927 results about "Ir camera" patented technology

The present invention relates to a mobile phone protective case with a rotating support, where the protective case includes a main body and a camera support assembly, the main body is provided with an accommodation cavity for accommodating a mobile phone, the camera support assembly is removably installed at a camera hole, the camera support assembly includes an installation base and a support, the support is rotationally connected to the installation base through a hinge, the support is configured to protect a camera of the mobile phone when closed, and the support is configured to support the mobile phone when opened.

Disclosed in the present invention is a photometric stereo-based device for rapidly detecting surface features of an object, capable of rapidly acquiring a plurality of images meeting the requirements of a photometric stereo algorithm. The device comprises a light emitting assembly, an acquisition assembly located above the light emitting assembly, and a data processing module; the acquisition assembly comprises a light path system and a camera group; the light path system comprises: a beam splitting prism group used for splitting reflected light from the surface of an object into a plurality of beams of light and respectively outputting the plurality of beams of light in different preset directions to form images to be processed, and a plurality of bandpass filters correspondingly arranged on the output sides of the plurality of beams of light used for forming said images, wherein the central wavelengths of the bandpass filters are respectively configured to be different wavebands of the reflected light from the surface of the object; the camera group comprises a plurality of cameras which are arranged behind the bandpass filters in a one-to-one correspondence mode and acquire filtered light beams; and the data processing module receives image information sent by the cameras, and performs compensation and photometric stereo reconstruction on the image information, so as to obtain surface features of the object. Further disclosed is a method implemented on the basis of the device.

The invention provides an unsupervised multi-view three-dimensional reconstruction method based on a bidirectional attention mechanism, and mainly solves the problem of low three-dimensional reconstruction quality in a weak texture region and a complex shielding scene in the prior art. The scheme comprises the following steps: 1) inputting a multi-view image and camera parameters; 2) preliminarily extracting multi-scale features of an input image through a bidirectional feature pyramid network Bi-FPN, taking the multi-scale features as input data, performing enhancement through an attention mechanism, and then calculating a fusion feature map; 3) performing multi-stage depth estimation from coarse to fine by using the fused feature map; 4) calculating an initial depth map of a data enhancement branch; 5) fusing multi-source information to optimize the initial depth map, and generating a final refined depth map; and 6) converting the refined depth map into a point cloud model to obtain a reconstruction result. According to the method, the multi-scale features of the image can be extracted and fused more efficiently, so that the three-dimensional reconstruction quality of a weak texture region and a complex shielding scene is remarkably improved.

The invention relates to the field of heliostat calibration, and discloses a scheduling method for calibrating heliostats through cooperation of artificial light sources and cameras in a large-scale heliostat field, and the method comprises the following steps: determining the heliostat range covered by each camera, determining the heliostat range covered by each artificial light source, and determining the heliostat range covered by each camera; the method comprises the following steps: determining the center and size of a light spot in an image shot by a camera when the heliostats are calibrated, determining the heliostats which can be calibrated in the coverage range of each artificial light source-camera combination, regarding each effective calibration combination as a node to construct an undirected graph, dividing the nodes into a plurality of groups through a graph grouping algorithm, and ensuring that the nodes in the same group are not connected with each other, therefore, all combinations in the same group can execute calibration in parallel. According to the method disclosed by the invention, the problem of affiliation misjudgment caused by light spot interference between heliostats is solved, possible shadow and shielding problems are avoided, dynamic scheduling of the artificial light source and the camera is realized, and invalid operation time is shortened.

The invention discloses a rail transit intelligent inspection robot positioning system based on laser SLAM and inertial navigation, and relates to the field of real-time positioning, the rail transit intelligent inspection robot positioning system is characterized in that data are synchronously acquired through a laser radar, an IMU, a speedometer and an RGB-D camera, and high precision in rail positioning is realized; the point cloud filtering module extracts orbit feature points through an improved voxel grid algorithm, and the inertial navigation initialization module calculates an initial pose based on IMU data and establishes a motion constraint model; the map feature layering module divides the point cloud into a track surface, equipment and a background layer; and the data tight coupling module optimizes the pose, and the dynamic object elimination module eliminates obstacles, so that the SLAM optimization precision is ensured. And the global positioning compensation module performs repositioning through particle filtering to generate a 6DoF pose estimation semantic map. The method has the advantages that by fusing the laser SLAM and inertial navigation technologies, high-precision and stable rail transit inspection is realized, the positioning precision and the environmental adaptability are optimized, and the intelligent operation and maintenance efficiency is improved.

