invention provides a multi-modal dynamic cooperative unmanned ship cluster autonomous obstacle avoidance method, which comprises the following steps: firstly, synchronizing LiDAR point cloud, camera images, radar data and AIS data through an IEEE 1588 protocol, and realizing cross-modal space alignment based on an external parameter matrix; secondly, a cross-modal attention mechanism and deformable convolution are combined to establish correlation between a channel and spatial dimensions, so that fused multi-modal features generate high-precision environmental semantic information; then, dynamically dividing unmanned ship task groups based on time-varying graph incremental modularity, and only updating a disturbed edge set to reduce calculation complexity; and finally, the unmanned ship leader optimizes a multi-target path by adopting an improved speed obstacle model (IVO-DWA), and the follower corrects the trajectory through a repulsive force potential field, so that real-time, safe and compliant cluster obstacle avoidance is realized.

The invention provides a camera external parameter calibration method and device based on image and point cloud matching, and the technical scheme of the invention is that a point cloud rendering view corresponding to an original point cloud is rotated, so that the point cloud rendering view and a camera image are superposed visually, and initial space association is established; and directly learning and solving accurate camera external parameters from the local relevance between the original point cloud intensity information and the image RGB information in an end-to-end manner by using a deep learning model. According to the scheme, the strict camera-radar orientation consistency requirement in a traditional method is not needed through rough matching, manual participation in the whole process of traditional calibration is replaced, transition from visual alignment to geometric alignment is achieved, and the calibration efficiency and scene adaptability are remarkably improved. Meanwhile, the scheme of the invention initiates a process of visual field cone cutting-perspective projection rasterization-intensity-RGB end-to-end matching, and compared with a traditional feature point method, the calculation complexity is greatly reduced, and the memory occupation is reduced by 80%.

The invention relates to the technical field of cabin loading and unloading automation, and provides a multi-sensor fusion cabin loading and unloading equipment cooperative positioning method and system. A laser radar is used for scanning a cabin, laser radar point cloud data are generated, and a laser radar coordinate system and a point cloud map are constructed; a monocular camera is adopted to obtain an operation area image of loading and unloading equipment, camera image data is generated, and a camera coordinate system is constructed; performing anti-interference processing on the laser radar point cloud data; performing illumination change influence resistance processing on the camera image data; fusing the laser radar point cloud data with the camera image data to obtain fused positioning information; calculating the distance and angle of loading and unloading equipment according to the fused positioning information, generating a moving instruction, and driving the loading and unloading equipment to perform cooperative positioning; a dynamic object in a cabin is detected through a laser radar and a monocular camera, multi-modal verification is carried out on a detection result, the dynamic object is filtered, and a point cloud map is updated in real time. The problems of insufficient positioning precision, GPS failure and large dynamic environment interference of a single sensor are solved.

The disclosed systems and techniques facilitate efficient detection and navigation of reduced drivability areas in driving environments. The disclosed techniques include, obtaining, using a sensing system of a vehicle, a set of camera images, a set of radar images, and / or a set of lidar images of an environment. The techniques further include generating, using a first neural network (NN), camera feature(s) characterizing the camera images, generating, using a second NN, radar features characterizing the radar images, and / or generating, using a third NN, lidar feature(s) characterizing the lidar images. The techniques further include processing the camera feature(s), the radar feature(s), and the lidar feature(s) to obtain an indication of a reduced drivability area in the environment.

The invention relates to the technical field of computer vision, image recognition and intelligent medicine management, in particular to a medicine bottle label content recognition method based on multiple cameras and YOLOv8. The method at least comprises the following steps: S1, deploying a medicine bottle label generation system, and generating a label; s2, multi-camera image acquisition and preprocessing; s3, carrying out chessboard calibration and space positioning; s4, medicine bottle label detection and label character recognition and structured analysis; and S5, system integration and application. According to the invention, through combination of multi-camera and multi-angle acquisition and checkerboard calibration positioning and combination with YOLOv8 label detection and OCR identification, high precision, high efficiency, end-to-end automation and system integration of medicine bottle label identification are realized, the defects of precision, efficiency, environmental adaptability and management integration in the prior art are overcome, and the system is suitable for popularization and application. The method has obvious technical advantages and practical value.

