200 results about "Perspective distortion" patented technology

The invention relates to the technical field of image super-resolution enhancement, in particular to a tablet computer image super-resolution enhancement method based on a generative adversarial network. The method comprises the following steps: collecting an image through a tablet computer, and carrying out regional illumination component calculation on the image to obtain detailed illumination component data; secondly, quantizing the motion out-of-focus fuzzy degree based on the illumination data, generating track fuzzy intensity sensing data, performing 3D modeling by combining the data, and estimating the distortion trend of the image; then, a shooting error is eliminated by using rendering visual angle distortion correction, a more real visual angle effect is generated, and super-resolution enhancement is performed on the image by using a generative adversarial network, and image details are improved. And finally, designing automatic firmware based on the super-resolution enhanced data, and embedding the automatic firmware into a tablet computer control system. According to the method, the image super-resolution enhancement technology is optimized, so that the image super-resolution enhancement technology is more perfect.

The invention discloses a geological structure fracture automatic identification and detection system based on deep learning, and relates to the technical field of geological structure fracture automatic identification and detection, and the geological structure fracture automatic identification and detection system comprises a fracture image analysis module, a distortion intelligent evaluation module, a form adaptive correction module, a correction efficiency evaluation module and an identification data management module. The distortion intelligent evaluation module is used for acquiring imaging parameter information of each region in the crack image in real time, judging the form distortion degree of each region in the crack image under a perspective distortion condition, and dividing each region in the crack image into a low distortion region, a medium distortion region and a high distortion region according to a judgment result; and the form self-adaptive correction module is used for constructing a fracture form correction mechanism according to the division result of each region in the fracture image. According to the invention, the problem of fracture shape distortion caused by the change of a shooting view angle is solved, accurate identification and automatic correction of the fracture are realized, and the detection precision and stability are improved.

The invention discloses a code reader distorted image correcting and decoding method and system based on machine vision. The method comprises the following steps: acquiring an original image containing a bar code through image acquisition equipment, preprocessing the original image, and extracting feature parameters; image distortion types including radial distortion and perspective distortion are judged according to the extracted features, and distortion correction is carried out by adopting a corresponding correction model; performing quality evaluation on the corrected image, and when a quality threshold value is met, repositioning a bar code area and performing decoding processing; the method can effectively solve the problem of image distortion caused by inherent defects of an optical system and a shooting angle in the bar code reading process, remarkably improves the accuracy and success rate of bar code recognition, and is suitable for bar code recognition application in the fields of industrial automation, logistics management and the like.

A method of identifying road distress on a road based on imagery of the road is disclosed. The method is performed by a processor and comprises the steps of: identifying a region of interest from road regions detected in the imagery of the road; detecting one or more road distresses present in the region of interest by identifying features associated with physical characteristics related to road distress in an original image comprising the region of interest; detecting one or more road distresses present in the region of interest by identifying features associated with physical characteristics related to road distress in a corrected image obtained by applying at least one of optical distortion correction and perspective distortion correction to the original image comprising the region of interest; and identifying one or more road distresses by combining the detection based on the original image and the detection based on the corrected image. Unlocking insights from Geo-Data, the present invention further relates to improvements in sustainability and environmental developments: together we create a safe and liveable world.

The invention relates to the field of intelligent traffic systems, and discloses an intelligent parking management system using artificial intelligence image recognition, and the system comprises an image collection module which is used for collecting a parking space image in a parking lot in real time, and is combined with at least one sensor unit which comprises a high-definition camera, an infrared thermal imager and a laser radar sensor; the image preprocessing module is used for preprocessing the acquired parking space image, removing noise in the image and enhancing the image; the geometric transformation module is used for converting the parking space image from the current view angle into a standard parking space model and eliminating view angle distortion; and the illumination optimization module is used for carrying out illumination correction on the parking space image and reducing the influence of illumination change on the parking space image. Through an image acquisition technology based on multi-sensor data fusion and in combination with a high-definition camera, an infrared thermal imager and a laser radar sensor, a parking space image can be accurately acquired under different illumination and complex environment conditions.