The invention discloses a dual-machine target positioning method based on semantic prior and spatial intersection constraint. The method comprises the following steps: 1, acquiring images of a camera 1 and a camera 2, and determining the spatial orientation of a camera coordinate system relative to a reference world coordinate system; step 2, obtaining a target normalized coordinate under a coordinate system of the camera 1; step 3, obtaining a target normalized coordinate under a coordinate system of the camera 2; 4, converting the normalized coordinates of the target into geometric rays in a three-dimensional space; 5, constructing an optimization function with the minimum distance sum of squares; step 6, performing semantic-level foreground and background division on the image content through a YOLOv5 network, and constructing a three-dimensional space feature point library of a semantic background; and 7, performing single-view semantic neighborhood retrieval and depth compensation by using the three-dimensional space feature point library of the semantic background constructed in the step 6, and realizing target three-dimensional positioning in a high-altitude long-distance complex scene. According to the invention, the detection robustness and the positioning precision of the high-altitude long-distance target in the environment are improved.

The invention discloses a large-view-field high-resolution imaging system based on polarization regulation and control, the large-view-field high-resolution imaging system comprises a metasurface system array and a camera along the direction of a light path, and the metasurface system array comprises a plurality of pairs of metasurface systems which are distributed in an array; each pair of metasurface systems comprises a first metasurface system and a second metasurface system; the first metasurface system is used for performing high-resolution imaging and large-view-field imaging on a first polarization light beam and a second polarization light beam of an incident light beam respectively; the second metasurface system is used for performing large-view-field imaging and high-resolution imaging on the first polarization-state light beam and the second polarization-state light beam respectively; the camera is used for acquiring and fusing images formed by different metasurface systems so as to capture a high-resolution central region of interest and surrounding environment information of a wide field of view. The system can efficiently realize higher imaging quality.

The invention relates to the technical field of medical instrument quality detection, in particular to a medical instrument intelligent defect detection method and system based on image processing. The method comprises the following steps: acquiring a multi-angle image through a high-resolution industrial camera, and adaptively adjusting illumination and camera parameters to realize image standardization; the image is divided into local Patch, and abnormal sensitive mapping is generated through multi-scale convolution filtering, feature enhancement and fusion; generating candidate defect areas through dynamic scoring, neighborhood aggregation, boundary optimization and multi-index layering based on the mapping; carrying out defect confirmation and grading by adopting multi-model cross validation and dynamic weight fusion; historical data and environmental factors are combined to predict a defect trend through an autoregression model, and a detection strategy is dynamically adjusted to realize closed-loop optimization. According to the method, the accuracy, robustness and efficiency of small defect detection are improved, and the method has adaptive and predictive capabilities.

The invention discloses a three-dimensional reconstruction method, device and equipment based on 3D Gaussian. The method comprises the steps of performing data preprocessing based on a three-dimensional point cloud output by sparse reconstruction of a target scene, a camera internal reference and a camera pose; initializing and training a 3D Gaussian model based on a real image, the three-dimensional point cloud after candidate object mask screening, a camera pose, a depth information graph and a scale alignment parameter to obtain a coarse-grained 3D Gaussian model of the target scene; generating a dynamic target mask by taking a rendered image generated by rendering the target scene based on the coarse-grained 3D Gaussian model as a reference and combining the real image and the candidate object mask; and on the basis of the coarse-grained 3D Gaussian model, the dynamic target mask and all input data obtained by initializing and training the coarse-grained 3D Gaussian model, carrying out refined reconstruction and optimization on the coarse-grained 3D Gaussian model. According to the method, unification of high precision, high efficiency and high robustness is realized, and the increasing actual industrial application requirements are met.