The invention relates to the technical field of vision assistance, and discloses an anesthesia puncture positioning method and system based on vision assistance, and the method comprises the steps: accurately positioning an anesthesia puncture point through multi-view image fusion, hyperspectral image processing, image preprocessing, illumination equalization, edge enhancement, depth feature extraction and the like. The method comprises the following steps: firstly, constructing an image coordinate system, collecting a plurality of camera images, and obtaining a fused clear image through a designed multi-view fusion algorithm and distance calculation; and in combination with a hyperspectral image fusion algorithm, the image quality is further improved. Then, residual mapping filtering denoising and local histogram enhancement are used for illumination equalization, and the image contrast and edge details are enhanced; a convolutional neural network is adopted, interest point detection is carried out, a Hessian matrix is utilized to describe image second-order changes, and local depth features are extracted. And accurate positioning of an anesthesia puncture point is realized through a weighted soft voting classifier and a dynamic threshold method.

The invention belongs to the technical field of laser radar-camera panoramic segmentation, and particularly relates to a multi-mode panoramic segmentation method for multi-view space-time alignment and implicit feature interaction, and the method is executed by a multi-mode panoramic segmentation network, and comprises the steps: S1, obtaining laser radar point cloud data and multi-view camera image data in the same scene; s2, performing double-branch feature coding on the laser radar point cloud data; performing multi-scale image feature extraction on the camera image data; s3, generating false point cloud features with geometric perception capability; s4, generating semantic pixel features; s5, performing implicit fusion on the pseudo point cloud features generated in the S3 and the semantic pixel features generated in the S4 to obtain cross-modal fusion features; and S6, based on the cross-modal fusion features obtained in the S5, generating a unified panoramic segmentation result containing semantic tags and instance IDs. The method can effectively improve the robustness and precision of multi-mode panoramic segmentation in a complex urban environment.

The invention provides a multi-vision fusion mining explosion-proof equipment explosion-proof performance evaluation method, and relates to the technical field of intelligent detection, and the method comprises the steps: 1, obtaining flame images of visible light and infrared light; the method specifically comprises the steps that flame images are captured at the same time through an industrial camera and a thermal imager, the images of the industrial camera are stored as a visible light image sequence, the images of the thermal imager are stored as an infrared image sequence, and a time label is bound with each frame of image; step 2, data processing of visible light and infrared images; step 3, optimizing the single-vision image data; the method specifically comprises the following steps: optimizing the features of a visible light image; a feature optimization step of the infrared image; standardizing the feature representation; step 4, feature level fusion of visible light and infrared vision; comprising a feature matrix construction step and a feature level fusion step. And step 5, quantitative evaluation of the explosion-proof performance is carried out based on the visual features.

The invention discloses a submarine topography reconstruction system and method based on optical mobile measuring and scanning, and relates to the technical field of ocean exploration, and the system comprises the following units: an image collection unit, which is used for coordinating the emission of 532nm pulse laser and the collection of a gating camera image through a synchronous sequential circuit, a triangulation measurement optical structure with separated emission and reception is adopted, and a submersible can be carried for mobile measurement and scanning; the system calibration unit is used for carrying out joint calibration on camera parameters and a laser plane so as to determine system calibration parameters, so that the spatial position of a laser stripe accurately corresponds to the actual coordinate of the target surface; and the image processing unit is used for preprocessing the image acquired by the system and further extracting the coordinates of the center line of the laser stripe in the preprocessed image. According to the invention, adjacent splicing can be carried out through continuously collected laser stripe images to construct two-dimensional submarine landform information, and three-dimensional reconstruction of submarine topography can be carried out by using generated point cloud data.

The invention provides a roadway surface and internal defect synchronous sensing method based on point cloud registration. The roadway surface and internal defect synchronous sensing method comprises the following steps: S1, multi-sensor joint calibration; s2, synchronously acquiring data; s3, data preprocessing; s4, projecting a camera image to the point cloud; s5, surface defect identification: S5.1, geometric defect identification based on point cloud; s5.2, performing visual defect identification based on the image, and marking an identification result in the point cloud; s6, integrating the geometric defect point cloud and the visual defect point cloud; s7, identifying internal defects; s8, determining an internal confirmation three-dimensional position; s9, marking an internal defect point cloud; and S10, uniformly visualizing the surface defects and the internal defects. According to the invention, through cooperative work of the laser radar, the ground penetrating radar and the camera, the same perception of tunnel defect surface and internal defects is realized through point cloud visualization.