The invention discloses a cross-modal fusion and multi-frequency coding view angle image generation method in an ultra-low orbit scene, and solves the problem of view angle condition generation and single image three-dimensional reconstruction method based on diffusion prior in the ultra-low orbit scene of an aircraft target. Due to insufficient condition information fusion capability, limited radiation or illumination characterization and insufficient cross-view-angle geometric constraint, a problem that a new view angle image with visual coherence and geometric consistency of texture details is difficult to obtain at the same time under large-range view angle and scale change is solved; according to the method, multi-order angle information of direction distribution is effectively captured through perceptual coding of a light direction vector, then the position of a light starting point coordinate is coded, and finally the pose of a camera pose parameter is coded, so that the influence of a relative pose on depth perception and perspective distortion in an image generation process can be depicted; the three coding results are used for a decoding stage of the potential diffusion model, and geometric consistency of detail recovery under a new view angle is effectively improved.

Methods, systems, and apparatus, including medium-encoded computer program products, for camera detection of projectile point of impact include: processing at least one of multiple images of a physical target in a field of view of a camera to determine an orientation of the physical target, correct spatial perspective distortion for the physical target, establish a metric for the physical target that relates a pixel-to-pixel distance with a real-world distance, or a combination thereof; comparing respective images in a sequence of images from the multiple images to identify image data representing a projectile having hit the physical target; determining a point of impact of the projectile on the physical target based on the image data representing the projectile having hit the physical target; and providing the determined point of impact for scoring and presentation on a display device.

The invention relates to the technical field of object recognition based on image and point cloud fusion, and discloses a machine vision-based obstacle detection method and system in a container moving process, and the method comprises the steps: obtaining three-dimensional point cloud data through a laser radar installed on container carrying equipment, and obtaining a two-dimensional image through an industrial camera; performing terrain reference correction on the three-dimensional point cloud data, and establishing an inclined coordinate system matched with the ground gradient; performing perspective distortion correction on the two-dimensional image based on the inclined coordinate system to obtain a corrected two-dimensional image; extracting edge features, angular point features and semantic features from the corrected two-dimensional image; extracting geometric features and reflection intensity features of the three-dimensional point cloud data under the inclined coordinate system; performing cross-modal fusion on the edge features, the angular point features, the semantic features, the geometric features and the reflection intensity features to generate fusion features; and the obstacle type is identified based on the fusion feature, and the obstacle sensing reliability in the complex industrial environment is improved.

The invention discloses a perspective distortion image-oriented monocular dressing human body reconstruction method, and relates to the technical field of three-dimensional vision. The invention provides a three-dimensional human body reconstruction framework fusing distortion correction, three-dimensional geometric representation and pseudo multi-view constraint, and image distortion is relieved by uniformly partitioning an image and using virtual view transformation to decouple the relevance between an image position and perspective distortion; a multi-scale attention enhancement network is designed, and a hourglass model and a channel attention mechanism are utilized to extract block features, so that the model can process perspective distortion images; the human body parameter model is coded into a frequency domain coefficient through Fourier transform, and the frequency domain coefficient serves as the basis of geometric representation; in order to further optimize high-frequency details, the invention provides a pseudo multi-view module, and the robustness of the network to perspective distortion is enhanced by simulating multi-view data enhancement. Experimental results show that compared with the prior art, the method disclosed by the invention has the advantages that the quantification and the qualitative property on the Thuman2.0 data set and the CumHumans data set are optimal.