The invention relates to a heading machine pose detection method based on a binocular linear camera, and belongs to the technical field of heading machine pose detection. Comprising the following steps: respectively acquiring a left imaging image and a right imaging image of an auxiliary detection plane through a left-eye linear camera and a right-eye linear camera in the tunneling process of the tunneling machine; respectively constructing four coordinate systems; determining five real points of the left imaging image and the right imaging image to obtain a left real point set and a right real point set, and calculating a carrier coordinate of each real point to form a carrier point set; determining camera coordinates of each real point in the left camera coordinate system and the right camera coordinate system to form a left camera point set and a right camera point set; mapping each real point in the right camera point set to a left camera coordinate system to form a camera point set, and determining a rotation matrix and a translation matrix between the carrier point set and the camera point set; and determining a pose detection result of the heading machine according to the rotation matrix and the translation matrix. According to the invention, the pose detection accuracy of the heading machine is high, and the precision is high.

The invention relates to the field of surveying and mapping, in particular to a laser-IMU (Inertial Measurement Unit) installation deviation calibration method based on vision assistance, a surveying and mapping method and a surveying and mapping system, a plurality of images under a plurality of laser radar calibration conditions are processed according to camera parameters and camera-IMU associated parameters, laser radar-IMU installation deviation is obtained, manual operation is avoided, and high-precision and high-efficiency deviation calibration is realized; in addition, according to the installation deviation of the laser radar-IMU, the relative space relation of the laser radar-IMU is adjusted, and accurate matching installation of the laser radar and the IMU is ensured; and according to the dynamic characteristics of the surveying and mapping path, constructing a corresponding relation between IMU data and point cloud data frames, and screening a point cloud data point which is most matched with the position point of the surveying and mapping path where each IMU data is located so as to construct a local point cloud map corresponding to the position point, thereby integrating and constructing a space map of a target area where the surveying and mapping path is located. According to the method, data omission can be reduced through local point cloud data processing and map construction, and the accuracy and credibility of spatial surveying and mapping positioning are improved.

The invention relates to the field of computer vision, and discloses a laser radar and camera joint calibration method, electronic equipment and a storage medium. The method comprises the following steps: acquiring point cloud data of a plurality of laser radars and image data of a camera; performing motion distortion compensation on the point cloud data to generate a global point cloud map; converting the global point cloud map into a plurality of three-dimensional Gaussian voxels with color attributes and covariance attributes; minimizing the first loss function by jointly optimizing the attribute of the three-dimensional Gaussian voxel and the external parameter of the camera so as to complete the calibration of the external parameter of the camera; wherein the first loss function comprises a color consistency constraint term and a rendering error term, and the color consistency constraint term is used for constraining that observed color attributes of a single three-dimensional Gaussian voxel at multiple moments and multiple visual angles are kept consistent; the rendering error term is a pixel color error between a projection image generated by the three-dimensional Gaussian voxel and image data of the camera. Therefore, the precision of external parameter calibration in a dynamic scene is improved.

The invention discloses a multi-modal depth sensing and grabbing system based on a transparent object, which relates to the field of robot operation and comprises a multispectral sensing module, a depth correction module, a grabbing posture generation module and a control module. The visual information and the thermal radiation information of a transparent object are comprehensively obtained by combining two perception modes of an RGB-D image and a thermal imaging (TIR) image, systematic error analysis is performed on a depth map, and error sources of an RGB-D camera and a TIR camera are detected. The system adopts an encoder-decoder model as a depth correction core, the model extracts complementary features of an RGB-D image and a TIR image through a modal exclusive encoder, feature alignment and integration are completed by using a feature fusion module, and the depth estimation precision on the transparent surface is effectively improved. And meanwhile, a Bayesian optimization method is adopted to carry out hyper-parameter optimization on the depth correction model, through hyper-parameter optimization and model fitting processing, the system effectively avoids the problems of over-fitting and under-fitting, and the robustness of the depth correction model and the transparent object grabbing precision are further improved.

In some embodiments, a method for detecting if an application was initiated due to an inadvertent interaction on a user device is disclosed. The method includes analyzing signals from one or more sensors of a user device to identify any obstructions nearby. By examining the media streams, the device can derive metrics indicating whether the cameras are obstructed. The metrics combined with sensor data, can suggest that the launch was due to an unintentional interaction, causing the initiation of a first mitigation operation. This first mitigation operation involves lowering the device's power state and dimming its display. Following the initiation of the first mitigation operation, the user interface is monitored to identify interactions reflecting an intent to launch the application. If a predetermined period elapses without such interactions, confirmation of the inadvertent application launch is made, prompting the execution of a second mitigation operation.

