232 results about "Perspective transformation" patented technology

The invention provides a core photograph automatic correction and cutting method based on YOLOv8-seg and perspective transformation, and belongs to the technical field of image processing, and the method comprises the steps: S1, collecting a core box image; s2, polygonal point sequence labeling; s3, obtaining an optimal segmentation model; and S4, cutting. Aiming at the problems of much manual intervention, low efficiency and poor adaptability in the existing core photograph processing, the invention provides a set of full-automatic end-to-end solution without preprocessing, realizes rapid and stable correction and cutting of the core photograph in a complex scene, relieves the mechanical labor burden of geological personnel, focuses on the core exploration work, and improves the core exploration efficiency. And the practical application requirements of large-scale geological exploration projects are met.

The invention provides a method and a device for mapping image coordinates to desktop coordinates, equipment and a storage medium, which are used for improving the positioning precision of a projection touch system in a real environment. The method comprises the steps of performing distortion correction on original gesture video data according to internal parameters of a camera and a lens distortion coefficient to obtain a corrected image frame sequence, and performing interaction space calibration according to the corrected image frame sequence to obtain a distortionless interaction plane, performing perspective change matrix solving on the distortionless interaction plane according to the target vertex coordinate set to obtain orthogonal mapping data, and when gesture key points are detected in the distortionless interaction plane, performing perspective transformation and normalization processing on gesture coordinates of the gesture key points according to the orthogonal mapping data to generate initial desktop control coordinates; and performing display mapping processing on the initial desktop control coordinates according to the physical attribute parameters of the target display to obtain desktop control coordinates.

The invention discloses a vehicle target detection tracking and trajectory data extraction method based on deep learning, and relates to the technical field of unmanned aerial vehicle aerial photography. The method comprises the following steps: carrying out stable frame processing on an unmanned aerial vehicle video, extracting feature points and feature vectors through an SURF algorithm, matching and screening reliable matching pairs through an FLANN algorithm, calculating a homography matrix through an RANSAC algorithm when a condition is met, carrying out perspective transformation to eliminate jitter, and outputting a stable video sequence; vehicle target detection: introducing an AIFI module to construct an improved YOLOv5OBB model, and outputting vehicle rotation bounding box parameters and categories after training; vehicle tracking and trajectory extraction are carried out, cross-frame tracking is realized based on a DeepSORT model, original trajectory data are preprocessed, and the speed, the acceleration, the additional lane number and the ID of an adjacent vehicle are calculated. According to the method, the problems of video jitter and insufficient detection precision are effectively solved, and the accuracy and continuity of track data extraction in the highway scene are improved.

The invention provides an industrial material segmentation and size measurement method based on deep learning. The method comprises the following steps: acquiring a to-be-processed industrial material image; inputting the image into the trained segmentation network model, and outputting to obtain a pixel-level segmentation mask; the segmentation network model is a hybrid network based on a U-Net architecture, a pre-trained ResNet50 is adopted by an encoder of the segmentation network model, and a convolutional block attention module (CBAM) is fused in jump connection between the encoder and a decoder; transforming the pixel-level segmentation mask to an aerial view space by using a perspective transformation matrix obtained by pre-calibration to obtain a corrected mask; in the aerial view space, geometric features of the corrected mask are calculated, and the physical size of the material is obtained through conversion according to a preset proportional scale. The device has the beneficial effects that high-precision and automatic segmentation and size measurement of industrial materials can be realized.

The invention provides a cowshed drivable area detection method based on fusion of a laser radar and a monocular camera, and the method comprises the steps: synchronously collecting real-time point cloud data and image data, carrying out the matching of the real-time point cloud data and a point cloud map constructed offline, and carrying out the calculation to obtain the pose state of a vehicle in a current map; the method comprises the following steps: inquiring and acquiring key points of a global induction area around a vehicle from a priori map constructed offline, and performing spatial mapping to form an image region of interest; pixel-level classification is carried out through a pre-constructed lightweight semantic segmentation model so as to output and obtain a binary segmentation mask; and mapping the binary segmentation mask from the image pixel coordinate system to the vehicle coordinate system through inverse perspective transformation, and generating a drivable area map under the vehicle coordinate system. According to the invention, through the core thought of priori map guidance, semantic fine recognition and coordinate system unified restoration, the drivable area detection of the cowshed is solved, and a basis is provided for a subsequent path planning module.

