1511 results about "Pixel pair" patented technology

In implementation of techniques for three-dimensional reconstructions based on Gaussian primitives, a computing device implements a reconstruction system to receive a first digital image depicting an object from a first angle and a second digital image depicting the object from a second angle. The reconstruction system segments the first digital image and the second digital image into patches. The reconstruction system then generates, using a machine learning model, three-dimensional Gaussian primitives that predict parameters of points of the object in a three-dimensional space that correspond on a per-pixel basis to pixels of the patches. The reconstruction system then forms a three-dimensional reconstruction of the object for display in a user interface by merging the three-dimensional Gaussian primitives.

The invention relates to the technical field of traditional Chinese medicinal material defect detection, in particular to a traditional Chinese medicinal material defect detection method based on vision, which comprises the following steps: regularly acquiring a time sequence image of a traditional Chinese medicinal material sample, acquiring a multi-view image, carrying out pixel alignment on the time sequence image, and carrying out structured organization on the multi-view image according to a shooting direction to associate camera parameters; and generating a registration time sequence image sequence and a multi-view image set. According to the method, the time sequence images of the traditional Chinese medicine samples are collected regularly, pixel alignment is carried out, image offset caused by environment illumination fluctuation and equipment jitter is eliminated, and time-space consistency of dynamic variable quantity calculation is ensured. Structured organization is carried out on multi-view-angle images according to shooting directions, camera parameters are associated, a geometric constraint relation between view angles is established, and the problem that three-dimensional reconstruction precision is insufficient due to view angle isolation in a traditional method is solved.

The invention discloses a medical image super-resolution reconstruction method based on multi-level attention guidance, and the method comprises the following steps: S10, constructing a deep learning network model based on a generative adversarial network architecture, which comprises a generator and a discriminator; the generator is based on an improved U-Net architecture, a hierarchical attention module and a dual-path feature processing module are configured in an encoder and a decoder of the generator, the hierarchical attention module adopts different attention strategies according to network levels to consider structure and texture, and the dual-path feature processing module separates and processes low-frequency and high-frequency information; the generator further comprises a multi-level feature fusion module for integrating the multi-scale features of the decoder, and an attention guide up-sampling module for final enhancement and dimension raising. The discriminator adopts a spectrum normalization U-Net architecture and uses multi-scale features for matching; s20, training the network model by adopting a composite loss function comprising pixels, adversarial, perception and total variation loss; and S30, inputting the low-resolution image into the trained model, and outputting a high-resolution image. According to the method, through deep fusion of multi-level attention and multi-scale feature processing, the image restoration quality can be remarkably improved, the texture detail definition can be enhanced, the anatomical structure accuracy can be ensured, and the noise robustness can be improved.

The invention discloses a shock wave overpressure field global measurement method based on multi-view image fusion, belongs to the technical field of explosive shock wave measurement, and is suitable for weapon equipment power evaluation and blasting safety analysis. The method comprises the following steps of: synchronously acquiring a time sequence image of the whole explosion process through a distributed multi-view high-speed imaging system, preprocessing the image by adopting pixel-by-pixel comparison, square operation enhancement and normalization processing, and positioning a shock wave edge contour; the blasting center coordinate is positioned through binocular parallax, the shock wave initial radius is determined by combining spatial domain analysis, and the key feature points of the front and rear edges of the wavefront are detected by using a dynamic search window and a radial gradient field. Based on multi-view constraints, a three-dimensional point cloud is generated through direct linear triangulation, and a three-dimensional wave front form is reconstructed in combination with least square spherical fitting. And finally, constructing a wavefront radius-time evolution model, and deducing a quantitative relationship between the instantaneous propagation velocity and the peak overpressure in combination with a Ranki ne-Huton iot relationship, thereby realizing the global high-precision calculation of the overpressure field.

The invention discloses a display panel and a driving method thereof, the display panel comprises a plurality of first switches and a plurality of second switches, N columns of pixels are arranged in a first display area, and M columns of pixels are arranged in a second display area; the first scanning line is connected with N columns of pixels in the first display area and M columns of pixels in the second display area, the second scanning line is connected with the N columns of pixels in the first display area, and each first switch is connected with two data lines corresponding to sub-pixels with the same color in the first display area and the second display area; each second switch is connected with a data driving chip and a data line corresponding to a pixel in the first display area; wherein N and M are natural numbers greater than or equal to 2. The two data lines corresponding to the sub-pixels with the same color in the first display area and the second display area respectively receive the same data signal or different data signals to realize display in different display modes by switching on or switching off the first switch and the second switch, so that insufficient charging under a high refresh rate is improved.