The invention discloses a product front and back side detection device, and particularly relates to the technical field of keyboard product detection equipment, and the product front and back side detection device comprises a top appearance visual detection mechanism and a bottom appearance visual detection mechanism. Through cooperation of the top appearance visual detection mechanism and the bottom appearance visual detection mechanism and combination of the adsorption transfer function of the manipulator mechanism, automatic continuous detection of the front and back surfaces of a product is realized, manual overturning and repeated transfer are not needed, the detection efficiency is greatly improved, collision damage possibly occurring in the transfer process is avoided, and the product quality is improved. The main camera can be accurately positioned through a lifting sliding table, a camera front-back adjusting knob and a camera left-right adjusting knob, four sets of side light-emitting plates can adjust the height and angle to provide adaptive illumination, clear images are ensured, the detection precision is improved, misjudgment is reduced, a buffer module of the mechanical arm mechanism can buffer taking and placing impact, full-process automatic operation reduces manual intervention, and the working efficiency is improved. The method is suitable for batch production and effectively guarantees the detection quality and efficiency.

The invention relates to a coastal zone sediment erosion pore scale flow field observation system and observation method. The observation system comprises a wave-flow water tank, a sediment accumulation body arranged in the wave-flow water tank and PIV equipment used for observing the internal flow field and the pore size of sediment. A wave making plate and a wave dissipating plate opposite to the wave making plate are arranged in the wave flow water tank, experimental fluid used for simulating waves is arranged between the wave making plate and the wave dissipating plate, and a wave height meter floats on the experimental fluid; the sediment accumulation body is formed by accumulation of transparent solid particles with the same refractive index as the experimental fluid; the PIV equipment comprises a laser light source and a camera, the laser light source is focused on the sediment accumulation body, and the camera directly faces the sediment accumulation body and is used for collecting images. Compared with the prior art, a clearer sediment pore internal flow field image is obtained through the combination of the refractive index matching technology and the image particle velocity measurement method, so that effective observation of the pore scale flow field eroded by the sediment in the coastal zone is realized.

For see-through AR displays, the current state of art uses geometric correction to lower the display location error, however this is far from an ideal solution and requires knowing range or pose of the devices. A dual-function optic for eye display & camera is disclosed, wherein a sensor camera imaging optics pipeline with a virtual projection point at an eye's retina reduces parallax errors to zero.

Systems and methods are disclosed that perform global human and camera motion estimation using a motion diffusion model that is attached to a control branch. For instance, using a controlled motion denoiser that comprises the motion diffusion model and the control branch, global human motions and the corresponding camera motions from “in-the-wild” videos may be estimated. Initially, SLAM may be used to initialize the camera motion and a pose estimation model may be used to estimate the local human motion. Combining the two, embodiments of the present disclosure initialize the global human motion. Then, during optimization and using a COIN system that includes the controlled motion denoiser and / or using a COIN algorithm, embodiments of the present disclosure enforce the global human and camera motion to satisfy a two-dimensional (2D) projection on videos and the motion distribution from the motion diffusion model.

Techniques of calibrating telepresence videoconferencing displays and cameras include providing 6 DoF camera locations and orientation vectors in display reference frame based on a plurality of images that indicate specified mirror-plane points of a mirror and specified reflected display plane points. In some implementations, the specified mirror-plane points are located at fiducial markers printed on the mirror. In some implementations, the specified reflected display plane points are located in a checkerboard pattern of fiducial markers on the display.

The invention discloses a laser radar real-time semantic segmentation method and system based on image and point cloud multi-view fusion knowledge distillation, and the method comprises the steps: obtaining laser radar point cloud data and camera RGB image data, building a projection mapping relation between point cloud and image pixels, and enabling the mapping relation to be used for searching a common effective projection point; laser radar point cloud data is converted into a voxel grid view and a distance plane view, a main and auxiliary double-branch architecture is constructed in combination with camera RGB image data, hierarchical compression features are fused through three-level mutual attention gating, and multi-modal multi-view fusion features are generated; respectively distilling the multi-modal multi-view fusion features to a main branch and an auxiliary branch by adopting a bidirectional knowledge distillation strategy, and synchronously optimizing the dual-branch feature extraction capability; the auxiliary branch is unidirectionally distilled to the main branch, so that the performance of the main branch is further improved; and migrating the semantic segmentation capability on the point cloud multi-view and image-point cloud multi-modal fusion features to a point cloud voxel network, thereby realizing real-time high-precision semantic segmentation of a radar point cloud single mode. According to the method, the precision and the real-time performance of point cloud semantic segmentation in a dynamic scene are remarkably improved.