The invention relates to the technical field of laser measurement and photoelectric detection, and discloses a laser boresight equipment high-precision calibration method and system based on region segmentation mapping, and the method comprises the steps: firstly collecting a standard grid frosted glass target plate image, extracting grid intersection point pixel coordinates through employing an SURF algorithm, and building a data set corresponding to physical coordinates; dividing the field of view into a plurality of sub-regions, and respectively resolving a perspective transformation matrix mapped from a pixel coordinate system to a physical coordinate system; secondly, collecting a light spot image, performing initial positioning by using a Canny operator and a least square method, fitting a sampling sequence through a Sigmoid function, and extracting edge sub-pixel coordinates in combination with a central difference method; and finally, calling a corresponding transformation matrix according to the sub-region where the light spot is located to obtain physical coordinates, and calculating the light spot jerk value, the maximum diameter and the included angle between the optical axis and the mechanical axis. According to the invention, image distortion is compensated through view field partition mapping, and the calibration precision and detection efficiency of the laser boresight equipment are improved in combination with a sub-pixel positioning technology.

The invention discloses a vehicle nameplate identification method, system and device and a storage medium, and the method comprises the steps: carrying out the multi-scale grid partitioning of an input image, extracting features, building a regional characteristic map to discriminate the imaging attribute category, and carrying out the differential image enhancement and fusion to generate a preprocessing image; constructing a multi-hypothesis verification model based on the geometric attribute and the layout relationship of the vehicle identification code to perform rotation correction; constructing a perspective transformation model by utilizing text line geometric constraint, and correcting spatial deformation through adaptive block perspective transformation and affine transformation; structured information is output by adopting a universal recognition and special engine collaborative decision-making mechanism; and finally carrying out image feature generation, repeated verification, semantic consistency verification and block chain evidence storage. According to the method, the problems of poor vehicle nameplate image quality, difficulty in geometric deformation correction and low identification accuracy in a complex imaging environment are effectively solved, and end-to-end high-precision identification from a low-quality image to credible structured information is realized.

Embodiments of the present disclosure disclose image alignment methods, devices, electronic equipment and computer readable media. A specific implementation of the method comprises: respectively performing distortion correction on a first image and a second image to obtain a third image and a fourth image; performing feature point matching on the third image and the fourth image to obtain a feature point information group sequence; determining whether a homography matrix corresponding to a current rotation angle exists; in response to the homography matrix not existing, determining the homography matrix according to the feature point information group sequence; and performing perspective transformation on the fourth image according to the homography matrix, so that the fourth image after the perspective transformation is aligned with the third image. This implementation does not need to set a complex hardware structure, thereby achieving efficient and accurate image alignment.

The application provides a cable joint meshing state detection method and device, electronic equipment and storage medium. The method comprises: calibrating a shooting unit to obtain shooting unit parameters of the shooting unit; performing position calibration according to the shooting unit parameters to obtain a first pose transformation relationship between the shooting unit and a moving contact of the cable joint; performing elliptical correction processing on an elliptical contour formed by perspective transformation of a circular contour at the bottom of a stationary contact of the cable joint according to the shooting unit parameters to obtain a second pose transformation relationship between the shooting unit and the stationary contact of the cable joint; and obtaining the meshing state of the cable joint according to the first pose transformation relationship and the second pose transformation relationship. The cable joint meshing state detection method provided in the application can further ensure the accuracy of position measurement and direction measurement of the meshing position of the cable joint.

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 invention relates to the technical field of transformer substation environment perception, and provides a transformer substation equipment abnormity discrimination method and system based on a difference image, and the method comprises the steps: making a transformer substation equipment difference data set; adjusting the size of the comparison picture to the size of the reference picture; similar feature points are extracted between the comparison picture and the reference picture, and position registration of the two pictures is achieved through perspective transformation; after position registration is completed, color correction is carried out on the pictures based on reference picture comparison; performing difference operation on the pair picture and the reference picture, and taking a difference picture generated by the difference operation as a detection image; inputting the difference image and the annotation data as training data into a network for model training to form a detection model; and inputting a detection image used for testing into the trained network model, and outputting predicted bounding box information and an abnormal probability by the network model. According to the method, the problems of low detection speed and low detection precision in a transformer substation equipment difference detection process of an existing anomaly detection algorithm are solved.