The invention discloses a seabed sonar image processing method based on semantic segmentation. The method comprises the following steps: S1, obtaining preprocessed sonar image blocks; s2, generating a submarine landform boundary priori map corresponding to the preprocessed sonar image blocks pixel by pixel; s3, an improved U-Net double-branch semantic segmentation network is constructed, the improved U-Net double-branch semantic segmentation network comprises a semantic branch and a boundary branch, and a landform mutual feedback gating module is configured in each scale jumper connection layer; s4, obtaining a fusion feature map; s5, deploying to the autonomous underwater vehicle to form an onboard reasoning model; and S6, performing online semantic segmentation reasoning on the preprocessed sonar image block acquired in real time by using the onboard reasoning model, and outputting a semantic category diagram and a boundary vector diagram corresponding to the preprocessed sonar image block. According to the method, the average mIoU index of the model is improved in a small-target and multi-category hybrid scene.

The invention discloses a one-way door control method, system and equipment based on image recognition, and belongs to the field of images. The method comprises the following steps: synchronously obtaining a polarized light image, binocular depth point cloud data and a face image of a target area; the polarized light image and the depth point cloud three-dimensional feature are fused through spatial registration, and a point cloud polarization joint feature is constructed; performing pixel alignment on the joint feature and the face image, and generating a dynamic mask by using polarization degree data of the polarized light image; under the constraint of a dynamic mask, performing cross-modal fusion on the face image and the spatial feature map by adopting a multi-scale attention mechanism, and extracting a multi-modal feature vector containing a face spatial structure, a material attribute and a texture feature; performing similarity comparison on the vector and a preset database, and outputting verification information through a classification network; and triggering a self-adaptive control instruction of the one-way access control system according to the mapping relationship between the verification information and the control strategy. The one-way door can be accurately and effectively controlled in a complex reflective scene.

The invention discloses a high-load data steganography method, system and device and a medium, and the method comprises the steps: obtaining secret data of a power system, and converting the secret data into a novenary sequence; a quantum key distribution system is utilized to generate a shared key, the novenary sequence is encrypted through an encryption key, encrypted information is obtained, and the shared key comprises the encryption key and a mapping key; determining a corresponding first unit matrix from a preset mapping library based on the mapping key, and performing non-overlapping tiling on the first unit matrix in a two-dimensional plane to obtain a second-order reference matrix; and scanning a preset carrier image into a non-overlapping first pixel pair sequence according to a Hilbert curve, and embedding the encrypted information into the first pixel pair sequence by using the second-order reference matrix to obtain a steganographic image, so that the data transmission security can be improved.

The invention relates to the technical field of cholesteric liquid crystal electronic paper display, and discloses a cholesteric liquid crystal electronic paper display panel and a driving method thereof, and the cholesteric liquid crystal electronic paper display panel comprises an upper substrate, a lower substrate, a grid scanning line and a signal line, the electrode strip blocks continuously extend in the direction of a grid scanning line, each electrode strip block corresponds to one row of pixels, a single pixel corresponds to one pixel electrode, the electrode strip block corresponding to each row of pixels is conducted with the ITO electrode in the corresponding row through a conductive spacer, the ITO electrode is located on the periphery of a display area of the display panel, and in the direction of a signal line, the ITO electrode is connected with the electrode strip block in the corresponding row. The size of a single pixel is larger than the width L of each electrode strip block and larger than or equal to the width L'of a pixel electrode, and the distance S between every two adjacent electrode strip blocks is larger than or equal to the width W of a grid scanning line. According to the display panel and the driving method thereof, by optimizing the common electrode structure and the driving time sequence, the display performance and the refreshing efficiency of cholesteric liquid crystal electronic paper are remarkably improved.