To reduce perspective distortion of a glass molded article obtained by subjecting a glass plate to a bending processing.SOLUTION: The present invention relates to a method for producing a glass molded article, the method including: attaching a flattened mold cloth, which is obtained by reducing irregularities on a surface of a mold cloth, to a forming mold; heating a glass plate to a temperature equal to or higher than a glass transition point; and press-forming the softened glass plate into a predetermined shape along the forming mold via the flattened mold cloth.SELECTED DRAWING: None

The invention discloses a power line image super-resolution recovery system and method for power transmission line patrol, and relates to the technical field of unmanned aerial vehicle image processing. The system provided by the invention comprises three core modules, namely a physical perception generator, a differentiable physical discriminator and a spatial transformation network (STN), wherein the generator is combined with a U-Net and a residual structure, and the output is ensured to accord with the geometry and material characteristics of a power line through physical constraint loss; the discriminator adopts a double-branch structure, and synchronously evaluates image authenticity and physical rationality; and the STN module corrects perspective distortion by using the pose data of the unmanned aerial vehicle to realize geometric alignment of the power line. The system integrates advanced components such as Swin Transformer, ViT and differentiable RANSAC, supports end-to-end processing from original Bayer data to a high-definition image, and outputs a defect detection confidence map with enhanced physical constraints. Compared with a traditional data driving method, the method strictly guarantees the physical credibility of a reconstruction result while improving the resolution, and is suitable for a power line inspection task in a complex environment.

Disclosed in the present invention is an obliquely translatable vehicle-mounted camera device. The vehicle-mounted camera device comprises a camera main body, an image acquisition unit, and an imaging lens; the imaging lens and the image acquisition unit are sequentially installed in the camera main body; the center of the image acquisition unit has a predetermined oblique offset relative to the intersection point of the optical axis of the imaging lens and the plane of the image acquisition unit. By using the camera device in which the central point of the image acquisition unit is obliquely translated, the effective area of the image acquisition unit can be used to the maximum extent, the problem of perspective distortion as a result of the camera device being obliquely installed towards the outer side of a vehicle body and the ground due to an acquisition angle requirement is avoided, and a field-of-view observation area satisfying the requirements of GB1508-2022 and ECE R46 regulations can be provided.

The invention discloses an instrument data reading and collecting system and method based on image recognition, relates to the field of instrument image recognition, introduces a specially trained Meter-CornerNet deep learning model, and can accurately position four key angular points of an instrument panel from an original image containing perspective distortion and uneven illumination. After the angular points are obtained, an inclined instrument area is corrected into a standard front rectangular view through perspective transformation, and illumination normalization is carried out, so that the interference of the angle and illumination problems on the image quality is improved. And then, digital segmentation and OCR identification are carried out on the processed high-quality image, a result is verified, and finally, an accurate reading is sent to an Internet of Things platform. According to the method, high robustness and high accuracy of the whole system in a complex real scene are ensured through a strategy of first accurate correction and then identification verification.

The invention provides an electrical equipment defect identification method and device based on pose fusion, the method is applied to an identification device comprising a data processing module, a model construction module and a defect identification module, and the method specifically comprises the following steps: obtaining historical image data and corresponding defect information, and aligning pose data with the historical image data according to a shooting timestamp; performing perspective distortion correction and deblurring processing on the historical image data according to the corresponding pose data to obtain a corresponding equipment area image, and training a YOLO model in combination with the corresponding pose data and defect information to obtain a power equipment defect identification model; and constructing a recognition sample according to the currently collected image data and pose data of the power equipment, and inputting the recognition sample into the power equipment defect recognition model to obtain a defect recognition result of the power equipment. According to the method, the pose data can be fused into the pre-training data preprocessing stage and the training stage of the model, the method can adapt to defect identification of power equipment in different pose scenes, and the accuracy is guaranteed.

The invention discloses an intelligent archive digital processing method and system, and the method comprises the steps: carrying out the synchronous scanning of a paper archive through a multi-spectral linear array scanner, and collecting the archive image data of a visible light wave band and a near-infrared wave band; performing non-uniform illumination correction and perspective distortion correction on the archive image data to generate a geometric and color standardized dual-channel fusion archive image; based on the dual-channel fusion file image, outputting structured layout description information including a text area, a table area, a signature area and an illustration area; generating a preliminary archive information atlas according to the structured layout description information; and based on the archive information atlas and a preset archive metadata specification, finally outputting a fully structured digital archive object conforming to an archive standard. According to the embodiment of the invention, the fully-structured digital archive object can be automatically generated in a high-fidelity manner, and the accuracy, integrity and knowledge degree of digital processing are improved.