The application discloses a two-stage COVID-19 antigen detection result discrimination method and device, relates to the field of image processing, and particularly relates to the application of image processing technology in the COVID-19 epidemic prevention and control field, and the method comprises the following steps: step 1, a plurality of quadrilateral regions recording detection results are obtained from a target picture, and a perspective transformation method is used to adjust the visual angle of the quadrilateral regions to obtain a corresponding number of rectangular regions; step 2, a classification network based on deep metric learning is used to identify the rectangular regions to obtain detection results. The application uses a deep metric learning method based on pairs, measures the similarity between sample groups, causes samples of the same type to be close to each other and samples of different types to be separated from each other, and processes fine-grained images at the instance level. The recognition accuracy of tail class samples is improved by adding a memory storage module to optimize the fine-grained image retrieval algorithm.

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.

This invention discloses a color balancing method, apparatus, electronic device, and storage medium. The method includes: acquiring four video images captured by a surround-view camera; performing distortion correction on the four video images to obtain four target video images; wherein the four video images are ultra-wide-angle images of the target object in four directions (front, back, left, and right); performing inverse perspective transformation on the four target video images to generate four top-view images; and performing color balancing on any one top-view image based on curve fitting to obtain the target top-view. This invention achieves color balancing through curve fitting, solving the problems of poor adaptability and low robustness of existing methods, and improving the adaptability and robustness of color balancing methods.

The invention discloses a method and a system for quickly inputting voting information. The method comprises the following steps: shooting a paper voting table containing a two-dimensional code to obtain an original image; performing document four-corner detection on the original image, and performing perspective transformation processing to obtain a coarse correction image; analyzing the two-dimensional code and pulling the corresponding electronic template; key points are extracted from the rough correction image and the template image for matching, and an initial key point pair set is obtained; when the quality is insufficient, the key point pairs are supplemented through OCR character graph matching; establishing an adaptive FFD control grid, and carrying out nonlinear deformation on the rough correction image until the rough correction image is aligned with a template pixel level to obtain a fine correction image; and clipping the ROI region according to the template definition file, executing check box classification identification to obtain structured voting data, and packaging the structured voting data into a preset format and outputting the structured voting data. According to the method, the heterogeneous second-level, high-robustness and traceable automatic information input aiming at the paper voting table form is realized.

The invention discloses a flatness online detection method and system based on a line laser contourgraph and a storage medium. The method comprises the following steps: acquiring two-dimensional height array data of the surface of a measured piece; unifying the image to a standard geometric coordinate system through four-side boundary straight line fitting and perspective transformation; aiming at an image splicing seam, calculating a robust statistic in a seam bandwidth, and adaptively selecting a constant offset mode or a line linear mode to carry out leveling processing; and a ruler method based on stability scoring is adopted, supporting point pairs are optimized under the condition that the jacking-free constraint condition is met, flatness indexes are calculated, and a visual thermodynamic diagram is generated. According to the method, the problems of low sampling coverage rate, large seam interference and poor detection consistency in the prior art are solved, and full-surface and high-precision flatness online detection on a high-beat production line is realized.

The invention discloses a personnel trajectory tracking method based on multi-camera posture detection, which comprises the following steps: extracting image data from a multi-camera video acquisition unit, and obtaining a mapping relation between pixel coordinates and physical world coordinates through a YOLO posture detection unit, a foot key point extraction unit and a perspective transformation calibration unit; the foot center position is extracted from the key points of the human body posture and converted through the perspective conversion unit to obtain physical world coordinate data, invalid data exceeding the monitoring range are filtered out through the physical range verification unit, and then smooth track coordinates are obtained through the Kalman filtering unit; the multi-camera detection coordinates and the historical trajectory data are subjected to coupling calculation through the trajectory fusion unit, and then the accurate trajectory of the personnel is provided through the visual output unit. According to the invention, the precision and stability of the whole personnel tracking system are improved, the utilization rate of installed monitoring equipment is greatly improved, and low-cost and high-precision trajectory tracking service is provided for personnel in a monitoring environment.