The invention provides a self-supervised end-to-end vision reconstruction method and system, and relates to the technical field of computer vision processing. The method comprises the following steps: firstly, acquiring multi-camera parameters and different-view-angle image data, including focal length, lens distortion and main coordinate point data of each camera, and pixel point coordinates of a current frame of a first camera and a reference frame of a second camera, then constructing an end-to-end training model, and calculating a re-projection error of pixel points of the current frame and the reference frame to obtain a multi-view-angle image; and solving the parameter update quantity by using a Gaussian Newton iteration method, iteratively optimizing the camera pose, the pixel corresponding relation and the depth data, and reconstructing a three-dimensional coordinate by combining the obtained data set after the re-projection error is converged, and converting and splicing to realize three-dimensional scene reconstruction. By implementing the scheme, end-to-end self-supervised training can be realized under the condition of not depending on manual annotation, so that three-dimensional visual reproduction is realized.

The invention provides a scene semantic occupancy prediction method based on probabilistic three-dimensional Gaussian, and the method comprises the steps: processing a multi-view camera image through employing a two-dimensional backbone network, generating multi-view and multi-scale image features, predicting the three-dimensional occupancy distribution aligned with pixels through the image features, and initializing the position attribute of the three-dimensional Gaussian according to the distribution; the method comprises the following steps: taking three-dimensional gauss as point cloud data, generating sparse voxel data through voxelization, and processing the sparse voxel data by using three-dimensional sparse convolution to complete feature interaction between the three-dimensional gauss; by using a cross attention mechanism, updating three-dimensional Gaussian features by image features; performing layer-by-layer iterative optimization on each attribute in the three-dimensional Gaussian features by using a Gaussian attribute fine tuning model; and aggregating geometric attributes and semantic attributes of the three-dimensional Gaussian by using a probability Gaussian superposition method, and generating a three-dimensional semantic occupation prediction result of the scene. According to the invention, the problem of efficient adaptive three-dimensional semantic occupancy prediction of an automatic driving scene can be solved.

The invention is suitable for the technical field of image annotation and intelligent transportation, and provides an intelligent image data annotation method and system based on multi-modal semantic fusion, and the method comprises the following steps: obtaining image data, point cloud data, voice data and environment information of the same time and space; performing semantic segmentation on the image data to obtain image segmentation information; performing alignment processing on the point cloud data and the image data to generate a 3D point cloud aligned with image pixels; according to the 3D point cloud, deducing the geometric contour of the shielded target to obtain point cloud geometric information; fusing the image segmentation information and the point cloud geometric information in combination with environment information to obtain image fusion information; and in combination with the voice data, marking visible targets and shielded targets in the image data with behavior semantics according to the image fusion information. According to the method, multi-modal data such as images, voices and point clouds are fused, so that semantic annotation can be accurately carried out on the occluded target in a complex scene.

The invention relates to the technical field of image processing, in particular to a food production line cleanliness monitoring method and system based on machine vision. The method comprises the steps of obtaining a Lab image of a to-be-detected conveyor belt of a food production line, determining an abnormal score value of a target pixel point in the Lab image, and constructing a data item edge weight of an energy function according to the abnormal score value of the pixel point in the Lab image; determining a periodic displacement amount of a periodic structure of the Lab image in the motion direction of the conveyor belt to be detected, and determining a periodic consistency factor of adjacent pixel pairs according to the periodic displacement amount; according to the method, a data item edge weight of an energy function is established, a smooth item edge weight of the energy function is established according to texture features and a period consistency factor, the energy function is established by using the data item edge weight and the smooth item edge weight, segmentation enabling the energy function to be minimized is performed on the Lab image, and a cleanliness monitoring result can be determined according to the minimum value of the energy function. According to the technical scheme, the cleanliness of the conveying belt of the food production line can be automatically monitored.

Examples are described for processing images to mask dynamic objects out of images to improve feature tracking between images. A device receives an image of an environment captured by an image sensor. The image depicts at least a static portion of the environment and a dynamic object in the environment. The device identifies a portion of the image that includes a depiction of the dynamic object. For example, the device can detect a bounding box around the dynamic object, or can detect which pixels in the image correspond to the dynamic object. The device generates a masked image at least by masking the portion of the image. The device identifies features in the masked image, and uses the features from the masked image for feature tracking from other images of the environment, masked or otherwise. The device can use this feature tracking for mapping, localization, and / or relocation.