The invention relates to the technical field of power distribution networks, and particularly discloses a power distribution network hot-line work discrimination method based on image recognition. The method is used for solving the problems that a small target of a grounding ring is difficult to stably identify under the condition of scale and posture change due to factors such as strong light glare, appliance shielding, strong reflection metal highlight and perspective distortion in live-line work of a power distribution network, installation in-place and safe distance are difficult to accurately judge, threshold calibers are not uniform, and linkage lag is caused by time sequence jitter. Comprising the steps of scene acquisition and adaptive homomorphic preprocessing, multi-scale target detection modeling, loss calculation and training convergence, target output and label merging, geometric measurement construction, operation state judgment, safety linkage and back-off alarm, record uploading and quality closed loop. According to the method, through cooperation of the adaptive homomorphic preprocessing, the improved YOLOX embedded with geometric priori, the assembly geometric quantity and the time sequence consistent criterion, the identification and the positioning of the grounding ring under the hard light shielding are more stable and accurate, and the wrong judgment and the missed judgment are reduced.

The invention relates to the technical field of image processing, and particularly provides an image perspective distortion correction method and device and a computer readable storage medium, and the method comprises the steps: obtaining an original face image; gridding processing is carried out on the original face image to obtain an initial deformation grid, and grids in the initial deformation grid correspond to pixel points in the original face image; and converting the original face image into a target distance face image based on a preset target distance. And based on the original face image and the target distance face image, determining coordinate change vectors of at least part of pixel points, and determining target coordinates of each grid in the initial deformation grid. Pixel points in the original face image are adjusted to the target coordinates of the corresponding grids, and a target image is generated. In this way, the accuracy of image perspective distortion correction can be improved.

The invention relates to the technical field of physical property testing, and discloses a mobile phone appearance multi-angle automatic detection system and method based on a multi-face reflection structure, and the system comprises a panorama collection unit which obtains an original panorama image formed by a to-be-detected mobile phone through a multi-face panorama reflection mechanism; the reference data storage unit is used for storing standardized three-dimensional model data of the mobile phone to be tested; the central processing unit is used for establishing the three-dimensional model data as a space measurement reference, constructing an optical path mapping matrix according to the distribution parameters, mapping a surface grid of the three-dimensional model data to a camera coordinate system to determine geometric distortion sampling coordinates, extracting pixels to complete space topology reconstruction, and generating a standardized surface expansion view; according to the invention, nonlinear perspective distortion in reflection imaging is eliminated, tight alignment of pixel sites and physical topology is realized, and physical balance with objective scales is provided for quantitative analysis of flaws.

A method and electronic apparatus may be provided, the method includes obtaining a first image, generating a projection image based on the first image, in which perspective distortion of the first image is corrected, generating a cognitive map according to a plurality of objects in the first image, wherein the cognitive map divides the first image into a plurality of areas of a first plurality of types, generating first mesh information for correcting the first image based on the projection image and the cognitive map, wherein the first mesh information comprises movement information for a plurality of pixel positions, and generating an output image by correcting the first image based on the first mesh information.

The invention belongs to the technical field of bearing bush detection, and particularly relates to an image distortion correction method for bearing bush surface defect detection, which comprises the following specific steps: measuring the size information of a to-be-detected bearing bush, including the inner diameter and the axial width; shooting the surface of the bearing bush to obtain a target bearing bush image which comprises a complete bearing bush surface; carrying out image preprocessing on the obtained bearing bush image; the problem that in an existing image-based defect detection method, due to perspective distortion, the detection precision of position shape distortion, length area and the like of a bearing bush curved surface defect area is insufficient is solved. According to the method, the perspective distortion of the three-dimensional structure of the curved surface of the bearing bush is effectively corrected, so that accurate recognition and quantification of surface defect characteristics are ensured, and the accuracy and reliability of bearing bush defect detection are improved.