This invention relates to the field of lawn maintenance technology, and discloses a fusion perception method, device, equipment, and medium suitable for autonomous mobile devices. By introducing dynamic inverse perspective transformation assisted by lidar, it achieves a visual image projected onto a three-dimensional real terrain. Combined with deep fusion of visual and lidar data, it generates a high-precision, semantically rich three-dimensional environment model. This model can effectively reflect the terrain undulations and obstacle distribution in complex lawn environments, achieving multimodal perception with good environmental adaptability and high real-time performance on a low-cost hardware platform, supporting accurate and safe navigation and efficient task execution for the autonomous mobile device. It breaks the strong assumption of an absolutely flat ground surface, utilizing real-time lidar data to construct a non-planar ground model, achieving deep fusion of visual and lidar information, thereby improving perception accuracy and robustness with low-cost hardware configuration.

The invention provides a face acne skin lesion detection matching method, system and device, and relates to the field of image processing, and the method comprises the steps: calling a face key point detection interface, and obtaining face key point coordinates on face images at different angles; obtaining a first mapping transformation reference point of each face key point corresponding to the side face and the front face; according to the first mapping transformation reference point, generating a perspective transformation matrix of mapping the side face to the front face; according to the perspective transformation matrix and the human face key point coordinates, mapping the skin lesion detection frame on the side face to a position corresponding to the front face; matching the skin lesion detection frame on the front face with the skin lesion detection frame mapped to the front face by the side face; and unifying the skin lesion type labels of the skin lesion with the front face and the side face successfully matched. According to the invention, the accuracy of acne evaluation results can be 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.

This invention discloses a method, device, and computer-readable storage medium for improving frame rate in game scenes. The method includes: identifying whether each element color value in the first element color data value set of the main scene model in the main buffer of the current frame is identical to an element color value in the second element color data value set of the simplified model in the secondary buffer; in the next frame, placing each element with the same element color value in the secondary buffer and waiting directly for image rendering; performing coordinate transformation, perspective transformation, and cropping operations on each element with different element color values, placing it in the main buffer, and waiting for image rendering. This achieves an efficient game scene frame rate improvement scheme, reduces computational load, saves processing resources, and enhances the user's gaming experience.

The application discloses a kind of rolling robot binocular vision SLAM data preprocessing methods, comprising: S01, measure the current three-axis Euler angle of rolling robot, obtain the current left and right eye image of rolling robot;S02, based on the inherent shaking cycle of rolling robot, yaw angle data in Euler angle is executed sliding window mean filtering, and the electronic image stabilization correction quantity is obtained from the reading of roll angle and pitch angle and the filtering result of yaw angle, according to the correction quantity and the cutting amount, perspective transformation and cutting are executed in turn, and the stabilized image is obtained;S03, the camera internal parameter corresponding to the stabilized image is obtained according to the cutting amount, and the relative external parameter between the cameras corresponding to the left and right eye stabilized image is obtained according to the correction quantity, to obtain the data that can be used for visual SLAM.The application can provide stable and reliable monitoring picture for rolling robot, and visual SLAM can be realized based on stable picture, which can be widely applied in the field of visual perception positioning of robot with strong shaking characteristics.

This invention discloses a depth image normalization method, apparatus, device, and gaze estimation system for gaze estimation, relating to the fields of computer vision and 3D data processing. It includes: constructing an original 3D coordinate map based on the original depth map; transforming and inversely mapping the original 3D coordinate map based on a perspective transformation matrix to obtain a normalized 3D coordinate map and the mapped 3D spatial coordinates of each pixel in the normalized 3D coordinate map in the original depth camera coordinate system; transforming the mapped 3D spatial coordinates to the target virtual camera coordinate system through rotation and translation operations in the perspective transformation matrix to obtain the final Z-coordinate of each pixel in the normalized 3D coordinate map, which serves as the normalized depth value for each pixel in the normalized 3D coordinate map; and generating a normalized depth map based on each pixel in the normalized 3D coordinate map and its corresponding normalized depth value, thereby eliminating geometric distortion and achieving accurate normalization of the depth image.