The method comprises the following steps: acquiring a multi-view video stream and LiDAR point cloud data of a cultural relic to be built, performing timestamp registration on the multi-view video stream and the LiDAR point cloud data, and establishing a spatial corresponding relationship between the multi-view video stream and the LiDAR point cloud data; gaussian primitive initialization is completed by using the three-dimensional coordinates of the LiDAR point cloud and color values on camera image pixels; dynamically adjusting a spherical harmonic coefficient of the Gaussian primitive through an illumination sensing network, a modulation network and a bias network; and constructing a multi-dimensional loss function, carrying out collaborative optimization on Gaussian primitive parameters, camera poses and weights of the illumination perception network, the modulation network and the bias network, and outputting high-precision three-dimensional Gaussian scene representation of the to-be-constructed cultural relics. According to the method, deep tight coupling of the point cloud data and the image information is realized, the complementary advantages of the multi-modal data are fully played, and the transmission and accumulation of errors among different steps are effectively avoided by performing integrated collaborative optimization on the scene, rendering and illumination, so that the reconstruction quality is remarkably improved.

The invention discloses a joint calibration method based on a rotary laser radar and a camera, and the method comprises the following steps: S1, carrying out the kinematic modeling of the camera, the laser radar and a motor rotating shaft based on a DH parameter method; s2, collecting synchronous laser radar point cloud data and camera image data of a motor rotating shaft at different angles; s3, processing the laser radar point cloud data; s4, processing the camera image data; s5, projecting the extracted laser point cloud depth continuous edge features to an image plane based on a current external parameter estimation value, and matching and associating the extracted laser point cloud depth continuous edge features with the image edge features extracted in the step S4; s6, constructing a projection residual equation about the external parameters according to the successfully matched feature pairs; and S7, the residual equation is linearized. According to the joint calibration method based on the rotary laser radar and the camera, high-precision and high-robustness automatic calibration is completed in an environment without an auxiliary target through kinematics modeling of a system and an advanced feature extraction matching technology. The invention further discloses a joint calibration device and system based on the rotary laser radar and the camera, and a storage medium.

The invention discloses a lifting frame control method and system capable of intelligently recognizing lifting points, and aims to solve the problems that traditional lifting operation depends on manpower, efficiency is low, and high-altitude operation risks exist. The core of the method is that automatic identification and accurate positioning of the lifting point are realized through combination of machine vision and automatic control. The system is composed of a PLC master control system, an industrial camera, a walking trolley, a servo motor and the like. The PLC acquires a product image through a camera, performs feature matching with a pre-stored template (by adopting ORB, RANSAC and other algorithms), and calls a successfully matched lifting point formula to drive the walking trolley to move; and if no matching template exists, calculating the position of the lifting point in real time based on image pixel coordinates and camera calibration parameters. In the downward moving process of the hanging bracket, fine adjustment and safety confirmation are achieved through a sensor contact signal, and finally hanging connection and hoisting are automatically completed. Full-process automation is achieved, manual climbing is thoroughly avoided, and operation safety and efficiency are greatly improved.

The present disclosure discloses an aircraft surveillance system. The system includes one or more radar sensors, one or more cameras, and one or more controllers. The radar sensors scan an area proximal to an aircraft and provide first data corresponding to scanning of the area. The cameras capture visuals of the area and provide second data corresponding to captured visuals. The controllers communicate with the radar sensors and cameras to receive the first data from one or more radar sensors and the second data from one or more cameras. The controllers further analyze the first data and the second data to identify presence of one or more undesirable objects proximal to the aircraft and provide a notification upon detection of the undesirable objects.