The invention discloses a farmland area determination method based on boundary determination and semantic segmentation of a G-Lap algorithm, and the method comprises the steps: collecting images of an agricultural region in multiple scenes through an unmanned plane, and constructing a multi-scene farmland image set; through a G-Lap algorithm, farmland edges in the multi-scene farmland image set are further enhanced to construct a multi-scene farmland training image set; using a multi-scene farmland training image set to carry out complete training of freezing and unfreezing trunks on the improved Deeplab model, and training for multiple rounds of convergence to obtain a final segmentation model; segmenting and extracting farmland area pixels in the test image set by using the trained segmentation model, counting the number of the pixels segmented by the segmentation model through an image binarization method, and obtaining the farmland area according to the number of the pixels in combination with the actual area corresponding to each pixel at different aerial photography heights. The method effectively solves the technical problems that an existing traditional farmland region extraction method is long in consumed time and low in efficiency, and a calculation method is lack of universality.

The invention discloses an ultra-low illumination-oriented SPAD active imaging data compression method, which comprises the following steps of: performing wavelet decomposition on histogram data of a single pixel in a time-frequency domain based on wavelet transform to obtain low-frequency data of each pixel after wavelet decomposition; by taking each pixel as a center, performing non-maximum suppression and data enhancement on the low-frequency data after the wavelet decomposition of the current pixel by using adjacent pixels to obtain processed compressed data; customizing different Gaussian kernel parameters for each pixel according to the possibility that each pixel is located at the boundary, and performing Gaussian filtering on the processed compressed data of each pixel to obtain filtered data; and performing depth estimation on the filtered data to obtain a final depth image. According to the method, the depth reconstruction performance of the laser pulse can be improved by utilizing the multi-resolution characteristic of wavelet transform, the space-time correlation of signal photons and the smoothness of Gaussian filtering.

The embodiment of the invention provides an indoor vehicle positioning method and device and a storage medium, and the method comprises the steps: obtaining a scene image corresponding to a target vehicle at a current moment when it is detected that the target vehicle drives into a positioning region of a target scene; edge point detection is carried out on the scene image to determine a target image area corresponding to a target calibration lattice, and the target calibration lattice is a calibration lattice assembled on the target vehicle. And extracting pixel coordinates corresponding to each pixel point in the target image area from the scene image. And according to a pre-calibrated internal reference matrix of the camera, the pixel coordinate corresponding to each pixel and the height from the target calibration grid to the ground, determining the camera coordinate of each pixel point projected to the camera coordinate system. And according to the pre-calibrated external parameter matrix of the camera, converting the camera coordinate corresponding to each pixel point into the world coordinate in the world coordinate system to determine the position information of the target vehicle in the positioning area, so that the vehicle position can be accurately positioned in the indoor scene.

The invention discloses a multi-modal ultrasonic image intelligent analysis system based on deep learning, particularly relates to the field of ultrasonic image intelligent analysis, and is used for solving the problems of speckle noise and displacement artifacts caused by sound beam attenuation during deep abdomen scanning of a bedside ultrasonic person with a relatively high in-vivo quality index. Partition noise reduction is guided through pixel noise distribution associated with depth and attenuation, and deep micro-textures are reserved; the color flow velocity and the elastic displacement are anchored through the noise reduction gray scale edge, three-mode sub-pixel alignment is completed, and cross-channel deformation and drift are eliminated; the self-attention fusion network synchronously extracts texture, speed and hardness features on the unified alignment surface to realize coupling of a complete biological structure and dynamic information; the texture residual error rate and the flow rate matching degree form complementary evaluation, noise distribution and alignment grid real-time self-adjustment are driven, frame-by-frame iteration convergence is achieved, and image definition and feature consistency are continuously improved.

The invention discloses a display device and a defect pixel compensation method of the display device, the display device comprises a display panel and a processor, the processor determines the defect type of a defect sub-pixel and an initial gray scale value corresponding to the defect sub-pixel; determining at least one target gray-scale value according to the defect type of the defective sub-pixel, the initial gray-scale value and a plurality of to-be-displayed gray-scale values corresponding to the defective pixel to which the defective sub-pixel belongs; based on the at least one target gray-scale value, updating the to-be-displayed gray-scale values corresponding to other sub-pixels in the defective pixels; and the display panel displays the to-be-displayed image after the plurality of to-be-displayed gray-scale values corresponding to the defect pixels are updated. According to the target gray-scale value, the to-be-displayed gray-scale values of other sub-pixels except the defective sub-pixels in the defective pixels are updated, so that the display color cast problem caused by the defective sub-pixels is compensated, the perceptibility of the defective sub-pixels in human eyes is reduced, and the display effect of the display device is improved.