The invention discloses an epipolar alignment method and device for an asymmetric binocular stereoscopic vision system and a storage medium, and the method comprises the steps: carrying out the calibration of two eyes in the asymmetric binocular stereoscopic vision system, and obtaining key parameters; the key parameters comprise internal parameters and external parameters; internal parameters are adjusted, and binocular calibration is carried out again and key parameters are obtained; based on the rotation matrixes in the reacquired external parameters, rotation quantity sharing is adopted to obtain rotation matrixes of the left image and the right image, then directional rotation is carried out on translation vectors in the reacquired external parameters, final rotation matrixes of the left image and the right image are obtained, and perspective transformation matrixes are calculated respectively; performing perspective transformation on the left and right images based on the perspective transformation matrix; and based on the left and right images after perspective transformation, searching a maximum common inscribed regular rectangle by adopting a central epipolar circular scanning method, further cutting the left and right images after perspective transformation, and then matching by adopting one-dimensional parallax. According to the method, the YZ parallax of the asymmetric binocular can be thoroughly eliminated, and more effective pictures can be reserved.

This invention relates to the field of image processing technology and discloses an image stitching method, apparatus, and device. The method includes: identifying multiple target structures in images to be stitched; determining a homography matrix using target feature points of the multiple target structures in different images to be stitched; the target feature points are determined based on the geometric shape of the target structures; performing a perspective transformation on one of the images to be stitched using the homography matrix to obtain a distorted image; and stitching the distorted image with another image to be stitched, which serves as a reference image. This invention can improve the accuracy of feature point recognition and registration, thereby ensuring the quality of image stitching.

The invention particularly relates to an ink printing surface quality detection method based on computer vision, and relates to the technical field of printing quality detection. Quality detection; tension and displacement parameters are collected and analyzed; and calibration and perspective transformation adjustment. According to the method, feature points are extracted, mismatching is eliminated in combination with an FLANN matcher and an RANSAC algorithm, and the deviation quantization precision of displacement, rotation and the like is improved to the pixel level; thin plate spline interpolation is introduced to construct a global deformation field, quantitative detection of local stretching and nonlinear distortion is realized for the first time, and the detection rate of local deformation type defects is increased; meanwhile, the weight factors are self-learned through the random forest model, the defect sensitivities of different products are adapted, the quality inspection judgment deviation is reduced, the misjudgment rate is reduced, the problems of missing detection and misjudgment in traditional detection are effectively solved, and the high-precision requirement for high-end printing of electronic circuits, anti-counterfeit labels and the like is met.

This application relates to the field of automotive electronics technology, proposing a method, electronic device, and computer program product for correcting images of a Type II rearview mirror. The method includes: acquiring a homography transformation matrix from the image plane of a real camera in the vehicle's electronic rearview mirror to the image plane of a virtual camera with an ideal viewing posture; wherein the homography transformation matrix is ​​determined based on the parameters of the first camera of the real camera and the second camera of the virtual camera; using the homography transformation matrix, performing perspective transformation processing on the initial Type II mirror image captured by the real camera to obtain a corrected target Type II mirror image. This method, by constructing a virtual camera with an ideal viewing posture in three-dimensional space and reprojecting the Type II mirror image captured by the real camera onto the viewpoint of the virtual camera using the homography transformation matrix, can correct the geometric distortion problem existing in the Type II mirror image, thereby improving the safety and comfort of vehicle driving.

The application discloses a kind of based on deep learning's drawing comparison method, comprising: obtaining first image based on original drawing and second image formed based on original drawing scanning;According to key point extraction, matching algorithm, reliable key point matching pair is obtained by first image and second image, and perspective transformation matrix is calculated according to reliable key point matching pair, and second image is aligned to first graph according to perspective transformation matrix, and reprojected second image is obtained after alignment;According to difference comparison model, difference prediction is carried out to first image and reprojected second image, and difference image is obtained;Difference block confirmation image is obtained by dividing difference image according to predetermined parameter, and finally difference confirmation is completed by user;It is more accurate to achieve for small difference capture and noise caused misjudgment processing, suitable for various drawing comparison, especially for drawing quality difference, and better results can be predicted.