A camera system has at least a first and a second camera each with an image sensor. Active areas of the image sensors of the first and second camera are determined and define an extended image space of a real-time panoramic video. A bounding box captures a fixed position in space or an object is set. The bounding box moves through extended image space. Image data determined only by the bounding box is harvested from camera image sensors and displayed within a window on a screen in real-time. Scan-line control of images sensors based on the bounding box is updated in real-time to form an e-gimbal. Steps of the e-gimbal are performed by a machine learning inference phase on a processor.

A position estimation apparatus estimates a position of a vehicle based on a first feature point of an object included in a camera image and a second feature point of the object included in map information. The apparatus includes a memory storing type information indicating a type of the object corresponding to the second feature point together with the map information, and a microprocessor performing: extracting the first feature point from the camera image, recognizing the type of the object corresponding to the first feature point based on the camera image, searching for the second feature point corresponding the first feature point from the map information based on the first feature point, the type of the object corresponding to the first feature point, and the type information, and estimating the position of the vehicle based on the first feature point and the second feature point.

The invention discloses a geological vision large model-based drilling camera intelligent interpretation method, which comprises the following steps of: 1) acquiring and preprocessing a drilling image, and constructing a sample library containing geological labels; 2) based on the existing general visual large model, embedding a geological feature attention module and a lithology classification adapter, performing special optimization in combination with deep coal mine geological features, and constructing a geological visual large model; 3) constructing a geological algorithm dictionary to convert geology knowledge experience into a computable algorithm module, and embedding the algorithm module into a geological vision large model; and 4) realizing sample adaptive diagnosis and repeated learning through an AI module. According to the method, the geologic vision large model is constructed, the geologic feature attention module and the geologic algorithm dictionary are embedded in the model, and a sample adaptive diagnosis and repeated learning mechanism is fused, so that the accuracy of geologic feature recognition in the borehole camera image can be remarkably improved, and efficient and accurate interpretation of the borehole image is realized; and accurate geological data support is provided for underground operation.

The invention relates to the technical field of image quality optimization, and discloses a hunting camera imaging quality optimization method based on multimode data fusion. The method comprises the following steps: acquiring a visible light image, a thermal infrared image, a laser ranging point cloud and inertial measurement unit data, and carrying out space-time alignment and registration to form synchronous multimode data. A controllable imaging parameter set and scene context description information are separated by performing joint feature extraction on synchronous data. And for each parameter to be optimized, the system retrieves a plurality of candidate strategies from the imaging optimization knowledge base by taking the current value and the scene context as query conditions, selects an optimal strategy through fusion decision calculation, and finally generates and executes a global imaging parameter optimization instruction set to control a camera to complete image capture. According to the method, intelligent optimization of imaging parameters based on depth scene understanding is realized, and the image quality and adaptability of the hunting camera in a complex environment are improved.

The invention relates to an external parameter adjusting method and device of a vehicle camera and vehicle equipment. The method comprises the following steps: reading vehicle landing data, and extracting three-dimensional target detection data and camera image data of a static or low-speed moving road environment target from the vehicle landing data; obtaining target pixel coordinates of a road environment target in the camera image data; constructing a coordinate index structure according to the target pixel coordinates; determining a plurality of candidate camera external parameters and two-dimensional projection coordinates of the candidate camera external parameters to the sampling points of the plurality of three-dimensional target detection data; querying the coordinate index structure, and determining a coordinate index result of the two-dimensional projection coordinates; and screening target camera external parameters of the vehicle camera from the plurality of candidate camera external parameters according to a projection error obtained by a coordinate index result. Therefore, statistical optimization can be carried out by using mass driving data, so that the camera external parameters with high reliability can be obtained by adjusting and calibrating without depending on a specific calibration scene.

To suppress an unintended situation for a user regarding handling of a camera image.SOLUTION: An information processing system includes: a communication control section for controlling data communication among multiple terminals including a first terminal and a second terminal that belong to the same group; a camera image acquisition section for acquiring a camera image of a camera corresponding to either terminal that belongs to the same group; a restriction setting section for setting restriction of display of a camera image of a camera corresponding to a terminal other than at least the second terminal in the second terminal according to a user's operation; a game application execution section for executing game processing according to a user's operation in the first terminal and outputting a first game image and first information; and a display mode determination section for determining a display mode of a first game image in the second terminal on the basis of the setting of restriction of display and first information.SELECTED DRAWING: Figure 1