The invention discloses an expressway pavement disease detection method and system based on unmanned aerial vehicle image analysis, and relates to the field of general image data processing.The method comprises the steps that an unmanned aerial vehicle is controlled to collect an initial pavement image; determining an environment interference area based on the initial pavement image and gray analysis; controlling the unmanned aerial vehicle to collect supplementary pavement images in the environment interference area; performing space projection transformation on the initial pavement image and the supplementary pavement image according to a preset included angle, and establishing a pixel corresponding relation of the environment interference area; calculating a gray difference value of corresponding pixel points of the environment interference area in the two acquired images based on the pixel corresponding relation, and generating a difference value matrix according to the gray difference value; performing compensation processing on the environment interference area in the initial road surface image according to the difference value matrix to obtain a compensated road surface image; and performing feature matching on the compensated pavement image based on a preset feature parameter to obtain pavement disease information of which the similarity with the preset feature parameter is greater than a preset threshold. The method is used for improving the accuracy of pavement disease detection.

The invention discloses a 6D pose estimation method and device based on online 3D Gaussian reconstruction and a medium, and the method comprises the steps: firstly obtaining the initial pose of a target object in an input RGB-D image through a feature point method, then optimizing the initial pose according to the depth corresponding to a matched pixel between two images, putting a new frame and the optimized pose into a local image, and carrying out the optimization of the initial pose. The method comprises the following steps that: firstly, a local image is obtained by a thread, the local image is matched with other frames by the other thread, the pose is further optimized, finally, the 3D Gaussian is initialized and optimized according to the image and the pose in the local image, and an obtained model provides new loss item auxiliary optimization for the previous pose optimization process in turn. The invention provides a method for solving the 6D pose estimation problem of any object, any model information related to the target object does not need to be provided during operation, and the universality of the 6D pose estimation method is improved.

The invention provides a pallet pose estimation method and system, and the method comprises the steps: obtaining a color image which comprises a pallet region and at least part of pixels of which are aligned, and point cloud data, and cutting the color image to obtain a target image which only comprises the pallet region; obtaining a point cloud of an area corresponding to the target image from the point cloud data according to an alignment relationship between the color image and the point cloud data, and obtaining a to-be-fitted point cloud; performing plane fitting on the to-be-fitted point cloud to obtain a plane fitting model; angle information and center coordinates of the pallet are calculated based on the plane fitting model and a preset reference coordinate system, and the angle information and the center coordinates of the pallet are utilized to construct a pose matrix of the pallet to obtain pose information of the pallet. The pallet pose estimation method provided by the invention has higher robustness and higher accuracy.

A camera misalignment detection system detecting a misalignment condition of one or more cameras for a vehicle includes one or more controllers that determine a set of matched pixel pairs, determine a feature matching ratio based on the set of matched pixel pairs, and calculate an alignment angle difference of the one or more cameras. In response to determining the feature matching ratio, the essential matrix inlier ratio, and the alignment angle difference each exceed respective threshold values, the controllers add the alignment angle difference to a queue including a sequence of historical alignment angle difference values. The controllers perform statistical filtering to determine a total number of historical alignment angle difference values within the queue that are inliers and determine a misalignment condition of the one or more cameras based on the total number of historical alignment angle difference values within the queue that are inliers.

A method includes obtaining a medical image and receiving a selection of both a template image and a region of interest within the template image. The method includes inputting both the medical image and the template image into a trained vision transformer model and outputting from the trained vision transformer model both pixel level feature vectors from the medical image and a reference pixel level feature vector from the region of interest of the template image. The method includes inputting both the pixel level feature vectors and the reference pixel level feature vector into a trained contrastive similarity metric learning model and outputting from the trained contrastive similarity metric learning model pixel that are similar to reference pixels. The method includes labeling the pixels in the medical image with a segmentation mask, wherein the pixels that are labeled in the medical image correspond to the region of interest.

The invention discloses an electronic component packaging defect detection method and system based on image acquisition, and relates to the field of image analysis, and the method comprises the steps: collecting a multi-mode image of a to-be-detected electronic component package; pixel alignment is carried out on the multi-modal image, the multi-modal image after pixel alignment is used as an R channel, a G channel and a B channel to be stacked, and a three-channel pseudo-color image is generated; inputting the three-channel pseudo-color image into a pre-trained convolutional auto-encoder model to obtain a reconstructed three-channel image; analyzing the difference between the three-channel pseudo-color image and the reconstructed three-channel image to obtain an analysis result, and generating a three-channel residual image according to the analysis result; generating a single-channel defect saliency map based on the three-channel residual image; and when a connected region of which the pixel value exceeds a preset pixel threshold value exists in the single-channel defect saliency map, judging that the connected region is a real defect. The detection false alarm rate can be effectively reduced, and the production efficiency is improved.