The invention provides a large-scale pavement reconstruction and drivable area automatic marking method for a well mine road, and belongs to the technical field of intelligent mines and automatic driving, and the method comprises the steps: collecting inertial integrated navigation, laser radar point cloud and camera image data; de-noising and segmenting laser radar point cloud data and camera image data, constructing a local surface element map and a global key frame pose sequence based on the pose initial value, and splicing to obtain a three-dimensional point cloud model; constructing a ground grid surface element based on a three-dimensional point cloud model and the segmented camera image, performing visibility screening on a view cone to obtain a visible surface element set, performing block rendering on the visible surface element, performing exposure self-adaption and reflective mask constraint combined optimization on a BEV rendering image at the same time, and constructing a pavement reconstruction model in combination with a Meshgrid grid. And finally, performing semantic annotation based on the pavement reconstruction model to generate an image data set with a label. The method is suitable for complex scenes of well industry and mining, and the pavement reconstruction precision and the labeling automation degree can be improved.

The invention discloses a prime lens and a camera, and relates to the technical field of prime lenses, the prime lens is provided with an object side and an image side which are oppositely arranged along the optical axis direction, the prime lens comprises a first lens group, a second lens group, a third lens group and a fourth lens group which are sequentially arranged from the object side to the image side, and the four lens groups comprise eleven lenses, the first lens, the fourth lens, the seventh lens, the eighth lens and the tenth lens have positive focal power; the second lens, the third lens, the fifth lens, the sixth lens, the ninth lens and the eleventh lens have negative focal power; wherein the third lens group is movably arranged along the extension direction of the optical axis and is used for focusing, so that the aperture number of the prime lens is adjustable in a range of 1.6-2.0, the variation range of a field angle is less than 5%, and the distortion degree of the prime lens is less than 3%. According to the technical scheme provided by the invention, the technical effects of large aperture and stable imaging are achieved through the arrangement and combination of the eleven lenses and the design of the lenses.

The invention relates to the technical field of door lock detection, and discloses a double-camera door lock detection system and method based on a millimeter wave radar. The method comprises the steps that millimeter wave radar transmitting antennas are sequentially activated to transmit linear frequency modulation continuous wave signals in a radar transmitting time slot, an RGB camera and a ToF camera are synchronously triggered to start exposure in a camera exposure time slot, an instantaneous phase difference is calculated according to radar echo intermediate frequency signals, and when the instantaneous phase difference is smaller than a phase change rate threshold value, the camera is triggered to start exposure. Calculating a target radial distance and an azimuth angle; calculating a distance difference value based on the target radial distance, calculating an angle difference value based on the azimuth angle, and establishing an associated target set according to the distance difference value and the angle difference value; and respectively calculating the radar channel confidence coefficient, the RGB channel confidence coefficient and the ToF channel confidence coefficient of each target in the associated target set, and outputting an unlocking trigger instruction. According to the method, the blind area detection time is shortened, and the detection robustness and the recognition accuracy in complex illumination and shielding scenes are improved.

The invention relates to the technical field of expressway monitoring, in particular to a traffic incident monitoring device integrating radar, camera and edge computing equipment, and the device comprises a data collection module which is used for collecting laser radar point cloud data and camera image data; the data processing module is used for carrying out preprocessing, frame-level time synchronization and IMU attitude correction on the collected data; the data fusion and target detection module is used for fusing the point cloud and the image data, generating spatial position, speed and appearance information of a target, and identifying traffic participants and behaviors thereof; and the event judgment and alarm module is used for judging a traffic abnormal event according to the fused target information and generating event alarm information. According to the invention, the laser radar point cloud data and the camera image data are collected in the integrated device, so that the problem of low target detection precision caused by the adoption of a single sensor for monitoring in most traditional traffic monitoring systems is solved.

The invention relates to the technical field of AR remote operation and maintenance, and provides an operation and maintenance system, an operation and maintenance terminal, an expert terminal, a server and a control method thereof, and the operation and maintenance system comprises an operation and maintenance unit which is used for collecting video information and depth data of a physical environment in real time; establishing a mapping relation between the physical environment and the virtual coordinate system according to the video information and the depth data of the physical environment to obtain a physical environment grid and a camera projection matrix; the expert unit is used for intercepting a static frame picture and generating marking information after selecting a marking type; the operation and maintenance unit is used for receiving the marking information and the marking type in real time; converting the labeling information into a camera space ray through the camera projection matrix; calculating a collision point of a camera space ray and the physical environment grid, and determining a three-dimensional space coordinate; and rendering the corresponding three-dimensional marking model in the display field of view of the operation and maintenance unit. The method is used for realizing remote maintenance of IT equipment, mapping expert guidance information to a physical environment and carrying out augmented reality visual display.