The invention provides a paver intelligent welt control method and system, and particularly relates to the technical field of road construction machinery intelligentization, the method comprises the following steps: firstly, using industrial cameras installed on two sides of a paver to collect an operation area image in real time, then extracting image edge features, and calculating a definition score; according to a scoring result, selecting different paths to generate target boundary position coordinates: if the score is greater than a set threshold value, adopting a gradient operator and a traditional edge detection algorithm of Hough transformation; and otherwise, applying a target detection model based on YOLO series deep learning. Afterwards, bird's-eye view transformation is carried out based on the target boundary position coordinates to eliminate perspective distortion, and transverse offset is obtained. And finally, generating a screed stretching control instruction according to the transverse offset, transmitting the screed stretching control instruction to a paver controller through a CAN (Controller Area Network) bus, and driving a telescopic oil cylinder to execute screed welting action so as to realize high-precision self-adaptive welting.

The invention discloses a laser-point-cloud-guided tunnel multi-view area-array camera distribution map generation method and system, and the method comprises the steps: carrying out the calculation of a spatial mapping relation between a synchronously collected multi-view image and corresponding laser radar point cloud data based on the calibration parameters of a camera and a radar; geometric correction is carried out on the original image, and brightness adaptive equalization preprocessing is carried out on the corrected image; carrying out image feature point extraction and matching on the ROI of the image based on a deep learning model; recording an image matching relationship in a metadata form and creating an index; and based on the constructed image matching relationship metadata and indexes, realizing rapid generation of a panoramic distribution map. According to the method, the generated distribution image splicing error caused by factors such as uneven image brightness and perspective distortion can be relieved; large-scale image data matching and fusion based on adjacent image coincidence ROI constraint significantly improve efficiency; and based on a lightweight deep learning model, the reliability of extracting and matching the feature points of the low-illumination and weak-texture image of the tunnel scene is improved.

Disclosed in the present invention are a camera system for non-directly-facing shooting and a camera system configuration method. The camera system comprises a camera body, an image acquisition unit and an imaging lens. The imaging lens and the image acquisition unit are respectively installed at the front end and the rear end of the camera body. The camera body inclines, with respect to a reference direction, at a first angle θ in a first direction; the imaging lens and the image acquisition unit incline, with respect to the main axis of the camera body, at a second angle α and a third angle β respectively in the direction opposite to the inclination direction of the camera body, the second angle α and the third angle β being different from each other. The present invention can shoot clearly without perspective distortion scenes in a larger range of depth of field at sides of or below an object, thereby providing a better field of view for people.

The invention discloses a 2D / 3D visual calibration plate for industrial manufacturing, which comprises a calibration plate body, a plurality of target patterns are arranged on the calibration plate body, the target patterns comprise circular patterns and / or square patterns, and the circular patterns are used for calculating an ellipse center point under imaging perspective deformation of an industrial robot camera; the checkered pattern comprises cross lines and digital labels and is used for calibrating the tail end of the industrial robot, and calibration comprises calculation of a coordinate transformation relation between a camera coordinate system and a robot coordinate system. According to the method, the calibration is more precise through the structural optimization of the calibration plate, the calibration process is simplified through the three-point method, the calculated amount is extremely small, the calibration precision and efficiency are remarkably improved, the coordinate axis orientation is assisted through the arrangement of the asymmetric pattern, the horizontal and vertical coordinate axes are judged through the assistance of the grid pattern, and the robustness is improved.

The application relates to the technical field of image data processing, in particular to a blister product defect intelligent detection method and system based on industrial vision. The method comprises the following steps: acquiring product images of to-be-detected blister products and calibration plate images of calibration plates respectively photographed on multiple detection stations; determining a relative offset amount of a finished product image relative to a finished calibration plate image based on the positions of physical marking points in the product images and the calibration plate images photographed on the multiple detection stations; evaluating a perspective distortion degree of the finished product image based on the morphological feature difference between a blister product region in the finished product image and a calibration plate region in the finished calibration plate image; and performing perspective transformation calibration on the finished product image based on the relative offset amount and the perspective distortion degree, wherein the calibrated finished product image is used for intelligent defect detection. The method can eliminate the influence of distortion on defect detection and improve the accuracy of defect detection.