The invention relates to a circuit board defect identification method and system based on multi-dimensional image data, and belongs to the technical field of data identification processing, and the method comprises the following steps: obtaining synchronous image data of a circuit board to be detected in a plurality of imaging modes; performing space-spectrum joint registration on each modal image to generate a multi-dimensional image cube with a unified coordinate system and pixel alignment; inputting the multi-dimensional image cube into a pre-trained multi-branch heterogeneous fusion neural network; generating a pixel-level defect probability graph by utilizing a defect sensing context decoder, and performing geometric constraint optimization on the probability graph by combining prior information of a circuit board design layout; outputting defect types, positions and confidence coefficients, and establishing an interpretable defect fingerprint database according to the multi-dimensional response characteristics of the defects; the method has the beneficial effects that false defect signals generated by image noise and circuit board surface texture interference can be effectively inhibited, the omission ratio and the false detection ratio are greatly reduced, and pixel-level accurate defect positioning is realized.

The invention discloses a chicken body size identification method, system and device and a storage medium, and belongs to the technical field of image detection, and the method comprises the steps: collecting a whole body side picture of a chicken, marking the key part of the whole body of the chicken, and generating a tag file; establishing a target detection model, and outputting a target bounding box position of the complete side of the chicken; establishing an image key point detection model, and identifying coordinate positions of key parts of the whole body of the chicken; calculating the real physical length corresponding to each pixel according to the actual size of the scale; and outputting the real area and the real perimeter of each key part as the body size parameters of the chicken. The method is accurate in target and key point recognition, has the advantages of high accuracy, can recognize the key point positions of the whole body of the chicken and calculate corresponding parameters only by inputting the manually shot images through automatic calculation, and is low in manual participation degree and high in automation degree.

The embodiment of the invention provides a check image correction method, device and equipment. The method comprises the following steps: acquiring an original deformation check image; processing the original deformation check image through a backbone network, a distortion encoder, a correction decoder and an output predictor in an image correction model in sequence to obtain a target pixel displacement field representing a pixel corresponding relation from the original deformation check image to a corrected check image; and according to the target pixel displacement field, performing bilinear difference distortion operation on the original deformed check image to obtain a corrected check image after the original deformed check image is corrected. An original deformed check image is encoded, an encoded feature map is learned, a corresponding pixel displacement field is generated by a correction decoder, and pixels are sampled from the original deformed check image using the predicted pixel displacement field to reconstruct a corrected check image. Automatic and high-quality deformation check image automatic correction is realized, the labor cost is reduced, and the correction accuracy is improved.

The invention discloses an SAR (Synthetic Aperture Radar) image target detection method and device combining superpixel segmentation and fusion. The method comprises the following steps: inputting an SAR image; uniformly segmenting the SAR image into a preset number of initial super-pixel regions, and taking the central point of each initial super-pixel region as an initial clustering center; updating a clustering center based on joint probability distribution of local entropy and gray level distribution; according to a self-adaptive pixel similarity measurement criterion, the pixels are distributed to the nearest clustering center; according to the self-adaptive pixel similarity measurement criterion, the similarity of pixel points is evaluated in a manner of combining a gray distance and a spatial distance and introducing a dynamic weight factor; constructing a gray-level co-occurrence matrix in six directions, calculating texture similarity among superpixels, and fusing the superpixels of which the texture similarity values are smaller than a set threshold value; and performing multistage rule screening on the SAR image after superpixel fusion, distinguishing target and background superpixels, and outputting a target detection result. According to the invention, the precision of the SAR image target detection technology is improved, and the calculation complexity is reduced.

Disclosed is a display device including a substrate, a plurality of pixels, a counter substrate, and at least one rib. The plurality of pixels is located over the substrate and each includes a light-emitting diode. The counter substrate is located over the plurality of pixels. The at least one rib is formed over the counter substrate, located over a region between the plurality of light-emitting diodes included in the plurality of pixels, and has a height larger than a height of the light-emitting diode.