166 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 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.

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.

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.

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.

The present invention provides a machine vision-based method for detecting damaged mesh in underwater cement cage nets, which relates to the technical field of mesh damage detection. The method comprises: controlling an ROV to move to a detection position, capturing an underwater image of the cement cage based on an RGB color space, and transmitting the underwater image to a control computer; the control computer converting the underwater image based on the RGB color space into an L*a*b* color space; identifying meshes of the cement cage net in the underwater image; identifying damaged meshes based on the area and central moment of each mesh in the underwater image; marking the damaged meshes in the underwater image, and outputting the marked underwater image. The present invention can accurately segment the mesh structure in complex underwater environments, offset perspective distortion caused by water flow and mesh topology changes caused by algae adhesion, increase the recognition rate of damaged meshes, achieve real-time continuous tracking of damaged meshes, and accurately locate damaged meshes, achieving high efficiency, low cost, and low risk.

The application discloses a visible light and laser radar aerial survey target, and relates to the field of aerial surveying and mapping, which comprises a transparent box body, a composite corner reflector and an adjustable supporting mechanism. A visible light image control pattern is arranged on the top plate of the transparent box body, and the geometric center of the visible light image control pattern is coaxially locked with the geometric center of the composite corner reflector with multiple symmetrical corner reflection units in the box. Regardless of the change of the ground slope and the angle of the box body, there is no relative displacement, and the systematic registration error caused by the center misalignment due to the two-time layout of the separated target is completely eliminated from the physical structure. The box body is adjusted to be absolutely horizontal through the adjustable supporting mechanism, the visible light image control pattern is guaranteed to be perpendicular to the unmanned aerial vehicle aerial photography optical axis without perspective distortion, the laser signal capture rate is improved through the configuration of the composite corner reflector comprising multiple symmetrical corner reflection units, and the aerial survey precision in mountainous areas is significantly improved.

The invention provides a control method and system of grabbing equipment and the grabbing equipment. The control method of the grabbing equipment comprises the steps that an original image of a cloth bearing device used for bearing cloth to be grabbed is obtained; correcting a target monitoring area in the original image into a rectangular shape through perspective transformation processing to obtain a to-be-processed image; identifying a target grabbing position of to-be-grabbed cloth in the to-be-processed image, wherein the to-be-grabbed cloth is located in the target monitoring area; and controlling the grabbing equipment to grab the target grabbing position of the to-be-grabbed cloth. Distortion of an original image is corrected through perspective transformation, a target monitoring area is corrected to be in a regular rectangular shape, the processed image can accurately reflect the actual position and shape of cloth, and interference of perspective distortion on cloth position measurement is effectively eliminated. And the target grabbing position of the to-be-grabbed cloth is recognized based on the corrected to-be-processed image, and the grabbing device is controlled to grab the target grabbing position, so that the accuracy of cloth grabbing control is improved.

The invention discloses a liquid level optical precision measurement method and system based on multi-depth reference characteristics, and belongs to the field of non-contact industrial measurement. The method comprises: taking an image including a luminescent liquid level, a container opening as a first reference feature, and at least one second reference feature located below the container and having a known size, from diagonally above the container; acquiring reference information such as the real size of each reference feature; based on the real size of each reference feature and the pixel size of each reference feature in the image, constructing a core effective scale conversion relation related to depth; and finally, the liquid level height is accurately solved by using the relation. According to the method, the concept of multi-depth reference is introduced, the problems of perspective distortion and scale uncertainty of a traditional single-reference-object vision method under the strabismus condition are fundamentally solved, and non-contact high-precision measurement of the liquid level is achieved.

The invention relates to the technical field of computer vision nondestructive testing, and discloses a concrete crack nondestructive testing quantitative analysis method based on a convolutional neural network. The method comprises the following steps: firstly, carrying out definition screening on a video stream, and generating a semantic mutual exclusion mask covering a crack region by utilizing semantic segmentation and morphological expansion; removing non-coplanar interference feature points in the crack region under the constraint of a mask, resolving a homography matrix only based on the feature points of the background region, and generating an orthoimage without perspective distortion; and finally, calculating the sub-pixel physical width of the crack in combination with the camera physical distance obtained by homography matrix decomposition and a direction adaptive Zernike moment positioning algorithm. According to the method, interference of crack textures on plane parameter calculation is eliminated through semantic constraint, geometric distortion caused by oblique shooting is corrected through virtual orthographic projection, and high-precision crack width quantitative detection can be achieved without external distance measuring equipment.

Systems, methods, and computer program product embodiments for an image correction system are disclosed herein. Embodiments operate by receiving an image of a physical object including graphics data with undefined perspective distortion. It is determined that an image transformation is to be performed on the received image. The image is provided to a neural network configured to generate a plurality of transformation parameters corresponding to the perspective distortion. The neural network is trained on a training dataset, the training dataset comprising: a first image set comprising an object having graphic data similar to the received image in a readable direction; and a second image set having a known perspective transformation. Image transformation is performed on the received image based on the values for the plurality of transformation parameters to generate a corrected image having graphic data oriented within a readable threshold.

The invention provides a field core box corner extraction and perspective distortion correction method based on multi-scale Retinex fusion. The field core box corner extraction and perspective distortion correction method comprises the following steps: S1, preprocessing a core box image based on anisotropic diffusion and multi-scale Retinex fusion; s2, core box angular point extraction based on topological constraint Shii-Tomasi and graph cut optimization interaction; s3, perspective correction based on thin plate spline transformation and Markov random field fusion; and S4, performing correction result precision verification and closed-loop feedback based on Hough transform straight line fitting and geometric moment invariant fusion. According to the method, accurate extraction of core box angular points and efficient distortion correction of images are realized, and meanwhile, full-dimension precision verification of correction results, adaptive migration of processing parameters of batch images and lightweight field deployment of algorithms are completed.

The invention relates to the technical field of image processing, and particularly provides an image perspective distortion correction method, electronic equipment and a computer readable storage medium, and the image perspective distortion correction method comprises the steps: obtaining an original face image; based on a preset target distance, the original face image is converted into a target distance face image, the target distance face image is an image formed by imaging a face in the original face image under the target distance, and the target distance is larger than the shooting distance of the original face image. And extracting facial features in the target distance face image. And performing face replay on the original face image based on the facial form features in the target distance face image to obtain a target image. Through the above mode, accurate image perspective distortion correction can be realized.

The application discloses a kind of pose closed loop planning system and method based on PLC six visual quality inspection, it is related to industrial robot quality inspection technical field, including: visual perception module, for obtaining RGB-D image;Pose closed loop control module, for receiving RGB-D image and output 6D pose data;Motion planning module, for generating double-arm motion trajectory;Flip optimization module, for generating optimal flip sorting set;Sorting execution module, for completing sorting and discharging action according to quality inspection result and control instruction;Robot controller, for data interaction and issue control instruction.The application does not need to rely on artificial flip or multiple fixtures, reduce the failure rate caused by incoming material position deviation etc., through double-arm alternate operation and Monte Carlo optimization flip sorting, realize PLC device six automatic continuous detection, with the combination of coarse positioning, fine positioning of double RGB-D camera, combined with the output 6D pose data of deep learning pose estimation model, effectively avoid perspective distortion and miss detection, false detection.