The present application belongs to the technical field of oil use peony phenotype parameter automatic extraction method, especially relates to a kind of oil use peony phenotype parameter automatic extraction method and system based on multi-organ image analysis, obtain the high-resolution original image of oil use peony multi-organ containing ColorChecker24 standard color card, the organ at least includes petal, blade, fruit pod;Color correction and geometric correction are executed to original image, 24 color blocks of color card are positioned using computer vision algorithm, color conversion matrix is constructed to map image from device-dependent RGB space to standard color space, while calculating pixel scale based on color block physical size;Four corner points of bearing tray are detected for fruit pod image, and perspective transformation matrix is constructed to complete perspective distortion correction.

The invention relates to the technical field of microscopic imaging, and provides a microscopic imaging method and system based on a transmission-type relay projection screen, and the method comprises the steps: S1, enabling a laser to emit a light beam to irradiate a sample, and enabling the diffraction light of the sample to be projected to the transmission-type relay projection screen; s2, shooting the transmission-type relay projection screen on the other side of the transmission-type relay projection screen, and recording a power spectrum image of the sample; wherein the laser, the sample and the shooting camera are on the same axis, and the axis is perpendicular to the transmission-type relay projection screen; and S3, reconstructing the power spectrum image of the sample to obtain a target reconstructed image. The scheme has the characteristics of coaxial arrangement, simple structure, easy integration, long working distance, large depth of field, wide field of view and high resolution, can effectively reduce perspective distortion, and is convenient to combine with other technologies. The system does not need scanning measurement or wavefront modulation, so that the cost, complexity and operation difficulty of the system are reduced.

This application provides an image processing method, including: performing stabilization on two adjacent image frames of a video stream acquired by a camera to obtain cropped areas, and performing perspective distortion correction on the cropped areas obtained through stabilization. Because the cropped areas output after stabilization is performed on the two adjacent frames include same or basically same content, it can be ensured that a subsequently generated video is smooth and stable. In this application, position relationships, for perspective distortion correction, between the cropped areas obtained through stabilization are further mapped, so that degrees or effects of perspective distortion correction of the adjacent frames of the subsequently generated video are the same, in other words, degrees of transformation of a same object in the images are the same.

The application provides a kind of visual quality inspection and machining precision closed-loop control method and system of intelligent manufacturing unit, specifically collect the image of workpiece to be inspected, obtain initial target frame by target detection network, segment workpiece image to obtain pixel mask and contour, calculate the minimum area of contour as size reference frame as circumscribed quadrilateral;Based on the matching relationship between the actual feature points in the reference frame and the preset template, calculate the homography matrix to correct the perspective distortion, get the corrected contour and the standard size frame;Sample along the normal direction of each side of the standard size frame to get edge point set, fit high-precision edge with random sampling consistency algorithm and calculate the proportion of in-point, if the proportion does not meet the standard, increase the sampling density and re-fit, compare the fitted workpiece size with the planned size, and construct the machining deviation state matrix;Combine historical deviation matrix and compensation parameters to update the transfer matrix of machining process online, generate compensation parameters through current deviation matrix and send to unit controller.

The application discloses a kind of based on machine vision's patch type infrared receiving head appearance detection method and system, belong to computer vision detection field.Method includes: through bottom collimating backlight source irradiation to be measured receiving head, utilize the image of two telecentric cameras of symmetrical arrangement Synchronous acquisition pin;Sub-pixel level feature positioning is carried out to image and rejects reflection false feature point;Based on binocular vision principle, reconstruct the three-dimensional space coordinates of pin end;Adopt lowest multipoint method to construct reference plane, calculate the vertical distance of each pin to reference plane as coplanarity deviation;Deviation and tolerance threshold are compared, and output determination signal.System includes object table, bottom collimating backlight source, double-side telecentric camera, hardware level synchronous trigger unit, image processing and three-dimensional reconstruction unit, coplanarity evaluation and determination unit.The application can high-precision measurement pin coplanarity, eliminate perspective distortion and stray light interference, suitable for on-line full inspection on automatic production line.